sha
null | last_modified
null | library_name
stringclasses 154
values | text
stringlengths 1
900k
| metadata
stringlengths 2
348k
| pipeline_tag
stringclasses 45
values | id
stringlengths 5
122
| tags
sequencelengths 1
1.84k
| created_at
stringlengths 25
25
| arxiv
sequencelengths 0
201
| languages
sequencelengths 0
1.83k
| tags_str
stringlengths 17
9.34k
| text_str
stringlengths 0
389k
| text_lists
sequencelengths 0
722
| processed_texts
sequencelengths 1
723
| tokens_length
sequencelengths 1
723
| input_texts
sequencelengths 1
61
| embeddings
sequencelengths 768
768
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral_gsm8k_sft_0
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7868
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 16
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7482 | 1.0 | 455 | 0.7868 |
### Framework versions
- Transformers 4.36.1
- Pytorch 2.0.1+cu117
- Datasets 2.16.1
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "mistral_gsm8k_sft_0", "results": []}]} | text-generation | weijie210/mistral_gsm8k_sft_0 | [
"transformers",
"tensorboard",
"safetensors",
"mistral",
"text-generation",
"trl",
"sft",
"generated_from_trainer",
"conversational",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T08:51:23+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #mistral #text-generation #trl #sft #generated_from_trainer #conversational #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mistral\_gsm8k\_sft\_0
======================
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7868
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 16
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 4
* total\_train\_batch\_size: 16
* total\_eval\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* num\_epochs: 1
### Training results
### Framework versions
* Transformers 4.36.1
* Pytorch 2.0.1+cu117
* Datasets 2.16.1
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 16\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* total\\_train\\_batch\\_size: 16\n* total\\_eval\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.36.1\n* Pytorch 2.0.1+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #tensorboard #safetensors #mistral #text-generation #trl #sft #generated_from_trainer #conversational #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 16\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* total\\_train\\_batch\\_size: 16\n* total\\_eval\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.36.1\n* Pytorch 2.0.1+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.0"
] | [
92,
148,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #mistral #text-generation #trl #sft #generated_from_trainer #conversational #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 16\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* total\\_train\\_batch\\_size: 16\n* total\\_eval\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.36.1\n* Pytorch 2.0.1+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.0"
] | [
-0.08638639003038406,
0.10855191946029663,
-0.004766938276588917,
0.08835691213607788,
0.09174834936857224,
0.03253064304590225,
0.15181443095207214,
0.1512031853199005,
-0.07402492314577103,
0.12124720215797424,
0.1147225871682167,
0.06760511547327042,
0.05656915903091431,
0.15426014363765717,
-0.026277780532836914,
-0.21292035281658173,
0.03833554685115814,
-0.013308478519320488,
-0.11224468797445297,
0.10890661180019379,
0.08549466729164124,
-0.10417306423187256,
0.09608869254589081,
-0.019164694473147392,
-0.13219192624092102,
-0.018215570598840714,
-0.032565101981163025,
-0.02727845311164856,
0.08542054891586304,
0.05544181913137436,
0.07552751898765564,
0.026361960917711258,
0.06777994334697723,
-0.22185441851615906,
0.0007683503208681941,
0.07703627645969391,
0.0019198444206267595,
0.07173244655132294,
0.0846574604511261,
0.0235665924847126,
0.10335568338632584,
-0.08500514924526215,
0.0465577132999897,
0.035726435482501984,
-0.11217917501926422,
-0.1914007067680359,
-0.08659864217042923,
0.06632308661937714,
0.10278042405843735,
0.06885695457458496,
-0.010962709784507751,
0.10164713114500046,
-0.04180249944329262,
0.08665669709444046,
0.23772870004177094,
-0.2596032917499542,
-0.07027959823608398,
0.06265155971050262,
0.04417732357978821,
0.083089180290699,
-0.10410947352647781,
-0.022459127008914948,
0.04416317120194435,
0.022438418120145798,
0.10269376635551453,
-0.003811570582911372,
0.009341518394649029,
-0.004524439573287964,
-0.13166409730911255,
-0.0669059231877327,
0.17061612010002136,
0.06314278393983841,
-0.00974537804722786,
-0.10205361247062683,
-0.07335349917411804,
-0.17307023704051971,
-0.03511321544647217,
-0.0069356560707092285,
0.04411117359995842,
-0.028026161715388298,
-0.027843983843922615,
0.02306857332587242,
-0.07046571373939514,
-0.08618295937776566,
0.010819039307534695,
0.0843227356672287,
0.06238703429698944,
-0.003113840240985155,
-0.0017085929866880178,
0.10175234824419022,
0.008425615727901459,
-0.16109995543956757,
-0.029198117554187775,
0.02295519784092903,
-0.056273531168699265,
-0.0199876818805933,
0.00016280122508760542,
0.012573396787047386,
0.06511791795492172,
0.16462965309619904,
-0.05905681848526001,
0.07545056194067001,
0.03740890696644783,
0.019434981048107147,
-0.08166250586509705,
0.14083395898342133,
-0.07223158329725266,
-0.054114002734422684,
-0.014463269151747227,
0.11514236032962799,
0.04130077362060547,
-0.0022610106971114874,
-0.08451579511165619,
0.03270334377884865,
0.1004570722579956,
0.047854818403720856,
-0.006932089105248451,
0.06046239659190178,
-0.05586276948451996,
-0.015757162123918533,
0.1017170399427414,
-0.10880906879901886,
0.035251133143901825,
0.03006524220108986,
-0.05356409773230553,
-0.01983296312391758,
0.014927387237548828,
-0.022608166560530663,
-0.021662527695298195,
0.054872065782547,
-0.0919012725353241,
-0.026422644034028053,
-0.07830101996660233,
-0.09329957515001297,
0.04037037864327431,
-0.040812328457832336,
-0.011290493421256542,
-0.07996832579374313,
-0.13950327038764954,
-0.03692101687192917,
0.03206684812903404,
-0.06304814666509628,
-0.06710943579673767,
-0.05173331871628761,
-0.1081521064043045,
0.03933122381567955,
0.0007796582067385316,
0.1036490947008133,
-0.06593120843172073,
0.06812993437051773,
0.02937176451086998,
0.05519317835569382,
0.06126202642917633,
0.04294091835618019,
-0.06668529659509659,
0.07978890836238861,
-0.15238511562347412,
0.054683469235897064,
-0.08915093541145325,
0.0690506249666214,
-0.11637993156909943,
-0.09114357084035873,
-0.006172648165374994,
-0.016162309795618057,
0.06307177990674973,
0.1145709902048111,
-0.13632486760616302,
-0.0605425126850605,
0.19315725564956665,
-0.09748353064060211,
-0.1394423246383667,
0.12678200006484985,
-0.0030564728658646345,
-0.04708121716976166,
0.03079167567193508,
0.13843755424022675,
0.13282975554466248,
-0.09381089359521866,
-0.030088486149907112,
-0.005287072155624628,
0.08328120410442352,
0.013914113864302635,
0.09159654378890991,
-0.007504072040319443,
0.048653919249773026,
0.023265788331627846,
-0.04872865229845047,
0.023751916363835335,
-0.06822693347930908,
-0.07842466235160828,
-0.05418788269162178,
-0.07592549920082092,
-0.004891593009233475,
0.0429631769657135,
0.023560382425785065,
-0.08889571577310562,
-0.11935613304376602,
0.012693461030721664,
0.11537080258131027,
-0.08663368225097656,
0.014339761808514595,
-0.061528634279966354,
0.08549819886684418,
-0.03204002603888512,
0.017014330253005028,
-0.14199453592300415,
-0.0979386493563652,
0.06231832876801491,
-0.08642718940973282,
-0.00034748855978250504,
0.014705199748277664,
0.0673312395811081,
0.08187374472618103,
-0.05446904897689819,
-0.0538189597427845,
-0.02046091854572296,
-0.007907803170382977,
-0.07827605307102203,
-0.21733565628528595,
-0.04918912425637245,
-0.02902393974363804,
0.13085928559303284,
-0.21356011927127838,
0.04039682075381279,
0.05071176588535309,
0.12563127279281616,
0.020930156111717224,
-0.03508063033223152,
0.005378939677029848,
0.0346769243478775,
-0.05251952260732651,
-0.09209644794464111,
0.0269775390625,
-0.0027431570924818516,
-0.0837443545460701,
-0.005753902718424797,
-0.16287411749362946,
0.13571473956108093,
0.09465697407722473,
0.044374506920576096,
-0.08370915800333023,
-0.008874310180544853,
-0.05502966046333313,
-0.06833828240633011,
-0.016304992139339447,
-0.03427499532699585,
0.114946648478508,
-0.002218480221927166,
0.11755402386188507,
-0.08487141877412796,
-0.06662499159574509,
0.023592596873641014,
0.0008195194532163441,
-0.01903243362903595,
0.13639108836650848,
0.04758121818304062,
-0.10084468871355057,
0.1567986011505127,
0.11701136827468872,
-0.06477881222963333,
0.11558113992214203,
-0.07932735234498978,
-0.07052110135555267,
-0.04468381032347679,
0.05747275426983833,
0.03468981012701988,
0.08500941097736359,
-0.09550264477729797,
0.0052833412773907185,
0.0233700443059206,
0.018879279494285583,
0.017683617770671844,
-0.16745321452617645,
0.007062916643917561,
0.02961765229701996,
-0.09352155774831772,
0.056880537420511246,
-0.01468784362077713,
-0.012580189853906631,
0.09017083048820496,
-0.0069055743515491486,
-0.05162519961595535,
0.004138156305998564,
-0.016167661175131798,
-0.07860933989286423,
0.2052561491727829,
-0.09836708754301071,
-0.13097834587097168,
-0.15227021276950836,
0.01979614980518818,
-0.05540813133120537,
-0.0013339711586013436,
0.037641990929841995,
-0.062113117426633835,
-0.05780639126896858,
-0.107320636510849,
0.004124383442103863,
0.007908939383924007,
0.020039880648255348,
0.027197211980819702,
0.006330409087240696,
0.07951316237449646,
-0.11599459499120712,
0.005912721157073975,
0.02041034586727619,
-0.07314110547304153,
0.013227281160652637,
0.013230284675955772,
0.10770942270755768,
0.1427455097436905,
0.027359789237380028,
0.005832338239997625,
-0.013687344267964363,
0.18020914494991302,
-0.08070864528417587,
0.01731569878757,
0.08947145193815231,
0.025851542130112648,
0.06520029157400131,
0.15622632205486298,
0.03809835761785507,
-0.0762050449848175,
0.0018098399741575122,
0.03499056398868561,
-0.02107304148375988,
-0.22660347819328308,
-0.026981240138411522,
-0.04686807468533516,
0.038607824593782425,
0.10421142727136612,
0.05957772955298424,
0.03349313139915466,
0.04334373027086258,
-0.04017544910311699,
0.046588677912950516,
0.03578697144985199,
0.07573332637548447,
0.06932462006807327,
0.05434345081448555,
0.11011993139982224,
-0.050492290407419205,
-0.024380691349506378,
0.04696433246135712,
0.011883288621902466,
0.2198018729686737,
-0.021551787853240967,
0.23948489129543304,
0.04167060926556587,
0.1287941336631775,
0.0038415694143623114,
0.0496499128639698,
0.003667710116133094,
-0.0006944691995158792,
0.008190855383872986,
-0.06136641278862953,
0.00011828819697257131,
0.04336453974246979,
-0.006454960443079472,
0.046054333448410034,
-0.06972530484199524,
0.03729480504989624,
0.06760725378990173,
0.26910459995269775,
0.05620695278048515,
-0.31745535135269165,
-0.07834704220294952,
0.038309454917907715,
-0.034322477877140045,
-0.029800985008478165,
0.025345398113131523,
0.15832340717315674,
-0.06954645365476608,
0.07790835201740265,
-0.05462093651294708,
0.08109088242053986,
-0.055475447326898575,
0.00969129242002964,
0.10240856558084488,
0.11292944103479385,
0.009790798649191856,
0.07451064884662628,
-0.2485707849264145,
0.24100792407989502,
0.004182352218776941,
0.04113984480500221,
-0.04596973583102226,
0.04839581251144409,
0.014827384613454342,
0.05590791627764702,
0.07824712246656418,
-0.011780506931245327,
-0.1105850487947464,
-0.15471963584423065,
-0.12564072012901306,
0.005576563999056816,
0.09551303088665009,
-0.059397391974925995,
0.11147303879261017,
-0.033230092376470566,
-0.032665759325027466,
0.04493347555398941,
-0.05032123997807503,
-0.07042102515697479,
-0.10878932476043701,
0.034550439566373825,
0.0048112040385603905,
-0.013625934720039368,
-0.09292101114988327,
-0.0820692703127861,
-0.09731476753950119,
0.17423410713672638,
-0.11480522900819778,
-0.04884925112128258,
-0.1133517324924469,
0.04856472089886665,
0.1303892731666565,
-0.09141978621482849,
0.037241511046886444,
-0.013644018210470676,
0.1174091100692749,
0.03287702798843384,
-0.05285510793328285,
0.09517789632081985,
-0.0648178979754448,
-0.22185324132442474,
-0.041568558663129807,
0.13051868975162506,
0.0034808171913027763,
0.04965243861079216,
-0.030239347368478775,
0.022723587229847908,
-0.005534294992685318,
-0.10106870532035828,
0.022276293486356735,
0.0672735869884491,
0.048542194068431854,
0.03729905188083649,
-0.04459673911333084,
-0.01037455815821886,
-0.036997806280851364,
-0.032688360661268234,
0.0812053456902504,
0.2835148572921753,
-0.09174934774637222,
0.02561582438647747,
0.052613720297813416,
-0.07246725261211395,
-0.18464434146881104,
-0.027480121701955795,
0.060771770775318146,
0.01876462996006012,
-0.0009853781666606665,
-0.15405070781707764,
0.05388256534934044,
0.10721726715564728,
-0.022660166025161743,
0.10557635128498077,
-0.34547531604766846,
-0.14133259654045105,
0.0703423023223877,
0.09325367957353592,
-0.030805686488747597,
-0.2003103792667389,
-0.07254394143819809,
-0.01606382429599762,
-0.12009432911872864,
0.051542431116104126,
-0.05782376974821091,
0.11198030412197113,
-0.036497656255960464,
0.004728697240352631,
0.009186695329844952,
-0.05891413614153862,
0.1662573218345642,
0.012318120338022709,
0.08634404838085175,
-0.05363057926297188,
0.010396905243396759,
0.09904277324676514,
-0.0793839693069458,
0.052719373255968094,
-0.13184572756290436,
0.04606860876083374,
-0.07737857103347778,
-0.012834507040679455,
-0.05776993930339813,
0.01500011421740055,
-0.05515265464782715,
-0.016408313065767288,
-0.0654745101928711,
0.03261416405439377,
0.06508532911539078,
-0.014724495820701122,
0.14052055776119232,
0.022384172305464745,
0.1284281611442566,
0.17278848588466644,
0.07294026762247086,
0.00004624006396625191,
-0.10949085652828217,
-0.01051550917327404,
-0.0036032823845744133,
0.04202389717102051,
-0.14121343195438385,
0.03627244383096695,
0.13422253727912903,
0.016541222110390663,
0.12838608026504517,
0.048870641738176346,
-0.06796056032180786,
-0.009727484546601772,
0.06905028969049454,
-0.12590838968753815,
-0.17044506967067719,
-0.007826324552297592,
0.001162331784144044,
-0.15023913979530334,
0.02416517399251461,
0.10267484933137894,
-0.056390244513750076,
0.0055524795316159725,
0.00921436958014965,
0.05883539468050003,
-0.017943503335118294,
0.21205705404281616,
0.0308415275067091,
0.08385223895311356,
-0.09025716036558151,
0.09667174518108368,
0.06334005296230316,
-0.08185485750436783,
0.016744162887334824,
0.0918123871088028,
-0.08389303088188171,
-0.033255089074373245,
0.08817330002784729,
0.11827408522367477,
0.019456621259450912,
-0.04793064296245575,
-0.12028708308935165,
-0.13736391067504883,
0.0779041200876236,
0.09822939336299896,
0.0647699311375618,
0.06271862983703613,
0.008613175712525845,
0.011811777018010616,
-0.0861387774348259,
0.13539272546768188,
0.06424523890018463,
0.08926961570978165,
-0.1612275093793869,
0.07867739349603653,
0.0014712180709466338,
0.00043948192615062,
-0.009361610747873783,
0.045269738882780075,
-0.12305805832147598,
-0.026031654328107834,
-0.11543332785367966,
0.02811249904334545,
-0.051957592368125916,
0.0003408065822441131,
0.005980563350021839,
-0.041130390018224716,
-0.050730347633361816,
0.0269155390560627,
-0.08000264316797256,
-0.0584699921309948,
-0.02554992027580738,
0.08125662803649902,
-0.12423154711723328,
-0.030691301450133324,
0.03425402566790581,
-0.11291765421628952,
0.08313895016908646,
0.022984616458415985,
0.03010234422981739,
0.007353892084211111,
-0.08191770315170288,
0.03581717982888222,
0.04370805621147156,
0.02509140968322754,
0.02277195081114769,
-0.14494480192661285,
-0.009017453528940678,
-0.023260729387402534,
-0.005509767681360245,
0.0033990468364208937,
0.028655149042606354,
-0.12320098280906677,
0.02063034474849701,
-0.04888369143009186,
-0.05363916605710983,
-0.05454937368631363,
0.04655425623059273,
0.09932679682970047,
-0.017902491614222527,
0.16347092390060425,
-0.07943041622638702,
0.030333857983350754,
-0.23100756108760834,
-0.002304708119481802,
0.01877945102751255,
-0.09829198569059372,
-0.08156117051839828,
-0.020773207768797874,
0.07563038170337677,
-0.056383877992630005,
0.12364640831947327,
-0.04748819023370743,
-0.0038347213994711637,
0.033753424882888794,
-0.02906445786356926,
0.037248995155096054,
0.06062198802828789,
0.1371268630027771,
0.022482406347990036,
-0.030985554680228233,
0.050114553421735764,
-0.01619892194867134,
0.08010431379079819,
0.041836198419332504,
0.15875592827796936,
0.1379023641347885,
-0.023217543959617615,
0.08618108928203583,
0.061753883957862854,
-0.11362606287002563,
-0.12592457234859467,
0.1090511754155159,
-0.08128947019577026,
0.12697459757328033,
-0.029692701995372772,
0.1466040462255478,
0.11724668741226196,
-0.19054143130779266,
0.03717142716050148,
-0.04825568571686745,
-0.08899133652448654,
-0.09504158049821854,
-0.09092312306165695,
-0.09603462368249893,
-0.1573937088251114,
-0.008546951226890087,
-0.11631108820438385,
0.032402995973825455,
0.0764799490571022,
0.028717536479234695,
0.013392038643360138,
0.1534639596939087,
0.04680433124303818,
0.025871993973851204,
0.03138282522559166,
0.027152154594659805,
-0.025499384850263596,
-0.03757611662149429,
-0.07567480206489563,
0.025449011474847794,
-0.02244826965034008,
0.04736620560288429,
-0.016025574877858162,
-0.004024949390441179,
0.07188843935728073,
-0.005117161199450493,
-0.08352470397949219,
0.015815628692507744,
-0.004679795354604721,
0.01070111058652401,
0.03827540948987007,
0.023092538118362427,
-0.012111231684684753,
-0.013338252902030945,
0.16009095311164856,
-0.0725538581609726,
-0.065116286277771,
-0.11062733829021454,
0.1837311089038849,
-0.005035307724028826,
-0.007369701284915209,
0.04077675938606262,
-0.06248468533158302,
-0.011378836818039417,
0.1664552390575409,
0.17828038334846497,
-0.01778697595000267,
-0.015852125361561775,
0.005413363687694073,
-0.014316149055957794,
-0.006930391304194927,
0.07700255513191223,
0.09973793476819992,
0.047289907932281494,
-0.04786233231425285,
-0.021906418725848198,
-0.010027918964624405,
-0.029265686869621277,
-0.05801749974489212,
0.05659197270870209,
0.007456429302692413,
0.008997393772006035,
-0.029831763356924057,
0.043329257518053055,
-0.03551623970270157,
-0.08273636549711227,
0.061283260583877563,
-0.1857861429452896,
-0.15551674365997314,
-0.028765633702278137,
0.09274373203516006,
-0.002375344280153513,
0.047734592109918594,
-0.004005549941211939,
-0.023156579583883286,
0.10752364993095398,
-0.02605072781443596,
-0.10066525638103485,
-0.08579621464014053,
0.04786919802427292,
-0.10471346229314804,
0.19947153329849243,
-0.030069870874285698,
0.039884887635707855,
0.13060420751571655,
0.0008108649053610861,
-0.13129064440727234,
0.045071616768836975,
0.07320968061685562,
-0.08836716413497925,
0.02749757096171379,
0.11484649032354355,
-0.0374370776116848,
0.09936550259590149,
0.04542135074734688,
-0.06466123461723328,
-0.020833998918533325,
-0.024277539923787117,
-0.025918668136000633,
-0.06479281932115555,
-0.021553609520196915,
-0.045467983931303024,
0.15225477516651154,
0.2046227902173996,
-0.05376356095075607,
-0.017282191663980484,
-0.024584777653217316,
0.03379198908805847,
0.04212237149477005,
0.10343282669782639,
-0.00023020189837552607,
-0.2591414749622345,
0.031018434092402458,
-0.00028742128051817417,
0.04186080023646355,
-0.2045089602470398,
-0.10419043153524399,
0.021070705726742744,
-0.036284007132053375,
-0.09517154097557068,
0.12009435147047043,
0.08874557912349701,
0.05108955502510071,
-0.05705806612968445,
-0.10538603365421295,
-0.060743845999240875,
0.15800878405570984,
-0.14762786030769348,
-0.09083496034145355
] |
null | null | transformers | # Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [openlm-research/open_llama_3b](https://huggingface.co/openlm-research/open_llama_3b)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed.
```bash
pip install transformers==4.29.2
pip install einops==0.6.1
pip install accelerate==0.19.0
pip install torch==2.0.0
```
```python
import torch
from transformers import pipeline
generate_text = pipeline(
model="Shishir1807/Moas_Explicit_OLM_v1",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?</s><|answer|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
import torch
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"Shishir1807/Moas_Explicit_OLM_v1",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"Shishir1807/Moas_Explicit_OLM_v1",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Shishir1807/Moas_Explicit_OLM_v1" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?</s><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
LlamaForCausalLM(
(model): LlamaModel(
(embed_tokens): Embedding(32000, 3200, padding_idx=0)
(layers): ModuleList(
(0-25): 26 x LlamaDecoderLayer(
(self_attn): LlamaAttention(
(q_proj): Linear(in_features=3200, out_features=3200, bias=False)
(k_proj): Linear(in_features=3200, out_features=3200, bias=False)
(v_proj): Linear(in_features=3200, out_features=3200, bias=False)
(o_proj): Linear(in_features=3200, out_features=3200, bias=False)
(rotary_emb): LlamaRotaryEmbedding()
)
(mlp): LlamaMLP(
(gate_proj): Linear(in_features=3200, out_features=8640, bias=False)
(down_proj): Linear(in_features=8640, out_features=3200, bias=False)
(up_proj): Linear(in_features=3200, out_features=8640, bias=False)
(act_fn): SiLUActivation()
)
(input_layernorm): LlamaRMSNorm()
(post_attention_layernorm): LlamaRMSNorm()
)
)
(norm): LlamaRMSNorm()
)
(lm_head): Linear(in_features=3200, out_features=32000, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. | {"language": ["en"], "library_name": "transformers", "tags": ["gpt", "llm", "large language model", "h2o-llmstudio"], "inference": false, "thumbnail": "https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico"} | text-generation | Shishir1807/Moas_Explicit_OLM_v1 | [
"transformers",
"pytorch",
"llama",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T08:54:50+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #llama #text-generation #gpt #llm #large language model #h2o-llmstudio #en #autotrain_compatible #text-generation-inference #region-us
| # Model Card
## Summary
This model was trained using H2O LLM Studio.
- Base model: openlm-research/open_llama_3b
## Usage
To use the model with the 'transformers' library on a machine with GPUs, first make sure you have the 'transformers', 'accelerate' and 'torch' libraries installed.
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
Alternatively, you can download h2oai_pipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the 'transformers' package, this will allow you to set 'trust_remote_code=False'.
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
## Quantization and sharding
You can load the models using quantization by specifying or . Also, sharding on multiple GPUs is possible by setting .
## Model Architecture
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in URL. Visit H2O LLM Studio to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. | [
"# Model Card",
"## Summary\n\nThis model was trained using H2O LLM Studio.\n- Base model: openlm-research/open_llama_3b",
"## Usage\n\nTo use the model with the 'transformers' library on a machine with GPUs, first make sure you have the 'transformers', 'accelerate' and 'torch' libraries installed.\n\n\n\n\n\nYou can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:\n\n\n\n\n\nAlternatively, you can download h2oai_pipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the 'transformers' package, this will allow you to set 'trust_remote_code=False'.\n\n\n\n\nYou may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:",
"## Quantization and sharding\n\nYou can load the models using quantization by specifying or . Also, sharding on multiple GPUs is possible by setting .",
"## Model Architecture",
"## Model Configuration\n\nThis model was trained using H2O LLM Studio and with the configuration in URL. Visit H2O LLM Studio to learn how to train your own large language models.",
"## Disclaimer\n\nPlease read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.\n\n- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.\n- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.\n- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.\n- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.\n- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.\n- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.\n\nBy using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it."
] | [
"TAGS\n#transformers #pytorch #llama #text-generation #gpt #llm #large language model #h2o-llmstudio #en #autotrain_compatible #text-generation-inference #region-us \n",
"# Model Card",
"## Summary\n\nThis model was trained using H2O LLM Studio.\n- Base model: openlm-research/open_llama_3b",
"## Usage\n\nTo use the model with the 'transformers' library on a machine with GPUs, first make sure you have the 'transformers', 'accelerate' and 'torch' libraries installed.\n\n\n\n\n\nYou can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:\n\n\n\n\n\nAlternatively, you can download h2oai_pipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the 'transformers' package, this will allow you to set 'trust_remote_code=False'.\n\n\n\n\nYou may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:",
"## Quantization and sharding\n\nYou can load the models using quantization by specifying or . Also, sharding on multiple GPUs is possible by setting .",
"## Model Architecture",
"## Model Configuration\n\nThis model was trained using H2O LLM Studio and with the configuration in URL. Visit H2O LLM Studio to learn how to train your own large language models.",
"## Disclaimer\n\nPlease read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.\n\n- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.\n- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.\n- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.\n- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.\n- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.\n- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.\n\nBy using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it."
] | [
59,
3,
33,
174,
34,
4,
42,
518
] | [
"passage: TAGS\n#transformers #pytorch #llama #text-generation #gpt #llm #large language model #h2o-llmstudio #en #autotrain_compatible #text-generation-inference #region-us \n# Model Card## Summary\n\nThis model was trained using H2O LLM Studio.\n- Base model: openlm-research/open_llama_3b## Usage\n\nTo use the model with the 'transformers' library on a machine with GPUs, first make sure you have the 'transformers', 'accelerate' and 'torch' libraries installed.\n\n\n\n\n\nYou can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:\n\n\n\n\n\nAlternatively, you can download h2oai_pipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the 'transformers' package, this will allow you to set 'trust_remote_code=False'.\n\n\n\n\nYou may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:## Quantization and sharding\n\nYou can load the models using quantization by specifying or . Also, sharding on multiple GPUs is possible by setting .## Model Architecture## Model Configuration\n\nThis model was trained using H2O LLM Studio and with the configuration in URL. Visit H2O LLM Studio to learn how to train your own large language models."
] | [
-0.08752601593732834,
0.1270565241575241,
-0.003469377290457487,
0.06385204195976257,
0.10115861147642136,
0.04594554752111435,
0.12329027056694031,
0.11918003112077713,
0.06289221346378326,
0.07954800128936768,
0.009517939761281013,
0.0417027622461319,
0.036419209092855453,
0.22788405418395996,
0.10739640891551971,
-0.2642940282821655,
-0.011424754746258259,
-0.05925930663943291,
-0.02539588324725628,
0.05334096401929855,
0.05876060947775841,
-0.06368233263492584,
0.06765555590391159,
-0.0068735359236598015,
-0.06859222054481506,
-0.009257486090064049,
-0.010527854785323143,
0.009818371385335922,
0.09691538661718369,
0.071579709649086,
0.00758299371227622,
0.049692302942276,
0.06198173388838768,
-0.15364959836006165,
0.030483556911349297,
0.10860170423984528,
0.01321346964687109,
0.043670013546943665,
0.004965838976204395,
-0.00116831308696419,
0.10996578633785248,
-0.05619123578071594,
0.06422089785337448,
0.05967229604721069,
-0.062797911465168,
-0.14107024669647217,
-0.03509378060698509,
-0.08831845968961716,
0.07631674408912659,
0.03703904151916504,
0.018511096015572548,
0.11046874523162842,
0.04739753529429436,
0.06900319457054138,
0.14260488748550415,
-0.13024811446666718,
-0.0551423504948616,
0.04893020540475845,
0.005504622124135494,
0.10501202195882797,
-0.03113264963030815,
0.009941911324858665,
-0.007708580698817968,
0.019118353724479675,
0.08087591826915741,
-0.05431219935417175,
0.04177872836589813,
-0.088102787733078,
-0.1172412857413292,
-0.015043486841022968,
0.10988180339336395,
-0.06537508964538574,
-0.05449184775352478,
-0.12659116089344025,
-0.16506843268871307,
-0.06131618469953537,
0.0031329221092164516,
0.006414016708731651,
0.04783864691853523,
0.005783127620816231,
0.02618211694061756,
-0.15539900958538055,
-0.08359787613153458,
-0.08976012468338013,
-0.02740160934627056,
0.21474219858646393,
0.07198315858840942,
0.006326181814074516,
-0.05290517210960388,
0.1777373105287552,
-0.08783009648323059,
-0.050935402512550354,
-0.0985272079706192,
-0.06565681844949722,
-0.1424306035041809,
0.027228999882936478,
-0.011660742573440075,
-0.06557917594909668,
0.04927759990096092,
0.21047762036323547,
-0.05232887715101242,
0.0694298967719078,
0.04283164441585541,
0.008148443885147572,
0.04711800068616867,
0.06206956133246422,
-0.0016217258526012301,
-0.017882460728287697,
0.04321098327636719,
-0.002788874786347151,
0.08027872443199158,
-0.06510491669178009,
-0.043481532484292984,
-0.043644458055496216,
-0.07899382710456848,
0.05546017736196518,
-0.008478216826915741,
0.051626067608594894,
0.004879380576312542,
-0.08113162219524384,
0.09097622334957123,
-0.1579728126525879,
-0.003761311061680317,
0.004212493076920509,
0.02400190383195877,
-0.008862914517521858,
0.06910794973373413,
-0.08423616737127304,
-0.046334270387887955,
-0.06657962501049042,
-0.020538508892059326,
0.016379470005631447,
-0.13556219637393951,
-0.052735090255737305,
-0.014455423690378666,
-0.05238182470202446,
-0.05338966101408005,
-0.12096606194972992,
-0.09298243373632431,
0.0006076783174648881,
0.06846141070127487,
-0.04950549453496933,
0.019469033926725388,
0.016587361693382263,
0.07080193608999252,
-0.023116668686270714,
0.013033621944487095,
-0.0059581040404737,
-0.013116643764078617,
-0.007429521065205336,
0.0749061331152916,
0.10563687235116959,
-0.07902935892343521,
0.01018449105322361,
-0.06451765447854996,
0.06999316066503525,
-0.1561914086341858,
0.14505493640899658,
-0.029218221083283424,
0.01741047389805317,
-0.05007597431540489,
-0.020393338054418564,
-0.05638088285923004,
-0.01582312397658825,
0.0535101555287838,
0.06548739969730377,
-0.14197658002376556,
-0.0390794537961483,
0.15241262316703796,
-0.16962887346744537,
-0.035423316061496735,
0.05574491620063782,
-0.012711620889604092,
0.12896965444087982,
0.03320957347750664,
0.0760173574090004,
0.22790496051311493,
-0.16324421763420105,
0.047385115176439285,
0.10227462649345398,
-0.01345224492251873,
0.026980634778738022,
0.060733240097761154,
0.01942690834403038,
0.05168217793107033,
0.06615819782018661,
-0.07310507446527481,
0.019286155700683594,
0.03467842936515808,
-0.0447961762547493,
-0.026570318266749382,
-0.05837322399020195,
-0.008998199366033077,
0.006379580590873957,
0.023521186783909798,
0.05707528814673424,
-0.0311601422727108,
0.0912863239645958,
0.17715896666049957,
-0.09853512048721313,
0.03944329544901848,
-0.08031021803617477,
0.07269696146249771,
0.008313293568789959,
0.006025403738021851,
-0.18333691358566284,
-0.08410527557134628,
0.08389413356781006,
-0.21080459654331207,
0.07205543667078018,
-0.0011995000531896949,
0.014359050430357456,
0.1431257277727127,
-0.003644409356638789,
0.007276076823472977,
0.027467159554362297,
-0.008460449054837227,
-0.020052632316946983,
-0.09013894945383072,
-0.04662173241376877,
-0.06983610987663269,
0.13915209472179413,
-0.003999568056315184,
0.06377415359020233,
0.11818690598011017,
0.02967268042266369,
0.014556735754013062,
-0.0408126562833786,
0.042071383446455,
-0.042274415493011475,
-0.008105774410068989,
-0.07886107265949249,
0.07168281078338623,
0.05904288962483406,
-0.03334388881921768,
-0.005783085711300373,
-0.18313494324684143,
-0.12154733389616013,
0.06366851925849915,
0.13515111804008484,
-0.06261661648750305,
-0.05791372060775757,
-0.04606885835528374,
-0.038538649678230286,
-0.08332963287830353,
-0.00546293007209897,
0.09631885588169098,
0.04805653169751167,
0.11091599613428116,
-0.069821797311306,
-0.014287960715591908,
0.02019626274704933,
-0.021206744015216827,
0.037222594022750854,
0.05258319899439812,
0.06449584662914276,
-0.06191611289978027,
-0.026785731315612793,
0.00813809223473072,
-0.08729560673236847,
0.18328838050365448,
0.07342861592769623,
-0.07503476738929749,
-0.049969885498285294,
0.012190023437142372,
0.009508821181952953,
0.11690206080675125,
-0.05265119671821594,
-0.004554889164865017,
0.003415231592953205,
-0.01598619855940342,
0.041525665670633316,
-0.12657210230827332,
0.07221802324056625,
-0.016067640855908394,
-0.0036026271991431713,
0.026370810344815254,
0.06685229390859604,
-0.07556318491697311,
0.008320754393935204,
-0.01568862795829773,
0.1352645605802536,
0.002652845811098814,
-0.0591731034219265,
-0.060534995049238205,
0.14274580776691437,
-0.09710425138473511,
-0.24067069590091705,
-0.11948736011981964,
-0.008060973137617111,
-0.08250860869884491,
-0.013242073357105255,
0.03456021472811699,
0.009803001768887043,
-0.009520064108073711,
-0.07695049047470093,
-0.04879722371697426,
0.005221052095293999,
-0.038733892142772675,
-0.10192789137363434,
0.04691785201430321,
0.04350881278514862,
-0.0984632596373558,
-0.02810007706284523,
0.029004300013184547,
-0.11466425657272339,
0.0751517117023468,
0.037907958030700684,
0.031063010916113853,
0.10534942895174026,
0.007187833543866873,
0.04352904111146927,
0.022883055731654167,
0.1493350863456726,
-0.056735385209321976,
0.0835375189781189,
0.20679980516433716,
0.056541502475738525,
0.08073364198207855,
0.09875382483005524,
0.04458960145711899,
-0.10943930596113205,
0.051935162395238876,
-0.011955901980400085,
-0.10257966816425323,
-0.083295077085495,
-0.06745757907629013,
-0.08090799301862717,
-0.026915552094578743,
0.1349150687456131,
0.05438642576336861,
-0.09092747420072556,
0.05645729601383209,
0.005003826692700386,
0.0016772134695202112,
0.007878091186285019,
0.10842283070087433,
0.11858148127794266,
-0.05004922300577164,
0.050036050379276276,
-0.03937898576259613,
0.027923109009861946,
0.09494385123252869,
0.12052348256111145,
0.1428852677345276,
-0.1019347533583641,
0.15934638679027557,
0.03886639326810837,
0.11453098058700562,
0.03986961394548416,
0.014158090576529503,
-0.008374071680009365,
0.0573807992041111,
-0.009400310926139355,
-0.07068478316068649,
-0.059648316353559494,
0.10727506130933762,
-0.07376266270875931,
-0.08108311146497726,
-0.008435603231191635,
0.06336570531129837,
0.043820928782224655,
0.18555817008018494,
0.03440820053219795,
-0.19754408299922943,
-0.05496556684374809,
-0.0068600173108279705,
-0.015513433143496513,
-0.05665820091962814,
0.028796633705496788,
0.09616319090127945,
-0.10321508347988129,
0.03194452449679375,
-0.058407824486494064,
0.05164554715156555,
-0.07593704760074615,
-0.027277594432234764,
0.09603145718574524,
0.21816055476665497,
0.013350541703402996,
0.09001550823450089,
-0.18372200429439545,
0.07524573802947998,
0.000621345650870353,
0.06313790380954742,
-0.028327587991952896,
0.035132136195898056,
0.05685526132583618,
0.11249849200248718,
0.13086794316768646,
0.02538326196372509,
-0.11748731881380081,
-0.08636852353811264,
-0.07312776893377304,
0.0652320384979248,
0.04958157613873482,
-0.07108284533023834,
0.015452527441084385,
-0.030652180314064026,
-0.01964164525270462,
-0.08117327094078064,
-0.03739503398537636,
-0.030391080304980278,
-0.1877816915512085,
0.08460967987775803,
-0.0481649711728096,
-0.00949187483638525,
-0.054699305444955826,
-0.01080784946680069,
0.06409980356693268,
0.10099778324365616,
-0.04830438271164894,
-0.11313394457101822,
-0.09654485434293747,
0.0006744993734173477,
0.058466553688049316,
-0.10541890561580658,
0.03220442682504654,
-0.05146001651883125,
0.17126739025115967,
-0.05414942651987076,
-0.11942405998706818,
0.03126714006066322,
-0.08082785457372665,
-0.09307189285755157,
-0.007176600396633148,
0.09053968638181686,
0.044079434126615524,
0.005636545363813639,
-0.018729425966739655,
0.009805463254451752,
-0.025771120563149452,
-0.10599859058856964,
-0.05625135451555252,
0.2513488829135895,
-0.025811893865466118,
-0.05952388793230057,
-0.11456164717674255,
0.09578681737184525,
-0.06577031314373016,
0.018673665821552277,
0.10390084236860275,
0.21550236642360687,
-0.1032569482922554,
0.18112067878246307,
0.16826869547367096,
-0.10811303555965424,
-0.2143986076116562,
-0.059702206403017044,
0.029465600848197937,
0.018309101462364197,
0.012797537259757519,
-0.2606036961078644,
0.08519063889980316,
0.04235808551311493,
-0.016813524067401886,
0.03520103171467781,
-0.2311278134584427,
-0.13389387726783752,
0.037367723882198334,
0.0741804763674736,
-0.025689175352454185,
-0.06512223184108734,
0.0030490700155496597,
-0.081024669110775,
0.007630707696080208,
0.11888203769922256,
-0.1496438980102539,
0.131562277674675,
-0.021780651062726974,
-0.01230069249868393,
0.0028183755930513144,
-0.05202103778719902,
0.0965798869729042,
-0.02620423212647438,
0.025079958140850067,
-0.003652761457487941,
0.06234535574913025,
0.07647620141506195,
-0.10469157248735428,
0.07356201857328415,
-0.013748574070632458,
0.06802316009998322,
0.023042071610689163,
-0.07805860787630081,
-0.03827829286456108,
0.049788180738687515,
-0.01745445840060711,
-0.10127739608287811,
-0.012023121118545532,
0.08857394754886627,
0.03817101940512657,
-0.024883411824703217,
-0.13031521439552307,
-0.05844568461179733,
-0.02112896926701069,
0.20647098124027252,
0.07849116623401642,
-0.10394540429115295,
-0.11958283931016922,
-0.002422123681753874,
0.014778899028897285,
0.07697652280330658,
-0.08539680391550064,
0.03354160115122795,
0.07476818561553955,
0.0819459930062294,
0.05489111691713333,
0.018664544448256493,
-0.0723445862531662,
0.02413950301706791,
0.016617627814412117,
-0.12568046152591705,
-0.18753214180469513,
-0.025753585621714592,
0.113136887550354,
-0.03373267501592636,
0.012502316385507584,
0.13264130055904388,
-0.027318453416228294,
-0.016117556020617485,
0.03504267707467079,
0.02491418458521366,
-0.018722770735621452,
0.10515306890010834,
0.012039407156407833,
0.022208096459507942,
-0.07870545238256454,
0.08283629268407822,
0.005588927771896124,
-0.0644584447145462,
-0.01170304138213396,
0.15298521518707275,
-0.16306118667125702,
-0.07279568910598755,
-0.12477708607912064,
-0.08306542783975601,
-0.042825885117053986,
-0.07870921492576599,
-0.030863676220178604,
0.05512509122490883,
-0.06473048031330109,
0.03463495522737503,
0.08341129869222641,
0.006203321274369955,
-0.07891906797885895,
0.02275395579636097,
-0.049239419400691986,
0.06603332608938217,
-0.03612102195620537,
0.05755406245589256,
-0.05997474491596222,
0.140984907746315,
0.07121483236551285,
0.09275195747613907,
-0.05266798660159111,
-0.11006218940019608,
-0.05196892470121384,
-0.0007747887866571546,
-0.008804813027381897,
0.028041275218129158,
-0.07114662230014801,
0.0007209033356048167,
0.015493111684918404,
0.029720770195126534,
0.003707506461068988,
0.05095914378762245,
-0.05384311452507973,
-0.012421010993421078,
-0.07630618661642075,
0.041613996028900146,
-0.06317896395921707,
0.03322996571660042,
0.0419016107916832,
-0.09197968244552612,
0.11213915050029755,
0.002259705914184451,
-0.07225152850151062,
0.009135284461081028,
-0.06337378174066544,
0.014892293140292168,
-0.051926590502262115,
0.05380769073963165,
0.018572578206658363,
-0.1253979206085205,
0.014448669739067554,
-0.0044350228272378445,
0.006761202123016119,
-0.043150924146175385,
0.11541040241718292,
-0.08602755516767502,
0.14135967195034027,
-0.006023877765983343,
-0.03152591362595558,
-0.07770774513483047,
0.0030278023332357407,
-0.06590993702411652,
0.12257856130599976,
0.009264502674341202,
-0.06041952967643738,
0.07267771661281586,
-0.09790720045566559,
-0.044259365648031235,
0.05921922251582146,
-0.0010759730357676744,
-0.0226306039839983,
-0.08838022500276566,
0.062296122312545776,
-0.0034898773301392794,
0.23033881187438965,
0.0004911532741971314,
0.06452246010303497,
-0.010336789302527905,
-0.06197970360517502,
-0.02651095762848854,
-0.023248374462127686,
0.05732008442282677,
0.001665086718276143,
0.07384046912193298,
0.02088695578277111,
0.05557841435074806,
-0.02945263497531414,
0.08331463485956192,
0.10849752277135849,
0.030726265162229538,
0.03607043996453285,
0.023534340783953667,
0.021650079637765884,
0.029870672151446342,
-0.14194811880588531,
0.09509388357400894,
-0.05304429680109024,
0.1055888682603836,
-0.1314825415611267,
0.11609114706516266,
0.08661192655563354,
-0.11013691872358322,
0.07411636412143707,
0.027590114623308182,
-0.08100450783967972,
-0.13403834402561188,
-0.18542461097240448,
-0.02820160984992981,
-0.1675868183374405,
-0.02612505480647087,
-0.03967016190290451,
0.009061762131750584,
0.0013933213194832206,
0.030002614483237267,
0.009816959500312805,
0.17507201433181763,
-0.028129298239946365,
-0.052790842950344086,
-0.06254613399505615,
0.013320195488631725,
0.03713598847389221,
0.062443807721138,
-0.046856705099344254,
0.024846479296684265,
0.0021509001962840557,
0.027927957475185394,
0.03574899211525917,
0.08007850497961044,
-0.00006552881677635014,
0.014266234822571278,
0.01647716388106346,
-0.04154030606150627,
0.01483506616204977,
-0.04768719896674156,
0.06827018409967422,
0.055734697729349136,
-0.08572910726070404,
-0.008498789742588997,
0.11748281866312027,
-0.050992418080568314,
-0.09289124608039856,
-0.1594693511724472,
0.28686752915382385,
-0.09384110569953918,
-0.028389230370521545,
0.006491228938102722,
-0.05651550740003586,
-0.053433094173669815,
0.18025276064872742,
0.1691702902317047,
-0.02831146866083145,
0.033014338463544846,
0.041588228195905685,
0.003079641843214631,
-0.03797256574034691,
0.13474594056606293,
0.00027764475089497864,
0.2201257348060608,
-0.02973928488790989,
0.024625126272439957,
0.0032165073789656162,
-0.03231928125023842,
-0.08916423469781876,
0.017328035086393356,
-0.06612890958786011,
0.05094829574227333,
-0.03012220561504364,
0.020786544308066368,
-0.015910528600215912,
-0.22265367209911346,
0.0574178472161293,
0.01629633828997612,
-0.06044808775186539,
0.006618396379053593,
-0.0003357213281560689,
-0.057492103427648544,
0.027351681143045425,
-0.06152405962347984,
0.013349697925150394,
0.16641096770763397,
-0.004934076219797134,
-0.09377063065767288,
-0.048455897718667984,
0.11698349565267563,
-0.05563100427389145,
0.13066928088665009,
-0.04705128073692322,
0.03050912916660309,
0.05782662332057953,
0.004250192549079657,
-0.0986311212182045,
0.07375186681747437,
0.009899133816361427,
-0.13089562952518463,
-0.00020521371334325522,
0.12174670398235321,
-0.026501290500164032,
0.02554837241768837,
0.010425231419503689,
-0.038271792232990265,
0.03630595654249191,
-0.07928667962551117,
0.01195435132831335,
-0.10508956015110016,
0.043812476098537445,
-0.09948951750993729,
0.1488182693719864,
0.15381784737110138,
-0.0013033439172431827,
-0.0383363738656044,
-0.06491547077894211,
0.06758107990026474,
-0.00022887816885486245,
-0.02883961796760559,
-0.022059602662920952,
-0.1221911683678627,
0.016190843656659126,
-0.018106430768966675,
-0.00020230890368111432,
-0.15781965851783752,
-0.09412618726491928,
0.021042706444859505,
-0.0002801070222631097,
0.030484801158308983,
0.10399387031793594,
0.021108629181981087,
0.05112693831324577,
-0.03702807053923607,
-0.1804930567741394,
-0.004677227232605219,
0.051562730222940445,
-0.18553435802459717,
-0.08213089406490326
] |
null | null | transformers |
# Pasta-Lake-7b 5bpw
Exllama quant of [Test157t/Pasta-Lake-7b](https://huggingface.co/Test157t/Pasta-Lake-7b)
## Other quants:
EXL2: [8bpw](https://huggingface.co/Kooten/Pasta-Lake-7b-8bpw-exl2), [6bpw](https://huggingface.co/Kooten/Pasta-Lake-7b-6bpw-exl2), [5bpw](https://huggingface.co/Kooten/Pasta-Lake-7b-5bpw-exl2), [4bpw](https://huggingface.co/Kooten/Pasta-Lake-7b-4bpw-exl2)
## Prompt format: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Input:
{input}
### Response:
```
## Contact
Kooten on discord
[ko-fi.com/kooten](https://ko-fi.com/kooten) if you would like to support me
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Test157t/Pasta-PrimaMaid-7b", "macadeliccc/WestLake-7B-v2-laser-truthy-dpo"]} | text-generation | Kooten/Pasta-Lake-7b-5bpw-exl2 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"base_model:Test157t/Pasta-PrimaMaid-7b",
"base_model:macadeliccc/WestLake-7B-v2-laser-truthy-dpo",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T08:54:57+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #mergekit #merge #base_model-Test157t/Pasta-PrimaMaid-7b #base_model-macadeliccc/WestLake-7B-v2-laser-truthy-dpo #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Pasta-Lake-7b 5bpw
Exllama quant of Test157t/Pasta-Lake-7b
## Other quants:
EXL2: 8bpw, 6bpw, 5bpw, 4bpw
## Prompt format: Alpaca
## Contact
Kooten on discord
URL if you would like to support me
| [
"# Pasta-Lake-7b 5bpw\nExllama quant of Test157t/Pasta-Lake-7b",
"## Other quants:\nEXL2: 8bpw, 6bpw, 5bpw, 4bpw",
"## Prompt format: Alpaca",
"## Contact\nKooten on discord\n\nURL if you would like to support me"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #base_model-Test157t/Pasta-PrimaMaid-7b #base_model-macadeliccc/WestLake-7B-v2-laser-truthy-dpo #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Pasta-Lake-7b 5bpw\nExllama quant of Test157t/Pasta-Lake-7b",
"## Other quants:\nEXL2: 8bpw, 6bpw, 5bpw, 4bpw",
"## Prompt format: Alpaca",
"## Contact\nKooten on discord\n\nURL if you would like to support me"
] | [
98,
27,
28,
8,
14
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #base_model-Test157t/Pasta-PrimaMaid-7b #base_model-macadeliccc/WestLake-7B-v2-laser-truthy-dpo #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Pasta-Lake-7b 5bpw\nExllama quant of Test157t/Pasta-Lake-7b## Other quants:\nEXL2: 8bpw, 6bpw, 5bpw, 4bpw## Prompt format: Alpaca## Contact\nKooten on discord\n\nURL if you would like to support me"
] | [
-0.16926653683185577,
0.08561262488365173,
-0.002724748570472002,
0.04486815631389618,
0.029025375843048096,
0.019406108185648918,
0.09277268499135971,
0.13064689934253693,
0.13985222578048706,
0.0320681594312191,
0.10585867613554001,
0.09492970257997513,
0.03175933659076691,
0.21660597622394562,
-0.07287737727165222,
-0.12337489426136017,
0.016439387574791908,
0.056421007961034775,
0.03206479921936989,
0.12657147645950317,
0.10485386848449707,
-0.07142683118581772,
0.09576649963855743,
-0.01853247359395027,
-0.11030596494674683,
0.0671735554933548,
0.0008259459864348173,
-0.06342913210391998,
0.09670355170965195,
0.05401112884283066,
0.0687192901968956,
0.08654314279556274,
-0.009067194536328316,
-0.150391086935997,
0.03887900710105896,
0.00794825330376625,
-0.002368313493207097,
0.03664839267730713,
0.08503004908561707,
0.012012810446321964,
-0.05778387561440468,
-0.029928380623459816,
-0.008591997437179089,
0.05923643708229065,
-0.07720872014760971,
0.0050261737778782845,
-0.09539864957332611,
0.08276739716529846,
0.0607968233525753,
0.08110404014587402,
-0.006628429517149925,
0.15421074628829956,
-0.05700986087322235,
0.0967964231967926,
0.1953974962234497,
-0.21016378700733185,
-0.08099541068077087,
0.047035347670316696,
0.08111728727817535,
0.038144178688526154,
0.006511111743748188,
0.023921877145767212,
0.08764684200286865,
-0.015402395278215408,
-0.11930252611637115,
-0.06619350612163544,
0.10743831843137741,
-0.049638569355010986,
-0.08634258806705475,
0.016094354912638664,
0.23222841322422028,
0.007603026926517487,
-0.08542737364768982,
0.0020532850176095963,
-0.0628499761223793,
-0.03856122866272926,
0.004746708553284407,
-0.08048409968614578,
-0.01323561742901802,
-0.010405082255601883,
0.026669327169656754,
-0.043420568108558655,
-0.0860842764377594,
-0.09271485358476639,
-0.06782830506563187,
0.17904117703437805,
0.017878053709864616,
-0.027252139523625374,
-0.03442808985710144,
0.08666203916072845,
-0.21097570657730103,
-0.14434155821800232,
-0.04938337206840515,
-0.09087865799665451,
0.03446624428033829,
-0.06738494336605072,
-0.03150611370801926,
-0.10266353189945221,
0.10957908630371094,
0.20419423282146454,
0.011130420491099358,
0.0624944232404232,
-0.006337551400065422,
0.0027046736795455217,
-0.010722588747739792,
0.09521765261888504,
-0.02200537547469139,
-0.13891813158988953,
0.06453306972980499,
0.07663491368293762,
0.026231886819005013,
0.023004570975899696,
-0.04374408349394798,
-0.09938028454780579,
0.05800047144293785,
0.05392298474907875,
0.04782060533761978,
0.014296186156570911,
-0.07336080819368362,
-0.03306520730257034,
0.2310134768486023,
-0.10769093781709671,
0.03517160564661026,
0.018752751871943474,
-0.01665440760552883,
0.13193956017494202,
0.024515969678759575,
0.012560788542032242,
-0.010993245989084244,
0.0006222125375643373,
-0.07208145409822464,
-0.026980625465512276,
-0.07280055433511734,
-0.025803012773394585,
0.08756652474403381,
-0.051015663892030716,
-0.004876249935477972,
-0.19039994478225708,
-0.18118451535701752,
-0.005339108407497406,
-0.016468394547700882,
-0.07150747627019882,
-0.01704196259379387,
0.0011343819787725806,
0.014592640101909637,
-0.029311738908290863,
0.0027715612668544054,
-0.010088476352393627,
-0.009692125022411346,
0.024929290637373924,
0.118064284324646,
0.062490254640579224,
-0.09080521017313004,
0.024469710886478424,
-0.10012995451688766,
0.06235755607485771,
-0.19791042804718018,
-0.020904555916786194,
-0.12218789011240005,
0.10341945290565491,
-0.11424605548381805,
-0.04672497510910034,
-0.07366541028022766,
-0.027966169640421867,
0.0923282578587532,
0.16938048601150513,
-0.16169370710849762,
-0.003851439571008086,
0.07817358523607254,
-0.15781058371067047,
-0.1342722326517105,
0.14419648051261902,
0.05538134649395943,
0.04324358329176903,
0.08458942174911499,
0.10262781381607056,
0.17824462056159973,
-0.12664231657981873,
-0.04028411582112312,
-0.013187301345169544,
0.0394052229821682,
-0.05675820633769035,
0.056720927357673645,
-0.0025289286859333515,
-0.08534134179353714,
0.053086720407009125,
0.057864896953105927,
-0.010725836269557476,
-0.02124214358627796,
-0.07115073502063751,
-0.10490047186613083,
-0.06935220211744308,
0.0986715778708458,
-0.000372276030248031,
0.012656759470701218,
-0.08262909948825836,
-0.04969983920454979,
-0.06255578994750977,
0.09651170670986176,
-0.01549903117120266,
-0.00245436723344028,
-0.064341239631176,
0.22985957562923431,
-0.08933304250240326,
-0.02015039511024952,
-0.08918526768684387,
-0.04154393449425697,
0.04847760125994682,
0.01545618288218975,
-0.010432141833007336,
0.004830631427466869,
0.07193588465452194,
0.010731433518230915,
-0.016016313806176186,
-0.038059983402490616,
0.03768732026219368,
0.02153722383081913,
-0.07002642750740051,
-0.08037204295396805,
0.010604479350149632,
-0.060913264751434326,
0.06502474099397659,
-0.11424162238836288,
0.04622892290353775,
0.160542830824852,
0.13729745149612427,
-0.0091891810297966,
0.0055148727260529995,
0.007655964232981205,
0.042235441505908966,
-0.008878268301486969,
-0.012501712888479233,
0.029946159571409225,
-0.04053056240081787,
-0.05844304338097572,
-0.008437065407633781,
-0.04664523899555206,
0.10422877222299576,
0.09281617403030396,
0.030710933730006218,
-0.05281052738428116,
-0.1178559735417366,
-0.019726203754544258,
-0.0028569521382451057,
0.028966190293431282,
-0.04503101855516434,
0.02581367827951908,
0.0031409801449626684,
0.06555919349193573,
-0.08561013638973236,
-0.011274914257228374,
0.044334784150123596,
-0.06767916679382324,
-0.05785026028752327,
0.11326771974563599,
0.02849504165351391,
-0.1547730267047882,
0.0720336064696312,
0.21300320327281952,
-0.06342841684818268,
0.12050143629312515,
0.03544686734676361,
-0.04830826073884964,
-0.016640543937683105,
0.07413408160209656,
0.005784250795841217,
0.10734709352254868,
-0.13331831991672516,
0.07617448270320892,
0.03316879644989967,
-0.00460860738530755,
0.03581885248422623,
-0.11208206415176392,
-0.015242493711411953,
-0.010197140276432037,
-0.025560077279806137,
0.004199638497084379,
0.058629464358091354,
-0.013687490485608578,
0.07087051123380661,
0.025855200365185738,
-0.0058033582754433155,
0.028509585186839104,
0.012311909347772598,
-0.06216641888022423,
0.21106182038784027,
-0.08771204948425293,
-0.183822900056839,
-0.1615758091211319,
-0.016545938327908516,
-0.06065374240279198,
-0.0013126709964126348,
0.06291184574365616,
-0.08852045983076096,
-0.03481578454375267,
-0.04419548809528351,
0.00224266923032701,
0.06035435199737549,
0.02568160742521286,
-0.020571919158101082,
0.015828095376491547,
0.028875956311821938,
-0.07010132074356079,
-0.04840023070573807,
0.039161890745162964,
-0.05327139422297478,
0.06994914263486862,
-0.07053742557764053,
0.13352730870246887,
0.09375789016485214,
-0.01950441300868988,
-0.004660324193537235,
-0.004747059661895037,
0.18138472735881805,
-0.0705086812376976,
0.0942312628030777,
0.25073856115341187,
-0.01456285547465086,
0.06840895861387253,
0.1272696852684021,
0.006093463394790888,
-0.06302006542682648,
0.032900914549827576,
-0.021442418918013573,
-0.022658947855234146,
-0.20766474306583405,
-0.06125522032380104,
-0.046439774334430695,
0.08224062621593475,
0.05411599203944206,
0.046112291514873505,
0.030074622482061386,
0.14630581438541412,
0.0045186979696154594,
-0.038524847477674484,
-0.023890694603323936,
0.07513109594583511,
0.21686120331287384,
-0.019243745133280754,
0.10620380938053131,
-0.04870089888572693,
-0.02120940014719963,
0.079536572098732,
0.17283819615840912,
0.09070674329996109,
0.016334999352693558,
0.04710825905203819,
0.07061025500297546,
0.10477893799543381,
0.04329909384250641,
0.051286887377500534,
0.025703083723783493,
-0.058269500732421875,
-0.0030536481644958258,
-0.09209815412759781,
0.006119954865425825,
0.03843385726213455,
-0.0814865231513977,
0.034648772329092026,
-0.05371824651956558,
-0.016402440145611763,
0.05066978558897972,
0.11909428983926773,
0.07192884385585785,
-0.2631883919239044,
-0.040888864547014236,
0.04013974219560623,
-0.0009504471672698855,
-0.027620218694210052,
-0.019751159474253654,
0.0017980493139475584,
-0.058672379702329636,
0.09736260026693344,
-0.024969173595309258,
0.08136360347270966,
0.035721421241760254,
-0.005985172465443611,
0.034812066704034805,
0.09133788198232651,
-0.02913125976920128,
0.09554708003997803,
-0.21516922116279602,
0.18373645842075348,
0.02295631915330887,
0.04316752403974533,
-0.05142378434538841,
0.03765585273504257,
-0.003490620292723179,
0.18725845217704773,
0.08472710102796555,
-0.012399696744978428,
0.025907034054398537,
-0.1275905817747116,
-0.12330960482358932,
0.05184770002961159,
0.026821034029126167,
0.05570893734693527,
0.047263506799936295,
0.001196402939967811,
-0.020681077614426613,
-0.03258184716105461,
0.05129342898726463,
-0.21032123267650604,
-0.06167188659310341,
0.07148944586515427,
-0.011204572394490242,
0.03598534315824509,
-0.07785408198833466,
-0.08120903372764587,
-0.05224233120679855,
0.19958356022834778,
-0.02659372240304947,
-0.04711182415485382,
-0.12564679980278015,
-0.07961434870958328,
0.08526977151632309,
-0.1118084117770195,
0.0509236678481102,
-0.05933219939470291,
0.06300975382328033,
-0.06785091757774353,
-0.06729986518621445,
0.10284670442342758,
-0.10539299994707108,
-0.15435482561588287,
-0.04047830030322075,
0.12486319243907928,
-0.04201792553067207,
0.0005599252181127667,
0.05958980694413185,
-0.021968519315123558,
-0.03600787743926048,
-0.08466143906116486,
-0.022584594786167145,
0.06605140119791031,
-0.024634676054120064,
-0.003242680337280035,
-0.08094415813684464,
-0.048851378262043,
-0.07065369188785553,
-0.00008534992230124772,
0.09520015120506287,
0.27604156732559204,
-0.002918071113526821,
0.012057994492352009,
0.09829258173704147,
-0.0438111238181591,
-0.18447962403297424,
0.052445150911808014,
-0.029132114723324776,
0.007660580333322287,
-0.04813198372721672,
-0.060767702758312225,
0.08823943138122559,
0.17606793344020844,
-0.029800081625580788,
0.14612561464309692,
-0.1871211677789688,
-0.12124349921941757,
0.025149788707494736,
0.0915672779083252,
0.13402059674263,
-0.1107674390077591,
-0.05852348357439041,
-0.06924034655094147,
-0.18831118941307068,
-0.026973892003297806,
-0.023187391459941864,
0.04835972562432289,
-0.06225311756134033,
0.03569575399160385,
-0.002494262298569083,
-0.07580921798944473,
0.13966502249240875,
-0.013016043230891228,
-0.01425819844007492,
-0.04544268548488617,
0.019000735133886337,
-0.003787518246099353,
-0.01113686803728342,
0.09890460222959518,
-0.04658200219273567,
0.021151214838027954,
-0.10192040354013443,
-0.04850722849369049,
-0.056359030306339264,
0.10594859719276428,
-0.06119707226753235,
-0.044714827090501785,
0.006050133612006903,
0.0656987875699997,
-0.07147897034883499,
-0.04229917377233505,
-0.012984152883291245,
-0.08241966366767883,
0.09226515889167786,
0.2177598774433136,
0.1377508044242859,
-0.12778626382350922,
0.008748851716518402,
0.046214375644922256,
-0.08681865781545639,
0.0430026613175869,
-0.06020893529057503,
0.03093510866165161,
0.0975102111697197,
-0.013594472780823708,
0.04822235926985741,
0.01217400748282671,
-0.06873051822185516,
0.0027897371910512447,
0.1059534028172493,
-0.15134119987487793,
-0.0059519074857234955,
-0.04153197631239891,
0.03494152054190636,
-0.09760580956935883,
0.08076156675815582,
0.1949848085641861,
-0.023787111043930054,
-0.03545069321990013,
-0.013677101582288742,
0.008779946714639664,
-0.07582991570234299,
0.19958104193210602,
0.11498278379440308,
0.0466822013258934,
-0.025971392169594765,
0.0503806546330452,
-0.05179031565785408,
-0.09339820593595505,
-0.036696333438158035,
0.08392950892448425,
-0.08281132578849792,
-0.11768902093172073,
-0.0481739267706871,
0.04959091171622276,
-0.07883629947900772,
-0.055642269551754,
-0.15939031541347504,
-0.08599284291267395,
0.029867418110370636,
0.1648363322019577,
0.09402921795845032,
-0.011892280541360378,
0.02906028740108013,
-0.047154489904642105,
-0.022082431241869926,
0.11543131619691849,
0.07873108983039856,
0.11372050642967224,
-0.12158992141485214,
0.010329054668545723,
-0.07059404999017715,
0.046598657965660095,
-0.04144958779215813,
0.023272983729839325,
-0.1460828185081482,
-0.03558914363384247,
-0.13715393841266632,
-0.0018138265004381537,
-0.09454949200153351,
0.0020938883535563946,
-0.05141182243824005,
-0.05308947712182999,
-0.03941948711872101,
0.008310781791806221,
-0.044659070670604706,
0.01474970206618309,
-0.019627010449767113,
0.03471887856721878,
-0.09778626263141632,
0.01582443155348301,
0.09062597155570984,
-0.04787175729870796,
0.02548344060778618,
-0.04635064676403999,
-0.030640047043561935,
-0.023338571190834045,
-0.17324672639369965,
0.050772588700056076,
0.04823591932654381,
0.022645501419901848,
0.037339307367801666,
-0.08773910254240036,
0.04436826705932617,
0.0640348345041275,
0.055005524307489395,
0.04873085394501686,
0.09940506517887115,
-0.11757977306842804,
0.014947102405130863,
0.0019783645402640104,
-0.13292363286018372,
-0.03815213963389397,
-0.00345158320851624,
0.1162717416882515,
0.008538452908396721,
0.08348871022462845,
-0.08286680281162262,
0.05134354159235954,
-0.11480712890625,
-0.004970074165612459,
0.010694686323404312,
-0.09192129969596863,
0.00883154571056366,
-0.0556737445294857,
0.04568927735090256,
0.0420297235250473,
0.10465571284294128,
-0.03306330740451813,
0.05249781534075737,
0.041957952082157135,
-0.07294610142707825,
0.1092018261551857,
0.036881305277347565,
0.15998488664627075,
0.01881222240626812,
0.030822038650512695,
-0.08983928710222244,
0.050859980285167694,
-0.0038328072987496853,
-0.02803667262196541,
0.05868423357605934,
0.18274874985218048,
-0.07695215195417404,
0.0794655904173851,
0.09295662492513657,
0.050588030368089676,
-0.03217845410108566,
-0.11523782461881638,
-0.09068667143583298,
0.04159931465983391,
-0.04112686961889267,
0.08109155297279358,
0.0881594642996788,
-0.14567214250564575,
-0.028267867863178253,
-0.012458615005016327,
-0.06800764054059982,
-0.10615068674087524,
-0.1559394747018814,
-0.11630641669034958,
-0.15068359673023224,
0.026230251416563988,
-0.08452516794204712,
0.016783801838755608,
-0.023908033967018127,
0.020746760070323944,
-0.03132402151823044,
0.21253322064876556,
0.012935423292219639,
-0.0071921939961612225,
0.07216852158308029,
0.015016858465969563,
0.022968679666519165,
0.03477603942155838,
-0.03289172798395157,
0.0209515281021595,
-0.03295927867293358,
-0.03966553881764412,
-0.005026379134505987,
-0.03470248728990555,
-0.01963910087943077,
-0.03196408599615097,
-0.07007373869419098,
-0.04203013703227043,
0.03267207741737366,
0.03134042024612427,
0.05266153812408447,
0.010936307720839977,
0.00712985172867775,
0.02123243175446987,
0.09693501889705658,
-0.022694354876875877,
-0.11216439306735992,
-0.09933768212795258,
0.15265928208827972,
-0.021381579339504242,
0.0746636837720871,
-0.03791738674044609,
-0.06614380329847336,
0.09116587042808533,
0.19935962557792664,
0.12468709796667099,
-0.010842196643352509,
0.010550888255238533,
0.009275762364268303,
0.014760305173695087,
0.006171237211674452,
0.06208939105272293,
0.047972746193408966,
0.05028745159506798,
-0.048286668956279755,
0.019400224089622498,
-0.013960042037069798,
-0.0442388541996479,
-0.12257110327482224,
-0.047508854418992996,
0.05586055666208267,
-0.025380514562129974,
-0.06494404375553131,
0.13393305242061615,
0.050396498292684555,
0.05728866904973984,
0.034027133136987686,
-0.13693352043628693,
-0.14219246804714203,
-0.045946717262268066,
0.13343890011310577,
-0.04741286113858223,
0.08152064681053162,
-0.056945160031318665,
-0.03551600128412247,
0.1210571676492691,
0.008612439036369324,
-0.07573115080595016,
-0.10752992331981659,
0.030995305627584457,
-0.050575241446495056,
0.0684528574347496,
-0.018011070787906647,
0.07087954133749008,
0.14069972932338715,
-0.021007554605603218,
-0.1081644743680954,
0.07012525200843811,
0.07142425328493118,
-0.10152718424797058,
0.05071904510259628,
0.04209700971841812,
0.008050997741520405,
0.08207984268665314,
0.06398407369852066,
-0.06273292005062103,
0.03354512155056,
-0.04672159627079964,
0.016969073563814163,
-0.10721544921398163,
0.095880426466465,
-0.09986478090286255,
0.13280992209911346,
0.12957105040550232,
-0.0556781105697155,
-0.000042849191231653094,
-0.05404464900493622,
0.09364952147006989,
0.05291860178112984,
-0.010324847884476185,
-0.012882638722658157,
-0.19289353489875793,
-0.02914935164153576,
0.03658014163374901,
0.04572853818535805,
-0.2179250419139862,
-0.08451829850673676,
-0.1264818161725998,
0.01958456076681614,
-0.07442475110292435,
0.09208835661411285,
0.09363783150911331,
0.01038285531103611,
0.005463517736643553,
-0.21964985132217407,
-0.041372887790203094,
0.09406254440546036,
-0.10667339712381363,
-0.05495133250951767
] |
null | null | transformers |
# Pasta-Lake-7b 4bpw
Exllama quant of [Test157t/Pasta-Lake-7b](https://huggingface.co/Test157t/Pasta-Lake-7b)
## Other quants:
EXL2: [8bpw](https://huggingface.co/Kooten/Pasta-Lake-7b-8bpw-exl2), [6bpw](https://huggingface.co/Kooten/Pasta-Lake-7b-6bpw-exl2), [5bpw](https://huggingface.co/Kooten/Pasta-Lake-7b-5bpw-exl2), [4bpw](https://huggingface.co/Kooten/Pasta-Lake-7b-4bpw-exl2)
## Prompt format: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Input:
{input}
### Response:
```
## Contact
Kooten on discord
[ko-fi.com/kooten](https://ko-fi.com/kooten) if you would like to support me
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Test157t/Pasta-PrimaMaid-7b", "macadeliccc/WestLake-7B-v2-laser-truthy-dpo"]} | text-generation | Kooten/Pasta-Lake-7b-4bpw-exl2 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"base_model:Test157t/Pasta-PrimaMaid-7b",
"base_model:macadeliccc/WestLake-7B-v2-laser-truthy-dpo",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T08:55:04+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #mergekit #merge #base_model-Test157t/Pasta-PrimaMaid-7b #base_model-macadeliccc/WestLake-7B-v2-laser-truthy-dpo #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Pasta-Lake-7b 4bpw
Exllama quant of Test157t/Pasta-Lake-7b
## Other quants:
EXL2: 8bpw, 6bpw, 5bpw, 4bpw
## Prompt format: Alpaca
## Contact
Kooten on discord
URL if you would like to support me
| [
"# Pasta-Lake-7b 4bpw\nExllama quant of Test157t/Pasta-Lake-7b",
"## Other quants:\nEXL2: 8bpw, 6bpw, 5bpw, 4bpw",
"## Prompt format: Alpaca",
"## Contact\nKooten on discord\n\nURL if you would like to support me"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #base_model-Test157t/Pasta-PrimaMaid-7b #base_model-macadeliccc/WestLake-7B-v2-laser-truthy-dpo #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Pasta-Lake-7b 4bpw\nExllama quant of Test157t/Pasta-Lake-7b",
"## Other quants:\nEXL2: 8bpw, 6bpw, 5bpw, 4bpw",
"## Prompt format: Alpaca",
"## Contact\nKooten on discord\n\nURL if you would like to support me"
] | [
98,
27,
28,
8,
14
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #base_model-Test157t/Pasta-PrimaMaid-7b #base_model-macadeliccc/WestLake-7B-v2-laser-truthy-dpo #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Pasta-Lake-7b 4bpw\nExllama quant of Test157t/Pasta-Lake-7b## Other quants:\nEXL2: 8bpw, 6bpw, 5bpw, 4bpw## Prompt format: Alpaca## Contact\nKooten on discord\n\nURL if you would like to support me"
] | [
-0.16919256746768951,
0.0861310064792633,
-0.0027180451434105635,
0.04593861103057861,
0.028731223195791245,
0.019892524927854538,
0.09327368438243866,
0.13093982636928558,
0.13956736028194427,
0.0313880480825901,
0.10581771284341812,
0.09485682100057602,
0.030898621305823326,
0.21357089281082153,
-0.0720696821808815,
-0.12409310042858124,
0.015881340950727463,
0.056409161537885666,
0.032153915613889694,
0.12647606432437897,
0.10416572540998459,
-0.07088272273540497,
0.09609878808259964,
-0.01808745041489601,
-0.11002582311630249,
0.06661117821931839,
0.0004747483762912452,
-0.06344200670719147,
0.0977867916226387,
0.05369747057557106,
0.06931596994400024,
0.0866735652089119,
-0.008592607453465462,
-0.14861789345741272,
0.038854047656059265,
0.0071078818291425705,
-0.0028124176897108555,
0.03674031049013138,
0.08407092094421387,
0.011122453026473522,
-0.058277327567338943,
-0.029172753915190697,
-0.008696768432855606,
0.058537665754556656,
-0.07765907049179077,
0.003257079515606165,
-0.096478670835495,
0.08171990513801575,
0.05924779176712036,
0.08166433870792389,
-0.006226021330803633,
0.15398892760276794,
-0.05577898025512695,
0.09644166380167007,
0.19290073215961456,
-0.21133682131767273,
-0.0805027186870575,
0.0458262637257576,
0.08146575838327408,
0.038182977586984634,
0.005716074723750353,
0.024380851536989212,
0.08733297139406204,
-0.013904232531785965,
-0.1204322874546051,
-0.06665460765361786,
0.10709639638662338,
-0.04907574877142906,
-0.08581828325986862,
0.016880735754966736,
0.23158051073551178,
0.007183954585343599,
-0.08620747923851013,
0.003236246993765235,
-0.06285485625267029,
-0.03806240111589432,
0.004489758983254433,
-0.07879360020160675,
-0.013391655869781971,
-0.010577972047030926,
0.02469691075384617,
-0.04382368549704552,
-0.08576169610023499,
-0.0916464701294899,
-0.06879167258739471,
0.18016692996025085,
0.017819540575146675,
-0.02632293663918972,
-0.03224882483482361,
0.08662214875221252,
-0.20848752558231354,
-0.144876629114151,
-0.049087099730968475,
-0.0900351032614708,
0.03525499254465103,
-0.06609616428613663,
-0.031675953418016434,
-0.09865206480026245,
0.10917411744594574,
0.20517653226852417,
0.008920288644731045,
0.06280303746461868,
-0.005798192694783211,
0.0031156507320702076,
-0.011915046721696854,
0.09523840993642807,
-0.022173874080181122,
-0.13872359693050385,
0.06531330943107605,
0.07590491324663162,
0.027201076969504356,
0.022426703944802284,
-0.045159343630075455,
-0.09864389151334763,
0.05693642795085907,
0.05391422659158707,
0.047002408653497696,
0.01560367364436388,
-0.07254792004823685,
-0.03284522891044617,
0.23262298107147217,
-0.10817189514636993,
0.035063572227954865,
0.018257133662700653,
-0.016296252608299255,
0.13152770698070526,
0.02509862557053566,
0.012595969252288342,
-0.010261302813887596,
-0.0008988396730273962,
-0.07157807052135468,
-0.026802338659763336,
-0.0721668004989624,
-0.02583169750869274,
0.08749838173389435,
-0.0487857386469841,
-0.0049857269041240215,
-0.19034534692764282,
-0.18132102489471436,
-0.005309120751917362,
-0.016198495402932167,
-0.07161983102560043,
-0.016845297068357468,
0.0025267426390200853,
0.014085573144257069,
-0.028782568871974945,
0.002628101035952568,
-0.011531160213053226,
-0.01028132438659668,
0.024585947394371033,
0.11776913702487946,
0.06303402781486511,
-0.09200171381235123,
0.02390468865633011,
-0.09968456625938416,
0.06276436895132065,
-0.19973137974739075,
-0.021317344158887863,
-0.12191157042980194,
0.10362266004085541,
-0.11256705224514008,
-0.04730349779129028,
-0.07186783850193024,
-0.027059640735387802,
0.09185449033975601,
0.16863559186458588,
-0.16160142421722412,
-0.0038383458741009235,
0.07638443261384964,
-0.15761899948120117,
-0.13465872406959534,
0.1447182595729828,
0.05491633340716362,
0.043138787150382996,
0.08419234305620193,
0.1030750498175621,
0.17782284319400787,
-0.12612590193748474,
-0.04016188159584999,
-0.012914016842842102,
0.038670215755701065,
-0.05567276477813721,
0.056212518364191055,
-0.0023253243416547775,
-0.08426234871149063,
0.052839308977127075,
0.05991273373365402,
-0.011630930937826633,
-0.020887592807412148,
-0.07097548991441727,
-0.10379377007484436,
-0.06867154687643051,
0.09918752312660217,
-0.0010364203481003642,
0.012292829342186451,
-0.08376839756965637,
-0.050733473151922226,
-0.06270570307970047,
0.09678307175636292,
-0.016584202647209167,
-0.0019391936948522925,
-0.06518269330263138,
0.22988300025463104,
-0.0886106789112091,
-0.02085156925022602,
-0.0882028117775917,
-0.04362810030579567,
0.047891948372125626,
0.018245119601488113,
-0.010080337524414062,
0.005730899050831795,
0.0721287801861763,
0.011864442378282547,
-0.01719929836690426,
-0.03801523894071579,
0.03855111822485924,
0.021634818986058235,
-0.0696076899766922,
-0.08010631799697876,
0.010241701267659664,
-0.06037071719765663,
0.06655964255332947,
-0.11471466720104218,
0.04591315612196922,
0.16164721548557281,
0.13700994849205017,
-0.009646988473832607,
0.006350899115204811,
0.00722895935177803,
0.042110949754714966,
-0.00902353785932064,
-0.013161337934434414,
0.030762208625674248,
-0.039454568177461624,
-0.05895392969250679,
-0.006639643106609583,
-0.046544961631298065,
0.10374974459409714,
0.09393219649791718,
0.028966769576072693,
-0.05317465960979462,
-0.1162966638803482,
-0.02005614899098873,
-0.0029080247040838003,
0.03054056130349636,
-0.046591851860284805,
0.029487263411283493,
0.002776246052235365,
0.06580047309398651,
-0.0851454809308052,
-0.011604676954448223,
0.043497972190380096,
-0.06806810200214386,
-0.056381672620773315,
0.1127888411283493,
0.02596307545900345,
-0.15260545909404755,
0.0718858540058136,
0.21137648820877075,
-0.06351252645254135,
0.11985594034194946,
0.03510655090212822,
-0.04676569998264313,
-0.01632317528128624,
0.07278706133365631,
0.006237001158297062,
0.1077524945139885,
-0.1321260631084442,
0.07611677795648575,
0.03326092287898064,
-0.004192059393972158,
0.036192793399095535,
-0.11293445527553558,
-0.015477965585887432,
-0.010921499691903591,
-0.026087315753102303,
0.004309064708650112,
0.0579228550195694,
-0.013652009889483452,
0.07101654261350632,
0.02615255117416382,
-0.00633953046053648,
0.029458219185471535,
0.012277131900191307,
-0.06223132088780403,
0.2100543975830078,
-0.08862760663032532,
-0.18330593407154083,
-0.16316525638103485,
-0.018401766195893288,
-0.060450293123722076,
-0.00047537896898575127,
0.06213974580168724,
-0.08879455924034119,
-0.034362487494945526,
-0.044232115149497986,
0.001603467739187181,
0.060041263699531555,
0.025133132934570312,
-0.020213626325130463,
0.015370014123618603,
0.029705768451094627,
-0.06992076337337494,
-0.04906091094017029,
0.039192359894514084,
-0.05368071794509888,
0.07118293642997742,
-0.07037225365638733,
0.1337798535823822,
0.09392216801643372,
-0.0196530744433403,
-0.005270505324006081,
-0.004074796102941036,
0.1808672845363617,
-0.07088787108659744,
0.09588375687599182,
0.2508968412876129,
-0.015764528885483742,
0.068731389939785,
0.12674687802791595,
0.0067763556726276875,
-0.06373456865549088,
0.03260102868080139,
-0.02153291366994381,
-0.023286910727620125,
-0.2073533684015274,
-0.06120448559522629,
-0.045922841876745224,
0.08192714303731918,
0.05310971289873123,
0.04607873782515526,
0.02875663712620735,
0.14648205041885376,
0.0043397932313382626,
-0.03778941556811333,
-0.02488905005156994,
0.07525713741779327,
0.2184952348470688,
-0.0195584986358881,
0.10664024949073792,
-0.048635680228471756,
-0.021437687799334526,
0.08022787421941757,
0.17295080423355103,
0.09033770114183426,
0.015623850747942924,
0.048029255121946335,
0.07121279835700989,
0.10501359403133392,
0.04372606799006462,
0.05050389841198921,
0.025200288742780685,
-0.05828535556793213,
-0.0030929269269108772,
-0.09209847450256348,
0.006363663822412491,
0.03828475624322891,
-0.08268602192401886,
0.03403685986995697,
-0.053174879401922226,
-0.01683804579079151,
0.050612930208444595,
0.11771614849567413,
0.07250973582267761,
-0.2653735280036926,
-0.0422198511660099,
0.04033517464995384,
-0.0006596786552108824,
-0.026979420334100723,
-0.019631754606962204,
0.0021386118605732918,
-0.05895250290632248,
0.09834220260381699,
-0.024490194395184517,
0.0807916447520256,
0.03404461592435837,
-0.005725040566176176,
0.036579832434654236,
0.09029478579759598,
-0.02927163615822792,
0.0957990363240242,
-0.21083714067935944,
0.18268807232379913,
0.022946076467633247,
0.042912013828754425,
-0.051175557076931,
0.037491749972105026,
-0.0021547151263803244,
0.1882864534854889,
0.0837353840470314,
-0.012027205899357796,
0.025834882631897926,
-0.12721532583236694,
-0.12318208813667297,
0.052209947258234024,
0.026230832561850548,
0.05592183396220207,
0.048703789710998535,
0.0019199014641344547,
-0.020525455474853516,
-0.03299928084015846,
0.05030735954642296,
-0.20844413340091705,
-0.06238071620464325,
0.07074327021837234,
-0.01070905290544033,
0.03642851486802101,
-0.07769212871789932,
-0.08173579722642899,
-0.05303032696247101,
0.20249268412590027,
-0.022479576990008354,
-0.04825926199555397,
-0.12571054697036743,
-0.08187780529260635,
0.0859365165233612,
-0.11158239096403122,
0.05005809664726257,
-0.058455754071474075,
0.06309623271226883,
-0.06687421351671219,
-0.06681690365076065,
0.10214261710643768,
-0.10540507733821869,
-0.154659703373909,
-0.0408545657992363,
0.12559926509857178,
-0.042360663414001465,
0.000777052016928792,
0.05971408262848854,
-0.021178849041461945,
-0.03605644777417183,
-0.08492564409971237,
-0.02195144072175026,
0.06378570199012756,
-0.021431319415569305,
-0.0044807433150708675,
-0.08158048987388611,
-0.043569132685661316,
-0.07000471651554108,
0.000009908699212246574,
0.09519479423761368,
0.2752794623374939,
-0.002707965672016144,
0.012029241770505905,
0.0981052815914154,
-0.043008480221033096,
-0.18567736446857452,
0.05113386735320091,
-0.02960391342639923,
0.007129962556064129,
-0.049156010150909424,
-0.06107132136821747,
0.08886716514825821,
0.17546038329601288,
-0.029460115358233452,
0.14761820435523987,
-0.18385136127471924,
-0.12159758061170578,
0.02383607067167759,
0.09143319725990295,
0.134095698595047,
-0.11125088483095169,
-0.05876750871539116,
-0.07019922137260437,
-0.1891697645187378,
-0.02819499559700489,
-0.025240756571292877,
0.04861855134367943,
-0.06214062124490738,
0.0389561764895916,
-0.003188919275999069,
-0.075227290391922,
0.14085015654563904,
-0.011591581627726555,
-0.014886868186295033,
-0.04662322625517845,
0.019121892750263214,
-0.0032010087743401527,
-0.011141173541545868,
0.09755152463912964,
-0.0458991564810276,
0.021242564544081688,
-0.10364352911710739,
-0.048634257167577744,
-0.056197039783000946,
0.10503474622964859,
-0.06093033775687218,
-0.04490263760089874,
0.005771873984485865,
0.06529440730810165,
-0.07200441509485245,
-0.04262653365731239,
-0.014481274411082268,
-0.08272921293973923,
0.09155162423849106,
0.21766377985477448,
0.13779129087924957,
-0.12931211292743683,
0.008166828192770481,
0.0469217412173748,
-0.08723139762878418,
0.04233816638588905,
-0.05960585176944733,
0.030629435554146767,
0.09745695441961288,
-0.01405490655452013,
0.04779501259326935,
0.012643047608435154,
-0.0684998407959938,
0.0037550691049546003,
0.10614047944545746,
-0.15141478180885315,
-0.003387933596968651,
-0.04095213860273361,
0.03214048221707344,
-0.09733906388282776,
0.08050545305013657,
0.19589461386203766,
-0.022820286452770233,
-0.03490231931209564,
-0.013285605236887932,
0.008373663760721684,
-0.07607331871986389,
0.19836224615573883,
0.11584250628948212,
0.04639521613717079,
-0.026228182017803192,
0.04958883672952652,
-0.05217308923602104,
-0.0920446589589119,
-0.03716474398970604,
0.08455591648817062,
-0.08373653143644333,
-0.11796489357948303,
-0.049993693828582764,
0.050185710191726685,
-0.07939734309911728,
-0.056524857878685,
-0.15993352234363556,
-0.08654392510652542,
0.029565436765551567,
0.16192522644996643,
0.09415538609027863,
-0.011661852709949017,
0.02840125374495983,
-0.04755357652902603,
-0.022168850526213646,
0.11438208073377609,
0.07815144211053848,
0.11363127082586288,
-0.12141366302967072,
0.010256629437208176,
-0.07079478353261948,
0.048081912100315094,
-0.041680607944726944,
0.02365470491349697,
-0.14539815485477448,
-0.0354536697268486,
-0.14017993211746216,
-0.0013547692215070128,
-0.09377229958772659,
0.0020663482137024403,
-0.051175761967897415,
-0.053042683750391006,
-0.0399191714823246,
0.008226788602769375,
-0.04481365904211998,
0.01461241114884615,
-0.018701748922467232,
0.034168876707553864,
-0.09699483215808868,
0.015381529927253723,
0.09062836319208145,
-0.04852348193526268,
0.02614014782011509,
-0.04544340446591377,
-0.030375249683856964,
-0.02358201891183853,
-0.16986075043678284,
0.04969635605812073,
0.04838497191667557,
0.02306210622191429,
0.03755953907966614,
-0.08735579997301102,
0.04341304302215576,
0.06348350644111633,
0.055008988827466965,
0.049196500331163406,
0.10055247694253922,
-0.11686395108699799,
0.01613413356244564,
0.0026157675310969353,
-0.13327646255493164,
-0.03839492425322533,
-0.00325010041706264,
0.11618181318044662,
0.008848322555422783,
0.08344785124063492,
-0.08333137631416321,
0.051043495535850525,
-0.11521793901920319,
-0.005432659760117531,
0.010566997341811657,
-0.09201152622699738,
0.010633999481797218,
-0.055839903652668,
0.04580569639801979,
0.042250391095876694,
0.10548673570156097,
-0.03395221754908562,
0.051003582775592804,
0.04123597964644432,
-0.0728704035282135,
0.10925735533237457,
0.03654366359114647,
0.15987327694892883,
0.0187019445002079,
0.030548367649316788,
-0.09086496382951736,
0.05034082382917404,
-0.003980875015258789,
-0.029520170763134956,
0.05702545866370201,
0.18154005706310272,
-0.07440536469221115,
0.07856129854917526,
0.09348707646131516,
0.05083513632416725,
-0.03236303851008415,
-0.11583293974399567,
-0.09099289774894714,
0.040956705808639526,
-0.040050309151411057,
0.08098294585943222,
0.08788739889860153,
-0.14506970345973969,
-0.02841874584555626,
-0.013174793682992458,
-0.06859467178583145,
-0.10525704175233841,
-0.15388289093971252,
-0.11603537946939468,
-0.15033872425556183,
0.026306089013814926,
-0.08438413590192795,
0.01613209955394268,
-0.021812070161104202,
0.02066791243851185,
-0.03127707913517952,
0.21224743127822876,
0.0114591671153903,
-0.00733281672000885,
0.07013899087905884,
0.015531360171735287,
0.02353549748659134,
0.03658435493707657,
-0.03328060731291771,
0.020836448296904564,
-0.03215152025222778,
-0.03974151238799095,
-0.004277539439499378,
-0.03350330889225006,
-0.01955980248749256,
-0.031921640038490295,
-0.07008274644613266,
-0.042528606951236725,
0.033476147800683975,
0.032960329204797745,
0.05218884348869324,
0.010616140440106392,
0.007360382936894894,
0.02117796428501606,
0.09609460085630417,
-0.022504521533846855,
-0.11296704411506653,
-0.09904982894659042,
0.15454646944999695,
-0.023004472255706787,
0.07376857846975327,
-0.03831943869590759,
-0.06615261733531952,
0.09118920564651489,
0.20091062784194946,
0.12737876176834106,
-0.01310036238282919,
0.0103031937032938,
0.009519622661173344,
0.014779332093894482,
0.006579091306775808,
0.06291193515062332,
0.04801718890666962,
0.04993264377117157,
-0.0484079010784626,
0.016460033133625984,
-0.015070840716362,
-0.043646249920129776,
-0.12378250062465668,
-0.04611502215266228,
0.05494542047381401,
-0.024938520044088364,
-0.0652204155921936,
0.13299767673015594,
0.05052890256047249,
0.0543474406003952,
0.03582064434885979,
-0.13700300455093384,
-0.1422828584909439,
-0.044852130115032196,
0.13337282836437225,
-0.0476311519742012,
0.08235499262809753,
-0.05710664018988609,
-0.03573863208293915,
0.12258543074131012,
0.00820060446858406,
-0.07571014761924744,
-0.10398343950510025,
0.031393811106681824,
-0.05175057053565979,
0.06824085861444473,
-0.01898716576397419,
0.0729118213057518,
0.14108054339885712,
-0.020724259316921234,
-0.10759831219911575,
0.07080675661563873,
0.0713287815451622,
-0.10220309346914291,
0.05103553086519241,
0.04130462184548378,
0.007977548986673355,
0.07909317314624786,
0.06444370746612549,
-0.06259340047836304,
0.03313986957073212,
-0.04525119811296463,
0.016019094735383987,
-0.10654457658529282,
0.09368596971035004,
-0.10047037154436111,
0.13320335745811462,
0.12957561016082764,
-0.05573373660445213,
0.0002492327184882015,
-0.05271875858306885,
0.09282287210226059,
0.05359528958797455,
-0.014241250231862068,
-0.0128518370911479,
-0.19364172220230103,
-0.029789216816425323,
0.03683853894472122,
0.04500024765729904,
-0.21996647119522095,
-0.08480002731084824,
-0.12568892538547516,
0.019812382757663727,
-0.0739055797457695,
0.09251724183559418,
0.09139009565114975,
0.010432526469230652,
0.005687684286385775,
-0.21538078784942627,
-0.04153412953019142,
0.09347984939813614,
-0.1070738360285759,
-0.055757325142621994
] |
null | null | transformers |
# Trinity
Trinity is a general purpose coding AI.
# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!
Check out the Prompt Enhancing feature! Access at: https://www.whiterabbitneo.com/
# Join Our Discord Server
Join us at: https://discord.gg/8Ynkrcbk92 (Updated on Dec 29th. Now permanent link to join)
# Sample Inference Code
```
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "/home/migel/models/Trinity"
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map="auto",
load_in_4bit=False,
load_in_8bit=True,
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
def generate_text(instruction):
tokens = tokenizer.encode(instruction)
tokens = torch.LongTensor(tokens).unsqueeze(0)
tokens = tokens.to("cuda")
instance = {
"input_ids": tokens,
"top_p": 1.0,
"temperature": 0.5,
"generate_len": 1024,
"top_k": 50,
}
length = len(tokens[0])
with torch.no_grad():
rest = model.generate(
input_ids=tokens,
max_length=length + instance["generate_len"],
use_cache=True,
do_sample=True,
top_p=instance["top_p"],
temperature=instance["temperature"],
top_k=instance["top_k"],
num_return_sequences=1,
)
output = rest[0][length:]
string = tokenizer.decode(output, skip_special_tokens=True)
answer = string.split("USER:")[0].strip()
return f"{answer}"
conversation = f"SYSTEM: You are an AI that can code. Answer with code."
while True:
user_input = input("You: ")
llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: "
answer = generate_text(llm_prompt)
print(answer)
conversation = f"{llm_prompt}{answer}"
# print(conversation)
json_data = {"prompt": user_input, "answer": answer}
# print(json_data)
# with open(output_file_path, "a") as output_file:
# output_file.write(json.dumps(json_data) + "\n")
```
write(json.dumps(json_data) + "\n")
```
| {"license": "other", "license_name": "deepseek-coder-33b", "license_link": "https://huggingface.co/deepseek-ai/deepseek-coder-33b-base/blob/main/LICENSE"} | text-generation | LoneStriker/Trinity-33B-v1.0-AWQ | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2024-02-09T09:01:24+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
|
# Trinity
!Trinity
Trinity is a general purpose coding AI.
# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!
Check out the Prompt Enhancing feature! Access at: URL
# Join Our Discord Server
Join us at: URL (Updated on Dec 29th. Now permanent link to join)
# Sample Inference Code
write(URL(json_data) + "\n")
'''
| [
"# Trinity\n\n\n!Trinity\n\n\nTrinity is a general purpose coding AI.",
"# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!\nCheck out the Prompt Enhancing feature! Access at: URL",
"# Join Our Discord Server\nJoin us at: URL (Updated on Dec 29th. Now permanent link to join)",
"# Sample Inference Code\n\n\n\nwrite(URL(json_data) + \"\\n\")\n\n'''"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n",
"# Trinity\n\n\n!Trinity\n\n\nTrinity is a general purpose coding AI.",
"# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!\nCheck out the Prompt Enhancing feature! Access at: URL",
"# Join Our Discord Server\nJoin us at: URL (Updated on Dec 29th. Now permanent link to join)",
"# Sample Inference Code\n\n\n\nwrite(URL(json_data) + \"\\n\")\n\n'''"
] | [
55,
17,
40,
25,
23
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n# Trinity\n\n\n!Trinity\n\n\nTrinity is a general purpose coding AI.# Our Offensive Cybersecurity Model WhiteRabbitNeo-33B-v1.2 model is now in beta!\nCheck out the Prompt Enhancing feature! Access at: URL# Join Our Discord Server\nJoin us at: URL (Updated on Dec 29th. Now permanent link to join)# Sample Inference Code\n\n\n\nwrite(URL(json_data) + \"\\n\")\n\n'''"
] | [
-0.08407554030418396,
0.155599445104599,
-0.003629707731306553,
0.06990107148885727,
0.13092148303985596,
0.013907522894442081,
0.17441563308238983,
0.017675066366791725,
0.05054430663585663,
0.02317153476178646,
0.1455802321434021,
0.10134031623601913,
0.006341624539345503,
0.14964987337589264,
-0.05210057646036148,
-0.03169906511902809,
0.08088522404432297,
-0.01229319628328085,
-0.05856779217720032,
0.06101348251104355,
0.05103064700961113,
-0.03870043531060219,
0.09011390060186386,
-0.09590983390808105,
0.010131681337952614,
0.024312548339366913,
0.016482369974255562,
-0.025409916415810585,
0.0591893196105957,
0.06632820516824722,
-0.005081514827907085,
0.03782854974269867,
-0.015990059822797775,
-0.08272108435630798,
0.05504922568798065,
0.040390077978372574,
-0.021910103037953377,
0.023601649329066277,
-0.053867172449827194,
0.042460907250642776,
-0.054730676114559174,
0.04118413105607033,
-0.040305305272340775,
0.0717279464006424,
-0.08477570116519928,
0.0646897479891777,
-0.14282192289829254,
0.02698725089430809,
0.06897394359111786,
0.1020820215344429,
0.03658534958958626,
0.23788389563560486,
-0.013652571476995945,
0.11339081823825836,
0.005033791996538639,
-0.2182561755180359,
-0.036126162856817245,
0.09326902031898499,
0.027457842603325844,
0.1954546719789505,
0.03860531374812126,
0.026358118280768394,
0.12371771782636642,
0.003397687803953886,
-0.05311193689703941,
-0.021088602021336555,
-0.074913889169693,
0.011067437008023262,
-0.10123841464519501,
-0.08692452311515808,
0.24835988879203796,
0.04125647246837616,
-0.028571579605340958,
0.07668451219797134,
-0.044382642954587936,
-0.05621073395013809,
-0.003716972190886736,
-0.0827079713344574,
-0.009736625477671623,
0.021405663341283798,
0.029516877606511116,
-0.06813633441925049,
-0.06821785867214203,
-0.021439747884869576,
-0.06372593343257904,
0.09914954006671906,
0.0331818088889122,
0.03717532381415367,
-0.09981679916381836,
-0.022479074075818062,
0.04405004903674126,
-0.08630747348070145,
-0.08794577419757843,
-0.04742487519979477,
0.09812814742326736,
-0.04028348997235298,
-0.08116134256124496,
-0.2139045000076294,
0.16435104608535767,
0.1507866531610489,
0.04080752283334732,
0.039210036396980286,
-0.14172039926052094,
0.02869938313961029,
-0.003588529536500573,
0.02310626208782196,
-0.03575635701417923,
-0.006110540125519037,
0.14963339269161224,
0.01612638495862484,
0.15333124995231628,
-0.02029426395893097,
-0.10683713108301163,
0.0060759857296943665,
0.07413547486066818,
0.136161670088768,
0.013703561387956142,
0.15987280011177063,
0.02343032695353031,
-0.005519188940525055,
0.23232506215572357,
-0.10635711252689362,
0.022051744163036346,
0.01862870715558529,
0.06399860978126526,
-0.05255744606256485,
0.04037082567811012,
-0.04850979149341583,
-0.10704682767391205,
-0.034490518271923065,
-0.05377744510769844,
-0.028745243325829506,
-0.054137684404850006,
-0.08945823460817337,
0.10528641194105148,
0.046182386577129364,
-0.0366925485432148,
-0.15119391679763794,
-0.024595966562628746,
0.01962437480688095,
-0.011809201911091805,
0.038429275155067444,
-0.0325857549905777,
-0.04406014829874039,
-0.08161289244890213,
-0.005876095034182072,
-0.028767596930265427,
-0.14650484919548035,
-0.0753098875284195,
0.05366608500480652,
0.0162945743650198,
0.0460708811879158,
-0.20579341053962708,
-0.01798536255955696,
-0.14850512146949768,
-0.0434102900326252,
-0.12263810634613037,
-0.025342827662825584,
-0.07344367355108261,
0.15390510857105255,
-0.027853650972247124,
0.03529978543519974,
-0.056640591472387314,
0.0187566839158535,
0.07462695240974426,
0.18760879337787628,
-0.1301194578409195,
0.038827985525131226,
0.18981194496154785,
-0.18368929624557495,
-0.18629509210586548,
0.14153483510017395,
0.014592878520488739,
-0.1499222218990326,
0.10585666447877884,
0.050513856112957,
0.11857801675796509,
-0.08010675013065338,
0.00599814485758543,
0.0025329957716166973,
-0.1819705367088318,
-0.11670643091201782,
-0.05258940905332565,
0.04534897580742836,
-0.13549312949180603,
0.0276664849370718,
0.07615692913532257,
0.03819042444229126,
-0.02513945661485195,
-0.013622978702187538,
-0.051926787942647934,
-0.0890665277838707,
-0.041012752801179886,
-0.04795796796679497,
-0.053290389478206635,
-0.13381066918373108,
-0.000436192232882604,
-0.0880729928612709,
0.08529304713010788,
-0.01110280491411686,
0.016385849565267563,
-0.1524081975221634,
0.07168835401535034,
-0.07397902011871338,
0.07747447490692139,
-0.028005806729197502,
-0.11555126309394836,
0.03741470351815224,
0.10675042867660522,
0.10840105265378952,
-0.06613177806138992,
0.07221448421478271,
-0.0062108831480145454,
0.04735840857028961,
0.01066604908555746,
0.11905132979154587,
0.02188993990421295,
-0.0838640034198761,
-0.06702538579702377,
0.002826565643772483,
-0.012535139918327332,
0.05719337239861488,
-0.15458977222442627,
0.025339361280202866,
0.13618673384189606,
0.05611912161111832,
-0.012826548889279366,
-0.004983048420399427,
0.038037292659282684,
-0.0026647725608199835,
-0.06365369260311127,
-0.029310837388038635,
-0.02187136746942997,
0.02345368079841137,
-0.02122851461172104,
0.16573061048984528,
-0.10146841406822205,
0.1058608815073967,
0.16875293850898743,
-0.07662253826856613,
0.012295976281166077,
0.1305491328239441,
0.003650340251624584,
0.03559102118015289,
0.13131870329380035,
-0.06342588365077972,
-0.1421542912721634,
0.004579066298902035,
0.0772305428981781,
-0.04276657477021217,
0.002750223036855459,
0.07944899052381516,
-0.08619891107082367,
-0.07582901418209076,
0.039174988865852356,
0.16054032742977142,
-0.30598077178001404,
0.09407949447631836,
0.12806960940361023,
-0.017469389364123344,
0.054812535643577576,
-0.028355609625577927,
-0.0018095169216394424,
-0.002898533595725894,
0.013119540177285671,
0.054359011352062225,
0.04054086282849312,
0.0015869918279349804,
0.01440880075097084,
-0.01600908488035202,
0.020384952425956726,
0.0255343746393919,
-0.06925546377897263,
-0.02708246000111103,
-0.013836230151355267,
-0.038849856704473495,
0.09134896099567413,
-0.0004272477235645056,
0.004981087055057287,
0.11638342589139938,
-0.05719166249036789,
-0.09729031473398209,
0.09360197931528091,
-0.007463982328772545,
-0.10083984583616257,
0.12950848042964935,
-0.09207979589700699,
-0.21217471361160278,
-0.15967847406864166,
-0.11317776143550873,
-0.056853532791137695,
0.0058861104771494865,
0.07282455265522003,
-0.07990936189889908,
-0.050464000552892685,
-0.16701923310756683,
-0.16918233036994934,
0.01001711841672659,
0.013164309784770012,
-0.029196608811616898,
-0.0015525963390246034,
0.0661485344171524,
-0.13212615251541138,
-0.015011793002486229,
-0.022382954135537148,
-0.1445034146308899,
0.07924285531044006,
-0.045028746128082275,
0.1345035582780838,
0.12487919628620148,
-0.03219394013285637,
0.030957434326410294,
-0.021995944902300835,
0.16457433998584747,
-0.027500798925757408,
0.034666042774915695,
0.1604483723640442,
-0.09243050962686539,
0.05328212305903435,
0.08366858959197998,
-0.017031678929924965,
-0.11461738497018814,
0.06400477141141891,
-0.011379726231098175,
-0.03195999190211296,
-0.15783613920211792,
-0.09330560266971588,
-0.041316721588373184,
0.09277916699647903,
0.00717338128015399,
0.07451421767473221,
0.10498100519180298,
0.037098731845617294,
-0.07096641510725021,
-0.0032788419630378485,
0.009410836733877659,
0.06790920346975327,
0.16431458294391632,
0.011142907664179802,
0.02267330512404442,
-0.13062113523483276,
-0.027326295152306557,
0.11254087090492249,
0.010105074383318424,
0.052698519080877304,
0.10353776812553406,
0.004303330089896917,
0.05265258997678757,
0.07577568292617798,
0.04411761462688446,
0.033379748463630676,
0.023535821586847305,
-0.006448620930314064,
-0.04126964136958122,
-0.06387192755937576,
-0.042465269565582275,
-0.03389347344636917,
-0.12275273352861404,
-0.033185526728630066,
-0.020196998491883278,
0.026084957644343376,
0.05750344321131706,
0.08248206228017807,
0.09252217411994934,
-0.2820602059364319,
-0.0991826057434082,
0.033139899373054504,
-0.023144394159317017,
-0.0020836738403886557,
0.0682372897863388,
0.025907963514328003,
0.03570489585399628,
0.1133449450135231,
0.00601206487044692,
0.06858915835618973,
-0.044513020664453506,
0.06593617796897888,
-0.05010386183857918,
0.005441212095320225,
-0.039081014692783356,
0.060797069221735,
-0.32248830795288086,
0.10995549708604813,
0.009562944062054157,
0.036892976611852646,
-0.05592117831110954,
-0.019914569333195686,
0.04826914519071579,
0.2429725080728531,
-0.035548098385334015,
0.0028502580244094133,
0.019856922328472137,
-0.0787375345826149,
-0.07234881818294525,
0.07376224547624588,
0.04025956615805626,
0.11362273246049881,
0.0648014098405838,
-0.015207010321319103,
0.011834877543151379,
-0.04343106597661972,
0.06875702738761902,
-0.1250215768814087,
-0.08771201223134995,
0.006593190133571625,
0.13039523363113403,
-0.0018766652792692184,
-0.021156014874577522,
0.0012095320271328092,
-0.1275109499692917,
0.134479820728302,
-0.06442391872406006,
0.003062431700527668,
-0.04712172970175743,
-0.008088747039437294,
-0.02500462904572487,
-0.06464213132858276,
-0.029214635491371155,
-0.042646896094083786,
0.021600820124149323,
-0.053923897445201874,
-0.11801014840602875,
0.05147938430309296,
-0.07783499360084534,
-0.03882535547018051,
-0.03139635547995567,
0.08883959800004959,
-0.04808852821588516,
-0.04371263086795807,
0.037728626281023026,
-0.03153381496667862,
0.0871686264872551,
-0.0947960764169693,
0.02691868133842945,
-0.007246151100844145,
-0.04625992476940155,
0.010938629508018494,
0.021340781822800636,
-0.1129748746752739,
-0.04862302541732788,
0.021516762673854828,
0.10259643197059631,
0.17210599780082703,
-0.021417757496237755,
-0.026875128969550133,
0.09950963407754898,
-0.06725858151912689,
-0.3005850613117218,
-0.09804460406303406,
-0.14106948673725128,
-0.061689481139183044,
0.04366184398531914,
-0.020401131361722946,
0.09115031361579895,
0.02835390903055668,
-0.07925485074520111,
0.1387266218662262,
-0.08239951729774475,
-0.03642375022172928,
0.04577602818608284,
0.08837810158729553,
0.18523769080638885,
-0.14430490136146545,
-0.07937479764223099,
-0.0551191084086895,
-0.17601683735847473,
0.14086700975894928,
-0.03758111596107483,
0.044004056602716446,
0.07927834987640381,
0.003802830120548606,
0.007627448532730341,
-0.061611052602529526,
-0.0031982387881726027,
-0.1356087028980255,
0.10608641803264618,
-0.15695461630821228,
-0.10643148422241211,
0.056229785084724426,
-0.046754103153944016,
0.11246704310178757,
-0.15351636707782745,
0.01224636472761631,
0.0460069514811039,
0.0007782743778079748,
0.022551484405994415,
0.08574710786342621,
0.016804393380880356,
0.029167205095291138,
-0.08772720396518707,
-0.03883283957839012,
-0.0347764678299427,
0.046224627643823624,
0.16165268421173096,
-0.022279800847172737,
0.03540516644716263,
0.12390132248401642,
0.1632874459028244,
-0.18770518898963928,
0.24009956419467926,
-0.0037968328688293695,
-0.07888293266296387,
0.0882844552397728,
-0.050569090992212296,
0.09189551323652267,
0.0012806806480512023,
-0.06133488193154335,
0.028766440227627754,
0.03261617198586464,
-0.059006381779909134,
0.03962571918964386,
0.10283121466636658,
-0.16634652018547058,
-0.16963331401348114,
0.021373236551880836,
-0.02189241722226143,
0.03670330345630646,
0.24067017436027527,
0.2313048392534256,
-0.0401885099709034,
-0.002566081937402487,
-0.053130608052015305,
0.06997178494930267,
-0.03986617922782898,
-0.028507061302661896,
0.06817135214805603,
0.0063477130606770515,
-0.061379995197057724,
0.057625219225883484,
0.09027311205863953,
-0.05603446438908577,
0.10297045856714249,
0.14129063487052917,
-0.07717891782522202,
-0.12298442423343658,
-0.08800052851438522,
0.0732988491654396,
0.08302994817495346,
-0.12591272592544556,
-0.056715041399002075,
-0.019332434982061386,
0.044030483812093735,
0.2634274363517761,
0.016423514112830162,
0.06510179489850998,
0.03497863933444023,
0.0282589141279459,
-0.08034635335206985,
0.028187934309244156,
-0.10528048127889633,
0.02068873681128025,
-0.11612378060817719,
0.06723257154226303,
-0.026941951364278793,
0.05108854919672012,
-0.03517695516347885,
-0.02669856697320938,
-0.08008835464715958,
-0.021447062492370605,
-0.05157255381345749,
-0.008192258886992931,
-0.0018440685234963894,
0.014551357366144657,
0.02543322928249836,
-0.06470579653978348,
-0.04464510828256607,
0.06334758549928665,
-0.023046093061566353,
-0.02840755134820938,
0.03228587284684181,
0.048144061118364334,
-0.13269998133182526,
-0.012731515802443027,
0.017657777294516563,
0.027876025065779686,
0.10413791239261627,
0.021982070058584213,
-0.1276341825723648,
0.04581930488348007,
-0.21592098474502563,
-0.0264150258153677,
0.06464134901762009,
0.06840871274471283,
-0.0056541492231190205,
0.11629264801740646,
-0.026533691212534904,
0.11696692556142807,
0.01504493597894907,
0.017289508134126663,
0.06501631438732147,
-0.01177018042653799,
0.01858152076601982,
0.03188304230570793,
-0.09057264029979706,
0.037346526980400085,
-0.02413015626370907,
0.0417441800236702,
-0.03608247637748718,
0.11501351743936539,
-0.09311536699533463,
-0.06179579347372055,
-0.17229749262332916,
-0.003849155968055129,
0.027278205379843712,
-0.11809257417917252,
-0.3556629717350006,
0.016097279265522957,
0.05296528711915016,
-0.030267931520938873,
0.08787763118743896,
-0.031055917963385582,
-0.12385234236717224,
0.04782040789723396,
-0.0042205690406262875,
-0.03454579785466194,
0.018897568807005882,
0.12363220006227493,
-0.012355229817330837,
-0.055622201412916183,
-0.07893233746290207,
-0.05683322623372078,
0.018414631485939026,
-0.13747729361057281,
0.033237751573324203,
0.03012029640376568,
0.036934949457645416,
0.082756906747818,
0.08533314615488052,
0.08964932709932327,
0.055755987763404846,
-0.11382001638412476,
-0.03855004906654358,
0.08201822638511658,
0.04383119195699692,
0.10290423780679703,
0.19706816971302032,
0.09066404402256012,
-0.050932902842760086,
-0.08626674860715866,
0.015364501625299454,
-0.10956074297428131,
-0.2622528672218323,
-0.11597444862127304,
-0.16596195101737976,
0.0003837996628135443,
-0.09013034403324127,
0.008290138095617294,
0.07621916383504868,
0.03604285046458244,
-0.11952682584524155,
0.07252833992242813,
0.1500934213399887,
0.03426327928900719,
-0.02865758165717125,
-0.07921980321407318,
-0.02214450016617775,
0.04183303564786911,
-0.020257918164134026,
0.025436151772737503,
0.002567749470472336,
0.03671247884631157,
0.07661526650190353,
0.11387708783149719,
0.11879903823137283,
-0.11597306281328201,
-0.09950969368219376,
0.006362352520227432,
0.04454468935728073,
-0.020362772047519684,
0.052684005349874496,
0.07668840885162354,
-0.004879618063569069,
0.045782413333654404,
0.09432049840688705,
0.005583308171480894,
-0.15324166417121887,
-0.04244473949074745,
0.3151407837867737,
0.016982613131403923,
0.11933063715696335,
-0.05759257823228836,
-0.029313713312149048,
0.02737567201256752,
0.19815373420715332,
0.16660520434379578,
-0.1411120444536209,
0.026838114485144615,
-0.07029976695775986,
0.015573655255138874,
0.026941785588860512,
0.13507331907749176,
0.04794374480843544,
0.1416776329278946,
0.047490138560533524,
-0.0018361855763942003,
-0.007842052727937698,
0.05345531925559044,
-0.08799692243337631,
-0.023013323545455933,
-0.07467802613973618,
0.011545924469828606,
-0.0014817959163337946,
0.09326358139514923,
-0.0400586873292923,
0.07008591294288635,
-0.1356258988380432,
-0.07885041832923889,
-0.020184820517897606,
0.03330191597342491,
0.07329511642456055,
-0.0034602645318955183,
0.04194709286093712,
-0.02265358529984951,
-0.023687317967414856,
0.005102986469864845,
-0.047554146498441696,
-0.0677969828248024,
-0.03939177468419075,
-0.015314689837396145,
-0.15044690668582916,
0.20825430750846863,
0.06085185334086418,
0.056645359843969345,
0.06718962639570236,
0.008222217671573162,
-0.14108268916606903,
0.08442921936511993,
-0.00020629040955100209,
0.029286323115229607,
0.0005624365294352174,
-0.13517403602600098,
0.010915618389844894,
0.06556779891252518,
0.07590524107217789,
-0.059123557060956955,
0.019866064190864563,
0.05998682975769043,
-0.0473342090845108,
-0.03174848109483719,
-0.0009246323024854064,
-0.06566862761974335,
0.0779414102435112,
0.022441701963543892,
-0.04858541861176491,
0.011701810173690319,
-0.04197203740477562,
-0.02992638200521469,
0.008953843265771866,
-0.060853540897369385,
-0.02477133274078369,
-0.13528645038604736,
-0.027400171384215355,
0.010809287428855896,
0.04954109713435173,
-0.1466245800256729,
0.049348440021276474,
-0.12545882165431976,
0.06941305100917816,
-0.08150969445705414,
0.040938060730695724,
0.23056328296661377,
-0.015982387587428093,
-0.04003457352519035,
0.03219161182641983,
0.009985161945223808,
0.136095330119133,
-0.08807916939258575,
-0.09715057164430618
] |
null | null | transformers |
This model was produced as part of respective B.Sc. Thesis: [**NLP Tasks with GreekLegalBERT v2**](https://pergamos.lib.uoa.gr/uoa/dl/frontend/el/browse/2971631#contents).
As far as we can discern, there are two unique models in the Greek NLP era: the general-purpose **Greek-BERT** model and the specific-domain **Greek-Legal-BERT-v1** model. In this
thesis, we focus on the generation and representation of the second version of GreekLegal-BERT, namely **GreekLegalBERT v2**, which was provided with more Legal Data than the first version.
Combined dataset that was used for current model's pretraining purposes is comprised of:
1. The **Raptarchis** dataset, also known as RAPTARCHIS47k, consisting of approximately 47 thousand legal resources, is a comprehensive collection of Greek legislation dating from the founding of the Greek state in 1834 through 2015.
2. **Nomothesi@**, a platform that makes Greek legislation available on the Web as linked open data, was built on the basis of the aforementioned principles.
3. **EuroParl**, Philipp Koehn’s team in Edinburgh was able to collect corpus parallel text from the European Parliament sessions in 11 languages from European Union, including Greek.
4. **EUR-LEX** provides online access to European Union (EU) legal documents that is both official and comprehensive, containing 57 thousand Greek EU legislative documents from the EUR-LEX portal.
5. **Hellenic Parliament Sessions**, All the available minutes of the plenary sessions of the Greek or Hellenic Parliament, from 3 July 1989 to 24 August 2021,
The current thesis' goal is to compare the three dinstict Greek NLP models, based on BERT model, between different downstream NLP tasks, notably in *Named Entity
Recognition*, *Natural Language Inference* and *Multiclass Classification on Raptarchis* dataset. | {"license": "mit"} | null | spyrosbriakos/greek_legal_bert_v2 | [
"transformers",
"pytorch",
"bert",
"pretraining",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-09T09:02:44+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #pretraining #license-mit #endpoints_compatible #region-us
|
This model was produced as part of respective B.Sc. Thesis: NLP Tasks with GreekLegalBERT v2.
As far as we can discern, there are two unique models in the Greek NLP era: the general-purpose Greek-BERT model and the specific-domain Greek-Legal-BERT-v1 model. In this
thesis, we focus on the generation and representation of the second version of GreekLegal-BERT, namely GreekLegalBERT v2, which was provided with more Legal Data than the first version.
Combined dataset that was used for current model's pretraining purposes is comprised of:
1. The Raptarchis dataset, also known as RAPTARCHIS47k, consisting of approximately 47 thousand legal resources, is a comprehensive collection of Greek legislation dating from the founding of the Greek state in 1834 through 2015.
2. Nomothesi@, a platform that makes Greek legislation available on the Web as linked open data, was built on the basis of the aforementioned principles.
3. EuroParl, Philipp Koehn’s team in Edinburgh was able to collect corpus parallel text from the European Parliament sessions in 11 languages from European Union, including Greek.
4. EUR-LEX provides online access to European Union (EU) legal documents that is both official and comprehensive, containing 57 thousand Greek EU legislative documents from the EUR-LEX portal.
5. Hellenic Parliament Sessions, All the available minutes of the plenary sessions of the Greek or Hellenic Parliament, from 3 July 1989 to 24 August 2021,
The current thesis' goal is to compare the three dinstict Greek NLP models, based on BERT model, between different downstream NLP tasks, notably in *Named Entity
Recognition*, *Natural Language Inference* and *Multiclass Classification on Raptarchis* dataset. | [] | [
"TAGS\n#transformers #pytorch #bert #pretraining #license-mit #endpoints_compatible #region-us \n"
] | [
31
] | [
"passage: TAGS\n#transformers #pytorch #bert #pretraining #license-mit #endpoints_compatible #region-us \n"
] | [
-0.056959133595228195,
0.041152745485305786,
-0.008454347960650921,
-0.023273972794413567,
0.1102408841252327,
0.026482291519641876,
0.11147106438875198,
0.0811445340514183,
0.0856197401881218,
-0.04723101854324341,
0.15740549564361572,
0.20923370122909546,
-0.03216475993394852,
-0.0031801960431039333,
-0.04823717102408409,
-0.24521315097808838,
0.06534331291913986,
0.0936758816242218,
-0.01892346329987049,
0.10914017260074615,
0.09242602437734604,
-0.10398200899362564,
0.04092603921890259,
0.00515582924708724,
-0.12750688195228577,
-0.0008429944282397628,
0.00975105818361044,
-0.07251431047916412,
0.15615040063858032,
0.028247009962797165,
0.16003581881523132,
0.07151959836483002,
-0.02444060891866684,
-0.19108456373214722,
0.020958317443728447,
-0.023202842101454735,
-0.08096706867218018,
0.03148169070482254,
0.03830832615494728,
-0.03448951616883278,
0.055684894323349,
0.07920029759407043,
0.014275009743869305,
0.05803310498595238,
-0.18478110432624817,
-0.19214922189712524,
-0.06507217139005661,
0.06860671937465668,
0.06639351695775986,
0.073807492852211,
0.02355441264808178,
0.14004141092300415,
-0.1600843071937561,
0.04396296292543411,
0.09200765937566757,
-0.36198705434799194,
0.01936272904276848,
0.1018236055970192,
0.09857017546892166,
0.0014756930759176612,
-0.0572953075170517,
0.0220529492944479,
0.0372389480471611,
0.006993111688643694,
-0.02449076808989048,
-0.07715106010437012,
0.03550836816430092,
0.07311637699604034,
-0.10852638632059097,
-0.08820714801549911,
0.21386975049972534,
-0.03050508163869381,
0.0021696703042834997,
0.006796075496822596,
-0.037762418389320374,
-0.0373125895857811,
-0.005313422996550798,
-0.009994600899517536,
0.007064866367727518,
0.10600399225950241,
0.01183714997023344,
-0.025376060977578163,
-0.15453222393989563,
0.01631244830787182,
-0.1968851536512375,
0.17899908125400543,
0.03771776705980301,
0.060022857040166855,
-0.17171959578990936,
0.08910679817199707,
0.004034536425024271,
-0.04573570564389229,
-0.018862729892134666,
-0.07747867703437805,
0.06250713020563126,
-0.03710223361849785,
-0.05105797201395035,
0.09446744620800018,
0.03912092745304108,
0.20645704865455627,
-0.0017335353186354041,
0.003942668437957764,
0.016101466491818428,
0.1361476480960846,
-0.03009026311337948,
0.08131398260593414,
0.06567535549402237,
0.061188358813524246,
-0.003843118902295828,
-0.12479790300130844,
-0.0023501659743487835,
0.013761871494352818,
-0.12073720991611481,
-0.06826440244913101,
-0.015880288556218147,
0.11427552998065948,
-0.024516990408301353,
0.026823896914720535,
-0.08843525499105453,
0.03066677413880825,
0.06135718524456024,
-0.011667212471365929,
-0.026499159634113312,
-0.003268661443144083,
0.03923529386520386,
0.05772315338253975,
-0.0031734469812363386,
0.018612459301948547,
-0.003407105803489685,
0.143795445561409,
-0.08286383002996445,
-0.016209015622735023,
-0.04072299227118492,
-0.002970094094052911,
0.0610942579805851,
-0.1301662176847458,
0.0932227298617363,
-0.11148200929164886,
-0.1308278739452362,
0.025443242862820625,
0.08193497359752655,
0.02644394524395466,
0.0027259059716016054,
0.014845187775790691,
-0.016852116212248802,
-0.05410179868340492,
-0.08124808222055435,
-0.04675197973847389,
-0.06368033587932587,
0.11424629390239716,
-0.041367433965206146,
-0.008836576715111732,
-0.1324423998594284,
0.048915665596723557,
-0.11625273525714874,
0.024859977886080742,
-0.034009899944067,
-0.055052485316991806,
-0.04338155686855316,
0.18802085518836975,
-0.012027154676616192,
-0.07707704603672028,
-0.09655902534723282,
0.05312814190983772,
-0.042753901332616806,
0.1232258677482605,
-0.050846729427576065,
-0.08816240727901459,
0.21778996288776398,
-0.10600857436656952,
-0.1943657547235489,
0.04355615749955177,
-0.025823067873716354,
0.10485527664422989,
0.07477855682373047,
0.20328272879123688,
0.03305011987686157,
-0.16452878713607788,
0.11151602119207382,
0.1486303210258484,
-0.1349554806947708,
-0.21790501475334167,
0.0608435682952404,
-0.06434880942106247,
-0.09794546663761139,
0.03088548593223095,
-0.05137289687991142,
0.08888417482376099,
-0.07948853075504303,
-0.05464992672204971,
-0.0037328482139855623,
-0.003714154474437237,
0.013857454061508179,
0.0738462433218956,
0.07708646357059479,
-0.10098159313201904,
-0.025253640487790108,
0.05589777231216431,
-0.005496291909366846,
0.0592033676803112,
0.04078128561377525,
-0.07652337849140167,
0.03181629627943039,
0.03148956969380379,
-0.044252485036849976,
-0.1440945714712143,
-0.03046337142586708,
-0.021755103021860123,
0.05627797171473503,
0.006696639116853476,
0.22416019439697266,
0.054826948791742325,
-0.04631515219807625,
-0.0040061199106276035,
-0.024467844516038895,
0.1004701480269432,
0.03173546493053436,
-0.030013207346200943,
-0.06998499482870102,
0.006049700081348419,
-0.03860661759972572,
-0.046095509082078934,
-0.061496999114751816,
0.02158461883664131,
0.09544581919908524,
0.07790078967809677,
-0.0342232920229435,
0.05927526578307152,
-0.06840220093727112,
0.04291670769453049,
-0.006502886302769184,
0.02013307809829712,
0.15892627835273743,
0.04781234264373779,
-0.05354251712560654,
0.13589632511138916,
-0.055497027933597565,
0.3037300705909729,
0.20220457017421722,
-0.24132731556892395,
-0.0026544611901044846,
-0.07682093232870102,
-0.07735801488161087,
0.01946810632944107,
0.07319744676351547,
0.03251844272017479,
0.09252936393022537,
0.012300140224397182,
0.12053176015615463,
-0.03868464007973671,
-0.05198632553219795,
-0.010405050590634346,
-0.05097529664635658,
-0.05138947442173958,
0.05491868779063225,
0.1376093178987503,
-0.191079780459404,
0.16385705769062042,
0.29203566908836365,
0.06336153298616409,
0.11378530412912369,
-0.09969619661569595,
0.0044546271674335,
0.0002015076606767252,
0.043476201593875885,
-0.043241385370492935,
0.09745696187019348,
-0.24898529052734375,
-0.028871092945337296,
0.05766555294394493,
0.0010785930790007114,
0.07311759889125824,
-0.2014012336730957,
-0.10425905138254166,
0.012512490153312683,
0.0012020281283184886,
-0.06541776657104492,
0.09309631586074829,
-0.018293794244527817,
0.056585557758808136,
0.0022970649879425764,
-0.09380749613046646,
0.14456544816493988,
-0.006265629082918167,
-0.07187975943088531,
0.14759881794452667,
-0.10451582819223404,
-0.16638533771038055,
-0.12568210065364838,
-0.09360267221927643,
0.0767967700958252,
0.01256017480045557,
0.10903976112604141,
-0.030868439003825188,
-0.036771662533283234,
0.08646968752145767,
-0.002579997293651104,
-0.15805035829544067,
-0.0018477077828720212,
-0.07018964737653732,
0.07501276582479477,
-0.08702901750802994,
-0.11009946465492249,
-0.08385495841503143,
-0.05167992785573006,
-0.06758622080087662,
0.07274533063173294,
-0.09305426478385925,
0.029850881546735764,
0.07807344198226929,
0.01907515525817871,
0.05386767536401749,
-0.07022164016962051,
0.21769754588603973,
-0.05465129762887955,
-0.03792130947113037,
0.1584843546152115,
-0.01905442401766777,
0.07112610340118408,
0.15627235174179077,
0.06342742592096329,
-0.08042553067207336,
-0.021528664976358414,
-0.0799219086766243,
-0.08225596696138382,
-0.238869771361351,
-0.07980936020612717,
-0.12173103541135788,
-0.00015122976037673652,
0.021281421184539795,
0.06529438495635986,
0.08897291123867035,
0.08407755196094513,
0.020104922354221344,
-0.0850248783826828,
-0.017892012372612953,
0.07276622951030731,
0.26835229992866516,
-0.047278836369514465,
0.08441480249166489,
-0.08027174323797226,
-0.06523454934358597,
0.06772729009389877,
0.06542079150676727,
0.19750843942165375,
0.1571795642375946,
0.03411347419023514,
0.14284446835517883,
0.18817846477031708,
0.12995679676532745,
0.08350671827793121,
-0.008940419182181358,
-0.038835134357213974,
-0.0336092934012413,
0.004251254256814718,
-0.052005279809236526,
0.0403541624546051,
0.057079944759607315,
-0.12264818698167801,
-0.05312037095427513,
-0.24511997401714325,
0.0186262559145689,
0.10853288322687149,
0.027066489681601524,
-0.1405411958694458,
-0.0012093745172023773,
0.061401620507240295,
-0.00042462264536879957,
-0.040435515344142914,
0.1225685104727745,
-0.040362972766160965,
-0.12059188634157181,
0.04340016469359398,
-0.04531329870223999,
0.09308622777462006,
0.03191937878727913,
0.08616259694099426,
0.02177150920033455,
-0.14764301478862762,
0.05790037289261818,
0.11215203255414963,
-0.3216373324394226,
0.31180980801582336,
-0.005336853675544262,
0.012666172347962856,
-0.036729078739881516,
-0.03308626264333725,
-0.014593685045838356,
0.20436590909957886,
0.15825767815113068,
0.017384976148605347,
-0.08648160099983215,
-0.09975580126047134,
0.07766789197921753,
0.019374102354049683,
0.09728267788887024,
-0.01527550257742405,
-0.06072821095585823,
-0.035915933549404144,
0.019996730610728264,
0.017979366704821587,
0.033877789974212646,
-0.024476421996951103,
-0.12953494489192963,
0.029798058792948723,
-0.005975052248686552,
0.06934390962123871,
-0.061118390411138535,
-0.0060099088586866856,
-0.0858035758137703,
0.13018639385700226,
-0.13802595436573029,
-0.05212893709540367,
-0.0788593590259552,
-0.16896629333496094,
0.0470505952835083,
-0.06452692300081253,
0.05396224185824394,
-0.06550666689872742,
-0.08723258972167969,
-0.1021556630730629,
-0.1363225132226944,
0.13192644715309143,
-0.10557591170072556,
-0.009628201834857464,
-0.06455542147159576,
0.20676662027835846,
-0.05056801438331604,
0.024121733382344246,
0.012590278871357441,
0.02041596733033657,
-0.08891083300113678,
-0.09405051171779633,
0.00640924321487546,
-0.06653439253568649,
0.08011354506015778,
-0.08105718344449997,
-0.09870283305644989,
0.07520043104887009,
0.02213370054960251,
-0.03211669996380806,
0.2133904993534088,
0.2605283260345459,
-0.04672921076416969,
0.1529558151960373,
0.20129536092281342,
-0.08956260234117508,
-0.2746005654335022,
-0.052718549966812134,
-0.1693735122680664,
-0.05321729555726051,
-0.01569109596312046,
-0.1680038720369339,
0.0869068056344986,
0.07064875215291977,
-0.0530480295419693,
0.07314493507146835,
-0.26071876287460327,
-0.07638639211654663,
0.18599319458007812,
0.02569887600839138,
0.44122520089149475,
-0.11468598246574402,
-0.07474304735660553,
0.04064829647541046,
-0.22138792276382446,
0.14696304500102997,
0.05021331086754799,
0.06331703811883926,
-0.01378432847559452,
0.012853387743234634,
-0.0048988801427185535,
-0.08660066872835159,
0.1278131604194641,
0.04277680069208145,
0.06050729751586914,
-0.077799491584301,
-0.09424236416816711,
0.09266785532236099,
0.019123606383800507,
-0.0011240644380450249,
0.023665938526391983,
0.02045867033302784,
-0.1388312131166458,
-0.031209558248519897,
-0.09775169938802719,
0.09683483093976974,
-0.013664918020367622,
-0.09487958997488022,
-0.03570445254445076,
0.03870192542672157,
-0.03688802942633629,
-0.02390020340681076,
0.25054091215133667,
-0.016916809603571892,
0.11881609261035919,
0.022944726049900055,
0.11198171228170395,
-0.1924239993095398,
-0.07690773904323578,
-0.076088547706604,
-0.06647050380706787,
0.06896615028381348,
-0.033914148807525635,
-0.010402344167232513,
0.15355168282985687,
-0.006448992528021336,
0.05522480607032776,
0.10305409878492355,
0.0024967726785689592,
-0.03194047883152962,
0.11117324978113174,
-0.17011946439743042,
-0.1105773076415062,
-0.025896498933434486,
0.03900938481092453,
0.10529909282922745,
0.08514908701181412,
0.057129163295030594,
0.01822345331311226,
0.0005973228253424168,
-0.0054868184961378574,
-0.02817070484161377,
-0.09053925424814224,
-0.011874753050506115,
0.07378308475017548,
0.017807254567742348,
-0.09653167426586151,
0.033879026770591736,
-0.012018902227282524,
-0.19888220727443695,
-0.049517255276441574,
0.06883900612592697,
-0.13148093223571777,
-0.09640815109014511,
-0.09699960798025131,
0.09141577780246735,
-0.17312149703502655,
-0.05939796194434166,
-0.004728594794869423,
-0.14296691119670868,
0.07130563259124756,
0.17491242289543152,
0.1094028577208519,
0.11957382410764694,
-0.02380933053791523,
-0.0058038136921823025,
0.025142455473542213,
-0.03777619078755379,
-0.09209631383419037,
0.02232109010219574,
-0.07622946053743362,
0.04106557369232178,
0.00800540391355753,
0.1011965274810791,
-0.0607965886592865,
-0.073517806828022,
-0.17234432697296143,
0.08265295624732971,
-0.07180172950029373,
-0.07985731214284897,
-0.1323574185371399,
-0.043499305844306946,
0.022664565593004227,
-0.10862057656049728,
-0.03471603989601135,
-0.02501794695854187,
-0.1365392804145813,
0.0719684436917305,
0.05230262503027916,
0.06218678504228592,
-0.06415506452322006,
-0.03045913204550743,
0.1134994626045227,
-0.019378777593374252,
0.07534998655319214,
0.07054097950458527,
-0.04168051481246948,
0.1156272366642952,
-0.10214559733867645,
-0.08935471624135971,
0.07870662957429886,
0.002551124896854162,
0.04422114044427872,
0.054911017417907715,
0.005778842139989138,
0.09943513572216034,
-0.004625395406037569,
0.0646529495716095,
-0.03802294284105301,
-0.1409788727760315,
-0.00273684598505497,
0.060754843056201935,
-0.1516602635383606,
-0.022927463054656982,
-0.08973217010498047,
0.1474010944366455,
0.0004916979814879596,
0.16768522560596466,
-0.005278400145471096,
0.05498291924595833,
-0.04801714047789574,
0.002801362192258239,
0.0025285370647907257,
-0.1244962140917778,
-0.02372303046286106,
-0.10553846508264542,
-0.04177132248878479,
-0.007314275950193405,
0.287516713142395,
-0.0019811084493994713,
-0.07236248254776001,
0.09603667259216309,
0.03537648916244507,
-0.016645295545458794,
0.003157848957926035,
0.2972029745578766,
0.0779145210981369,
-0.012197797186672688,
-0.1110362634062767,
0.07299812883138657,
-0.04060682654380798,
-0.1365952491760254,
0.12314163148403168,
0.1960551142692566,
0.01054392009973526,
0.06482372432947159,
0.0348946675658226,
0.011151869781315327,
-0.11265235394239426,
-0.1336185485124588,
0.08133716881275177,
0.01872869022190571,
-0.02374044992029667,
0.1588507741689682,
0.15525458753108978,
-0.09417629987001419,
0.0474424883723259,
-0.03711261227726936,
-0.021116871386766434,
-0.1685999035835266,
-0.09484080225229263,
-0.016956528648734093,
-0.06978794932365417,
0.052539948374032974,
-0.021464020013809204,
0.00031583564123138785,
0.15149615705013275,
0.05668191239237785,
-0.051449958235025406,
0.036117274314165115,
0.010538197122514248,
-0.03720248118042946,
-0.003725723596289754,
-0.00802889559417963,
0.05129154026508331,
-0.12675608694553375,
-0.038215626031160355,
-0.10568587481975555,
-0.09174695611000061,
-0.05438874289393425,
0.00888998992741108,
-0.07359683513641357,
-0.03775116428732872,
-0.09224219620227814,
-0.05830623582005501,
-0.04253363236784935,
0.08345687389373779,
0.007820694707334042,
0.1315821409225464,
-0.026726821437478065,
0.04215142875909805,
0.03965580463409424,
0.15284232795238495,
-0.02791057527065277,
-0.11547910422086716,
0.012483320198953152,
0.19006623327732086,
0.08093948662281036,
0.08686412870883942,
0.02684800885617733,
0.04014211893081665,
-0.006036616861820221,
0.30524328351020813,
0.2739561200141907,
-0.03586457297205925,
0.04106833413243294,
0.053067997097969055,
0.02267444133758545,
0.09316615015268326,
0.13475196063518524,
0.07856129854917526,
0.270524799823761,
-0.13502834737300873,
-0.03219005465507507,
-0.05006470903754234,
0.010869858786463737,
-0.08401791751384735,
0.08815852552652359,
0.024778226390480995,
-0.07020135968923569,
-0.05573881417512894,
0.10624469816684723,
-0.1197061538696289,
0.05194637551903725,
0.09402409940958023,
-0.1408090442419052,
-0.025740118697285652,
-0.02197199873626232,
0.18740946054458618,
-0.004958379548043013,
0.0880899578332901,
-0.05124330520629883,
-0.08275637030601501,
0.056534625589847565,
0.03261864557862282,
-0.24578538537025452,
-0.0762060284614563,
0.14537867903709412,
0.086802639067173,
0.07610438019037247,
-0.022187067195773125,
0.06485845148563385,
0.06028242036700249,
0.07275542616844177,
-0.05417828634381294,
0.06028513237833977,
0.05781978368759155,
-0.0645403191447258,
-0.09496116638183594,
-0.11966504901647568,
0.017161641269922256,
-0.039219439029693604,
0.015622003003954887,
-0.18481411039829254,
0.08044425398111343,
-0.03308127075433731,
-0.10752157866954803,
-0.06680549681186676,
0.059053998440504074,
-0.057121433317661285,
0.061071932315826416,
0.02570534497499466,
0.017896246165037155,
-0.06664323806762695,
-0.056687310338020325,
-0.011025150306522846,
0.0878123864531517,
-0.11201540380716324,
-0.07781858742237091,
-0.012026415206491947,
-0.04407089576125145,
0.08052442967891693,
-0.0202640350908041,
-0.10732780396938324,
-0.04245850071310997,
-0.03706076368689537,
0.046264585107564926,
-0.14177796244621277,
0.032593563199043274,
0.03873834386467934,
0.04649819806218147,
0.005642430391162634,
-0.0679548978805542,
0.018470030277967453,
0.0110373105853796,
-0.08856201171875,
-0.06772717088460922
] |
null | null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | null | m-newhauser/wiley-instruct-demo | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-09T09:04:12+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
31,
6,
3,
82,
28,
3,
4,
9,
9,
10,
42,
20,
3,
4,
5,
9,
11,
13,
3,
12,
5,
4,
5,
3,
4,
9,
53,
9,
8,
6,
3,
14,
8,
7,
9,
4
] | [
"passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
] | [
-0.06646376848220825,
0.2168014943599701,
-0.00225935154594481,
0.023818302899599075,
0.1271018385887146,
-0.001635765191167593,
0.04218708351254463,
0.13324736058712006,
-0.020175931975245476,
0.11144465953111649,
0.046588581055402756,
0.09377603232860565,
0.09928803145885468,
0.18404334783554077,
0.04859916493296623,
-0.2059975117444992,
0.007056170143187046,
-0.09090408682823181,
0.014076028019189835,
0.1116579994559288,
0.13719257712364197,
-0.10291384905576706,
0.08272874355316162,
-0.04045208916068077,
-0.02019004337489605,
0.00012576708104461432,
-0.09259183704853058,
-0.07032395154237747,
0.06885425746440887,
0.06264153122901917,
0.051234472543001175,
0.001456156256608665,
0.09140396863222122,
-0.2864592671394348,
0.017265573143959045,
0.08406311273574829,
0.0027674848679453135,
0.06290827691555023,
0.07236549258232117,
-0.07389893382787704,
0.11328595131635666,
-0.08021481335163116,
0.13019037246704102,
0.08625296503305435,
-0.062064990401268005,
-0.23071379959583282,
-0.07525765895843506,
0.0963398814201355,
0.12251301854848862,
0.06215599179267883,
-0.022921854630112648,
0.15455181896686554,
-0.06248689442873001,
0.012971068732440472,
0.1294165402650833,
-0.11526761949062347,
-0.05572471022605896,
0.061741601675748825,
0.11775490641593933,
0.10740239918231964,
-0.14110268652439117,
-0.0017287094378843904,
0.04900608956813812,
0.029121357947587967,
0.08589313924312592,
0.022661056369543076,
0.12003941088914871,
0.04652795568108559,
-0.13695219159126282,
-0.04037507623434067,
0.12011898308992386,
0.038862764835357666,
-0.06446044892072678,
-0.2168138176202774,
-0.006778308190405369,
-0.0601806715130806,
-0.014732478186488152,
-0.07019448280334473,
0.039128515869379044,
-0.02470310963690281,
0.07317749410867691,
-0.04465159401297569,
-0.1063927412033081,
-0.0421026237308979,
0.0892222449183464,
0.07748593389987946,
0.011527054943144321,
-0.02519804798066616,
0.04627908393740654,
0.13455867767333984,
0.05402068421244621,
-0.10399353504180908,
-0.07017925381660461,
-0.06942764669656754,
-0.09420394152402878,
-0.04035796597599983,
0.056760527193546295,
0.031942449510097504,
0.02665667235851288,
0.22703726589679718,
0.016653569415211678,
0.04155244305729866,
0.0224777739495039,
0.01032855175435543,
0.043662428855895996,
0.0955500528216362,
-0.05303520709276199,
-0.15660029649734497,
-0.04072032496333122,
0.09077946096658707,
-0.0027527001220732927,
-0.036689214408397675,
-0.03966725245118141,
0.03849169611930847,
0.06843466311693192,
0.13122352957725525,
0.07552056759595871,
-0.017929591238498688,
-0.04813180863857269,
-0.030096933245658875,
0.23523783683776855,
-0.1493375599384308,
0.04426715523004532,
-0.02271856553852558,
-0.01804111897945404,
-0.03908449783921242,
0.03597262129187584,
0.022118929773569107,
-0.000004518366949923802,
0.09706240892410278,
-0.058981191366910934,
-0.05378659814596176,
-0.10168042778968811,
-0.03272576630115509,
0.04088849574327469,
-0.013975566253066063,
-0.010589460842311382,
-0.09025166928768158,
-0.09490354359149933,
-0.04766594246029854,
0.05537205561995506,
-0.05123869329690933,
-0.03770573064684868,
0.009465423412621021,
-0.08151785284280777,
-0.005444355774670839,
-0.005417742300778627,
0.10699385404586792,
-0.03222226724028587,
0.04445803165435791,
-0.027600755915045738,
0.05225523188710213,
0.09919606149196625,
0.031576547771692276,
-0.0773419588804245,
0.0561848059296608,
-0.22559374570846558,
0.07503069192171097,
-0.11481974273920059,
0.04335082694888115,
-0.1704932004213333,
-0.042439818382263184,
0.005444696638733149,
0.0139949731528759,
0.013206101022660732,
0.12720820307731628,
-0.19255615770816803,
-0.01654396951198578,
0.13260798156261444,
-0.09212633967399597,
-0.118110790848732,
0.07884611934423447,
-0.029701577499508858,
0.1624738723039627,
0.04682036489248276,
-0.027025915682315826,
0.09224298596382141,
-0.16434773802757263,
-0.07092688232660294,
-0.00949116237461567,
-0.01727987825870514,
0.12109188735485077,
0.07512219995260239,
-0.05991523340344429,
0.046571120619773865,
0.02832140028476715,
-0.038078423589468,
-0.04424772411584854,
-0.050857074558734894,
-0.10884185880422592,
-0.01070026308298111,
-0.08987759798765182,
0.04065500199794769,
-0.01250192429870367,
-0.07916021347045898,
-0.029885273426771164,
-0.18612512946128845,
-0.0030564051121473312,
0.10038342326879501,
0.0035033065360039473,
-0.005652366206049919,
-0.08666291832923889,
0.026358824223279953,
-0.03112892620265484,
-0.008404186926782131,
-0.16764774918556213,
-0.04399421438574791,
0.046902090311050415,
-0.16094985604286194,
0.020117372274398804,
-0.06413903087377548,
0.06334125250577927,
0.03641495108604431,
-0.05590536445379257,
-0.0248766727745533,
-0.01730942726135254,
0.011945613659918308,
-0.05083848536014557,
-0.18994836509227753,
-0.056277405470609665,
-0.037882111966609955,
0.149809330701828,
-0.25956398248672485,
0.032966937869787216,
0.051140617579221725,
0.14649195969104767,
0.00406361510977149,
-0.05115427449345589,
0.01429014839231968,
-0.05360214412212372,
-0.054652128368616104,
-0.06746816635131836,
-0.006135428790003061,
-0.027576493099331856,
-0.05147203803062439,
0.019243421033024788,
-0.1755700707435608,
-0.021410830318927765,
0.09424154460430145,
0.12876708805561066,
-0.1486445665359497,
-0.018640631809830666,
-0.048725154250860214,
-0.06339836865663528,
-0.0715010017156601,
-0.07038594037294388,
0.10712739825248718,
0.0513901449739933,
0.04796046018600464,
-0.07435787469148636,
-0.07092321664094925,
0.02726263552904129,
0.006906150374561548,
-0.03382374346256256,
0.08727246522903442,
0.05199531093239784,
-0.09209315478801727,
0.0756213590502739,
0.1092359870672226,
0.07177663594484329,
0.09363535046577454,
0.01574566215276718,
-0.11756632477045059,
-0.028492970392107964,
0.036266472190618515,
0.02740776725113392,
0.1465986967086792,
-0.05952361226081848,
0.04016614332795143,
0.04494241625070572,
-0.04170418903231621,
0.022319864481687546,
-0.08787637203931808,
0.024075502529740334,
0.025203049182891846,
-0.0034381982404738665,
0.06284574419260025,
-0.02525499276816845,
-0.0050758360885083675,
0.07016654312610626,
0.047779910266399384,
0.04621000960469246,
0.009655474685132504,
-0.01720241829752922,
-0.1047825813293457,
0.16950392723083496,
-0.0951867327094078,
-0.269941508769989,
-0.17632324993610382,
0.026197833940386772,
0.04035249724984169,
-0.022378476336598396,
0.031619444489479065,
-0.07056326419115067,
-0.10630585998296738,
-0.1060405746102333,
-0.002429972169920802,
0.01714223250746727,
-0.06364088505506516,
-0.0741225928068161,
0.07348573952913284,
0.04382912442088127,
-0.14902326464653015,
0.038552410900592804,
0.055694397538900375,
-0.057955220341682434,
-0.0233661737293005,
0.09118817001581192,
0.12397737801074982,
0.14583967626094818,
-0.021366750821471214,
-0.028626007959246635,
0.029004426673054695,
0.19620531797409058,
-0.13469526171684265,
0.10371150821447372,
0.13814030587673187,
-0.04545360431075096,
0.08360563963651657,
0.1560150384902954,
0.029186224564909935,
-0.08317049592733383,
0.05044832453131676,
0.04082648828625679,
-0.043159641325473785,
-0.2666129767894745,
-0.0534592866897583,
0.012832709588110447,
-0.06255637854337692,
0.09786593168973923,
0.10183793306350708,
0.11542957276105881,
0.034910861402750015,
-0.07166364789009094,
-0.043925940990448,
-0.0058974819257855415,
0.11737963557243347,
-0.05490213260054588,
-0.012639665976166725,
0.07686592638492584,
-0.05086168646812439,
0.005355054512619972,
0.10266812145709991,
0.02973790094256401,
0.17442677915096283,
0.020399179309606552,
0.11231429129838943,
0.06195578724145889,
0.08633565157651901,
0.0007386076031252742,
0.02951662428677082,
0.05147615820169449,
0.017203815281391144,
-0.002300140680745244,
-0.10421168059110641,
-0.006156572140753269,
0.1449710875749588,
0.028103826567530632,
0.029669636860489845,
-0.0018948549404740334,
-0.005003341939300299,
0.05121048167347908,
0.1746254414319992,
-0.011592294089496136,
-0.22072425484657288,
-0.0845772922039032,
0.06936841458082199,
-0.06218599155545235,
-0.12968985736370087,
-0.026130788028240204,
0.045467354357242584,
-0.17519839107990265,
0.026703642681241035,
-0.027433741837739944,
0.0919293761253357,
-0.09345759451389313,
-0.02221956104040146,
0.03687324374914169,
0.084866963326931,
-0.014529162086546421,
0.08703910559415817,
-0.14498743414878845,
0.11886418610811234,
0.02978132851421833,
0.09024628251791,
-0.11081171780824661,
0.07909037172794342,
-0.007550720125436783,
0.009180475026369095,
0.19379350543022156,
-0.011335089802742004,
-0.03514958545565605,
-0.08774717897176743,
-0.11210042238235474,
-0.013537433929741383,
0.12687496840953827,
-0.1243172138929367,
0.08773399889469147,
-0.015198243781924248,
-0.044079482555389404,
0.00937260314822197,
-0.12100647389888763,
-0.17273177206516266,
-0.19628387689590454,
0.05585884302854538,
-0.09575839340686798,
0.025643249973654747,
-0.11914430558681488,
-0.07089093327522278,
-0.02952558360993862,
0.241120383143425,
-0.1745356321334839,
-0.06510113179683685,
-0.1468164622783661,
-0.046294767409563065,
0.1662203073501587,
-0.04437198117375374,
0.0718095526099205,
-0.0208172257989645,
0.20345525443553925,
0.005988610442727804,
-0.004939318168908358,
0.06724198162555695,
-0.08892562240362167,
-0.16873881220817566,
-0.06771010160446167,
0.1510489284992218,
0.11680185794830322,
0.04907919466495514,
-0.002248800592496991,
0.0011772146681323647,
-0.016943959519267082,
-0.1137804463505745,
-0.0033210667315870523,
0.16037839651107788,
0.03878779336810112,
0.025986969470977783,
-0.05243593826889992,
-0.08797456324100494,
-0.06899320334196091,
-0.06853509694337845,
0.06221301481127739,
0.19590823352336884,
-0.10376439243555069,
0.1700313836336136,
0.147536963224411,
-0.07305635511875153,
-0.23175598680973053,
0.035342130810022354,
0.04983805492520332,
0.0014306638622656465,
0.04886869341135025,
-0.18252557516098022,
0.10521943867206573,
0.019543392583727837,
-0.05505957826972008,
0.13485197722911835,
-0.1557481735944748,
-0.1552847921848297,
0.0722852572798729,
0.03904085233807564,
-0.22423844039440155,
-0.1354004591703415,
-0.09622503817081451,
-0.05825018882751465,
-0.14065024256706238,
0.06054598465561867,
-0.002136280992999673,
0.015948504209518433,
0.03500790148973465,
-0.0015643214574083686,
0.027123261243104935,
-0.058935679495334625,
0.18609118461608887,
-0.004065449349582195,
0.020676052197813988,
-0.060264769941568375,
-0.0478842556476593,
0.09839435666799545,
-0.06130504235625267,
0.12208222597837448,
0.004057085141539574,
0.01594383642077446,
-0.10362856835126877,
-0.048314861953258514,
-0.04328322783112526,
0.05154227837920189,
-0.07548051327466965,
-0.10070807486772537,
-0.043625857681035995,
0.08841723203659058,
0.07005169242620468,
-0.03383097052574158,
0.00549331633374095,
-0.07189501076936722,
0.10019614547491074,
0.17795267701148987,
0.17573626339435577,
0.009926567785441875,
-0.07241068035364151,
0.01677953451871872,
-0.04142116755247116,
0.044231921434402466,
-0.2513144314289093,
0.03756171092391014,
0.06098250672221184,
0.029438555240631104,
0.09217222779989243,
-0.020435843616724014,
-0.1820858269929886,
-0.04050002992153168,
0.08094815909862518,
-0.05452597141265869,
-0.22617179155349731,
-0.019085140898823738,
0.0954197570681572,
-0.2020406424999237,
-0.007372708059847355,
0.03995226323604584,
-0.048725228756666183,
-0.023169852793216705,
0.00010950004070764408,
0.06317184865474701,
0.002471912419423461,
0.09773622453212738,
0.0735151618719101,
0.09715340286493301,
-0.08337292820215225,
0.10562895983457565,
0.10150538384914398,
-0.09572599828243256,
0.03605884686112404,
0.06754924356937408,
-0.05300498008728027,
-0.043293699622154236,
0.03665391728281975,
0.033023297786712646,
0.005234600510448217,
-0.060321882367134094,
0.013913018628954887,
-0.036497246474027634,
0.044923391193151474,
0.08326134830713272,
0.03754979372024536,
-0.013354414142668247,
0.06462216377258301,
0.03401726484298706,
-0.10898099094629288,
0.10366570204496384,
0.01731540448963642,
0.04105307161808014,
-0.08384523540735245,
-0.019968897104263306,
0.035425446927547455,
0.030576206743717194,
-0.01765924133360386,
-0.02306121215224266,
-0.02860277332365513,
-0.01614218018949032,
-0.14299540221691132,
-0.023106401786208153,
-0.07243485748767853,
0.006181265693157911,
0.014656842686235905,
-0.031884219497442245,
-0.011233693920075893,
0.02475680410861969,
-0.06979699432849884,
-0.07426341623067856,
-0.006949664559215307,
0.09833318740129471,
-0.15115703642368317,
0.008848577737808228,
0.06907843053340912,
-0.11088496446609497,
0.08190931379795074,
-0.008411259390413761,
0.016245156526565552,
0.022527478635311127,
-0.15448406338691711,
0.05601610988378525,
0.0008648968650959432,
0.01916889287531376,
0.025886621326208115,
-0.16471809148788452,
0.004104440100491047,
-0.04661374166607857,
-0.02149827405810356,
-0.00004464812809601426,
-0.02647159807384014,
-0.12325995415449142,
0.06858719140291214,
-0.015622655861079693,
-0.035931166261434555,
-0.02701525390148163,
0.0539589487016201,
0.07888586074113846,
-0.027474910020828247,
0.10445091128349304,
-0.008690856397151947,
0.04941811040043831,
-0.16801609098911285,
-0.02470702864229679,
-0.04982255399227142,
0.019377702847123146,
0.009884213097393513,
-0.007693959400057793,
0.04183054715394974,
-0.00976533442735672,
0.21883612871170044,
-0.05075952783226967,
0.1607085019350052,
0.05847611650824547,
-0.017352959141135216,
-0.0007513365126214921,
0.06180921941995621,
0.05997028574347496,
0.04658793285489082,
0.009480604901909828,
0.023740366101264954,
-0.022450892254710197,
-0.006695089396089315,
-0.15932634472846985,
0.01890849508345127,
0.14999441802501678,
0.06301083415746689,
0.024745315313339233,
0.05866100639104843,
-0.12775006890296936,
-0.12135478109121323,
0.09311001747846603,
-0.026755332946777344,
0.00928465835750103,
-0.08245618641376495,
0.1358020007610321,
0.14980104565620422,
-0.14000412821769714,
0.05256148427724838,
-0.06134212389588356,
-0.05217423290014267,
-0.10388828068971634,
-0.12032219022512436,
-0.05887215584516525,
-0.053666237741708755,
0.002330566756427288,
-0.03760887682437897,
0.054546963423490524,
0.03344334661960602,
-0.009351172484457493,
-0.00022941511997487396,
0.13597318530082703,
-0.019751882180571556,
-0.0028988157864660025,
0.048313532024621964,
0.03693558648228645,
0.02373051457107067,
-0.05275435373187065,
0.02940409444272518,
0.02539868652820587,
0.032232340425252914,
0.06546790152788162,
0.033412106335163116,
-0.047448933124542236,
0.03804153576493263,
-0.0025254099164158106,
-0.11207924783229828,
0.019641218706965446,
-0.00460948096588254,
-0.0742158442735672,
0.1268945336341858,
0.0407399944961071,
0.010224059224128723,
-0.03741471841931343,
0.24361543357372284,
-0.06653323769569397,
-0.06378097087144852,
-0.13251738250255585,
0.10491154342889786,
-0.0027236645109951496,
0.06476365029811859,
0.023412218317389488,
-0.1284150779247284,
0.005243356805294752,
0.13858191668987274,
0.12181595712900162,
0.0045748427510261536,
0.009228081442415714,
0.0518609918653965,
0.0025186820421367884,
-0.06998204439878464,
0.054019294679164886,
0.06992026418447495,
0.12919506430625916,
-0.07847554981708527,
0.07680778950452805,
0.0006860480643808842,
-0.08370215445756912,
-0.02947772853076458,
0.11312682181596756,
-0.0409729965031147,
0.03491825982928276,
-0.047444481402635574,
0.10916327685117722,
-0.05787910893559456,
-0.29412412643432617,
0.02350960113108158,
-0.09588567912578583,
-0.15202060341835022,
-0.018367812037467957,
0.05944539234042168,
-0.02624768204987049,
0.018029648810625076,
0.06971040368080139,
-0.06011629104614258,
0.20098382234573364,
0.0335683599114418,
-0.07864278554916382,
-0.0664360448718071,
0.04837050288915634,
-0.06564252078533173,
0.2949807047843933,
0.008418165147304535,
0.02863333560526371,
0.10770907253026962,
-0.03253700211644173,
-0.18271861970424652,
0.010723991319537163,
0.1133992001414299,
-0.08056149631738663,
0.08200647681951523,
0.19000613689422607,
-0.012578671798110008,
0.1209007054567337,
0.05294662341475487,
-0.047376248985528946,
0.04217283055186272,
-0.03389401361346245,
-0.051268599927425385,
-0.10752558708190918,
0.058453381061553955,
-0.05909625440835953,
0.15447644889354706,
0.10152646154165268,
-0.05671518296003342,
-0.004550917539745569,
-0.05555408447980881,
0.04875178262591362,
0.01804669201374054,
0.12263146042823792,
0.02951994352042675,
-0.1865430772304535,
0.032826557755470276,
-0.01144319772720337,
0.10186848044395447,
-0.25588861107826233,
-0.08421015739440918,
0.08833149075508118,
-0.011924264021217823,
-0.05105875805020332,
0.10560628771781921,
0.057650718837976456,
0.04243382066488266,
-0.043439045548439026,
-0.10480839014053345,
-0.02186836116015911,
0.14663739502429962,
-0.1469624787569046,
-0.025013303384184837
] |
null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.1 | {"library_name": "peft", "base_model": "microsoft/Orca-2-7b"} | null | Mahdish720/Orca2_7b_Enlighten_V2 | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:microsoft/Orca-2-7b",
"region:us"
] | 2024-02-09T09:07:33+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-microsoft/Orca-2-7b #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
### Framework versions
- PEFT 0.7.1 | [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.7.1"
] | [
"TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-microsoft/Orca-2-7b #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.7.1"
] | [
35,
6,
3,
54,
28,
3,
4,
9,
9,
10,
42,
20,
3,
4,
5,
9,
11,
13,
3,
12,
5,
4,
5,
3,
4,
9,
53,
9,
8,
6,
3,
14,
8,
7,
9,
4,
11
] | [
"passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-microsoft/Orca-2-7b #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.7.1"
] | [
-0.10618973523378372,
0.1971147656440735,
-0.00381654966622591,
0.036631640046834946,
0.08770972490310669,
0.01854133978486061,
0.05497859790921211,
0.12813685834407806,
-0.03863305225968361,
0.11048749834299088,
0.07341744750738144,
0.10969670861959457,
0.105040043592453,
0.20203325152397156,
-0.0012966364156454802,
-0.19861847162246704,
0.02139122225344181,
-0.09614627063274384,
-0.0022512651048600674,
0.12511758506298065,
0.14395175874233246,
-0.09522505104541779,
0.0720684826374054,
-0.0239417664706707,
-0.009468638338148594,
-0.03838854283094406,
-0.07127993553876877,
-0.03333776816725731,
0.03416121006011963,
0.050804078578948975,
0.0621163509786129,
-0.00021623764769174159,
0.08660129457712173,
-0.26594847440719604,
0.017818452790379524,
0.03721968084573746,
-0.001943187671713531,
0.08273389935493469,
0.09705840796232224,
-0.046885620802640915,
0.12162739783525467,
-0.03917781636118889,
0.13768808543682098,
0.07937876880168915,
-0.10253401845693588,
-0.2221055030822754,
-0.06965944170951843,
0.07913213968276978,
0.16641119122505188,
0.0801788792014122,
-0.04410179331898689,
0.1412626951932907,
-0.10261870175600052,
0.017294367775321007,
0.037256382405757904,
-0.07405421882867813,
-0.07650510966777802,
0.06540417671203613,
0.11662546545267105,
0.06706123054027557,
-0.14547666907310486,
-0.03887031972408295,
0.0217878520488739,
0.03668012470006943,
0.07650400698184967,
0.022720327600836754,
0.14762245118618011,
0.032784443348646164,
-0.1453368365764618,
-0.04493362456560135,
0.12071768194437027,
0.04110703244805336,
-0.03430795297026634,
-0.22050072252750397,
0.010086390189826488,
-0.09776271879673004,
-0.01823943480849266,
-0.04718133062124252,
0.04060598835349083,
-0.0020075750071555376,
0.09033843874931335,
-0.026170924305915833,
-0.100609190762043,
-0.008530312217772007,
0.08303554356098175,
0.05157824978232384,
0.014711081981658936,
-0.026639003306627274,
0.007611172739416361,
0.11300835758447647,
0.06220462545752525,
-0.1242714673280716,
-0.06949635595083237,
-0.06684989482164383,
-0.035634029656648636,
-0.04790652543306351,
0.02977760136127472,
0.03952083736658096,
0.05828556790947914,
0.23805804550647736,
-0.016870947554707527,
0.050166498869657516,
0.05723642185330391,
0.026532618328928947,
0.039060160517692566,
0.09659081697463989,
-0.04771219566464424,
-0.13536280393600464,
-0.017068615183234215,
0.09630972146987915,
-0.01171107031404972,
-0.02158503606915474,
-0.05034147948026657,
0.03572267293930054,
0.027827566489577293,
0.1156773567199707,
0.09984491765499115,
-0.013943536207079887,
-0.08217910677194595,
-0.04919402301311493,
0.21506783366203308,
-0.14311204850673676,
0.04919176921248436,
0.025448350235819817,
-0.014757552184164524,
-0.04672994837164879,
0.005945988465100527,
0.020660076290369034,
-0.022653214633464813,
0.0939987301826477,
-0.06050204858183861,
-0.03317388892173767,
-0.11293721199035645,
-0.011187302879989147,
0.034685201942920685,
0.02016262151300907,
-0.022923056036233902,
-0.040847234427928925,
-0.0588805228471756,
-0.095309317111969,
0.10520575195550919,
-0.07310595363378525,
-0.05990780517458916,
-0.03215822950005531,
-0.10969334095716476,
0.016764560714364052,
0.026066655293107033,
0.10603976249694824,
-0.02387496642768383,
0.04355183616280556,
-0.011960704810917377,
0.06135258078575134,
0.07104213535785675,
0.03605236858129501,
-0.07560913264751434,
0.06250568479299545,
-0.18871109187602997,
0.09586707502603531,
-0.07628541439771652,
0.031651485711336136,
-0.1504390686750412,
-0.01138822827488184,
0.0016071689315140247,
0.015393962152302265,
0.032978903502225876,
0.1549881100654602,
-0.196641206741333,
-0.02960774302482605,
0.155679389834404,
-0.09595544636249542,
-0.11657753586769104,
0.03784172609448433,
-0.04920769855380058,
0.16536928713321686,
0.010163074359297752,
-0.0044162459671497345,
0.08383046835660934,
-0.1484757959842682,
-0.02055061049759388,
-0.035237304866313934,
-0.0005435437196865678,
0.10204095393419266,
0.09348205476999283,
-0.07342615723609924,
0.03928602114319801,
0.014321982860565186,
-0.03953295573592186,
-0.03290767967700958,
-0.05470160022377968,
-0.1166023313999176,
-0.0026100887916982174,
-0.08322036266326904,
0.02535358816385269,
-0.014671139419078827,
-0.08054468780755997,
-0.005928071215748787,
-0.16350208222866058,
-0.03228916972875595,
0.07519150525331497,
0.013793259859085083,
-0.016991527751088142,
-0.0955195277929306,
0.028883252292871475,
-0.03760034963488579,
-0.02854895032942295,
-0.15998928248882294,
-0.017670515924692154,
0.018287107348442078,
-0.14740516245365143,
0.018273144960403442,
-0.10501641780138016,
0.074969083070755,
0.013825565576553345,
-0.06717631965875626,
-0.03659772127866745,
-0.011628889478743076,
0.017088931053876877,
-0.054194893687963486,
-0.23408359289169312,
-0.018113210797309875,
-0.05869583785533905,
0.1628042757511139,
-0.23424066603183746,
0.034124016761779785,
0.0406978614628315,
0.12999466061592102,
0.013593031093478203,
-0.05895257741212845,
0.022999126464128494,
-0.06448452919721603,
-0.024164890870451927,
-0.0720604956150055,
-0.004725452046841383,
-0.0036142210010439157,
-0.04251086711883545,
0.01716987043619156,
-0.11652109771966934,
-0.04751298949122429,
0.106407530605793,
0.08897891640663147,
-0.1681375950574875,
-0.021646074950695038,
-0.04523686319589615,
-0.06879933178424835,
-0.0720347985625267,
-0.05877712741494179,
0.09757047146558762,
0.05234421417117119,
0.0267522931098938,
-0.07385362684726715,
-0.061263274401426315,
0.008940516039729118,
-0.028607701882719994,
-0.027512896806001663,
0.11464472860097885,
0.06918273866176605,
-0.11189072579145432,
0.09679325670003891,
0.08581472933292389,
0.02129501849412918,
0.0759253054857254,
-0.021505458280444145,
-0.1092173308134079,
-0.03598343953490257,
0.040468789637088776,
0.019929958507418633,
0.15032769739627838,
-0.07785537093877792,
0.06408177316188812,
0.04061705619096756,
-0.029577357694506645,
0.04652158543467522,
-0.09519176930189133,
0.0071509950794279575,
0.007260014768689871,
-0.0171283558011055,
0.014971485361456871,
-0.0276881642639637,
0.008550763130187988,
0.08348838239908218,
0.05075235664844513,
0.03246467933058739,
0.044140566140413284,
-0.0250060074031353,
-0.1274888664484024,
0.17470179498195648,
-0.09316796064376831,
-0.24472719430923462,
-0.16820769011974335,
0.05390369892120361,
0.05348911136388779,
-0.022503366693854332,
0.03312837705016136,
-0.0441620796918869,
-0.09967173635959625,
-0.08304793387651443,
0.013216604478657246,
0.03721335902810097,
-0.06705427169799805,
-0.07494043558835983,
0.06949879974126816,
0.04211503267288208,
-0.10794112086296082,
0.03537525609135628,
0.05989625304937363,
-0.018856678158044815,
0.01651381328701973,
0.04555605724453926,
0.08732810616493225,
0.1758585423231125,
-0.01039975881576538,
-0.00928798783570528,
0.058279428631067276,
0.2695869207382202,
-0.1537441909313202,
0.11158756166696548,
0.11896926164627075,
-0.07087850570678711,
0.07636561989784241,
0.1957930028438568,
0.03226091340184212,
-0.10144366323947906,
0.03059815801680088,
0.03427952900528908,
-0.018232395872473717,
-0.2779908776283264,
-0.04327758774161339,
-0.0160931758582592,
-0.09916119277477264,
0.0757770836353302,
0.08895676583051682,
0.09176262468099594,
0.03690662235021591,
-0.055185429751873016,
-0.11822911351919174,
0.03199027478694916,
0.10295635461807251,
-0.03456820920109749,
0.005926842335611582,
0.08184146881103516,
-0.02608991228044033,
0.014682522974908352,
0.09744776040315628,
-0.01789107732474804,
0.1709003895521164,
0.07137720286846161,
0.10516706109046936,
0.07416309416294098,
0.0815570130944252,
-0.00034488787059672177,
0.017288120463490486,
0.03351142629981041,
0.0158697497099638,
0.01641188934445381,
-0.08336567133665085,
0.030748702585697174,
0.11483093351125717,
0.04740532860159874,
0.03300268203020096,
0.013172744773328304,
-0.047878801822662354,
0.05427973344922066,
0.19146905839443207,
0.018465112894773483,
-0.20893564820289612,
-0.07784978300333023,
0.05268217995762825,
-0.08274048566818237,
-0.1363910287618637,
-0.020338181406259537,
0.019210513681173325,
-0.16663150489330292,
0.007211408577859402,
-0.03979663923382759,
0.10393691062927246,
-0.08107740432024002,
-0.043186452239751816,
0.0793745368719101,
0.07058115303516388,
-0.023416994139552116,
0.06521710008382797,
-0.2084905207157135,
0.1355094611644745,
0.013066593557596207,
0.0759325698018074,
-0.08627057075500488,
0.09191017597913742,
-0.005046086851507425,
-0.005530162714421749,
0.17423158884048462,
-0.0005879457457922399,
-0.07508141547441483,
-0.05670727416872978,
-0.09343022853136063,
-0.008987785317003727,
0.09848847985267639,
-0.13167327642440796,
0.06907707452774048,
-0.018328983336687088,
-0.03707737475633621,
0.0020753066055476665,
-0.0724877119064331,
-0.11295408755540848,
-0.1645212024450302,
0.048518016934394836,
-0.098029226064682,
0.036133743822574615,
-0.090285524725914,
-0.0590464286506176,
0.010996081866323948,
0.17757685482501984,
-0.17994914948940277,
-0.08201328665018082,
-0.13675685226917267,
-0.09034254401922226,
0.17025253176689148,
-0.0429559126496315,
0.07843764126300812,
-0.001866536564193666,
0.17395247519016266,
0.024251237511634827,
0.0006633479497395456,
0.09757154434919357,
-0.08687365800142288,
-0.19071713089942932,
-0.059147100895643234,
0.15548576414585114,
0.12852312624454498,
0.03708873689174652,
-0.008959502913057804,
0.028720643371343613,
-0.0590386763215065,
-0.10786951333284378,
0.030636092647910118,
0.12469958513975143,
0.0761980414390564,
-0.014859439805150032,
-0.03752870112657547,
-0.1109326034784317,
-0.06977134943008423,
-0.06241106241941452,
0.0035169313196092844,
0.2056959867477417,
-0.07300760596990585,
0.15952734649181366,
0.12710294127464294,
-0.05723908171057701,
-0.2016434371471405,
0.04269564524292946,
0.057255733758211136,
0.01870768703520298,
0.03815086558461189,
-0.1830911934375763,
0.09441105276346207,
0.012399313040077686,
-0.06737114489078522,
0.1402689814567566,
-0.1653527319431305,
-0.14519880712032318,
0.09123377501964569,
0.04093989357352257,
-0.2384648621082306,
-0.14097970724105835,
-0.0966392308473587,
-0.02302105724811554,
-0.11082181334495544,
0.07113248854875565,
0.019801020622253418,
0.02250228449702263,
0.030436113476753235,
0.020097600296139717,
0.02698669768869877,
-0.04814952239394188,
0.22402450442314148,
-0.03839182108640671,
0.006457359064370394,
-0.05332751199603081,
-0.10250423848628998,
0.03884890675544739,
-0.04778243973851204,
0.09054248034954071,
0.002824208000674844,
0.026656068861484528,
-0.13623519241809845,
-0.04839232191443443,
-0.05864228680729866,
0.022702520713210106,
-0.09289108961820602,
-0.08564868569374084,
-0.03343956544995308,
0.10732293874025345,
0.0890049859881401,
-0.028902823105454445,
0.003494016593322158,
-0.10268197953701019,
0.06854226440191269,
0.20496013760566711,
0.18767546117305756,
0.05570884421467781,
-0.06370138376951218,
0.011786351911723614,
-0.037035856395959854,
0.03580610081553459,
-0.2153090238571167,
0.040679797530174255,
0.06080049276351929,
0.023512791842222214,
0.08429305255413055,
-0.01273441407829523,
-0.1393039971590042,
-0.06965430080890656,
0.0769234448671341,
-0.03594182804226875,
-0.15929065644741058,
-0.014728506095707417,
0.04769804701209068,
-0.2170686423778534,
-0.05200590938329697,
0.018508076667785645,
-0.018742142245173454,
-0.04042856767773628,
0.030738485977053642,
0.07592222094535828,
-0.03495589643716812,
0.10147131979465485,
0.08810993283987045,
0.09354449808597565,
-0.09771488606929779,
0.07325815409421921,
0.08787036687135696,
-0.04296207055449486,
0.013535130769014359,
0.105666883289814,
-0.043001338839530945,
-0.03330978751182556,
0.08736565709114075,
0.09761033952236176,
0.01991661638021469,
-0.055157728493213654,
0.016152141615748405,
-0.05010398477315903,
0.06247786805033684,
0.1063852459192276,
0.034304630011320114,
-0.0074755605310201645,
0.05169546976685524,
0.03367455303668976,
-0.10430806130170822,
0.10846146196126938,
0.05906328931450844,
0.021783027797937393,
-0.04757574200630188,
-0.03614373132586479,
-0.013863909058272839,
-0.011468719691038132,
-0.017406923696398735,
-0.003599856747314334,
-0.0934719368815422,
-0.004877177532762289,
-0.09617750346660614,
0.03983952850103378,
-0.06945174187421799,
0.015827352181077003,
0.03392733633518219,
-0.06158975884318352,
0.0038428509142249823,
-0.0001573809568071738,
-0.07370559126138687,
-0.051487114280462265,
-0.01796772889792919,
0.07957905530929565,
-0.13219621777534485,
0.02182556502521038,
0.07616664469242096,
-0.1077919453382492,
0.0716969296336174,
0.015566335059702396,
0.002884232671931386,
0.003929962404072285,
-0.17351999878883362,
0.06133164092898369,
-0.01882101036608219,
-0.006358025595545769,
0.011440162546932697,
-0.21036261320114136,
-0.0154409846290946,
-0.03738025948405266,
-0.05217836797237396,
0.013350010849535465,
-0.030904343351721764,
-0.12824003398418427,
0.08908949047327042,
-0.009952842257916927,
-0.0880347192287445,
-0.00955529510974884,
0.04197089746594429,
0.09411463141441345,
-0.024732986465096474,
0.1285848617553711,
-0.018209103494882584,
0.08161046355962753,
-0.16851496696472168,
-0.005474268924444914,
-0.01738573983311653,
0.03401794657111168,
-0.015268024057149887,
-0.027805175632238388,
0.060217034071683884,
-0.011171204969286919,
0.17627958953380585,
-0.01602022908627987,
0.07568328827619553,
0.0554058663547039,
-0.004066098015755415,
0.020904231816530228,
0.08384142071008682,
0.060911115258932114,
-0.0017344829393550754,
-0.0037681388203054667,
0.04457107558846474,
-0.009304962120950222,
-0.05118849128484726,
-0.15535283088684082,
0.08766099810600281,
0.15953435003757477,
0.05618080496788025,
0.01872834749519825,
0.03776494041085243,
-0.10401520878076553,
-0.07041718065738678,
0.13549020886421204,
-0.003839237382635474,
-0.04055698961019516,
-0.07541660219430923,
0.16960836946964264,
0.13404011726379395,
-0.1997339129447937,
0.07675880193710327,
-0.06634767353534698,
-0.05196135491132736,
-0.12341835349798203,
-0.15468063950538635,
-0.07176002860069275,
-0.04106424003839493,
-0.029932530596852303,
-0.056176163256168365,
0.042212337255477905,
0.05326555296778679,
0.007434878498315811,
-0.019103221595287323,
0.10967529565095901,
0.02822841703891754,
-0.015918580815196037,
0.0457778200507164,
0.05247054621577263,
0.034603431820869446,
-0.10731033980846405,
0.011406616307795048,
0.0037906309589743614,
0.024712739512324333,
0.057688113301992416,
0.030159348621964455,
-0.05574353411793709,
0.008955842815339565,
-0.025801055133342743,
-0.12147317826747894,
0.03576640784740448,
-0.01538157183676958,
-0.04431416839361191,
0.14925436675548553,
0.03558792546391487,
0.01762775331735611,
-0.014868280850350857,
0.23639313876628876,
-0.0700271874666214,
-0.0814172625541687,
-0.1577623188495636,
0.06256107240915298,
-0.0852060317993164,
0.03368265926837921,
0.03326357901096344,
-0.11432744562625885,
0.01677393727004528,
0.1569201946258545,
0.11653155833482742,
-0.019747840240597725,
0.011213196441531181,
0.06759397685527802,
0.0013508994597941637,
-0.04075029864907265,
0.011305999010801315,
0.050689298659563065,
0.14361944794654846,
-0.08139775693416595,
0.06573817133903503,
-0.006895786616951227,
-0.07408809661865234,
-0.011755159124732018,
0.09530974179506302,
-0.005785473622381687,
0.0009571519331075251,
-0.07018253952264786,
0.14029860496520996,
-0.09942395985126495,
-0.228440523147583,
0.0686710849404335,
-0.0672549456357956,
-0.14889465272426605,
-0.052193496376276016,
0.034896120429039,
-0.019010011106729507,
0.015432458370923996,
0.06885647773742676,
-0.05470969155430794,
0.1667276918888092,
0.04123064875602722,
-0.03454127907752991,
-0.0802687555551529,
0.05740036442875862,
-0.12365879863500595,
0.2739740312099457,
0.015415440313518047,
0.06018485128879547,
0.10637787729501724,
-0.01536504365503788,
-0.14853015542030334,
0.007921189069747925,
0.11051692068576813,
-0.06996574252843857,
0.07852768152952194,
0.16755405068397522,
-0.0054679871536791325,
0.13356631994247437,
0.062090758234262466,
-0.05197472497820854,
0.03797238692641258,
-0.06508079171180725,
-0.05072522908449173,
-0.10153912752866745,
0.0870303213596344,
-0.08236028254032135,
0.1545427143573761,
0.1265910118818283,
-0.06675361841917038,
-0.008890656754374504,
-0.015397691167891026,
0.09351600706577301,
0.01114069577306509,
0.10253257304430008,
0.00885540060698986,
-0.19222067296504974,
0.032602544873952866,
0.02216813527047634,
0.1167881041765213,
-0.21350543200969696,
-0.06760527193546295,
0.0570640005171299,
-0.02695588208734989,
-0.07386038452386856,
0.10943581163883209,
0.04401180520653725,
0.029914693906903267,
-0.04238364100456238,
-0.04408970847725868,
0.0050428397953510284,
0.15485717356204987,
-0.12144074589014053,
-0.010064765810966492
] |
null | null | transformers |
# Model Card for llama2_telugu
Welcome to the `eswardivi/llama2_telugu` model page on Hugging Face. This model is a result of fine-tuning the powerful `PosteriorAI/godavari-telugu-llama2-7B` model on the `ravithejads/telugu_alpaca_ft` dataset to better serve the Telugu-speaking community.
## Overview
- **Base Model**: `PosteriorAI/godavari-telugu-llama2-7B`, a state-of-the-art Telugu language model based on the LLaMA architecture, offering advanced natural language understanding and generation capabilities.
- **Fine-Tuning Dataset**: `ravithejads/telugu_alpaca_ft`, a curated dataset specifically designed to fine-tune language models for Telugu language tasks .
- **Target Application**: Enhanced communication, education, and technology access for the Telugu-speaking community, addressing the gap in AI for Indic languages .
## Usage
To use this model, you can leverage the Hugging Face API, SDK, or Transformers library. Below is a simple Python example using the Transformers library:
```python
from transformers import pipeline
pipe = pipeline(
"text-generation",
model="eswardivi/llama2_telugu",
device_map="auto",
model_kwargs={"load_in_8bit": True}
)
def create_prompt(instruction: str, input: str = "") -> str:
prompt = f"""
You are a helpful assistant.
### Instruction:
{instruction}
### Input:
{input}
### Response:
"""
return prompt
instruction = "Krindi samaacharam prakaram google news app eppudu release ayyindi?"
input = "Google News is a news aggregator service developed by Google. It presents a continuous flow of links to articles organized from thousands of publishers and magazines. Google News is available as an app on Android, iOS, and the Web. Google released a beta version in September 2002 and the official app in January 2006."
prompt = create_prompt(instruction, input)
print(prompt)
out = pipe(
prompt,
num_return_sequences=1,
max_new_tokens=1024,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(out[0]['generated_text'])
```
## Training Details
This model was fine-tuned using Axolotl.
## Contributions
Contributions to this model are welcome. Feel free to submit issues, feature requests, or pull requests via Huggingface.
## Contact
For inquiries or collaborations, please contact the model maintainer at `[email protected]`.
--- | {"language": ["te"], "library_name": "transformers", "datasets": ["ravithejads/telugu_alpaca_ft"]} | text-generation | eswardivi/llama2_telugu | [
"transformers",
"safetensors",
"llama",
"text-generation",
"te",
"dataset:ravithejads/telugu_alpaca_ft",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T09:17:21+00:00 | [] | [
"te"
] | TAGS
#transformers #safetensors #llama #text-generation #te #dataset-ravithejads/telugu_alpaca_ft #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for llama2_telugu
Welcome to the 'eswardivi/llama2_telugu' model page on Hugging Face. This model is a result of fine-tuning the powerful 'PosteriorAI/godavari-telugu-llama2-7B' model on the 'ravithejads/telugu_alpaca_ft' dataset to better serve the Telugu-speaking community.
## Overview
- Base Model: 'PosteriorAI/godavari-telugu-llama2-7B', a state-of-the-art Telugu language model based on the LLaMA architecture, offering advanced natural language understanding and generation capabilities.
- Fine-Tuning Dataset: 'ravithejads/telugu_alpaca_ft', a curated dataset specifically designed to fine-tune language models for Telugu language tasks .
- Target Application: Enhanced communication, education, and technology access for the Telugu-speaking community, addressing the gap in AI for Indic languages .
## Usage
To use this model, you can leverage the Hugging Face API, SDK, or Transformers library. Below is a simple Python example using the Transformers library:
## Training Details
This model was fine-tuned using Axolotl.
## Contributions
Contributions to this model are welcome. Feel free to submit issues, feature requests, or pull requests via Huggingface.
## Contact
For inquiries or collaborations, please contact the model maintainer at 'URL.902@URL'.
--- | [
"# Model Card for llama2_telugu\n\nWelcome to the 'eswardivi/llama2_telugu' model page on Hugging Face. This model is a result of fine-tuning the powerful 'PosteriorAI/godavari-telugu-llama2-7B' model on the 'ravithejads/telugu_alpaca_ft' dataset to better serve the Telugu-speaking community.",
"## Overview\n\n- Base Model: 'PosteriorAI/godavari-telugu-llama2-7B', a state-of-the-art Telugu language model based on the LLaMA architecture, offering advanced natural language understanding and generation capabilities.\n- Fine-Tuning Dataset: 'ravithejads/telugu_alpaca_ft', a curated dataset specifically designed to fine-tune language models for Telugu language tasks .\n- Target Application: Enhanced communication, education, and technology access for the Telugu-speaking community, addressing the gap in AI for Indic languages .",
"## Usage\n\nTo use this model, you can leverage the Hugging Face API, SDK, or Transformers library. Below is a simple Python example using the Transformers library:",
"## Training Details\n\nThis model was fine-tuned using Axolotl.",
"## Contributions\n\nContributions to this model are welcome. Feel free to submit issues, feature requests, or pull requests via Huggingface.",
"## Contact\n\nFor inquiries or collaborations, please contact the model maintainer at 'URL.902@URL'.\n\n---"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #te #dataset-ravithejads/telugu_alpaca_ft #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for llama2_telugu\n\nWelcome to the 'eswardivi/llama2_telugu' model page on Hugging Face. This model is a result of fine-tuning the powerful 'PosteriorAI/godavari-telugu-llama2-7B' model on the 'ravithejads/telugu_alpaca_ft' dataset to better serve the Telugu-speaking community.",
"## Overview\n\n- Base Model: 'PosteriorAI/godavari-telugu-llama2-7B', a state-of-the-art Telugu language model based on the LLaMA architecture, offering advanced natural language understanding and generation capabilities.\n- Fine-Tuning Dataset: 'ravithejads/telugu_alpaca_ft', a curated dataset specifically designed to fine-tune language models for Telugu language tasks .\n- Target Application: Enhanced communication, education, and technology access for the Telugu-speaking community, addressing the gap in AI for Indic languages .",
"## Usage\n\nTo use this model, you can leverage the Hugging Face API, SDK, or Transformers library. Below is a simple Python example using the Transformers library:",
"## Training Details\n\nThis model was fine-tuned using Axolotl.",
"## Contributions\n\nContributions to this model are welcome. Feel free to submit issues, feature requests, or pull requests via Huggingface.",
"## Contact\n\nFor inquiries or collaborations, please contact the model maintainer at 'URL.902@URL'.\n\n---"
] | [
65,
91,
135,
43,
16,
32,
27
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #te #dataset-ravithejads/telugu_alpaca_ft #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for llama2_telugu\n\nWelcome to the 'eswardivi/llama2_telugu' model page on Hugging Face. This model is a result of fine-tuning the powerful 'PosteriorAI/godavari-telugu-llama2-7B' model on the 'ravithejads/telugu_alpaca_ft' dataset to better serve the Telugu-speaking community.## Overview\n\n- Base Model: 'PosteriorAI/godavari-telugu-llama2-7B', a state-of-the-art Telugu language model based on the LLaMA architecture, offering advanced natural language understanding and generation capabilities.\n- Fine-Tuning Dataset: 'ravithejads/telugu_alpaca_ft', a curated dataset specifically designed to fine-tune language models for Telugu language tasks .\n- Target Application: Enhanced communication, education, and technology access for the Telugu-speaking community, addressing the gap in AI for Indic languages .## Usage\n\nTo use this model, you can leverage the Hugging Face API, SDK, or Transformers library. Below is a simple Python example using the Transformers library:## Training Details\n\nThis model was fine-tuned using Axolotl.## Contributions\n\nContributions to this model are welcome. Feel free to submit issues, feature requests, or pull requests via Huggingface.## Contact\n\nFor inquiries or collaborations, please contact the model maintainer at 'URL.902@URL'.\n\n---"
] | [
-0.05771476402878761,
0.1363273710012436,
-0.007277547847479582,
0.0598653145134449,
0.08330663293600082,
0.018571970984339714,
0.12553489208221436,
0.08794185519218445,
0.0548369437456131,
0.05427232384681702,
-0.029185835272073746,
0.0029920157976448536,
0.1098431870341301,
0.14064431190490723,
0.00615632813423872,
-0.22287027537822723,
0.0236883033066988,
-0.065644770860672,
0.02830137312412262,
0.07586947083473206,
0.12895338237285614,
-0.026577068492770195,
0.09653671085834503,
0.0100331986322999,
-0.013461069203913212,
0.03099919483065605,
0.03107081726193428,
-0.0502743199467659,
0.07453146576881409,
0.06257405877113342,
0.12802930176258087,
-0.04225332662463188,
0.0376824289560318,
-0.19695299863815308,
0.043487798422575,
0.06056351587176323,
-0.00811480637639761,
0.015599182806909084,
0.0769745409488678,
-0.04509991407394409,
0.14813683927059174,
-0.07572755962610245,
0.03956761956214905,
0.07012245059013367,
-0.12242666631937027,
-0.048720747232437134,
-0.07380492240190506,
0.040774278342723846,
0.07327988743782043,
0.08103475719690323,
-0.045020610094070435,
0.12466833740472794,
-0.01257912628352642,
0.0683952271938324,
0.031212441623210907,
-0.21754644811153412,
-0.0619925893843174,
0.04575648158788681,
0.04038393497467041,
0.054868489503860474,
-0.08489000052213669,
0.028875891119241714,
0.0038619437254965305,
-0.011635435745120049,
0.026882434263825417,
-0.04777173325419426,
-0.08697064965963364,
-0.05422254651784897,
-0.050408896058797836,
0.006460404489189386,
0.12976497411727905,
-0.0033070838544517756,
-0.06280836462974548,
-0.11510040611028671,
-0.09279650449752808,
0.09869063645601273,
0.0001758645084919408,
-0.05249805003404617,
0.012053554877638817,
0.04831662401556969,
0.12830790877342224,
-0.06255999952554703,
-0.10143745690584183,
0.03299045190215111,
-0.06089833006262779,
0.08199663460254669,
0.040175892412662506,
0.011695710942149162,
-0.03181011974811554,
0.012435679323971272,
-0.04498197138309479,
-0.09918557852506638,
-0.016398582607507706,
-0.061990246176719666,
-0.1090245246887207,
-0.0075612617656588554,
-0.010411104187369347,
-0.13732074201107025,
0.03615771234035492,
0.1798858940601349,
-0.07049483060836792,
0.05111885443329811,
-0.05064056068658829,
-0.0606708750128746,
0.010947992093861103,
0.09757712483406067,
-0.07633411139249802,
-0.09223341196775436,
0.06699388474225998,
-0.004332113079726696,
0.06849818676710129,
-0.03282127529382706,
-0.0016844394849613309,
-0.058104682713747025,
-0.04781833291053772,
0.07525179535150528,
0.05159391835331917,
0.058820221573114395,
-0.056929729878902435,
-0.053417596966028214,
0.08452660590410233,
-0.14523161947727203,
0.02775622345507145,
0.0046034082770347595,
-0.019270578399300575,
0.06979192048311234,
-0.005890707019716501,
0.024576958268880844,
-0.07283914089202881,
-0.10329972207546234,
-0.056959621608257294,
0.01723102480173111,
-0.08480420708656311,
-0.07597518712282181,
0.02666240744292736,
-0.10325337201356888,
-0.038101229816675186,
-0.12513040006160736,
-0.15491966903209686,
-0.02066909708082676,
0.045831725001335144,
-0.08830943703651428,
-0.019379109144210815,
-0.01038164459168911,
0.0030478034168481827,
0.02600860968232155,
0.002629823051393032,
-0.09105498343706131,
-0.013601182028651237,
0.006770309526473284,
-0.013344534672796726,
0.0627559944987297,
0.045059047639369965,
0.010290982201695442,
-0.0345122329890728,
0.032674528658390045,
-0.07815533876419067,
0.11219780892133713,
-0.0787748321890831,
0.02705116756260395,
-0.09012296050786972,
0.06358931213617325,
0.03129255771636963,
0.00704196235165,
0.004055541940033436,
0.17474700510501862,
-0.18518707156181335,
0.019107777625322342,
0.16741694509983063,
-0.106160968542099,
-0.04935532808303833,
0.14156845211982727,
0.002080290112644434,
0.06184978038072586,
0.10185117274522781,
0.1348092406988144,
0.1421157717704773,
-0.12276625633239746,
-0.08234673738479614,
-0.01873406581580639,
-0.04005999490618706,
0.07942638546228409,
0.06361541897058487,
-0.013944484293460846,
0.10487633943557739,
0.024098213762044907,
0.05735861882567406,
0.025733957067131996,
0.014904159121215343,
-0.049943119287490845,
0.0008029501186683774,
-0.042184263467788696,
0.0126106021925807,
-0.0061323014087975025,
-0.019380738958716393,
-0.004437694326043129,
-0.07950011640787125,
-0.07542729377746582,
0.10909850150346756,
-0.011494601145386696,
0.03900858014822006,
-0.1883586347103119,
0.07904475927352905,
-0.000964610546361655,
-0.006767249200493097,
-0.1217961311340332,
-0.08391700685024261,
0.05215629190206528,
-0.0516483373939991,
0.09072129428386688,
-0.04745364189147949,
0.05315248668193817,
-0.008657547645270824,
-0.04622989520430565,
-0.002411354100331664,
0.02603612281382084,
-0.02986256405711174,
-0.006136697251349688,
-0.11953452229499817,
0.03834160789847374,
-0.056474972516298294,
0.20723815262317657,
-0.13473545014858246,
0.036618225276470184,
0.06373442709445953,
0.1495143622159958,
0.05781480297446251,
-0.02519642747938633,
0.0487506166100502,
-0.016462497413158417,
0.0008058647508732975,
-0.06523117423057556,
0.007265593856573105,
-0.02868102863430977,
-0.010885603725910187,
0.06608635187149048,
-0.11724758893251419,
-0.03479255735874176,
0.06787430495023727,
0.06321477890014648,
-0.03009083867073059,
0.04493032395839691,
0.03424455225467682,
-0.003359017660841346,
-0.02516980655491352,
-0.00459583755582571,
0.1126532107591629,
0.05058727413415909,
0.06404803693294525,
-0.05586675554513931,
0.011192022822797298,
-0.028409915044903755,
-0.08472037315368652,
-0.012408924289047718,
0.024845682084560394,
0.0193525068461895,
-0.12194626778364182,
0.07579466700553894,
-0.004525978583842516,
-0.006753668654710054,
0.1847478449344635,
0.002918773330748081,
-0.06030237674713135,
-0.026808099821209908,
0.0020842382218688726,
0.019928650930523872,
0.05428081378340721,
0.019461141899228096,
0.011603876017034054,
0.04100344330072403,
-0.023243658244609833,
0.010317616164684296,
-0.05234609544277191,
0.015779675915837288,
-0.0011521336855366826,
-0.0354776494204998,
-0.002665694337338209,
0.08722975105047226,
0.010402305983006954,
0.08467928320169449,
-0.031852174550294876,
0.07126587629318237,
-0.034460172057151794,
-0.015514801256358624,
-0.08140550553798676,
0.11181160807609558,
-0.12754662334918976,
-0.32416561245918274,
-0.13282497227191925,
-0.04379856213927269,
-0.06349019706249237,
0.0070262206718325615,
-0.0031942061614245176,
-0.11317885667085648,
-0.09352907538414001,
-0.08400386571884155,
0.04651905223727226,
0.1011168360710144,
-0.07788898050785065,
-0.08341842889785767,
0.001483264728449285,
0.05580829083919525,
-0.1295311599969864,
0.025259954854846,
0.054168637841939926,
-0.11403930932283401,
0.05618676543235779,
-0.018026016652584076,
0.05546940490603447,
0.03645724058151245,
0.013312914408743382,
-0.027763741090893745,
0.011190889403223991,
0.17737312614917755,
-0.11358049511909485,
0.16203202307224274,
0.19141563773155212,
0.028627291321754456,
0.051798395812511444,
0.21649210155010223,
-0.0031660948880016804,
-0.059573542326688766,
0.002657637931406498,
0.05511626973748207,
0.008506693877279758,
-0.2704352140426636,
-0.07976435124874115,
-0.06354537606239319,
-0.013263423927128315,
0.005600492004305124,
0.043292246758937836,
0.029461219906806946,
0.06803959608078003,
-0.06221967935562134,
0.025219788774847984,
0.04590631276369095,
0.0623047836124897,
0.1409703493118286,
0.008232837542891502,
0.048834092915058136,
-0.0726727694272995,
0.02373848296701908,
0.10850353538990021,
0.05617240443825722,
0.18478257954120636,
0.016956591978669167,
0.1388077586889267,
0.021312927827239037,
0.13145045936107635,
0.038281869143247604,
0.008729629218578339,
-0.02532409504055977,
0.008572135120630264,
-0.01624060980975628,
-0.010883558541536331,
-0.10939856618642807,
0.06274767965078354,
0.0913950577378273,
-0.04730619490146637,
0.08413820713758469,
0.041810743510723114,
0.05951550230383873,
0.11771410703659058,
0.02710612118244171,
-0.14831885695457458,
-0.041709642857313156,
0.08473064750432968,
-0.07763195782899857,
-0.1184060126543045,
-0.011349907144904137,
0.05551011487841606,
-0.1008291095495224,
0.1211739107966423,
-0.05590597540140152,
0.07122810184955597,
-0.024979691952466965,
-0.034853942692279816,
-0.05956781655550003,
0.0737592875957489,
-0.001259622978977859,
0.05631447583436966,
-0.19919519126415253,
0.11725125461816788,
0.03308885917067528,
0.08632681518793106,
0.01188425812870264,
0.037229299545288086,
0.05021735653281212,
0.11676972359418869,
0.0767684280872345,
0.011462562717497349,
0.1077808067202568,
-0.043978460133075714,
-0.08734510838985443,
0.04219026118516922,
0.042616359889507294,
-0.018303973600268364,
0.09160087257623672,
-0.048474378883838654,
0.006235244683921337,
-0.05475129932165146,
0.01887127384543419,
-0.1817481368780136,
-0.17438414692878723,
0.029356354847550392,
-0.011391431093215942,
0.10745660960674286,
-0.04827480390667915,
-0.025737319141626358,
-0.06508048623800278,
0.06823334097862244,
-0.05118181183934212,
-0.08676207810640335,
-0.11636380106210709,
-0.058107566088438034,
0.11771822720766068,
-0.10021375864744186,
0.04809242859482765,
-0.032237425446510315,
0.12372227758169174,
-0.022118564695119858,
-0.02472318522632122,
0.007378551177680492,
-0.046240728348493576,
-0.04573927819728851,
-0.013793888501822948,
0.0722515732049942,
0.0642307847738266,
0.01791231520473957,
0.05068383738398552,
0.026471855118870735,
-0.0030058890115469694,
-0.07150962203741074,
-0.030459029600024223,
0.1521064043045044,
-0.05840207636356354,
0.07206457108259201,
-0.04059455171227455,
-0.21503190696239471,
-0.10329626500606537,
-0.03866512328386307,
0.09773743897676468,
0.14240846037864685,
-0.010973340831696987,
0.12915298342704773,
0.11722336709499359,
-0.09664388000965118,
-0.1621481031179428,
-0.06836526095867157,
0.0016912521095946431,
0.054087068885564804,
0.032817937433719635,
-0.18820683658123016,
0.016522210091352463,
-0.009165546856820583,
-0.008624467067420483,
-0.06010967120528221,
-0.07272854447364807,
-0.12574192881584167,
0.07172710448503494,
0.0469834990799427,
-0.014896511100232601,
-0.12818419933319092,
-0.03502916544675827,
-0.05806760489940643,
-0.08498512208461761,
0.12134239822626114,
-0.14708788692951202,
0.044430311769247055,
0.011361329816281796,
0.11035145074129105,
0.01381214614957571,
0.005279242526739836,
0.10657058656215668,
-0.03839593753218651,
0.026369217783212662,
-0.0751790925860405,
0.044660523533821106,
0.11003968864679337,
-0.017589395865797997,
0.09207511693239212,
-0.0016042193165048957,
-0.007706095930188894,
-0.18105901777744293,
-0.049014005810022354,
-0.03990289196372032,
0.07649435102939606,
-0.030532728880643845,
-0.03800702840089798,
-0.04116872325539589,
0.08750895410776138,
0.06401681900024414,
-0.0077555738389492035,
-0.023745831102132797,
-0.06841861456632614,
0.04828326031565666,
0.08788178861141205,
0.19218556582927704,
-0.026901185512542725,
0.034494586288928986,
-0.022992826998233795,
0.006251215469092131,
0.028855551034212112,
-0.10018546879291534,
0.0164126455783844,
0.03760995715856552,
-0.031050540506839752,
0.114267997443676,
-0.0560845285654068,
-0.09905438125133514,
0.056795477867126465,
0.07949468493461609,
-0.016889257356524467,
-0.18748043477535248,
0.017372976988554,
-0.004838692955672741,
-0.07533403486013412,
-0.031218495219945908,
0.1493399292230606,
-0.021705832332372665,
-0.056190408766269684,
-0.005472913850098848,
0.05705764889717102,
0.014524580910801888,
0.04010705277323723,
0.04614819958806038,
-0.01086535956710577,
-0.06732623279094696,
0.0748213529586792,
0.07589834928512573,
-0.14279896020889282,
0.023279186338186264,
0.13638494908809662,
-0.10166863352060318,
-0.08496889472007751,
-0.043951500207185745,
0.041415173560380936,
-0.038790274411439896,
-0.032597221434116364,
-0.005292566958814859,
-0.07766170799732208,
-0.011791973374783993,
0.050358258187770844,
0.0016879154136404395,
-0.0019451226107776165,
-0.04304329305887222,
-0.026291698217391968,
-0.04884936287999153,
0.09371181577444077,
0.09986872971057892,
-0.013036169111728668,
-0.13081787526607513,
0.028564466163516045,
0.029136190190911293,
-0.06253434717655182,
-0.016596943140029907,
-0.0389268696308136,
-0.05000779405236244,
-0.03197566419839859,
-0.1412201076745987,
0.03731749951839447,
-0.10245190560817719,
0.04051748663187027,
-0.014287416823208332,
-0.05849971994757652,
-0.032126348465681076,
0.012970530427992344,
-0.0355415977537632,
-0.020196883007884026,
-0.03343368321657181,
0.10366963595151901,
-0.12495435029268265,
0.007709911093115807,
0.08133410662412643,
-0.03635147586464882,
0.10195370018482208,
0.03994734212756157,
-0.054583583027124405,
0.06415235996246338,
-0.12904217839241028,
0.01173008605837822,
-0.07347224652767181,
0.06278744339942932,
0.031807586550712585,
-0.15875478088855743,
0.006303056143224239,
0.025103293359279633,
-0.000018451488358550705,
-0.007310668937861919,
0.08403100818395615,
-0.0321018323302269,
0.06790406256914139,
-0.015709562227129936,
-0.058372270315885544,
-0.06301490217447281,
0.05052154138684273,
0.0819457545876503,
0.013494863174855709,
0.13460682332515717,
-0.06523501873016357,
0.049353841692209244,
-0.11918040364980698,
0.006444366183131933,
-0.009437779895961285,
0.008657432161271572,
-0.06775002926588058,
-0.0588562898337841,
0.054195601493120193,
-0.0046849241480231285,
0.051977138966321945,
0.033552344888448715,
-0.0008137637050822377,
0.030782882124185562,
-0.03704264387488365,
-0.06070013716816902,
-0.002143046585842967,
0.047605399042367935,
0.05861424654722214,
0.02422839216887951,
0.015199909918010235,
-0.08492358773946762,
-0.025524243712425232,
-0.007566244341433048,
0.007540246471762657,
0.12545371055603027,
0.030671807006001472,
0.03029041737318039,
0.026445074006915092,
-0.05252256989479065,
-0.055259671062231064,
-0.004636761266738176,
-0.1738550066947937,
-0.02730867825448513,
-0.05206203833222389,
0.06060776486992836,
0.1989472508430481,
-0.13724377751350403,
0.08165732026100159,
-0.02375621721148491,
-0.07417873293161392,
-0.07932784408330917,
-0.16504280269145966,
-0.08938440680503845,
-0.05189632624387741,
0.030360147356987,
-0.11994899064302444,
0.07617364823818207,
0.06505350768566132,
0.01760619878768921,
-0.0036237668246030807,
0.1477983146905899,
-0.07431432604789734,
-0.10386920720338821,
0.036283668130636215,
0.038096655160188675,
0.03731965273618698,
0.07974155247211456,
0.03297591209411621,
-0.06150173395872116,
0.0451497808098793,
0.04104713350534439,
0.0628155767917633,
0.02417755499482155,
0.043370556086301804,
-0.08809596300125122,
-0.05200785771012306,
0.009867955930531025,
0.02726408652961254,
-0.027406131848692894,
0.14522378146648407,
0.05170244351029396,
-0.01709669828414917,
0.011355545371770859,
0.18625935912132263,
-0.0153473736718297,
-0.114643894135952,
-0.18584734201431274,
0.09114182740449905,
-0.024798182770609856,
0.0007973284809850156,
0.007124616764485836,
-0.08410128206014633,
-0.005459440406411886,
0.155160054564476,
0.16038867831230164,
-0.05146310478448868,
0.019381025806069374,
-0.031944189220666885,
0.02606607973575592,
-0.0558551661670208,
0.13171552121639252,
0.025942226871848106,
0.17698204517364502,
-0.06154303252696991,
0.0767781212925911,
-0.03157168999314308,
-0.048981815576553345,
-0.10066059231758118,
0.10400277376174927,
-0.07573676109313965,
-0.0018578297458589077,
-0.0353502593934536,
0.11433305591344833,
-0.06389638036489487,
-0.11365987360477448,
-0.043178267776966095,
-0.05445128679275513,
-0.09179740399122238,
-0.03811204433441162,
0.020107565447688103,
0.03404180333018303,
0.028403567150235176,
0.037457775324583054,
-0.02068992704153061,
0.168903186917305,
0.05035334825515747,
-0.057037170976400375,
0.006048291455954313,
0.15701118111610413,
-0.07984091341495514,
0.10698170959949493,
0.004425145220011473,
0.06713749468326569,
0.09007207304239273,
-0.012648033909499645,
-0.106186643242836,
0.04679037258028984,
0.053133927285671234,
0.021903716027736664,
0.02502581663429737,
0.1192624568939209,
0.026338398456573486,
0.05064548924565315,
0.08955775946378708,
-0.0028589291032403708,
0.05599583312869072,
0.004585952032357454,
0.022258685901761055,
-0.06639892607927322,
0.1164565309882164,
-0.1481235772371292,
0.1313115805387497,
0.12695686519145966,
-0.023944826796650887,
-0.009972683154046535,
-0.0480165034532547,
0.00925857201218605,
0.014586432836949825,
0.05424756929278374,
-0.0364580973982811,
-0.13295015692710876,
-0.03203807398676872,
-0.025411127135157585,
0.08181129395961761,
-0.14420990645885468,
-0.03903196007013321,
-0.06505665928125381,
-0.004377842880785465,
-0.0679561123251915,
0.13575352728366852,
0.05807248875498772,
0.0252203531563282,
-0.005700341425836086,
-0.11614616215229034,
0.019529221579432487,
0.09860128909349442,
-0.08488590270280838,
-0.04005855694413185
] |
null | null | diffusers |
# InstructPix2Pix: Learning to Follow Image Editing Instructions
GitHub: https://github.com/timothybrooks/instruct-pix2pix
<img src='https://instruct-pix2pix.timothybrooks.com/teaser.jpg'/>
## Example
To use `InstructPix2Pix`, install `diffusers` using `main` for now. The pipeline will be available in the next release
```bash
pip install diffusers accelerate safetensors transformers
```
```python
import PIL
import requests
import torch
from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler
model_id = "timbrooks/instruct-pix2pix"
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None)
pipe.to("cuda")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
url = "https://raw.githubusercontent.com/timothybrooks/instruct-pix2pix/main/imgs/example.jpg"
def download_image(url):
image = PIL.Image.open(requests.get(url, stream=True).raw)
image = PIL.ImageOps.exif_transpose(image)
image = image.convert("RGB")
return image
image = download_image(url)
prompt = "turn him into cyborg"
images = pipe(prompt, image=image, num_inference_steps=10, image_guidance_scale=1).images
images[0]
``` | {"license": "mit", "tags": ["image-to-image"]} | image-to-image | Yeongtak/General_Corruption_LCMv2 | [
"diffusers",
"safetensors",
"image-to-image",
"license:mit",
"diffusers:StableDiffusionInstructPix2PixPipeline",
"region:us"
] | 2024-02-09T09:17:26+00:00 | [] | [] | TAGS
#diffusers #safetensors #image-to-image #license-mit #diffusers-StableDiffusionInstructPix2PixPipeline #region-us
|
# InstructPix2Pix: Learning to Follow Image Editing Instructions
GitHub: URL
<img src='URL
## Example
To use 'InstructPix2Pix', install 'diffusers' using 'main' for now. The pipeline will be available in the next release
| [
"# InstructPix2Pix: Learning to Follow Image Editing Instructions\nGitHub: URL\n<img src='URL",
"## Example\n\nTo use 'InstructPix2Pix', install 'diffusers' using 'main' for now. The pipeline will be available in the next release"
] | [
"TAGS\n#diffusers #safetensors #image-to-image #license-mit #diffusers-StableDiffusionInstructPix2PixPipeline #region-us \n",
"# InstructPix2Pix: Learning to Follow Image Editing Instructions\nGitHub: URL\n<img src='URL",
"## Example\n\nTo use 'InstructPix2Pix', install 'diffusers' using 'main' for now. The pipeline will be available in the next release"
] | [
46,
31,
38
] | [
"passage: TAGS\n#diffusers #safetensors #image-to-image #license-mit #diffusers-StableDiffusionInstructPix2PixPipeline #region-us \n# InstructPix2Pix: Learning to Follow Image Editing Instructions\nGitHub: URL\n<img src='URL## Example\n\nTo use 'InstructPix2Pix', install 'diffusers' using 'main' for now. The pipeline will be available in the next release"
] | [
-0.06270132213830948,
0.03062521293759346,
-0.004737253300845623,
0.018846597522497177,
0.09972266107797623,
-0.00542087247595191,
0.19084447622299194,
0.07815226912498474,
0.15752355754375458,
0.027989588677883148,
0.13402768969535828,
0.11158602684736252,
0.02337092161178589,
0.06033369526267052,
-0.02696346864104271,
-0.23479388654232025,
0.018524877727031708,
-0.04381163418292999,
0.06527696549892426,
0.07267878949642181,
0.10418106615543365,
-0.05841108784079552,
0.09047459810972214,
-0.02395772933959961,
-0.1425630897283554,
-0.018981678411364555,
0.07567765563726425,
-0.024906642735004425,
0.08141639083623886,
0.049865808337926865,
0.0960347130894661,
0.11284437030553818,
0.14203210175037384,
-0.08487726747989655,
0.0454343780875206,
0.06792028248310089,
0.005911828484386206,
0.03416866809129715,
0.05594974383711815,
0.0490555576980114,
-0.07679840922355652,
0.012349770404398441,
0.0052728536538779736,
-0.04611596092581749,
-0.06460148096084595,
0.03439455106854439,
-0.02342246100306511,
0.11588332802057266,
0.1519107073545456,
-0.009292437694966793,
0.0695214718580246,
0.03590523079037666,
0.01251399889588356,
0.08257114887237549,
0.17455706000328064,
-0.16041915118694305,
-0.02525959350168705,
0.06140463426709175,
0.06219477578997612,
0.13077688217163086,
-0.05938004329800606,
0.08489349484443665,
-0.00593344634398818,
-0.016763418912887573,
-0.011575012467801571,
-0.06243186816573143,
-0.01696058362722397,
-0.09742144495248795,
-0.07133625447750092,
-0.06582258641719818,
0.12835679948329926,
0.021463308483362198,
-0.08525922894477844,
-0.07699558138847351,
-0.09165903180837631,
0.05739983171224594,
-0.08601994067430496,
-0.05585984140634537,
0.04272887855768204,
0.03143962100148201,
-0.0019189327722415328,
-0.16320355236530304,
-0.16133148968219757,
-0.08095920830965042,
0.041662197560071945,
0.1666399985551834,
0.03398912772536278,
0.09114134311676025,
-0.06008807197213173,
0.11883419007062912,
-0.03009778819978237,
-0.08512511104345322,
-0.013571747578680515,
-0.10202739387750626,
0.08627308160066605,
0.06459996104240417,
0.05917591229081154,
-0.08226428925991058,
0.04854697361588478,
0.16272957623004913,
-0.10809013992547989,
0.005563220474869013,
-0.023839157074689865,
0.1565045267343521,
0.029429443180561066,
-0.022153912112116814,
-0.05446551367640495,
0.1921280473470688,
0.07520084828138351,
0.015398471616208553,
0.0422096848487854,
-0.03869056701660156,
-0.09695731848478317,
-0.0025323873851448298,
-0.057675983756780624,
0.042291950434446335,
0.025993645191192627,
-0.017242377623915672,
-0.07271398603916168,
-0.03606077656149864,
0.2698342204093933,
-0.035866156220436096,
0.029376082122325897,
-0.060127753764390945,
0.004777394235134125,
0.17258387804031372,
0.1284991204738617,
0.01792251691222191,
-0.04117130860686302,
0.08575417101383209,
-0.017781168222427368,
0.028092121705412865,
0.00489561865106225,
-0.0060619087889790535,
-0.0017688587540760636,
-0.05592627450823784,
0.03690357506275177,
-0.193647563457489,
-0.07772164791822433,
0.018666092306375504,
0.09878304600715637,
0.01869453676044941,
0.10306742787361145,
0.06719166785478592,
-0.004101661499589682,
0.02159188501536846,
-0.010267329402267933,
-0.1949051171541214,
-0.051128409802913666,
0.05114183574914932,
-0.10708877444267273,
0.06517206877470016,
-0.15312640368938446,
-0.015556231141090393,
-0.052847184240818024,
0.052070844918489456,
-0.17936870455741882,
-0.033006951212882996,
-0.08413009345531464,
-0.011480319313704967,
-0.05584559589624405,
-0.060368504375219345,
-0.03234468773007393,
0.035703498870134354,
0.024904603138566017,
0.20184041559696198,
-0.08965008705854416,
0.007403538096696138,
0.19673681259155273,
-0.19708935916423798,
-0.06053069606423378,
0.09864319115877151,
0.03174969181418419,
0.048029836267232895,
0.047169413417577744,
0.09463441371917725,
0.006436448078602552,
-0.32683852314949036,
0.11677975207567215,
0.1107955127954483,
-0.14567382633686066,
0.0032024059910327196,
0.04418354108929634,
0.07739263027906418,
0.11099779605865479,
0.004255516920238733,
-0.1198604479432106,
0.08568375557661057,
-0.04875462129712105,
0.015847068279981613,
-0.04245496541261673,
-0.006149118300527334,
-0.05644591152667999,
0.011146632954478264,
-0.004734775982797146,
0.024518322199583054,
-0.04180646315217018,
0.008092687465250492,
0.04174697771668434,
-0.03431243449449539,
0.01888367161154747,
-0.0844050869345665,
0.09605055302381516,
-0.024242281913757324,
-0.02612369693815708,
-0.027917657047510147,
-0.011496499180793762,
-0.01254003681242466,
0.15926998853683472,
-0.02818416804075241,
-0.002223055576905608,
0.05525819584727287,
0.07499666512012482,
-0.0017761933850124478,
-0.05930381640791893,
0.029965035617351532,
0.007163294591009617,
-0.03101382963359356,
-0.07514476031064987,
-0.02027454972267151,
-0.06606634706258774,
-0.005587917752563953,
-0.16062502562999725,
0.02896704152226448,
0.021578511223196983,
0.07127662003040314,
0.05924565717577934,
-0.04596428945660591,
-0.004453784320503473,
-0.11787955462932587,
-0.03313896805047989,
-0.10285373032093048,
0.06314686685800552,
0.04900116100907326,
0.06571949273347855,
0.15842507779598236,
0.05968143045902252,
0.13525958359241486,
0.10567234456539154,
-0.15104347467422485,
-0.08479750156402588,
-0.06038489192724228,
-0.044286273419857025,
0.03813021257519722,
-0.041286349296569824,
-0.016988661140203476,
-0.027714043855667114,
0.07112402468919754,
0.07941602170467377,
-0.06193262338638306,
0.038176264613866806,
0.06424658745527267,
-0.13532917201519012,
-0.03279384598135948,
-0.048428699374198914,
0.18529842793941498,
-0.25156447291374207,
0.029856093227863312,
0.13892978429794312,
0.008372100070118904,
0.0792650356888771,
0.008082429878413677,
-0.08058888465166092,
0.02476564608514309,
0.14403888583183289,
0.035935044288635254,
0.12431104481220245,
0.073260098695755,
-0.03880038484930992,
0.027420340105891228,
-0.06391435116529465,
-0.009326704777777195,
-0.15288923680782318,
-0.04701362922787666,
-0.004484522622078657,
-0.017269741743803024,
0.014742146246135235,
-0.03562109172344208,
-0.09104308485984802,
0.07755810767412186,
-0.026828773319721222,
-0.015614104457199574,
0.007140354719012976,
-0.00975540466606617,
-0.052495136857032776,
0.10489872843027115,
-0.06007827818393707,
-0.2517848312854767,
-0.13882122933864594,
0.001031189109198749,
0.016146652400493622,
0.05345582216978073,
0.05055321380496025,
-0.04959065094590187,
-0.0299171581864357,
-0.04753195494413376,
-0.11378683149814606,
0.04129331558942795,
-0.042788632214069366,
-0.04885400831699371,
0.018289821222424507,
-0.0035478619392961264,
-0.08413027971982956,
-0.04009318724274635,
0.019336167722940445,
-0.05822770670056343,
0.1361246556043625,
-0.04268939048051834,
0.05054529756307602,
0.06353477388620377,
-0.012909295037388802,
-0.022008681669831276,
0.0388670489192009,
0.1424509435892105,
-0.09178202599287033,
0.1379839926958084,
0.24759744107723236,
-0.06137355417013168,
0.07751885801553726,
0.016748379915952682,
0.056003764271736145,
-0.1324625462293625,
0.00013794799451716244,
-0.0626654326915741,
-0.08569097518920898,
-0.09017009288072586,
-0.02781238965690136,
-0.032406821846961975,
0.006402131635695696,
0.1422576606273651,
0.04835733398795128,
-0.05083131417632103,
0.1462671458721161,
-0.06260357797145844,
0.016325142234563828,
0.08580945432186127,
0.1071636974811554,
-0.11467409133911133,
-0.09277211129665375,
0.02980848029255867,
0.0006628796691074967,
-0.014644226990640163,
0.1135694682598114,
0.09640239179134369,
0.06393853574991226,
0.0212389063090086,
0.03577471897006035,
0.08497798442840576,
0.09271221607923508,
0.11341209709644318,
0.1515936255455017,
-0.01552556362003088,
-0.014247954823076725,
-0.030385559424757957,
-0.04293125122785568,
-0.10559301823377609,
0.04655981436371803,
-0.03376658633351326,
-0.14079615473747253,
0.012680542655289173,
0.11302293837070465,
-0.0021473621018230915,
0.13142533600330353,
0.0261253509670496,
-0.23509956896305084,
-0.0298150684684515,
-0.00004007344614365138,
0.047280751168727875,
-0.10411829501390457,
0.06526797264814377,
0.21036425232887268,
-0.05831265076994896,
-0.02605156973004341,
-0.06645934283733368,
0.062081098556518555,
-0.10227172821760178,
-0.015218596905469894,
0.020326625555753708,
-0.051825523376464844,
0.012264267541468143,
0.020334826782345772,
-0.0579381100833416,
0.06958916038274765,
0.0026951502077281475,
0.004514383152127266,
-0.03659011051058769,
0.01163067389279604,
0.054187968373298645,
0.23118607699871063,
0.2040235549211502,
0.029896501451730728,
-0.028212575241923332,
-0.10090509802103043,
-0.06992346793413162,
-0.014963873662054539,
0.09681909531354904,
0.010734528303146362,
-0.0406058244407177,
-0.0265872310847044,
-0.031067507341504097,
-0.006174023728817701,
-0.17767363786697388,
-0.1702955663204193,
-0.1577739417552948,
0.024292707443237305,
-0.020343761891126633,
0.02730092778801918,
-0.0847562775015831,
0.030402066186070442,
0.04261186346411705,
0.1981898993253708,
-0.04589380323886871,
-0.07129692286252975,
-0.12567855417728424,
0.029917439445853233,
-0.015089866705238819,
-0.0075693982653319836,
0.07662784308195114,
-0.03527657687664032,
0.11722883582115173,
-0.04647451639175415,
-0.09718839079141617,
0.048607565462589264,
-0.14813362061977386,
-0.06739722937345505,
-0.05088667571544647,
0.09837338328361511,
-0.026419689878821373,
-0.08714580535888672,
0.05655219033360481,
-0.007756391074508429,
0.06301448494195938,
-0.11117856204509735,
0.1032627671957016,
0.027620485052466393,
0.003095665480941534,
-0.010628840886056423,
-0.10434375703334808,
0.0590519942343235,
0.016069568693637848,
0.01872667297720909,
0.09427902102470398,
0.17088583111763,
-0.02976084128022194,
0.03612426668405533,
0.1837470829486847,
-0.03146393597126007,
-0.24174827337265015,
-0.09810180962085724,
0.0037304952275007963,
-0.07487715035676956,
0.028596488758921623,
-0.13862766325473785,
0.10075300186872482,
0.11243823915719986,
-0.0374043807387352,
0.24226078391075134,
-0.22655993700027466,
-0.07074827700853348,
0.0074806720949709415,
0.16179858148097992,
0.10885974019765854,
-0.14757880568504333,
-0.02268380858004093,
-0.06183909624814987,
-0.18224216997623444,
0.09103698283433914,
0.010752324014902115,
0.03879723697900772,
0.031090952455997467,
-0.0509297139942646,
0.007904100231826305,
-0.06614311784505844,
0.06643640995025635,
-0.14828896522521973,
0.06967610865831375,
-0.098373182117939,
-0.018133260309696198,
0.042099479585886,
-0.025376610457897186,
0.07731976360082626,
-0.021042918786406517,
0.06224469095468521,
-0.04520556703209877,
-0.001576008158735931,
0.00631213653832674,
0.05860217288136482,
0.013005886226892471,
-0.04677007719874382,
-0.022738385945558548,
0.019258731976151466,
-0.03616529330611229,
0.05535916984081268,
0.12806737422943115,
0.023736629635095596,
-0.08456053584814072,
0.16520006954669952,
-0.07040080428123474,
-0.060118164867162704,
-0.09873846173286438,
-0.11171054095029831,
-0.032784998416900635,
0.09851036965847015,
-0.09284977614879608,
0.030252737924456596,
0.009896019473671913,
0.056784726679325104,
0.08591772615909576,
0.025122907012701035,
0.01948617585003376,
0.052036359906196594,
0.08057758957147598,
-0.14154572784900665,
-0.03757207468152046,
-0.0009115742868743837,
0.17782047390937805,
0.15568143129348755,
0.01727069541811943,
0.18954594433307648,
-0.05938268080353737,
0.018701709806919098,
-0.03949957713484764,
0.12456128001213074,
-0.06896030157804489,
0.03430534526705742,
0.048896946012973785,
-0.02458721026778221,
-0.04722311347723007,
0.0780799463391304,
0.002844930160790682,
-0.04670548811554909,
-0.053995531052351,
-0.011633575893938541,
-0.06499118357896805,
-0.09100934118032455,
0.0042608389630913734,
-0.06896914541721344,
-0.04489624872803688,
-0.050446562469005585,
0.00043097411980852485,
0.02979089878499508,
-0.0669531375169754,
-0.12183938175439835,
0.0117095448076725,
-0.04867848381400108,
0.019582474604249,
0.023945115506649017,
-0.051825959235429764,
0.011228098534047604,
-0.032011859118938446,
0.08746515959501266,
-0.2083609253168106,
-0.026075545698404312,
0.022258559241890907,
-0.0040726871229708195,
-0.08524411171674728,
-0.0037058540619909763,
-0.13317686319351196,
0.02922624535858631,
-0.044212546199560165,
0.1479024589061737,
-0.0734153687953949,
-0.031253598630428314,
0.022730015218257904,
-0.01958097144961357,
0.010613229125738144,
0.01716921105980873,
-0.06658686697483063,
-0.013629596680402756,
-0.022310826927423477,
0.04147656634449959,
-0.06882329285144806,
-0.018200218677520752,
0.027075719088315964,
-0.06824076175689697,
0.06306128203868866,
0.06168542429804802,
-0.07387480139732361,
0.014616812579333782,
-0.2495841085910797,
-0.08162657171487808,
0.16194367408752441,
0.02068670652806759,
-0.04293540492653847,
0.10289087891578674,
0.06891486793756485,
0.04368105158209801,
-0.043355878442525864,
-0.06631540507078171,
0.09591499716043472,
-0.08452741801738739,
0.014659782871603966,
-0.05812501534819603,
-0.04634177312254906,
-0.0740974023938179,
0.01545911468565464,
0.11772020161151886,
0.039234600961208344,
0.12805584073066711,
-0.08711837977170944,
0.052214715629816055,
-0.06538912653923035,
0.0024066551122814417,
0.0749262273311615,
-0.05241876840591431,
0.08456411957740784,
-0.04529409110546112,
-0.025993529707193375,
-0.04069806635379791,
0.1378137320280075,
-0.007994847372174263,
-0.07866933941841125,
0.004375701770186424,
-0.08823634684085846,
-0.02000300958752632,
0.059005219489336014,
0.11455735564231873,
0.02470189332962036,
0.021122287958860397,
-0.25461652874946594,
-0.004390987567603588,
0.11777162551879883,
-0.15779653191566467,
0.026107603684067726,
0.19778090715408325,
-0.10450233519077301,
0.08350376039743423,
0.04718237742781639,
0.003190291114151478,
-0.09860450774431229,
-0.11001043766736984,
-0.06201371178030968,
0.10208816826343536,
-0.04866727069020271,
0.012680021114647388,
0.1277311146259308,
-0.026170535013079643,
-0.024009045213460922,
0.013652187772095203,
-0.004934497643262148,
-0.06952941417694092,
-0.16787460446357727,
-0.041261427104473114,
-0.15425120294094086,
0.03437139466404915,
-0.012349453754723072,
0.05182225629687309,
0.040192488580942154,
0.07183488458395004,
-0.03697219118475914,
0.06234879791736603,
-0.03831364959478378,
-0.07989669591188431,
0.10317598283290863,
0.030004411935806274,
-0.032822076231241226,
0.01289561577141285,
-0.0704156905412674,
0.08275628089904785,
0.03389141336083412,
0.039220768958330154,
0.041494932025671005,
0.07598071545362473,
0.025450488552451134,
0.007405594922602177,
-0.050986792892217636,
-0.03144410625100136,
0.031677719205617905,
-0.058921363204717636,
0.05770903453230858,
0.014498456381261349,
0.0602528378367424,
0.012133929878473282,
0.11007847636938095,
-0.04285719245672226,
-0.10030955821275711,
-0.08232830464839935,
0.03128303214907646,
-0.07357288151979446,
0.09080550074577332,
-0.06341339647769928,
-0.10314204543828964,
-0.02535897120833397,
0.18663857877254486,
0.09026665985584259,
-0.13258059322834015,
0.017752408981323242,
0.002996324561536312,
0.007457292173057795,
-0.04926350340247154,
0.17526544630527496,
0.003983200993388891,
0.18684934079647064,
0.005397026892751455,
-0.08813861012458801,
-0.010146756656467915,
-0.04340912401676178,
-0.11122380197048187,
-0.07809733599424362,
-0.0028998320922255516,
0.0001578018709551543,
-0.115394726395607,
0.09110113233327866,
-0.021975528448820114,
-0.19860942661762238,
0.09574229270219803,
0.007695966865867376,
0.019702402874827385,
0.013009641319513321,
0.04015018790960312,
0.01724683865904808,
0.03752140700817108,
-0.07000715285539627,
-0.016941096633672714,
0.03175012022256851,
0.0406259223818779,
-0.06397120654582977,
0.06446604430675507,
-0.01342653390020132,
-0.08610363304615021,
0.17409458756446838,
0.004186034668236971,
0.08340605348348618,
-0.025294864550232887,
-0.02752744033932686,
-0.0747867077589035,
0.034919895231723785,
-0.03567498177289963,
-0.08223652094602585,
-0.03606850653886795,
0.1271669864654541,
-0.04670092090964317,
-0.07208263128995895,
0.01614820398390293,
-0.14909188449382782,
-0.002313909586519003,
0.08070335537195206,
-0.06273261457681656,
-0.09969054907560349,
0.029179411008954048,
-0.1101008802652359,
0.10245124995708466,
0.0061202277429401875,
0.015353074297308922,
-0.018975799903273582,
-0.03699732944369316,
0.1291191279888153,
0.1353660523891449,
-0.09560301154851913,
0.036532048135995865,
-0.07423526793718338,
-0.06310637295246124,
-0.0023193613160401583,
0.05510759726166725,
-0.012844010256230831,
0.029432659968733788,
-0.08781016618013382,
0.02126985974609852,
-0.06407123059034348,
0.0549483522772789,
0.1635793149471283,
0.05525321140885353,
-0.013610372319817543,
-0.1669686883687973,
0.053848542273044586,
0.04792773723602295,
0.00019464045180939138,
-0.050314076244831085
] |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-cnn-samsum-finetuned
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1355
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0921 | 1.0 | 74 | 0.1355 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "facebook/bart-large-cnn", "model-index": [{"name": "bart-cnn-samsum-finetuned", "results": []}]} | text2text-generation | Prakruthirai/bart-cnn-samsum-finetuned | [
"transformers",
"tensorboard",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-large-cnn",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-09T09:18:16+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-large-cnn #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bart-cnn-samsum-finetuned
=========================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1355
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-large-cnn #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Tokenizers 0.15.1"
] | [
69,
98,
4,
27
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-large-cnn #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Tokenizers 0.15.1"
] | [
-0.08062048256397247,
0.04007747769355774,
-0.002616533311083913,
0.0946478545665741,
0.13191582262516022,
0.013042204082012177,
0.1639844924211502,
0.10497909784317017,
-0.051026638597249985,
0.05500735342502594,
0.15631841123104095,
0.10833866894245148,
0.002504728501662612,
0.15604911744594574,
-0.04487567022442818,
-0.2231065034866333,
-0.0030954473186284304,
0.04176873341202736,
-0.04939819872379303,
0.11539214849472046,
0.08623341470956802,
-0.12927651405334473,
0.09925834834575653,
-0.0031678706873208284,
-0.21823564171791077,
0.01608913205564022,
0.025746148079633713,
-0.04618535563349724,
0.14251691102981567,
0.038415733724832535,
0.13243113458156586,
0.026570066809654236,
0.07801607996225357,
-0.18412554264068604,
0.01613949052989483,
0.04335819557309151,
-0.006044598296284676,
0.0743756890296936,
0.028255745768547058,
0.022694125771522522,
0.11229351162910461,
-0.06613081693649292,
0.06334705650806427,
0.020932232961058617,
-0.13703463971614838,
-0.18446341156959534,
-0.06606416404247284,
0.034416563808918,
0.0801815390586853,
0.08815019577741623,
-0.020516052842140198,
0.11280437558889389,
-0.060052189975976944,
0.09314560145139694,
0.2326761782169342,
-0.3066239655017853,
-0.07930997014045715,
0.048123668879270554,
0.06092086806893349,
0.07210969179868698,
-0.11180219054222107,
-0.013157285749912262,
0.08147040009498596,
0.03380320593714714,
0.12319302558898926,
-0.04113605618476868,
0.0061955940909683704,
-0.0016334286192432046,
-0.13386301696300507,
-0.02081928215920925,
0.1408088356256485,
0.05140062794089317,
-0.03925580158829689,
-0.056226830929517746,
-0.04994429647922516,
-0.13904424011707306,
-0.04290671646595001,
-0.044908493757247925,
0.028513647615909576,
-0.02124304696917534,
-0.11114475131034851,
-0.029588492587208748,
-0.11859768629074097,
-0.07164168357849121,
-0.05226974934339523,
0.1546551138162613,
0.02104724943637848,
-0.007820199243724346,
-0.04032221436500549,
0.0913136750459671,
-0.03398434817790985,
-0.13770875334739685,
0.030193865299224854,
0.025579435750842094,
0.015834389254450798,
-0.05892730876803398,
-0.05160577595233917,
-0.08698319643735886,
0.02037203311920166,
0.14159204065799713,
-0.043726399540901184,
0.05555140599608421,
0.00958907138556242,
0.05135507509112358,
-0.11083374917507172,
0.16756634414196014,
-0.011319875717163086,
-0.047118596732616425,
0.005657876841723919,
0.0752968043088913,
0.01355164684355259,
-0.015360040590167046,
-0.13724128901958466,
0.0209304541349411,
0.09840555489063263,
-0.002137294504791498,
-0.06591319292783737,
0.07307697832584381,
-0.03716825693845749,
0.005032552871853113,
0.005376317538321018,
-0.06626464426517487,
0.0392967127263546,
-0.004069963004440069,
-0.03856303170323372,
-0.04975948482751846,
0.011414830572903156,
0.01855096034705639,
0.01880989596247673,
0.11434993892908096,
-0.09900202602148056,
0.01264691911637783,
-0.08421362191438675,
-0.1019878089427948,
0.017284996807575226,
-0.057763878256082535,
0.026079759001731873,
-0.12055815011262894,
-0.15545524656772614,
-0.016760090366005898,
0.045612968504428864,
-0.02449829876422882,
-0.030424613505601883,
-0.059256404638290405,
-0.06691768020391464,
0.020103491842746735,
-0.01843736320734024,
0.0774809718132019,
-0.05332314968109131,
0.09965827316045761,
0.05528441444039345,
0.07026858627796173,
-0.05673848092556,
0.028328634798526764,
-0.10212526470422745,
0.02598649449646473,
-0.2025158852338791,
0.016989514231681824,
-0.038303159177303314,
0.08136831969022751,
-0.06229942664504051,
-0.07428448647260666,
-0.05349127948284149,
0.022066816687583923,
0.0694182813167572,
0.09548566490411758,
-0.17592452466487885,
-0.09478681534528732,
0.1924019455909729,
-0.0819038450717926,
-0.1374339908361435,
0.13596804440021515,
-0.06244318559765816,
0.08283267915248871,
0.07394678145647049,
0.18946963548660278,
0.05338084325194359,
-0.10178092867136002,
0.0133718466386199,
-0.015154794789850712,
0.03622331842780113,
-0.022224659100174904,
0.04138987138867378,
0.005648914724588394,
-0.0025190829765051603,
0.018319590017199516,
-0.010004940442740917,
0.05525492504239082,
-0.0859316810965538,
-0.07382088899612427,
-0.03559691831469536,
-0.08808355033397675,
0.05548515170812607,
0.049347929656505585,
0.07898938655853271,
-0.12546062469482422,
-0.08247672766447067,
0.047819990664720535,
0.04769689589738846,
-0.05987578257918358,
0.019585059955716133,
-0.06783244758844376,
0.07118402421474457,
-0.05286819487810135,
-0.01513662375509739,
-0.1396676003932953,
-0.04060806334018707,
0.013677304610610008,
-0.005376419052481651,
0.039664190262556076,
0.04413711652159691,
0.08479805290699005,
0.06831088662147522,
-0.05654680356383324,
-0.016581468284130096,
-0.020052185282111168,
0.0019755414687097073,
-0.13498449325561523,
-0.18557202816009521,
0.00421377457678318,
-0.037182122468948364,
0.10786615312099457,
-0.24896381795406342,
0.05265090987086296,
0.013807074166834354,
0.09327013790607452,
0.033090654760599136,
-0.01896706223487854,
-0.06316327303647995,
0.06814639270305634,
-0.03424782678484917,
-0.04782719910144806,
0.05416460335254669,
0.014879811555147171,
-0.10811328142881393,
-0.041055306792259216,
-0.16188684105873108,
0.19864830374717712,
0.1387118250131607,
-0.10887230187654495,
-0.09541381895542145,
-0.01602974720299244,
-0.04988081380724907,
-0.02492484822869301,
-0.03282153978943825,
-0.020432239398360252,
0.16529157757759094,
-0.009479601867496967,
0.146639883518219,
-0.07651913911104202,
-0.04769875481724739,
0.03831317275762558,
-0.04945574328303337,
0.01376065518707037,
0.09000107645988464,
0.09169108420610428,
-0.10015437752008438,
0.1445619910955429,
0.18596655130386353,
-0.06754528731107712,
0.17037154734134674,
-0.015779782086610794,
-0.0542878620326519,
-0.034489408135414124,
0.006801103241741657,
0.00236634467728436,
0.1149243414402008,
-0.15154403448104858,
-0.016714345663785934,
-0.007844093255698681,
0.008928801864385605,
0.020065514370799065,
-0.21073156595230103,
-0.04686864838004112,
0.046991415321826935,
-0.03681950643658638,
0.023379743099212646,
-0.009366801008582115,
-0.004783543758094311,
0.09998716413974762,
0.017545493319630623,
-0.08362364023923874,
0.041241224855184555,
-0.0006726235151290894,
-0.09005607664585114,
0.19641496241092682,
-0.057588037103414536,
-0.162613645195961,
-0.13823671638965607,
-0.06709599494934082,
-0.02628866583108902,
0.03269367292523384,
0.07228999584913254,
-0.08468766510486603,
-0.02584291435778141,
-0.0792754739522934,
0.030217939987778664,
0.030196208506822586,
0.040863245725631714,
0.033747199922800064,
-0.000421578879468143,
0.07188478857278824,
-0.090776726603508,
-0.019493665546178818,
-0.03880220279097557,
-0.06583301723003387,
0.02485760673880577,
0.01732727140188217,
0.12037596851587296,
0.1283460110425949,
-0.025736277922987938,
0.0031379202846437693,
-0.03487294912338257,
0.2361719161272049,
-0.0870494395494461,
-0.021323785185813904,
0.11235714703798294,
-0.025325138121843338,
0.04684685543179512,
0.125977024435997,
0.06853163987398148,
-0.09624301642179489,
0.021742792800068855,
0.031015923246741295,
-0.04358793422579765,
-0.1729527711868286,
-0.024728145450353622,
-0.039519764482975006,
0.030794452875852585,
0.11267656087875366,
0.030965156853199005,
0.06168360635638237,
0.07313359528779984,
0.03241392970085144,
0.10074678063392639,
-0.018111784011125565,
0.08601334691047668,
0.09671594947576523,
0.03956127539277077,
0.1261695921421051,
-0.042427774518728256,
-0.08928946405649185,
0.03987666219472885,
-0.02101549319922924,
0.19070711731910706,
0.054090842604637146,
0.12478207796812057,
0.05491281673312187,
0.13756035268306732,
0.005736726336181164,
0.06219404190778732,
-0.012625211849808693,
-0.07607454806566238,
-0.013090086169540882,
-0.04339165240526199,
-0.019998667761683464,
0.04964716359972954,
-0.07943922281265259,
0.048530224710702896,
-0.1112440973520279,
0.0022211414761841297,
0.059358373284339905,
0.21671682596206665,
0.042132940143346786,
-0.3176002502441406,
-0.08615507185459137,
0.013140005059540272,
-0.0428515188395977,
-0.01941866986453533,
0.039960652589797974,
0.15068738162517548,
-0.0648576021194458,
0.03394957259297371,
-0.06705237925052643,
0.07946370542049408,
-0.017060035839676857,
0.07765227556228638,
0.045764848589897156,
0.0675295814871788,
-0.01394791342318058,
0.05954748019576073,
-0.3047923147678375,
0.27551957964897156,
0.007246705237776041,
0.07025565952062607,
-0.05858692154288292,
-0.0083352355286479,
0.023461265489459038,
0.05921978875994682,
0.07655379921197891,
-0.02627772092819214,
-0.07636415213346481,
-0.19724559783935547,
-0.0341293029487133,
0.03021712228655815,
0.11203129589557648,
-0.04741748794913292,
0.1067458838224411,
-0.031107384711503983,
0.011845703236758709,
0.08214360475540161,
0.017924955114722252,
-0.07330811768770218,
-0.09561079740524292,
-0.017564933747053146,
0.027168843895196915,
-0.0342877134680748,
-0.07831495255231857,
-0.08760768920183182,
-0.11149883270263672,
0.15630649030208588,
-0.04460879787802696,
0.0012055342085659504,
-0.09444651752710342,
0.06828710436820984,
0.03250735625624657,
-0.07899890094995499,
0.03381277248263359,
0.01869911514222622,
0.0987311378121376,
0.0019857946317642927,
-0.05078411102294922,
0.1254415363073349,
-0.06760518252849579,
-0.1698060929775238,
-0.08345463871955872,
0.11020450294017792,
0.02129930816590786,
0.05453116446733475,
0.011688767932355404,
0.003387858159840107,
-0.025361688807606697,
-0.08806345611810684,
0.02849585749208927,
-0.06520848721265793,
0.030488453805446625,
-0.027826886624097824,
-0.038244761526584625,
0.019928373396396637,
-0.053777437657117844,
-0.031250156462192535,
0.16247771680355072,
0.29009342193603516,
-0.09380792826414108,
-0.009591445326805115,
0.06261656433343887,
-0.054894253611564636,
-0.18779359757900238,
0.03478126600384712,
0.024746837094426155,
0.0029836585745215416,
0.042223621159791946,
-0.13287733495235443,
0.11353030055761337,
0.10392791032791138,
-0.014680393971502781,
0.10866168886423111,
-0.27591297030448914,
-0.14134657382965088,
0.11335840076208115,
0.14040689170360565,
0.1550147533416748,
-0.15513314306735992,
-0.037348657846450806,
-0.03628038987517357,
-0.10609838366508484,
0.09361150115728378,
-0.1228756457567215,
0.10932952165603638,
-0.010585373267531395,
0.08582374453544617,
0.006821338087320328,
-0.05691792070865631,
0.1057218611240387,
-0.010830117389559746,
0.11410116404294968,
-0.07690228521823883,
-0.04935084655880928,
0.03292040526866913,
-0.04646508768200874,
0.010310901328921318,
-0.11042796075344086,
0.008290193043649197,
-0.055518604815006256,
-0.0322694256901741,
-0.05983107164502144,
0.04436371475458145,
-0.047280341386795044,
-0.06327348202466965,
-0.04555344954133034,
0.018614793196320534,
0.018488246947526932,
-0.01204966101795435,
0.1287994235754013,
-0.007822141982614994,
0.18655656278133392,
0.1265363097190857,
0.08715666085481644,
-0.08619462698698044,
-0.017066624015569687,
0.006816608365625143,
-0.03495674207806587,
0.05871801823377609,
-0.1300041228532791,
0.03409473970532417,
0.12116265296936035,
0.013432558625936508,
0.15359729528427124,
0.07770057767629623,
-0.030304379761219025,
0.03046407923102379,
0.08717359602451324,
-0.17024976015090942,
-0.10869535803794861,
-0.021193932741880417,
-0.03717738017439842,
-0.11079590767621994,
0.049550529569387436,
0.1242547482252121,
-0.06924913078546524,
0.011427625082433224,
-0.01674717850983143,
-0.010854756459593773,
-0.05803753063082695,
0.16578148305416107,
0.0627356767654419,
0.04486643150448799,
-0.07058391720056534,
0.0368059016764164,
0.029755311086773872,
-0.0619751438498497,
0.017510510981082916,
0.07507506012916565,
-0.06777030974626541,
-0.03611583262681961,
0.04190351441502571,
0.22695229947566986,
-0.046579498797655106,
-0.036083027720451355,
-0.15318045020103455,
-0.12109490483999252,
0.06088954955339432,
0.2151348888874054,
0.08617080748081207,
0.012868569232523441,
-0.03185631334781647,
0.04015785828232765,
-0.11331727355718613,
0.09272343665361404,
0.030852530151605606,
0.0828426331281662,
-0.1374095380306244,
0.15886804461479187,
-0.00450673745945096,
0.009664175100624561,
-0.03400903195142746,
0.04272298887372017,
-0.1334092617034912,
-0.006796094588935375,
-0.12551242113113403,
-0.030353713780641556,
-0.03184885159134865,
-0.012586308643221855,
0.0034504674840718508,
-0.06115301698446274,
-0.06761622428894043,
-0.007935602217912674,
-0.10085446387529373,
-0.013695474714040756,
0.043486371636390686,
0.05424631014466286,
-0.11502804607152939,
-0.019438927993178368,
0.030100515112280846,
-0.06123916804790497,
0.056515179574489594,
0.025438522920012474,
0.028972718864679337,
0.05339941382408142,
-0.1757836788892746,
0.044989775866270065,
0.059546731412410736,
0.002047434216365218,
0.04678887501358986,
-0.05981113389134407,
-0.018066726624965668,
-0.007656237576156855,
0.0758841261267662,
0.02875622920691967,
0.06274356693029404,
-0.1268896609544754,
0.019062336534261703,
-0.009892500005662441,
-0.09489466995000839,
-0.056716881692409515,
0.016620038077235222,
0.07730624824762344,
-0.004883607849478722,
0.1881057173013687,
-0.10377421975135803,
0.02939903922379017,
-0.2148323953151703,
0.007859225384891033,
0.016635535284876823,
-0.10192420333623886,
-0.07849577069282532,
-0.07761695981025696,
0.046642936766147614,
-0.04737959802150726,
0.14474518597126007,
0.0020125957671552896,
0.02621515467762947,
0.042929843068122864,
-0.058986131101846695,
0.022233672440052032,
0.014424867928028107,
0.18993395566940308,
0.033693354576826096,
-0.05752495676279068,
0.00033739686477929354,
0.048567090183496475,
0.11068699508905411,
0.06995883584022522,
0.1824197918176651,
0.15221631526947021,
-0.050704680383205414,
0.10212887078523636,
0.05860044062137604,
-0.026273397728800774,
-0.1508779376745224,
0.0463092103600502,
-0.05400652438402176,
0.07954716682434082,
-0.03313383460044861,
0.20783762633800507,
0.1252002716064453,
-0.15874405205249786,
0.012857825495302677,
-0.046239662915468216,
-0.0722183957695961,
-0.10082016885280609,
-0.06835678219795227,
-0.09855581074953079,
-0.15076522529125214,
0.013101927936077118,
-0.10523787140846252,
0.01882876455783844,
0.06533492356538773,
0.009566741064190865,
-0.02248489297926426,
0.2002643346786499,
0.021350232884287834,
0.0332186296582222,
0.06619048863649368,
0.002098759636282921,
-0.028150318190455437,
-0.10783956199884415,
-0.07268895953893661,
-0.02396516315639019,
-0.019404422491788864,
0.018905827775597572,
-0.050998881459236145,
-0.042832933366298676,
0.01247609406709671,
-0.0034595238976180553,
-0.10192254185676575,
0.010301094502210617,
0.023049965500831604,
0.05677084997296333,
0.025207126513123512,
-0.004736816044896841,
0.010023092851042747,
-0.010239608585834503,
0.1993418186903,
-0.0772000402212143,
-0.06363918632268906,
-0.09600601345300674,
0.18846441805362701,
0.025611676275730133,
0.02737662009894848,
-0.02177221141755581,
-0.0694572925567627,
0.03144169971346855,
0.24940864741802216,
0.1722979098558426,
-0.08985137939453125,
0.014239535667002201,
-0.0036997031420469284,
-0.00429567089304328,
-0.02708698995411396,
0.09462226182222366,
0.10356870293617249,
0.049217190593481064,
-0.06865935772657394,
-0.039743732661008835,
-0.032276760786771774,
-0.009833921678364277,
-0.03203222528100014,
0.05264008417725563,
0.047313813120126724,
0.032287098467350006,
-0.06323585659265518,
0.052783627063035965,
-0.035940974950790405,
-0.09026286005973816,
0.038745660334825516,
-0.2060079127550125,
-0.1256653368473053,
0.007880588993430138,
0.11959783732891083,
-0.026531174778938293,
0.07442992180585861,
-0.024551473557949066,
-0.010625169612467289,
0.03146521747112274,
-0.031890563666820526,
-0.06651816517114639,
-0.0683637410402298,
0.06593120843172073,
-0.11185716837644577,
0.23277564346790314,
-0.050329264253377914,
0.0382237583398819,
0.12954671680927277,
0.04888990893959999,
-0.06086771562695503,
0.07481317222118378,
0.04369515925645828,
-0.06123632937669754,
0.005285014398396015,
0.0993301123380661,
-0.0298312995582819,
0.10826045274734497,
0.07472432404756546,
-0.16225098073482513,
0.029948310926556587,
-0.054316431283950806,
-0.06984418630599976,
-0.05366791412234306,
-0.03146657347679138,
-0.05256244167685509,
0.1417052298784256,
0.19919715821743011,
-0.0282067209482193,
-0.000494716630782932,
-0.04026423394680023,
0.02415022999048233,
0.07534823566675186,
0.07033718377351761,
-0.04888109862804413,
-0.24292634427547455,
0.01053660362958908,
0.09930971264839172,
-0.02561783790588379,
-0.30288708209991455,
-0.11019884049892426,
-0.010241168551146984,
-0.04028543084859848,
-0.07850677520036697,
0.08970282226800919,
0.1086060106754303,
0.05905865505337715,
-0.05544127896428108,
-0.12308058887720108,
-0.07045008987188339,
0.1633414775133133,
-0.12453193962574005,
-0.10109540075063705
] |
null | null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | null | poteminr/mistral-rudrec_extended_instruction | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-09T09:20:30+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
31,
6,
3,
82,
28,
3,
4,
9,
9,
10,
42,
20,
3,
4,
5,
9,
11,
13,
3,
12,
5,
4,
5,
3,
4,
9,
53,
9,
8,
6,
3,
14,
8,
7,
9,
4
] | [
"passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
] | [
-0.06646376848220825,
0.2168014943599701,
-0.00225935154594481,
0.023818302899599075,
0.1271018385887146,
-0.001635765191167593,
0.04218708351254463,
0.13324736058712006,
-0.020175931975245476,
0.11144465953111649,
0.046588581055402756,
0.09377603232860565,
0.09928803145885468,
0.18404334783554077,
0.04859916493296623,
-0.2059975117444992,
0.007056170143187046,
-0.09090408682823181,
0.014076028019189835,
0.1116579994559288,
0.13719257712364197,
-0.10291384905576706,
0.08272874355316162,
-0.04045208916068077,
-0.02019004337489605,
0.00012576708104461432,
-0.09259183704853058,
-0.07032395154237747,
0.06885425746440887,
0.06264153122901917,
0.051234472543001175,
0.001456156256608665,
0.09140396863222122,
-0.2864592671394348,
0.017265573143959045,
0.08406311273574829,
0.0027674848679453135,
0.06290827691555023,
0.07236549258232117,
-0.07389893382787704,
0.11328595131635666,
-0.08021481335163116,
0.13019037246704102,
0.08625296503305435,
-0.062064990401268005,
-0.23071379959583282,
-0.07525765895843506,
0.0963398814201355,
0.12251301854848862,
0.06215599179267883,
-0.022921854630112648,
0.15455181896686554,
-0.06248689442873001,
0.012971068732440472,
0.1294165402650833,
-0.11526761949062347,
-0.05572471022605896,
0.061741601675748825,
0.11775490641593933,
0.10740239918231964,
-0.14110268652439117,
-0.0017287094378843904,
0.04900608956813812,
0.029121357947587967,
0.08589313924312592,
0.022661056369543076,
0.12003941088914871,
0.04652795568108559,
-0.13695219159126282,
-0.04037507623434067,
0.12011898308992386,
0.038862764835357666,
-0.06446044892072678,
-0.2168138176202774,
-0.006778308190405369,
-0.0601806715130806,
-0.014732478186488152,
-0.07019448280334473,
0.039128515869379044,
-0.02470310963690281,
0.07317749410867691,
-0.04465159401297569,
-0.1063927412033081,
-0.0421026237308979,
0.0892222449183464,
0.07748593389987946,
0.011527054943144321,
-0.02519804798066616,
0.04627908393740654,
0.13455867767333984,
0.05402068421244621,
-0.10399353504180908,
-0.07017925381660461,
-0.06942764669656754,
-0.09420394152402878,
-0.04035796597599983,
0.056760527193546295,
0.031942449510097504,
0.02665667235851288,
0.22703726589679718,
0.016653569415211678,
0.04155244305729866,
0.0224777739495039,
0.01032855175435543,
0.043662428855895996,
0.0955500528216362,
-0.05303520709276199,
-0.15660029649734497,
-0.04072032496333122,
0.09077946096658707,
-0.0027527001220732927,
-0.036689214408397675,
-0.03966725245118141,
0.03849169611930847,
0.06843466311693192,
0.13122352957725525,
0.07552056759595871,
-0.017929591238498688,
-0.04813180863857269,
-0.030096933245658875,
0.23523783683776855,
-0.1493375599384308,
0.04426715523004532,
-0.02271856553852558,
-0.01804111897945404,
-0.03908449783921242,
0.03597262129187584,
0.022118929773569107,
-0.000004518366949923802,
0.09706240892410278,
-0.058981191366910934,
-0.05378659814596176,
-0.10168042778968811,
-0.03272576630115509,
0.04088849574327469,
-0.013975566253066063,
-0.010589460842311382,
-0.09025166928768158,
-0.09490354359149933,
-0.04766594246029854,
0.05537205561995506,
-0.05123869329690933,
-0.03770573064684868,
0.009465423412621021,
-0.08151785284280777,
-0.005444355774670839,
-0.005417742300778627,
0.10699385404586792,
-0.03222226724028587,
0.04445803165435791,
-0.027600755915045738,
0.05225523188710213,
0.09919606149196625,
0.031576547771692276,
-0.0773419588804245,
0.0561848059296608,
-0.22559374570846558,
0.07503069192171097,
-0.11481974273920059,
0.04335082694888115,
-0.1704932004213333,
-0.042439818382263184,
0.005444696638733149,
0.0139949731528759,
0.013206101022660732,
0.12720820307731628,
-0.19255615770816803,
-0.01654396951198578,
0.13260798156261444,
-0.09212633967399597,
-0.118110790848732,
0.07884611934423447,
-0.029701577499508858,
0.1624738723039627,
0.04682036489248276,
-0.027025915682315826,
0.09224298596382141,
-0.16434773802757263,
-0.07092688232660294,
-0.00949116237461567,
-0.01727987825870514,
0.12109188735485077,
0.07512219995260239,
-0.05991523340344429,
0.046571120619773865,
0.02832140028476715,
-0.038078423589468,
-0.04424772411584854,
-0.050857074558734894,
-0.10884185880422592,
-0.01070026308298111,
-0.08987759798765182,
0.04065500199794769,
-0.01250192429870367,
-0.07916021347045898,
-0.029885273426771164,
-0.18612512946128845,
-0.0030564051121473312,
0.10038342326879501,
0.0035033065360039473,
-0.005652366206049919,
-0.08666291832923889,
0.026358824223279953,
-0.03112892620265484,
-0.008404186926782131,
-0.16764774918556213,
-0.04399421438574791,
0.046902090311050415,
-0.16094985604286194,
0.020117372274398804,
-0.06413903087377548,
0.06334125250577927,
0.03641495108604431,
-0.05590536445379257,
-0.0248766727745533,
-0.01730942726135254,
0.011945613659918308,
-0.05083848536014557,
-0.18994836509227753,
-0.056277405470609665,
-0.037882111966609955,
0.149809330701828,
-0.25956398248672485,
0.032966937869787216,
0.051140617579221725,
0.14649195969104767,
0.00406361510977149,
-0.05115427449345589,
0.01429014839231968,
-0.05360214412212372,
-0.054652128368616104,
-0.06746816635131836,
-0.006135428790003061,
-0.027576493099331856,
-0.05147203803062439,
0.019243421033024788,
-0.1755700707435608,
-0.021410830318927765,
0.09424154460430145,
0.12876708805561066,
-0.1486445665359497,
-0.018640631809830666,
-0.048725154250860214,
-0.06339836865663528,
-0.0715010017156601,
-0.07038594037294388,
0.10712739825248718,
0.0513901449739933,
0.04796046018600464,
-0.07435787469148636,
-0.07092321664094925,
0.02726263552904129,
0.006906150374561548,
-0.03382374346256256,
0.08727246522903442,
0.05199531093239784,
-0.09209315478801727,
0.0756213590502739,
0.1092359870672226,
0.07177663594484329,
0.09363535046577454,
0.01574566215276718,
-0.11756632477045059,
-0.028492970392107964,
0.036266472190618515,
0.02740776725113392,
0.1465986967086792,
-0.05952361226081848,
0.04016614332795143,
0.04494241625070572,
-0.04170418903231621,
0.022319864481687546,
-0.08787637203931808,
0.024075502529740334,
0.025203049182891846,
-0.0034381982404738665,
0.06284574419260025,
-0.02525499276816845,
-0.0050758360885083675,
0.07016654312610626,
0.047779910266399384,
0.04621000960469246,
0.009655474685132504,
-0.01720241829752922,
-0.1047825813293457,
0.16950392723083496,
-0.0951867327094078,
-0.269941508769989,
-0.17632324993610382,
0.026197833940386772,
0.04035249724984169,
-0.022378476336598396,
0.031619444489479065,
-0.07056326419115067,
-0.10630585998296738,
-0.1060405746102333,
-0.002429972169920802,
0.01714223250746727,
-0.06364088505506516,
-0.0741225928068161,
0.07348573952913284,
0.04382912442088127,
-0.14902326464653015,
0.038552410900592804,
0.055694397538900375,
-0.057955220341682434,
-0.0233661737293005,
0.09118817001581192,
0.12397737801074982,
0.14583967626094818,
-0.021366750821471214,
-0.028626007959246635,
0.029004426673054695,
0.19620531797409058,
-0.13469526171684265,
0.10371150821447372,
0.13814030587673187,
-0.04545360431075096,
0.08360563963651657,
0.1560150384902954,
0.029186224564909935,
-0.08317049592733383,
0.05044832453131676,
0.04082648828625679,
-0.043159641325473785,
-0.2666129767894745,
-0.0534592866897583,
0.012832709588110447,
-0.06255637854337692,
0.09786593168973923,
0.10183793306350708,
0.11542957276105881,
0.034910861402750015,
-0.07166364789009094,
-0.043925940990448,
-0.0058974819257855415,
0.11737963557243347,
-0.05490213260054588,
-0.012639665976166725,
0.07686592638492584,
-0.05086168646812439,
0.005355054512619972,
0.10266812145709991,
0.02973790094256401,
0.17442677915096283,
0.020399179309606552,
0.11231429129838943,
0.06195578724145889,
0.08633565157651901,
0.0007386076031252742,
0.02951662428677082,
0.05147615820169449,
0.017203815281391144,
-0.002300140680745244,
-0.10421168059110641,
-0.006156572140753269,
0.1449710875749588,
0.028103826567530632,
0.029669636860489845,
-0.0018948549404740334,
-0.005003341939300299,
0.05121048167347908,
0.1746254414319992,
-0.011592294089496136,
-0.22072425484657288,
-0.0845772922039032,
0.06936841458082199,
-0.06218599155545235,
-0.12968985736370087,
-0.026130788028240204,
0.045467354357242584,
-0.17519839107990265,
0.026703642681241035,
-0.027433741837739944,
0.0919293761253357,
-0.09345759451389313,
-0.02221956104040146,
0.03687324374914169,
0.084866963326931,
-0.014529162086546421,
0.08703910559415817,
-0.14498743414878845,
0.11886418610811234,
0.02978132851421833,
0.09024628251791,
-0.11081171780824661,
0.07909037172794342,
-0.007550720125436783,
0.009180475026369095,
0.19379350543022156,
-0.011335089802742004,
-0.03514958545565605,
-0.08774717897176743,
-0.11210042238235474,
-0.013537433929741383,
0.12687496840953827,
-0.1243172138929367,
0.08773399889469147,
-0.015198243781924248,
-0.044079482555389404,
0.00937260314822197,
-0.12100647389888763,
-0.17273177206516266,
-0.19628387689590454,
0.05585884302854538,
-0.09575839340686798,
0.025643249973654747,
-0.11914430558681488,
-0.07089093327522278,
-0.02952558360993862,
0.241120383143425,
-0.1745356321334839,
-0.06510113179683685,
-0.1468164622783661,
-0.046294767409563065,
0.1662203073501587,
-0.04437198117375374,
0.0718095526099205,
-0.0208172257989645,
0.20345525443553925,
0.005988610442727804,
-0.004939318168908358,
0.06724198162555695,
-0.08892562240362167,
-0.16873881220817566,
-0.06771010160446167,
0.1510489284992218,
0.11680185794830322,
0.04907919466495514,
-0.002248800592496991,
0.0011772146681323647,
-0.016943959519267082,
-0.1137804463505745,
-0.0033210667315870523,
0.16037839651107788,
0.03878779336810112,
0.025986969470977783,
-0.05243593826889992,
-0.08797456324100494,
-0.06899320334196091,
-0.06853509694337845,
0.06221301481127739,
0.19590823352336884,
-0.10376439243555069,
0.1700313836336136,
0.147536963224411,
-0.07305635511875153,
-0.23175598680973053,
0.035342130810022354,
0.04983805492520332,
0.0014306638622656465,
0.04886869341135025,
-0.18252557516098022,
0.10521943867206573,
0.019543392583727837,
-0.05505957826972008,
0.13485197722911835,
-0.1557481735944748,
-0.1552847921848297,
0.0722852572798729,
0.03904085233807564,
-0.22423844039440155,
-0.1354004591703415,
-0.09622503817081451,
-0.05825018882751465,
-0.14065024256706238,
0.06054598465561867,
-0.002136280992999673,
0.015948504209518433,
0.03500790148973465,
-0.0015643214574083686,
0.027123261243104935,
-0.058935679495334625,
0.18609118461608887,
-0.004065449349582195,
0.020676052197813988,
-0.060264769941568375,
-0.0478842556476593,
0.09839435666799545,
-0.06130504235625267,
0.12208222597837448,
0.004057085141539574,
0.01594383642077446,
-0.10362856835126877,
-0.048314861953258514,
-0.04328322783112526,
0.05154227837920189,
-0.07548051327466965,
-0.10070807486772537,
-0.043625857681035995,
0.08841723203659058,
0.07005169242620468,
-0.03383097052574158,
0.00549331633374095,
-0.07189501076936722,
0.10019614547491074,
0.17795267701148987,
0.17573626339435577,
0.009926567785441875,
-0.07241068035364151,
0.01677953451871872,
-0.04142116755247116,
0.044231921434402466,
-0.2513144314289093,
0.03756171092391014,
0.06098250672221184,
0.029438555240631104,
0.09217222779989243,
-0.020435843616724014,
-0.1820858269929886,
-0.04050002992153168,
0.08094815909862518,
-0.05452597141265869,
-0.22617179155349731,
-0.019085140898823738,
0.0954197570681572,
-0.2020406424999237,
-0.007372708059847355,
0.03995226323604584,
-0.048725228756666183,
-0.023169852793216705,
0.00010950004070764408,
0.06317184865474701,
0.002471912419423461,
0.09773622453212738,
0.0735151618719101,
0.09715340286493301,
-0.08337292820215225,
0.10562895983457565,
0.10150538384914398,
-0.09572599828243256,
0.03605884686112404,
0.06754924356937408,
-0.05300498008728027,
-0.043293699622154236,
0.03665391728281975,
0.033023297786712646,
0.005234600510448217,
-0.060321882367134094,
0.013913018628954887,
-0.036497246474027634,
0.044923391193151474,
0.08326134830713272,
0.03754979372024536,
-0.013354414142668247,
0.06462216377258301,
0.03401726484298706,
-0.10898099094629288,
0.10366570204496384,
0.01731540448963642,
0.04105307161808014,
-0.08384523540735245,
-0.019968897104263306,
0.035425446927547455,
0.030576206743717194,
-0.01765924133360386,
-0.02306121215224266,
-0.02860277332365513,
-0.01614218018949032,
-0.14299540221691132,
-0.023106401786208153,
-0.07243485748767853,
0.006181265693157911,
0.014656842686235905,
-0.031884219497442245,
-0.011233693920075893,
0.02475680410861969,
-0.06979699432849884,
-0.07426341623067856,
-0.006949664559215307,
0.09833318740129471,
-0.15115703642368317,
0.008848577737808228,
0.06907843053340912,
-0.11088496446609497,
0.08190931379795074,
-0.008411259390413761,
0.016245156526565552,
0.022527478635311127,
-0.15448406338691711,
0.05601610988378525,
0.0008648968650959432,
0.01916889287531376,
0.025886621326208115,
-0.16471809148788452,
0.004104440100491047,
-0.04661374166607857,
-0.02149827405810356,
-0.00004464812809601426,
-0.02647159807384014,
-0.12325995415449142,
0.06858719140291214,
-0.015622655861079693,
-0.035931166261434555,
-0.02701525390148163,
0.0539589487016201,
0.07888586074113846,
-0.027474910020828247,
0.10445091128349304,
-0.008690856397151947,
0.04941811040043831,
-0.16801609098911285,
-0.02470702864229679,
-0.04982255399227142,
0.019377702847123146,
0.009884213097393513,
-0.007693959400057793,
0.04183054715394974,
-0.00976533442735672,
0.21883612871170044,
-0.05075952783226967,
0.1607085019350052,
0.05847611650824547,
-0.017352959141135216,
-0.0007513365126214921,
0.06180921941995621,
0.05997028574347496,
0.04658793285489082,
0.009480604901909828,
0.023740366101264954,
-0.022450892254710197,
-0.006695089396089315,
-0.15932634472846985,
0.01890849508345127,
0.14999441802501678,
0.06301083415746689,
0.024745315313339233,
0.05866100639104843,
-0.12775006890296936,
-0.12135478109121323,
0.09311001747846603,
-0.026755332946777344,
0.00928465835750103,
-0.08245618641376495,
0.1358020007610321,
0.14980104565620422,
-0.14000412821769714,
0.05256148427724838,
-0.06134212389588356,
-0.05217423290014267,
-0.10388828068971634,
-0.12032219022512436,
-0.05887215584516525,
-0.053666237741708755,
0.002330566756427288,
-0.03760887682437897,
0.054546963423490524,
0.03344334661960602,
-0.009351172484457493,
-0.00022941511997487396,
0.13597318530082703,
-0.019751882180571556,
-0.0028988157864660025,
0.048313532024621964,
0.03693558648228645,
0.02373051457107067,
-0.05275435373187065,
0.02940409444272518,
0.02539868652820587,
0.032232340425252914,
0.06546790152788162,
0.033412106335163116,
-0.047448933124542236,
0.03804153576493263,
-0.0025254099164158106,
-0.11207924783229828,
0.019641218706965446,
-0.00460948096588254,
-0.0742158442735672,
0.1268945336341858,
0.0407399944961071,
0.010224059224128723,
-0.03741471841931343,
0.24361543357372284,
-0.06653323769569397,
-0.06378097087144852,
-0.13251738250255585,
0.10491154342889786,
-0.0027236645109951496,
0.06476365029811859,
0.023412218317389488,
-0.1284150779247284,
0.005243356805294752,
0.13858191668987274,
0.12181595712900162,
0.0045748427510261536,
0.009228081442415714,
0.0518609918653965,
0.0025186820421367884,
-0.06998204439878464,
0.054019294679164886,
0.06992026418447495,
0.12919506430625916,
-0.07847554981708527,
0.07680778950452805,
0.0006860480643808842,
-0.08370215445756912,
-0.02947772853076458,
0.11312682181596756,
-0.0409729965031147,
0.03491825982928276,
-0.047444481402635574,
0.10916327685117722,
-0.05787910893559456,
-0.29412412643432617,
0.02350960113108158,
-0.09588567912578583,
-0.15202060341835022,
-0.018367812037467957,
0.05944539234042168,
-0.02624768204987049,
0.018029648810625076,
0.06971040368080139,
-0.06011629104614258,
0.20098382234573364,
0.0335683599114418,
-0.07864278554916382,
-0.0664360448718071,
0.04837050288915634,
-0.06564252078533173,
0.2949807047843933,
0.008418165147304535,
0.02863333560526371,
0.10770907253026962,
-0.03253700211644173,
-0.18271861970424652,
0.010723991319537163,
0.1133992001414299,
-0.08056149631738663,
0.08200647681951523,
0.19000613689422607,
-0.012578671798110008,
0.1209007054567337,
0.05294662341475487,
-0.047376248985528946,
0.04217283055186272,
-0.03389401361346245,
-0.051268599927425385,
-0.10752558708190918,
0.058453381061553955,
-0.05909625440835953,
0.15447644889354706,
0.10152646154165268,
-0.05671518296003342,
-0.004550917539745569,
-0.05555408447980881,
0.04875178262591362,
0.01804669201374054,
0.12263146042823792,
0.02951994352042675,
-0.1865430772304535,
0.032826557755470276,
-0.01144319772720337,
0.10186848044395447,
-0.25588861107826233,
-0.08421015739440918,
0.08833149075508118,
-0.011924264021217823,
-0.05105875805020332,
0.10560628771781921,
0.057650718837976456,
0.04243382066488266,
-0.043439045548439026,
-0.10480839014053345,
-0.02186836116015911,
0.14663739502429962,
-0.1469624787569046,
-0.025013303384184837
] |
null | null | transformers |
# Fine-tuned DistilBERT-base-uncased for IMDB Classification
# Model Description
DistilBERT is a transformer model that performs sentiment analysis. I fine-tuned the model on IMDB dataset with the purpose of classifying positive reviews from the bad ones. The model predicts these 2 classes.
The model is a fine-tuned version of [DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert).
It was fine-tuned on IMDB dataset [https://huggingface.co/datasets/imdb].
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on IMDB dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2265
- Accuracy: 0.9312
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2273 | 1.0 | 1563 | 0.2471 | 0.9122 |
| 0.1524 | 2.0 | 3126 | 0.2265 | 0.9312 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
# How to Use
```python
from transformers import pipeline
classifier = pipeline("text-classification", model="LukeGPT88/imdb_text_classifier")
classifier("I see it and it was awesome.")
```
```python
Output:
[{'label': 'POSITIVE', 'score': 0.9958052635192871}]
```
# Contact
Please reach out to [[email protected]]([email protected]) if you have any questions or feedback.
| {"language": "en", "tags": ["distilbert", "sentiment", "positive", "negative", "review", "imdb"]} | text-classification | LukeGPT88/imdb_text_classifier | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"sentiment",
"positive",
"negative",
"review",
"imdb",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-09T09:24:40+00:00 | [] | [
"en"
] | TAGS
#transformers #tensorboard #safetensors #distilbert #text-classification #sentiment #positive #negative #review #imdb #en #autotrain_compatible #endpoints_compatible #region-us
| Fine-tuned DistilBERT-base-uncased for IMDB Classification
==========================================================
Model Description
=================
DistilBERT is a transformer model that performs sentiment analysis. I fine-tuned the model on IMDB dataset with the purpose of classifying positive reviews from the bad ones. The model predicts these 2 classes.
The model is a fine-tuned version of DistilBERT.
It was fine-tuned on IMDB dataset [URL
This model is a fine-tuned version of distilbert-base-uncased on IMDB dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2265
* Accuracy: 0.9312
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
How to Use
==========
Contact
=======
Please reach out to luca.flammia@URL if you have any questions or feedback.
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1\n\n\nHow to Use\n==========\n\n\nContact\n=======\n\n\nPlease reach out to luca.flammia@URL if you have any questions or feedback."
] | [
"TAGS\n#transformers #tensorboard #safetensors #distilbert #text-classification #sentiment #positive #negative #review #imdb #en #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1\n\n\nHow to Use\n==========\n\n\nContact\n=======\n\n\nPlease reach out to luca.flammia@URL if you have any questions or feedback."
] | [
59,
98,
4,
64
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #distilbert #text-classification #sentiment #positive #negative #review #imdb #en #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1\n\n\nHow to Use\n==========\n\n\nContact\n=======\n\n\nPlease reach out to luca.flammia@URL if you have any questions or feedback."
] | [
-0.09704980254173279,
0.0863727480173111,
-0.0031167471315711737,
0.08889330923557281,
0.14211991429328918,
-0.01644398272037506,
0.09691118448972702,
0.1417587697505951,
-0.030657554045319557,
0.06941455602645874,
0.09391927719116211,
0.12879027426242828,
0.010990745387971401,
0.1122175008058548,
-0.12588511407375336,
-0.1879701018333435,
-0.057391490787267685,
0.02701340988278389,
-0.08480466902256012,
0.10163953900337219,
0.10306713730096817,
-0.12091748416423798,
0.09887450188398361,
-0.023263409733772278,
-0.12429974973201752,
0.016403647139668465,
0.008345060981810093,
-0.02011610008776188,
0.09885331243276596,
0.06683925539255142,
0.11931732296943665,
0.06762822717428207,
0.0352398082613945,
-0.21009700000286102,
0.035721130669116974,
0.03624415025115013,
0.011327015236020088,
0.08376488089561462,
0.0731697678565979,
-0.04944007471203804,
0.15000881254673004,
-0.15473675727844238,
0.04422962665557861,
0.0849086120724678,
-0.1051994264125824,
-0.19289469718933105,
-0.1279190480709076,
0.022750215604901314,
0.1446484476327896,
0.0768592432141304,
-0.04921472817659378,
0.14433489739894867,
-0.06562911719083786,
0.1371656358242035,
0.2372336983680725,
-0.19380730390548706,
-0.07969777286052704,
0.05332069844007492,
0.05267708748579025,
0.08805420249700546,
-0.13558626174926758,
0.015970228239893913,
0.09558325260877609,
0.000761758245062083,
0.08478310704231262,
-0.05182475969195366,
-0.046104978770017624,
0.008404836989939213,
-0.14714443683624268,
-0.004383457358926535,
0.13769355416297913,
0.0863911435008049,
-0.07930262386798859,
-0.06547210365533829,
-0.01243163738399744,
-0.12592293322086334,
-0.0037577226758003235,
-0.06538745015859604,
0.03631295636296272,
-0.06330946832895279,
-0.12134861201047897,
-0.04761112853884697,
-0.11058487743139267,
-0.08103319257497787,
-0.026699868962168694,
0.09698290377855301,
0.033242858946323395,
-0.017866062000393867,
-0.014561750926077366,
0.06783390045166016,
0.0012831524945795536,
-0.10404278337955475,
-0.02525087259709835,
0.028893418610095978,
-0.03553592041134834,
-0.0983935296535492,
-0.044151537120342255,
-0.12104105949401855,
0.028452467173337936,
0.08428771048784256,
-0.06047699600458145,
0.062400344759225845,
-0.017265526577830315,
-0.014493867754936218,
-0.048541102558374405,
0.132809117436409,
-0.028030572459101677,
-0.0270090289413929,
0.026737559586763382,
0.04824195057153702,
-0.011811849661171436,
-0.023164479061961174,
-0.06514713168144226,
0.009619071148335934,
0.12102683633565903,
0.10383930057287216,
-0.009970102459192276,
0.03632127121090889,
-0.03766311705112457,
-0.012454835698008537,
0.027523409575223923,
-0.07440081238746643,
0.07419860363006592,
0.04416075348854065,
-0.08006590604782104,
0.0414343997836113,
0.039784807711839676,
-0.003709911135956645,
-0.010918335057795048,
0.0919465720653534,
-0.07032103091478348,
0.009193521924316883,
-0.10133717209100723,
-0.10857003927230835,
0.06418097764253616,
-0.011588792316615582,
-0.006424339022487402,
-0.12058153003454208,
-0.1278403103351593,
-0.042437005788087845,
0.018832460045814514,
-0.02179439552128315,
-0.009639788419008255,
-0.03689780458807945,
-0.09479495882987976,
-0.004509590100497007,
-0.010088262148201466,
0.10532861202955246,
-0.036352477967739105,
0.08762109279632568,
0.08428621292114258,
0.07551461458206177,
-0.0068908329121768475,
0.03638395667076111,
-0.05594886094331741,
0.020122945308685303,
-0.1388119012117386,
0.033404406160116196,
-0.11715541034936905,
0.08840146660804749,
-0.10641214996576309,
-0.13214409351348877,
0.048426687717437744,
-0.023349113762378693,
0.05819658935070038,
0.1328112781047821,
-0.14318425953388214,
-0.041464414447546005,
0.09340734034776688,
-0.07186298817396164,
-0.1588297337293625,
0.17668700218200684,
-0.03444560244679451,
-0.02248038537800312,
0.09526897966861725,
0.09953923523426056,
0.07466370612382889,
-0.139419287443161,
-0.0013659910764545202,
-0.05081562697887421,
0.018534516915678978,
0.011052701622247696,
0.05393613874912262,
-0.006444444414228201,
-0.005378329195082188,
-0.0016844217898324132,
-0.031221352517604828,
0.021196529269218445,
-0.10216253250837326,
-0.0939030647277832,
-0.05658431351184845,
-0.10826286673545837,
0.08380673825740814,
0.10397104173898697,
0.05314746871590614,
-0.11764850467443466,
-0.05302732437849045,
0.0300323236733675,
0.0787743479013443,
-0.04463905096054077,
0.004934228491038084,
-0.08685498684644699,
0.061624616384506226,
-0.03446934372186661,
-0.02287478744983673,
-0.1531153917312622,
-0.07308007031679153,
-0.022915951907634735,
0.005841227248311043,
-0.006203132215887308,
0.07325313240289688,
0.07300709933042526,
0.023084158077836037,
-0.08500570058822632,
0.002231685910373926,
0.0301166083663702,
-0.0008939621620811522,
-0.10979297012090683,
-0.1891518086194992,
0.020540183410048485,
-0.025076862424612045,
0.18132376670837402,
-0.29280588030815125,
0.027621757239103317,
0.10568743944168091,
0.06853295117616653,
0.06243104860186577,
-0.03968696668744087,
0.015820670872926712,
0.0774507150053978,
-0.03335416316986084,
-0.03039192408323288,
0.04209977760910988,
-0.05268232896924019,
-0.13741137087345123,
-0.04454881697893143,
-0.13974490761756897,
0.1508466899394989,
0.12341772764921188,
-0.02773207612335682,
-0.11113891005516052,
0.009777448140084743,
-0.035218529403209686,
-0.00025488180108368397,
-0.04493733122944832,
0.052564989775419235,
0.10671104490756989,
0.024118296802043915,
0.10984835028648376,
-0.05475574731826782,
-0.05955280736088753,
-0.008898766711354256,
-0.1088324636220932,
0.016911741346120834,
0.1457364559173584,
0.03201235085725784,
-0.07530611008405685,
0.11278006434440613,
0.1027308776974678,
-0.14334669709205627,
0.0663108378648758,
-0.036440640687942505,
-0.06724289059638977,
-0.03252715617418289,
0.050225529819726944,
0.06935391575098038,
0.10081056505441666,
-0.09897961467504501,
0.005263743456453085,
0.02027714066207409,
-0.023575514554977417,
-0.0009856245014816523,
-0.1801624596118927,
-0.024983739480376244,
0.054109830409288406,
-0.07678818702697754,
-0.02389562502503395,
0.028517043218016624,
0.008338531479239464,
0.08865896612405777,
-0.004161398392170668,
0.040030986070632935,
-0.006031050346791744,
-0.03241569176316261,
-0.12338422238826752,
0.18046830594539642,
-0.034052833914756775,
-0.1700781136751175,
-0.10841044783592224,
-0.05528574436903,
-0.07141123712062836,
0.019327929243445396,
0.04816138744354248,
-0.14042113721370697,
-0.04889651760458946,
-0.0664227306842804,
0.03230897709727287,
0.03842148557305336,
0.009995962493121624,
-0.02509399875998497,
0.021867861971259117,
0.09224192798137665,
-0.09342264384031296,
0.016125241294503212,
-0.0126549256965518,
-0.07140868157148361,
0.06204257532954216,
0.044323112815618515,
0.15076205134391785,
0.1622311770915985,
-0.004016230348497629,
-0.010052437894046307,
-0.03922910615801811,
0.18021121621131897,
-0.11652760952711105,
-0.012966325506567955,
0.15798155963420868,
-0.021843265742063522,
0.0694819763302803,
0.12896715104579926,
0.057323746383190155,
-0.1035238578915596,
0.06795772910118103,
0.10438915342092514,
-0.02755911462008953,
-0.24050162732601166,
-0.0068974546156823635,
-0.05086356773972511,
0.01683901436626911,
0.009101422503590584,
0.028971130028367043,
0.11946487426757812,
0.08664381504058838,
0.021289268508553505,
-0.02146492339670658,
-0.03230508044362068,
0.09767526388168335,
0.09446121752262115,
0.02602045051753521,
0.10725870728492737,
-0.07055699825286865,
-0.04431704059243202,
0.07183181494474411,
-0.03642648458480835,
0.1932075470685959,
-0.00007506187830585986,
0.15800541639328003,
0.06995774805545807,
0.15447604656219482,
-0.013163604773581028,
-0.01696724258363247,
-0.0026640319265425205,
-0.07917776703834534,
0.01858091540634632,
-0.03858247399330139,
-0.04753543436527252,
0.010459070093929768,
0.004138309042900801,
0.08422783017158508,
-0.12039109319448471,
-0.018534399569034576,
0.05563095584511757,
0.16963067650794983,
0.032161712646484375,
-0.27033114433288574,
-0.08617684990167618,
0.050910305231809616,
-0.05664965510368347,
0.03686380758881569,
-0.04204896092414856,
0.11846678704023361,
-0.12439006567001343,
0.03653381019830704,
-0.07758916169404984,
0.12912225723266602,
-0.09494408965110779,
0.019152088090777397,
-0.044168706983327866,
0.08289282768964767,
-0.014248218387365341,
0.06353530287742615,
-0.25060316920280457,
0.22614119946956635,
-0.0015977876028046012,
0.08070862293243408,
-0.04851394146680832,
-0.015278588980436325,
0.0796523243188858,
0.11097323149442673,
0.06869979202747345,
-0.011393454857170582,
0.0034562323708087206,
-0.21634908020496368,
0.00368133164010942,
0.01397364679723978,
0.11709632724523544,
-0.002511108061298728,
0.10260463505983353,
-0.021575063467025757,
-0.0019538514316082,
0.05537476763129234,
-0.01805959641933441,
-0.14890997111797333,
-0.047885406762361526,
0.02678721211850643,
-0.0029284281190484762,
0.019038910046219826,
-0.07826578617095947,
-0.09821707755327225,
-0.08430936932563782,
0.18610018491744995,
-0.022676631808280945,
-0.04310142993927002,
-0.13962653279304504,
0.07684735953807831,
0.06651981174945831,
-0.10493461787700653,
0.03059237264096737,
0.01356563065201044,
0.10820824652910233,
0.047091975808143616,
0.007716052234172821,
0.06580549478530884,
-0.07098028808832169,
-0.19093413650989532,
-0.03329696133732796,
0.124219611287117,
0.05403263121843338,
0.05461736395955086,
0.019579116255044937,
-0.012754431925714016,
-0.009373683482408524,
-0.12658776342868805,
0.032412994652986526,
0.018643055111169815,
0.045657578855752945,
0.012948457151651382,
0.04057454317808151,
-0.0015033079544082284,
-0.11151865124702454,
-0.05308941379189491,
0.1975279152393341,
0.30070289969444275,
-0.062562957406044,
0.009842638857662678,
-0.012553809210658073,
-0.02931852452456951,
-0.1522439420223236,
0.0879545733332634,
0.06647792458534241,
0.008816872723400593,
0.06151370704174042,
-0.08111400157213211,
0.07163762301206589,
0.06330753862857819,
-0.01619253121316433,
0.09852604568004608,
-0.23794934153556824,
-0.11835998296737671,
0.05893588438630104,
0.21392793953418732,
-0.004784695338457823,
-0.1934468299150467,
-0.0751572921872139,
-0.01573740318417549,
-0.12301404029130936,
0.057509761303663254,
-0.07842695713043213,
0.0933305025100708,
0.003196154022589326,
0.16204963624477386,
0.04146527498960495,
-0.04356130585074425,
0.16558007895946503,
-0.04252178221940994,
0.08352719247341156,
-0.07937749475240707,
-0.0026968098245561123,
0.001982135232537985,
-0.07173023372888565,
0.07236559689044952,
-0.10416901856660843,
0.02529742196202278,
-0.110181525349617,
-0.03637782856822014,
-0.08239099383354187,
0.05632627010345459,
-0.05790063366293907,
-0.024543527513742447,
-0.050133198499679565,
0.04956771060824394,
0.0410684198141098,
-0.028804456815123558,
0.051697712391614914,
-0.07224605232477188,
0.13974657654762268,
0.10492686927318573,
0.15956218540668488,
-0.011425819247961044,
-0.02337292954325676,
0.04409274458885193,
-0.03838241845369339,
0.020493315532803535,
-0.20356468856334686,
0.07094690948724747,
0.0982123613357544,
0.036741673946380615,
0.1876434087753296,
0.04584561660885811,
-0.04532753676176071,
0.03738055005669594,
0.08296304941177368,
-0.100061796605587,
-0.14851711690425873,
-0.004958036355674267,
-0.03575381636619568,
-0.18403755128383636,
0.05662402883172035,
0.09199562668800354,
-0.035837698727846146,
0.00408985186368227,
0.01613702066242695,
0.010546015575528145,
-0.008929340168833733,
0.14246368408203125,
0.1598089039325714,
0.04964163526892662,
-0.05865209922194481,
0.02068478986620903,
0.017460372298955917,
-0.1693425178527832,
0.015695318579673767,
0.07553579658269882,
-0.05359215289354324,
-0.06636173278093338,
0.0972612276673317,
0.14727318286895752,
-0.031397975981235504,
-0.020262567326426506,
-0.11644413322210312,
-0.12251360714435577,
0.025742055848240852,
0.1781466007232666,
0.08657972514629364,
-0.007139788940548897,
0.0072517697699368,
0.07615474611520767,
-0.13614322245121002,
0.12722793221473694,
0.10429474711418152,
0.06015661731362343,
-0.16548024117946625,
0.1414308249950409,
-0.029210688546299934,
0.0244758278131485,
-0.027782831341028214,
0.04057209938764572,
-0.12111380696296692,
-0.02447386085987091,
-0.12004996091127396,
-0.03540842607617378,
-0.08716541528701782,
0.02675849199295044,
0.00360229704529047,
-0.10716398805379868,
-0.062421903014183044,
0.01700459234416485,
-0.10574879497289658,
-0.062249984592199326,
0.057645875960588455,
0.08124995976686478,
-0.17253929376602173,
-0.0760638564825058,
0.0820600837469101,
-0.03672031685709953,
0.07465506345033646,
0.005392801482230425,
-0.0017195302061736584,
0.03536715731024742,
-0.17390640079975128,
0.04654834046959877,
0.05306859314441681,
-0.004825386684387922,
0.05873807147145271,
-0.15788033604621887,
-0.02119290828704834,
-0.033029139041900635,
0.11439108848571777,
0.062684565782547,
0.0924825370311737,
-0.1243242472410202,
-0.024973157793283463,
-0.06035604327917099,
-0.07158495485782623,
-0.061295319348573685,
0.06494776159524918,
0.06990522146224976,
0.01899927854537964,
0.16664859652519226,
-0.09288093447685242,
0.08196786791086197,
-0.17991843819618225,
0.014473026618361473,
-0.008613168261945248,
-0.054080985486507416,
-0.09122065454721451,
-0.04942521080374718,
0.057443492114543915,
-0.060708221048116684,
0.0381050780415535,
-0.029561763629317284,
0.1254974752664566,
0.04434836283326149,
-0.01750006154179573,
0.10257645696401596,
-0.004394379444420338,
0.18673262000083923,
-0.0063655744306743145,
-0.03779997304081917,
0.0568147748708725,
0.05038077384233475,
0.027578221634030342,
-0.07639483362436295,
0.18593516945838928,
0.15244042873382568,
-0.07983873784542084,
0.08478199690580368,
-0.03286351263523102,
-0.0025167244020849466,
-0.05567333102226257,
0.002915718127042055,
-0.05298050865530968,
0.047735750675201416,
-0.05127725005149841,
0.16083715856075287,
0.11795852333307266,
-0.15536034107208252,
0.033056166023015976,
-0.04394214600324631,
-0.09664727747440338,
-0.09141107648611069,
-0.060925014317035675,
-0.08258792757987976,
-0.16577251255512238,
0.025128662586212158,
-0.12577351927757263,
0.02851719968020916,
0.0038412646390497684,
0.03629596158862114,
-0.017279060557484627,
0.20478059351444244,
-0.017511310055851936,
0.021418670192360878,
0.06743906438350677,
-0.004795086104422808,
-0.050040353089571,
-0.03267015516757965,
0.013435740023851395,
0.001910133520141244,
0.002075061434879899,
0.04273064807057381,
-0.011452805250883102,
-0.09876739978790283,
0.06466744840145111,
-0.04451502487063408,
-0.1268119066953659,
0.0020489085000008345,
0.06620311737060547,
0.10401805490255356,
0.1304512768983841,
-0.0025950127746909857,
0.021374551579356194,
-0.022473478689789772,
0.2530328929424286,
-0.10516197979450226,
-0.005193421151489019,
-0.11830529570579529,
0.18762314319610596,
0.03262118995189667,
0.00041651117498986423,
-0.042346030473709106,
-0.11627815663814545,
0.052442293614149094,
0.23593737185001373,
0.1196788102388382,
-0.06194295734167099,
0.0035806538071483374,
-0.04092293977737427,
0.0029668074566870928,
-0.015153302811086178,
0.05372631922364235,
0.08093130588531494,
0.00526753906160593,
-0.064630426466465,
0.000976720592007041,
-0.04161743074655533,
-0.022184673696756363,
-0.02874252013862133,
0.051104556769132614,
0.04070163145661354,
0.0029260246083140373,
-0.06023992598056793,
0.14194907248020172,
-0.05926593020558357,
-0.11495666950941086,
0.04106886312365532,
-0.1628515124320984,
-0.12894384562969208,
-0.008481587283313274,
0.10479068011045456,
0.010346841998398304,
0.037709128111600876,
0.0003680610388983041,
0.015026255510747433,
0.1359042525291443,
-0.0023099901154637337,
-0.06617730110883713,
-0.04435142129659653,
0.08692912012338638,
-0.05290231481194496,
0.1345997303724289,
-0.039146993309259415,
0.04883488640189171,
0.1657901555299759,
-0.013495828956365585,
-0.09776248782873154,
0.06151589751243591,
0.07813738286495209,
-0.1584552526473999,
0.05569346249103546,
0.17730297148227692,
-0.009634393267333508,
0.05553468316793442,
0.06603595614433289,
-0.14776311814785004,
0.004631485790014267,
-0.07330786436796188,
-0.01981564238667488,
-0.10112012922763824,
0.07848989218473434,
-0.09594926238059998,
0.09941159188747406,
0.2213173359632492,
-0.062263522297143936,
0.046549923717975616,
-0.05227566510438919,
0.023559028282761574,
0.051306162029504776,
0.01389340776950121,
-0.03933892771601677,
-0.2085936963558197,
0.03121800906956196,
0.018650127574801445,
-0.025638122111558914,
-0.2640765905380249,
-0.09160392731428146,
-0.0011093288194388151,
-0.03450635075569153,
-0.053816989064216614,
0.08395452052354813,
0.10509591549634933,
0.030782759189605713,
-0.017398567870259285,
-0.18154293298721313,
-0.06395380198955536,
0.17044498026371002,
-0.14345061779022217,
-0.05277297645807266
] |
null | null | transformers |
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/dgSNbTl.jpg" alt="Mii-LLM" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://buy.stripe.com/8wM00Sf3vb3H3pmfYY">Want to contribute? Please donate! This will let us work on better datasets and models!</a></p>
</div>
</div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Maestrale chat beta ༄
By @efederici and @mferraretto
## Model description
- **Language Model**: Mistral-7b for the Italian language, continued pre-training for Italian on a curated large-scale high-quality corpus.
- **Fine-Tuning**: SFT performed on convs/instructions for three epochs.
**v0.3**
- Function calling
- Reduced default system prompt to avoid wasting tokens (pre-alignment)
This model uses ChatML prompt format:
```
<|im_start|>system
Sei un assistente utile.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Usage:
```python
from transformers import (
AutoTokenizer,
AutoModelForCausalLM,
GenerationConfig,
TextStreamer
)
import torch
tokenizer = AutoTokenizer.from_pretrained("mii-llm/maestrale-chat-v0.3-beta")
model = AutoModelForCausalLM.from_pretrained("mii-llm/maestrale-chat-v0.3-beta", load_in_8bit=True, device_map="auto")
gen = GenerationConfig(
do_sample=True,
temperature=0.7,
repetition_penalty=1.2,
top_k=50,
top_p=0.95,
max_new_tokens=500,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.convert_tokens_to_ids("<|im_end|>")
)
messages = [
{"role": "system", "content": "Sei un assistente utile."},
{"role": "user", "content": "{prompt}"}
]
with torch.no_grad(), torch.backends.cuda.sdp_kernel(
enable_flash=True,
enable_math=False,
enable_mem_efficient=False
):
temp = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(temp, return_tensors="pt").to("cuda")
streamer = TextStreamer(tokenizer, skip_prompt=True)
_ = model.generate(
**inputs,
streamer=streamer,
generation_config=gen
)
```
## Intended uses & limitations
It's a beta sft version, but it's not `aligned`. It's a first test. We are working on alignment data and evals.
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) | {"language": ["it"], "license": "cc-by-nc-4.0", "tags": ["sft", "it", "mistral", "chatml", "axolotl"], "prompt_template": "<|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant", "model-index": [{"name": "maestrale-chat-v0.3-beta", "results": []}]} | text-generation | mii-llm/maestrale-chat-v0.3-beta | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"sft",
"it",
"chatml",
"axolotl",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T09:26:06+00:00 | [] | [
"it"
] | TAGS
#transformers #safetensors #mistral #text-generation #sft #it #chatml #axolotl #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.URL alt="Mii-LLM" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="URL to contribute? Please donate! This will let us work on better datasets and models!</a></p>
</div>
</div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
# Maestrale chat beta ༄
By @efederici and @mferraretto
## Model description
- Language Model: Mistral-7b for the Italian language, continued pre-training for Italian on a curated large-scale high-quality corpus.
- Fine-Tuning: SFT performed on convs/instructions for three epochs.
v0.3
- Function calling
- Reduced default system prompt to avoid wasting tokens (pre-alignment)
This model uses ChatML prompt format:
## Usage:
## Intended uses & limitations
It's a beta sft version, but it's not 'aligned'. It's a first test. We are working on alignment data and evals.
<img src="URL alt="Built with Axolotl" width="200" height="32"/> | [
"# Maestrale chat beta ༄\n\nBy @efederici and @mferraretto",
"## Model description\n\n- Language Model: Mistral-7b for the Italian language, continued pre-training for Italian on a curated large-scale high-quality corpus.\n- Fine-Tuning: SFT performed on convs/instructions for three epochs.\n\nv0.3\n- Function calling\n- Reduced default system prompt to avoid wasting tokens (pre-alignment)\n\nThis model uses ChatML prompt format:",
"## Usage:",
"## Intended uses & limitations\n\nIt's a beta sft version, but it's not 'aligned'. It's a first test. We are working on alignment data and evals.\n\n<img src=\"URL alt=\"Built with Axolotl\" width=\"200\" height=\"32\"/>"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #sft #it #chatml #axolotl #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Maestrale chat beta ༄\n\nBy @efederici and @mferraretto",
"## Model description\n\n- Language Model: Mistral-7b for the Italian language, continued pre-training for Italian on a curated large-scale high-quality corpus.\n- Fine-Tuning: SFT performed on convs/instructions for three epochs.\n\nv0.3\n- Function calling\n- Reduced default system prompt to avoid wasting tokens (pre-alignment)\n\nThis model uses ChatML prompt format:",
"## Usage:",
"## Intended uses & limitations\n\nIt's a beta sft version, but it's not 'aligned'. It's a first test. We are working on alignment data and evals.\n\n<img src=\"URL alt=\"Built with Axolotl\" width=\"200\" height=\"32\"/>"
] | [
71,
19,
92,
4,
76
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #sft #it #chatml #axolotl #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Maestrale chat beta ༄\n\nBy @efederici and @mferraretto## Model description\n\n- Language Model: Mistral-7b for the Italian language, continued pre-training for Italian on a curated large-scale high-quality corpus.\n- Fine-Tuning: SFT performed on convs/instructions for three epochs.\n\nv0.3\n- Function calling\n- Reduced default system prompt to avoid wasting tokens (pre-alignment)\n\nThis model uses ChatML prompt format:## Usage:## Intended uses & limitations\n\nIt's a beta sft version, but it's not 'aligned'. It's a first test. We are working on alignment data and evals.\n\n<img src=\"URL alt=\"Built with Axolotl\" width=\"200\" height=\"32\"/>"
] | [
-0.11260295659303665,
-0.0635606050491333,
-0.0031246766448020935,
0.013447987847030163,
0.10546489804983139,
0.000025086990717682056,
0.09184077382087708,
0.10788703709840775,
0.09667155891656876,
0.10863303393125534,
0.05355515703558922,
-0.013492132537066936,
0.038029056042432785,
0.14017896354198456,
-0.12338531762361526,
-0.05648723617196083,
0.08633436262607574,
-0.019034242257475853,
0.11714497953653336,
0.08677123486995697,
0.1381506323814392,
-0.0801207646727562,
0.08235296607017517,
-0.020424028858542442,
-0.17639213800430298,
0.03689035400748253,
0.003142185974866152,
-0.015404956415295601,
0.07461533695459366,
0.07610427588224411,
0.07766148447990417,
0.03712364658713341,
0.010997316800057888,
-0.10313218086957932,
0.027233794331550598,
0.03151498734951019,
0.046319570392370224,
0.011003638617694378,
0.11346479505300522,
0.008746030740439892,
0.08711373060941696,
0.01107268501073122,
-0.01528182439506054,
0.044261474162340164,
-0.07165978848934174,
-0.08588632196187973,
-0.12295863777399063,
-0.04988083988428116,
0.0774296447634697,
0.09533952176570892,
-0.02690758928656578,
0.1328475922346115,
-0.08713721483945847,
0.06902343034744263,
0.08649275451898575,
-0.28804919123649597,
-0.054957300424575806,
0.07453823834657669,
0.028652505949139595,
0.07353805005550385,
0.0026363045908510685,
0.04959527403116226,
0.053120192140340805,
0.03510148823261261,
-0.03796498849987984,
-0.039375532418489456,
-0.0023769126273691654,
-0.06031246855854988,
-0.15133224427700043,
-0.05086927115917206,
0.29333561658859253,
-0.0004507072735577822,
-0.07826636731624603,
-0.09122217446565628,
-0.021187562495470047,
0.02073800377547741,
0.019637800753116608,
-0.06849569827318192,
-0.01967480406165123,
0.034972790628671646,
0.1560152769088745,
-0.07109067589044571,
-0.1416512429714203,
-0.0327581949532032,
-0.09962999075651169,
0.12021010369062424,
0.014244155026972294,
0.04030371084809303,
-0.04678332060575485,
0.04500916972756386,
-0.10729656368494034,
-0.07635342329740524,
-0.11291083693504333,
-0.051388923078775406,
-0.06887145340442657,
-0.07472380250692368,
-0.07431455701589584,
-0.22362077236175537,
0.1036672294139862,
0.19149060547351837,
-0.09285728633403778,
0.04213585704565048,
-0.029483960941433907,
0.09978026896715164,
-0.005993582308292389,
0.17327207326889038,
0.059004802256822586,
-0.01785723865032196,
0.0419059582054615,
0.019903678447008133,
0.05476166307926178,
-0.00986065436154604,
-0.08755205571651459,
-0.10408946871757507,
-0.04508393630385399,
0.06379720568656921,
0.03269641473889351,
0.015425819903612137,
-0.012397995218634605,
-0.019816702231764793,
0.08719738572835922,
-0.1523844599723816,
0.048518020659685135,
0.038803912699222565,
-0.023726318031549454,
0.06446031481027603,
0.045803822576999664,
-0.0017339217010885477,
-0.056898050010204315,
0.007757297717034817,
-0.009162272326648235,
0.016103683039546013,
-0.09834840148687363,
-0.09373441338539124,
0.052474137395620346,
-0.014106950722634792,
-0.09367765486240387,
-0.19669485092163086,
-0.076557457447052,
-0.0751376524567604,
0.04415734484791756,
-0.028465133160352707,
0.08417010307312012,
-0.016096757724881172,
-0.014642547816038132,
0.02747822180390358,
0.0062514361925423145,
-0.0415516272187233,
-0.05145179480314255,
0.0035810619592666626,
0.019079778343439102,
0.04650242254137993,
-0.11321841180324554,
0.02853304147720337,
-0.14429540932178497,
0.02308778092265129,
-0.12839895486831665,
0.08827979862689972,
-0.046361666172742844,
0.025126758962869644,
-0.07908087968826294,
0.02927803248167038,
-0.08090966194868088,
0.01890835538506508,
0.06783774495124817,
0.2047715038061142,
-0.1644512563943863,
0.003603135235607624,
0.12204696983098984,
-0.18169765174388885,
-0.1330099105834961,
0.1705794632434845,
0.06694521754980087,
0.034896884113550186,
0.06491255760192871,
0.18703529238700867,
0.053364384919404984,
-0.11856191605329514,
-0.05019626021385193,
0.053452420979738235,
-0.036313027143478394,
-0.022050226107239723,
0.10184771567583084,
-0.07700440287590027,
-0.028741799294948578,
0.02027774602174759,
-0.1443794071674347,
0.021735914051532745,
-0.04226585477590561,
-0.049077872186899185,
-0.11050835996866226,
-0.03540041297674179,
-0.013389228843152523,
0.010070067830383778,
-0.002177063375711441,
-0.0911446362733841,
-0.07341465353965759,
-0.0964270532131195,
0.077250637114048,
-0.062427278608083725,
0.05319290980696678,
-0.12552472949028015,
0.1438271403312683,
-0.047201402485370636,
0.04614565521478653,
-0.1482597291469574,
-0.11841392517089844,
0.0823604166507721,
0.09461382031440735,
-0.025859054177999496,
0.023648768663406372,
0.08846081048250198,
0.02991420030593872,
-0.0008651410462334752,
-0.01783929020166397,
0.0738530084490776,
-0.04210488870739937,
-0.06942195445299149,
-0.12613409757614136,
-0.061689455062150955,
-0.09405872970819473,
0.11960954964160919,
-0.2351698875427246,
0.019265370443463326,
0.08021523058414459,
0.07924659550189972,
0.01324881985783577,
-0.030285147950053215,
-0.0691385343670845,
-0.010988879948854446,
-0.09335319697856903,
-0.03864939883351326,
0.04103393480181694,
0.0483488105237484,
-0.02874400094151497,
0.12079918384552002,
-0.14298659563064575,
0.006303871050477028,
0.061938196420669556,
0.03149428218603134,
-0.07663574069738388,
-0.08379130810499191,
-0.06260386109352112,
0.005657890811562538,
-0.04656050726771355,
-0.07214781641960144,
0.004013297613710165,
-0.004210971295833588,
0.1129440888762474,
-0.06191469356417656,
-0.00538366474211216,
0.0004974795156158507,
-0.06719460338354111,
-0.09421258419752121,
0.10344972461462021,
-0.015242732129991055,
-0.18753811717033386,
0.03387900069355965,
0.11669543385505676,
-0.09111815690994263,
0.10031938552856445,
-0.018936291337013245,
-0.10103580355644226,
0.014618506655097008,
0.1903771609067917,
0.025316147133708,
0.026202527806162834,
-0.10520908236503601,
-0.008092615753412247,
0.03698261082172394,
0.0698866918683052,
0.03719102591276169,
-0.09677480906248093,
0.050325289368629456,
0.010363961569964886,
-0.06254297494888306,
0.07838591933250427,
0.06213546171784401,
-0.048559073358774185,
0.10247284919023514,
0.0029769798275083303,
-0.0795968547463417,
0.017724711447954178,
-0.038164373487234116,
-0.15157699584960938,
0.1337878704071045,
-0.06777859479188919,
-0.175644189119339,
-0.1210697591304779,
-0.04130779206752777,
-0.06133222579956055,
0.05054841190576553,
0.10144595056772232,
-0.062193553894758224,
-0.06870352476835251,
-0.10140393674373627,
-0.02150909975171089,
-0.009615210816264153,
0.0008933361386880279,
0.02487899921834469,
-0.04075056314468384,
-0.0011015809141099453,
-0.07004701346158981,
-0.028970971703529358,
0.001275614951737225,
-0.08238949626684189,
0.03521406650543213,
-0.075399249792099,
0.1451789289712906,
0.20736251771450043,
-0.045041464269161224,
0.07358169555664062,
0.0064737857319414616,
0.19915564358234406,
-0.07958859950304031,
0.03521869331598282,
0.21644559502601624,
-0.02497413195669651,
0.011895254254341125,
0.13136827945709229,
-0.013387845829129219,
-0.03990771621465683,
0.04624883830547333,
-0.02214962989091873,
-0.07084745913743973,
-0.23461246490478516,
-0.05210495740175247,
-0.07944231480360031,
-0.07149863243103027,
-0.017817839980125427,
0.08389835804700851,
0.022262761369347572,
0.046320222318172455,
-0.04663591831922531,
-0.11935630440711975,
0.10909686237573624,
0.04949485510587692,
0.13922499120235443,
-0.0011971647618338466,
0.07414352148771286,
-0.08973032981157303,
-0.008731754496693611,
0.09509701281785965,
-0.0002587349445093423,
0.16206902265548706,
0.00879260990768671,
0.07969161868095398,
0.07992124557495117,
0.019752275198698044,
0.0057000103406608105,
-0.0019072870491072536,
0.01897534169256687,
-0.01988290064036846,
0.02298733778297901,
-0.09767508506774902,
0.02076086401939392,
0.005541860591620207,
0.00412336690351367,
0.003789904061704874,
-0.08458444476127625,
0.06706584244966507,
0.07170890271663666,
0.23891372978687286,
0.07653554528951645,
-0.24117474257946014,
-0.051275961101055145,
0.00466145807877183,
0.038514088839292526,
-0.07938634604215622,
-0.008798536844551563,
0.17988982796669006,
-0.07153400778770447,
0.05893564596772194,
0.015554814599454403,
0.09709040820598602,
0.0007948247366584837,
-0.0023258051369339228,
0.05588812753558159,
0.0737496018409729,
0.013760232366621494,
0.049317654222249985,
-0.26280778646469116,
0.12348037213087082,
0.04133576899766922,
0.13487498462200165,
-0.0363297164440155,
0.058360934257507324,
0.017601294443011284,
0.22564095258712769,
0.11484073847532272,
-0.004734023939818144,
-0.10527727007865906,
-0.0718613937497139,
-0.03417273983359337,
-0.011842883192002773,
0.11033041775226593,
0.04409350827336311,
0.06668319553136826,
-0.08351048827171326,
-0.025029776617884636,
0.0008088699541985989,
0.09883588552474976,
-0.12637518346309662,
-0.10311032831668854,
0.06797735393047333,
0.18021409213542938,
-0.03689102455973625,
-0.023788975551724434,
-0.021683957427740097,
-0.07554589211940765,
0.21092797815799713,
-0.04890056699514389,
-0.01753266714513302,
-0.11937655508518219,
-0.08904170244932175,
0.03915230184793472,
-0.0381842665374279,
0.005744315683841705,
-0.06739850342273712,
0.14935852587223053,
-0.042420800775289536,
-0.09674911201000214,
0.033282339572906494,
-0.12213734537363052,
-0.009715475142002106,
-0.024607665836811066,
0.11621014773845673,
-0.02068137563765049,
-0.011571124196052551,
0.09840623289346695,
0.024139216169714928,
-0.014356274157762527,
-0.06179442256689072,
0.08081607520580292,
0.1066538542509079,
-0.052305590361356735,
0.10825513303279877,
-0.09758152812719345,
-0.13611038029193878,
-0.00678930664435029,
-0.03543698787689209,
0.11067643761634827,
0.24790474772453308,
-0.08580400049686432,
-0.0011368799023330212,
0.03462012857198715,
-0.09603563696146011,
-0.23084071278572083,
0.10258974134922028,
0.04329996928572655,
0.045153066515922546,
-0.05025347322225571,
-0.029994117096066475,
0.03481057286262512,
0.10250816494226456,
-0.03910652548074722,
0.17749319970607758,
-0.3111703097820282,
-0.06836605817079544,
0.04215792939066887,
0.003134885337203741,
0.18726055324077606,
-0.13386814296245575,
-0.0888749286532402,
-0.023385904729366302,
-0.10026028007268906,
-0.026222944259643555,
-0.05902394652366638,
0.09867152571678162,
0.02441474236547947,
-0.1039065420627594,
0.05166812986135483,
-0.04472816735506058,
0.161620631814003,
-0.03821994736790657,
0.021623555570840836,
-0.06015479937195778,
0.10361681133508682,
0.0335087776184082,
-0.07947695255279541,
0.1894894689321518,
0.03845320641994476,
-0.007090688217431307,
-0.0657438114285469,
-0.020118849352002144,
-0.05048949271440506,
0.1387275755405426,
-0.005529520567506552,
0.031815215945243835,
-0.021943503990769386,
-0.023611383512616158,
0.026825344190001488,
0.040842439979314804,
0.10583043843507767,
-0.12309982627630234,
0.10913362354040146,
0.17752140760421753,
0.13314610719680786,
-0.10940855741500854,
-0.0788334533572197,
0.024079937487840652,
-0.041164662688970566,
0.040257763117551804,
0.04067444056272507,
0.04356974735856056,
0.10271167755126953,
-0.008505064062774181,
0.10011523962020874,
0.04307347163558006,
-0.06361217051744461,
-0.040963318198919296,
0.12388619035482407,
-0.08019241690635681,
-0.15642084181308746,
-0.04539978876709938,
0.04027076065540314,
-0.05389103665947914,
0.06675368547439575,
0.17811645567417145,
-0.0027200214099138975,
0.023248042911291122,
-0.0022801340091973543,
0.035230234265327454,
-0.04328416660428047,
0.1140420064330101,
0.014954276382923126,
0.006616916973143816,
-0.05790024623274803,
0.12123371660709381,
0.04482588917016983,
-0.07510058581829071,
0.012875177897512913,
0.14811626076698303,
-0.10780666023492813,
-0.08657779544591904,
-0.04003698751330376,
0.09053216874599457,
-0.08446921408176422,
-0.10123670846223831,
-0.06293909251689911,
-0.1336231827735901,
0.013700466603040695,
0.11950691044330597,
0.06486532092094421,
0.03209445998072624,
0.025932494550943375,
0.0027733815368264914,
-0.07705405354499817,
0.08202492445707321,
0.024157309904694557,
0.0690869465470314,
-0.11984226107597351,
0.10217863321304321,
-0.023911654949188232,
0.03201308101415634,
-0.03034784458577633,
-0.03932153433561325,
-0.08374833315610886,
0.01748495176434517,
-0.12332434207201004,
0.041788775473833084,
-0.007646871730685234,
-0.03888148441910744,
0.023496240377426147,
-0.031373657286167145,
-0.05451018735766411,
0.03379156067967415,
-0.07393326610326767,
-0.025425110012292862,
-0.035230182111263275,
0.03525720164179802,
-0.08768323063850403,
-0.02760762721300125,
-0.01897547021508217,
-0.07659224420785904,
0.05330774933099747,
0.07586892694234848,
-0.06769974529743195,
-0.029300950467586517,
-0.19114673137664795,
-0.033234383910894394,
0.07042592018842697,
0.027136897668242455,
0.013814275152981281,
-0.07400597631931305,
0.02127923257648945,
0.10139821469783783,
-0.016018923372030258,
0.04909516125917435,
0.06299623847007751,
-0.05391297489404678,
0.007286496460437775,
-0.07161585986614227,
-0.05507722124457359,
-0.03159550577402115,
0.09795641154050827,
0.05707043036818504,
0.14823675155639648,
0.08567629009485245,
-0.11658245325088501,
0.015370078384876251,
-0.073316290974617,
-0.014463932253420353,
0.010223190300166607,
-0.043561022728681564,
-0.13296939432621002,
0.01709892973303795,
0.05414634570479393,
-0.014776806347072124,
0.01885909028351307,
0.04684912785887718,
0.07146166265010834,
0.05012505501508713,
-0.06838449835777283,
0.02502412348985672,
-0.005006212275475264,
0.11524833738803864,
-0.005771405063569546,
-0.007325325161218643,
0.057400841265916824,
0.024250857532024384,
0.017382822930812836,
-0.02538677677512169,
0.1550341695547104,
0.1520339548587799,
0.022639958187937737,
0.16204668581485748,
0.014886492863297462,
-0.08187036216259003,
-0.06875060498714447,
-0.06679342687129974,
-0.05133071541786194,
0.1151222437620163,
-0.09166060388088226,
0.13866284489631653,
0.17116513848304749,
-0.12153716385364532,
-0.007978950627148151,
-0.09314363449811935,
-0.038297273218631744,
-0.16446702182292938,
-0.1250259131193161,
-0.07108792662620544,
-0.12251567840576172,
-0.04574187099933624,
-0.11293676495552063,
0.07243646681308746,
-0.015161721967160702,
0.007960506714880466,
-0.03496556356549263,
0.1436700075864792,
-0.06235255300998688,
-0.053645286709070206,
0.09795650094747543,
-0.08404360711574554,
0.07731571793556213,
-0.021005239337682724,
-0.03090980090200901,
0.09658544510602951,
-0.056903738528490067,
0.061041686683893204,
0.0715833529829979,
0.07668321579694748,
0.020774757489562035,
-0.0507594533264637,
-0.05125416815280914,
0.007029837928712368,
0.07505423575639725,
0.05548198148608208,
0.06869170814752579,
0.0584745854139328,
-0.05123811960220337,
0.005695364437997341,
0.22030623257160187,
-0.04255760833621025,
-0.07906888425350189,
-0.1455969512462616,
0.12499745935201645,
0.002617096761241555,
0.08903735131025314,
-0.07147914171218872,
-0.11409785598516464,
-0.017361553385853767,
0.15351490676403046,
0.035352498292922974,
-0.03404645249247551,
-0.03283601999282837,
-0.012984816916286945,
0.010504225268959999,
0.0077379317954182625,
0.07573643326759338,
0.03900768235325813,
0.21694646775722504,
-0.03246438503265381,
0.06137099862098694,
-0.026781484484672546,
0.05592796206474304,
-0.1077212244272232,
0.07545609027147293,
-0.03298594430088997,
-0.002247350988909602,
-0.043660275638103485,
0.0627056211233139,
0.0322735458612442,
-0.006257263012230396,
-0.060015447437763214,
-0.07987730205059052,
-0.05315641686320305,
-0.016890088096261024,
0.02913547120988369,
-0.04258979856967926,
0.09697452187538147,
-0.08769908547401428,
-0.025354953482747078,
0.061727020889520645,
-0.0512990839779377,
-0.032048776745796204,
-0.1375969797372818,
0.04004797711968422,
-0.08708493411540985,
0.22712689638137817,
0.01204084511846304,
0.11225689202547073,
0.08871594071388245,
0.015335844829678535,
-0.1537778675556183,
0.03927118331193924,
0.003943876828998327,
-0.061716433614492416,
0.065867118537426,
0.04116611182689667,
-0.0612364336848259,
0.15524272620677948,
0.06526271253824234,
-0.1454353779554367,
0.05528280511498451,
0.08410535752773285,
0.0035135590005666018,
-0.08382096886634827,
0.012949604541063309,
-0.08930308371782303,
0.1179877370595932,
0.1547207534313202,
-0.005998969543725252,
0.022014379501342773,
-0.03662930056452751,
0.044781461358070374,
0.058531273156404495,
0.10583534091711044,
-0.016702700406312943,
-0.19855108857154846,
0.04274601489305496,
0.04059085622429848,
0.006452980451285839,
-0.20478582382202148,
-0.09913397580385208,
-0.0970580130815506,
0.024750083684921265,
-0.06065104901790619,
0.05143314227461815,
0.1449858844280243,
0.031247220933437347,
-0.04369302839040756,
-0.14027822017669678,
0.045252468436956406,
0.14160582423210144,
-0.058615729212760925,
-0.06325768679380417
] |
null | null | transformers | ---
base_model:
- EmbeddedLLM/Mistral-7B-Merge-14-v0
- janai-hq/trinity-v1
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [EmbeddedLLM/Mistral-7B-Merge-14-v0](https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0) as a base.
### Models Merged
The following models were included in the merge:
* [janai-hq/trinity-v1](https://huggingface.co/janai-hq/trinity-v1)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: EmbeddedLLM/Mistral-7B-Merge-14-v0
parameters:
density: 0.5
weight: 0.5
- model: janai-hq/trinity-v1
parameters:
density: 0.5
weight: 0.3
merge_method: ties
base_model: EmbeddedLLM/Mistral-7B-Merge-14-v0
parameters:
normalize: true
dtype: float16
```
| {"language": ["en"], "license": "apache-2.0"} | text-generation | SonaliSN/MergedModelTest | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"arxiv:2306.01708",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T09:26:18+00:00 | [
"2306.01708"
] | [
"en"
] | TAGS
#transformers #safetensors #mistral #text-generation #en #arxiv-2306.01708 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ---
base_model:
- EmbeddedLLM/Mistral-7B-Merge-14-v0
- janai-hq/trinity-v1
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the TIES merge method using EmbeddedLLM/Mistral-7B-Merge-14-v0 as a base.
### Models Merged
The following models were included in the merge:
* janai-hq/trinity-v1
### Configuration
The following YAML configuration was used to produce this model:
| [
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the TIES merge method using EmbeddedLLM/Mistral-7B-Merge-14-v0 as a base.",
"### Models Merged\n\nThe following models were included in the merge:\n* janai-hq/trinity-v1",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #en #arxiv-2306.01708 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the TIES merge method using EmbeddedLLM/Mistral-7B-Merge-14-v0 as a base.",
"### Models Merged\n\nThe following models were included in the merge:\n* janai-hq/trinity-v1",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
65,
18,
4,
38,
27,
17
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #en #arxiv-2306.01708 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# merge\n\nThis is a merge of pre-trained language models created using mergekit.## Merge Details### Merge Method\n\nThis model was merged using the TIES merge method using EmbeddedLLM/Mistral-7B-Merge-14-v0 as a base.### Models Merged\n\nThe following models were included in the merge:\n* janai-hq/trinity-v1### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
-0.083101786673069,
-0.1686832755804062,
-0.0009700588998384774,
0.013669115491211414,
0.1251678615808487,
0.047152046114206314,
0.1556241661310196,
0.04858376085758209,
0.029257941991090775,
-0.0400552898645401,
0.03917611390352249,
0.05619078129529953,
0.041882067918777466,
0.1438363790512085,
-0.0639219582080841,
-0.16695411503314972,
0.07566329091787338,
-0.015812503173947334,
-0.2010408192873001,
0.09486120194196701,
0.13542582094669342,
-0.03588059917092323,
0.13437281548976898,
0.031312569975852966,
-0.17251749336719513,
0.05637330561876297,
-0.03528956323862076,
0.01144463662058115,
0.11244668066501617,
0.12030718475580215,
0.0635533481836319,
0.015987344086170197,
-0.010370081290602684,
-0.1052093580365181,
0.05673627555370331,
-0.015682697296142578,
-0.01761254295706749,
0.02825499325990677,
0.06767965853214264,
0.01021010521799326,
0.10106547176837921,
-0.06115097925066948,
-0.023052021861076355,
0.07189720869064331,
-0.09742671996355057,
-0.05268421396613121,
-0.10768850147724152,
0.04539060965180397,
0.17453217506408691,
0.050782397389411926,
-0.02444744110107422,
0.05318687483668327,
0.023110121488571167,
0.08649155497550964,
0.032905805855989456,
-0.30041229724884033,
0.012943582609295845,
0.0923033058643341,
0.06871347874403,
-0.07385049760341644,
0.0718894824385643,
0.03476633131504059,
0.09715843200683594,
-0.009167426265776157,
-0.013827239163219929,
-0.05464266613125801,
0.07543586939573288,
-0.04408445581793785,
-0.13175784051418304,
-0.0047530620358884335,
0.2646026313304901,
-0.0012714066542685032,
-0.00018288761202711612,
-0.0075959921814501286,
-0.13144844770431519,
0.059159863740205765,
-0.00774307269603014,
-0.021604765206575394,
-0.010011172853410244,
0.05586031451821327,
0.11085701733827591,
-0.05477316305041313,
-0.10593315958976746,
0.0004663766303565353,
-0.17204555869102478,
0.10723831504583359,
0.07615352421998978,
0.04859613627195358,
-0.14420928061008453,
0.056513406336307526,
-0.07508478313684464,
-0.09618021547794342,
0.011992053128778934,
-0.03971625491976738,
-0.05070081353187561,
-0.022259319201111794,
-0.12132804095745087,
-0.14544785022735596,
0.11361917108297348,
0.1461135745048523,
-0.08301462233066559,
0.03177465870976448,
0.07555317133665085,
0.07261420041322708,
0.028964772820472717,
-0.0022259510587900877,
-0.12973593175411224,
-0.12297030538320541,
0.0803859531879425,
-0.042502496391534805,
0.10016977041959763,
0.017400560900568962,
-0.18278391659259796,
-0.04443948343396187,
0.003724345238879323,
0.006246674340218306,
-0.0077108112163841724,
0.1390930712223053,
-0.020206697285175323,
-0.03936821222305298,
0.14382612705230713,
-0.06594143062829971,
0.014984725043177605,
-0.030442053452134132,
0.007327240891754627,
-0.07378257811069489,
0.13844835758209229,
0.06364531069993973,
-0.015757838264107704,
0.027285484597086906,
-0.05015609785914421,
0.008507569320499897,
-0.0632353201508522,
-0.09219446778297424,
0.009383904747664928,
-0.016420211642980576,
0.014384778216481209,
-0.1141710877418518,
-0.2530587911605835,
0.004185444209724665,
0.051073744893074036,
-0.023361794650554657,
-0.025196613743901253,
-0.04107285663485527,
0.025570088997483253,
-0.004744675476104021,
-0.016414223238825798,
0.02390592359006405,
-0.020880168303847313,
-0.05668340250849724,
-0.04143250733613968,
0.02496054582297802,
-0.20945227146148682,
0.04333554953336716,
-0.0977616086602211,
0.11925975978374481,
-0.05971604585647583,
0.09313147515058517,
0.031652070581912994,
0.11997876316308975,
-0.08433791249990463,
0.005613453220576048,
0.004184052813798189,
0.02726856619119644,
0.07878213375806808,
0.14956511557102203,
-0.14402031898498535,
-0.0591699481010437,
0.054803457111120224,
-0.13125695288181305,
-0.17837046086788177,
0.12294548749923706,
-0.003000762080773711,
0.09795204550027847,
0.06294763833284378,
0.20096327364444733,
0.10160815715789795,
-0.020537428557872772,
0.03596258908510208,
0.02253888174891472,
-0.0366002693772316,
-0.0593012273311615,
0.052210673689842224,
0.004473251290619373,
-0.17856843769550323,
0.043595973402261734,
0.04119139164686203,
0.16568714380264282,
-0.024331023916602135,
-0.048907771706581116,
-0.0833507850766182,
-0.028846656903624535,
0.04252735897898674,
-0.04669167846441269,
0.0434255488216877,
-0.07283564656972885,
0.04397284612059593,
0.10113807767629623,
0.09931997954845428,
-0.03988061472773552,
0.04832816123962402,
-0.022846486419439316,
0.08059972524642944,
-0.10375478863716125,
0.06785032898187637,
-0.07409361004829407,
-0.01953793317079544,
-0.036001190543174744,
0.0009617085452191532,
0.05096876248717308,
0.06150801107287407,
0.05153714120388031,
0.014636600390076637,
-0.05113711580634117,
-0.015471525490283966,
0.17048542201519012,
0.05335105583071709,
-0.048418495804071426,
-0.19030159711837769,
-0.026991872116923332,
-0.05379783362150192,
0.26573386788368225,
-0.028725024312734604,
0.07560452818870544,
-0.046951066702604294,
0.17917400598526,
-0.06290820240974426,
0.10145546495914459,
0.04215360805392265,
0.02797512337565422,
-0.07172779738903046,
0.004194421693682671,
0.08357034623622894,
0.06711800396442413,
-0.1778482049703598,
0.20160552859306335,
-0.15450146794319153,
0.023615550249814987,
0.1253901869058609,
-0.026443704962730408,
0.01338607631623745,
-0.08484719693660736,
-0.010856484062969685,
-0.07337636500597,
0.09186025708913803,
-0.053747355937957764,
0.08053431659936905,
-0.009455290623009205,
0.15726402401924133,
-0.05843018740415573,
-0.02839483506977558,
0.0028327845502644777,
-0.08465539664030075,
-0.05776655301451683,
0.06636340171098709,
-0.04002032428979874,
-0.3140232563018799,
0.13858111202716827,
0.23311465978622437,
0.007963300682604313,
0.08065199851989746,
0.0009349014726467431,
0.034080687910318375,
-0.054065436124801636,
-0.006723693106323481,
-0.03433748707175255,
-0.017737634479999542,
-0.09881257265806198,
0.025009548291563988,
0.05981345474720001,
0.027433793991804123,
0.07906746864318848,
-0.10501652210950851,
0.04053114354610443,
0.051692716777324677,
0.020631546154618263,
0.09100145846605301,
0.11517979949712753,
-0.013004230335354805,
0.05496995896100998,
0.006881197448819876,
-0.01692708395421505,
0.05420428141951561,
0.015283817425370216,
-0.1526169776916504,
0.17543916404247284,
-0.20169246196746826,
-0.23264336585998535,
-0.18518856167793274,
-0.12480927258729935,
-0.11902152001857758,
0.004475053399801254,
0.11318615078926086,
-0.0656735748052597,
-0.063687264919281,
-0.09922900050878525,
0.0941704660654068,
-0.0072995321825146675,
0.005916733294725418,
-0.016646498814225197,
-0.028061408549547195,
0.02617787756025791,
-0.08982887864112854,
-0.014531106688082218,
-0.007890388369560242,
0.008241567760705948,
0.06107465922832489,
-0.08878340572118759,
0.1178203746676445,
0.1420752853155136,
-0.019386177882552147,
0.016739094629883766,
-0.04640712961554527,
0.1948108971118927,
-0.0054500955156981945,
0.1010139063000679,
0.15393424034118652,
-0.09844832122325897,
0.05474476516246796,
0.28305932879447937,
-0.007646132726222277,
-0.026809388771653175,
0.03463377803564072,
-0.07579703629016876,
-0.06898792088031769,
-0.18637615442276,
-0.14367668330669403,
-0.1275690346956253,
-0.04150201007723808,
0.009146600030362606,
0.062143053859472275,
0.08008313924074173,
0.06613043695688248,
-0.09530998766422272,
0.04097859188914299,
0.03126375004649162,
0.05958389863371849,
0.237672820687294,
-0.007904186844825745,
0.07124053686857224,
-0.08368296921253204,
-0.014546537771821022,
0.0791529193520546,
0.06649328768253326,
0.1748155951499939,
0.062374360859394073,
0.09648638963699341,
0.12129204720258713,
0.05085182189941406,
0.05437411367893219,
0.07966164499521255,
-0.04857509583234787,
0.02991623990237713,
-0.05697285383939743,
-0.06764283031225204,
0.06208360195159912,
0.07673218846321106,
-0.14653372764587402,
0.07307305932044983,
-0.04657392203807831,
0.03591590002179146,
0.09687670320272446,
0.12519846856594086,
0.10364510118961334,
-0.25037431716918945,
-0.0837462842464447,
0.07508569210767746,
0.05397775024175644,
-0.02456984668970108,
-0.028239279985427856,
-0.036227524280548096,
0.002130059292539954,
0.21908622980117798,
-0.0038586370646953583,
0.11871064454317093,
0.0982237458229065,
0.009639264084398746,
0.006237062159925699,
0.08946157246828079,
0.0022743914742022753,
0.06204717233777046,
-0.2252064049243927,
0.2131158411502838,
0.01792447455227375,
-0.016960475593805313,
0.019467556849122047,
0.03495520353317261,
0.04586447402834892,
0.3249219059944153,
-0.006312374956905842,
0.027660101652145386,
-0.038617923855781555,
0.051365435123443604,
-0.039662569761276245,
0.023578116670250893,
-0.0472848005592823,
0.038339562714099884,
0.0534263476729393,
-0.09141075611114502,
-0.02955356426537037,
0.032957397401332855,
0.17220081388950348,
-0.11430863291025162,
-0.14772848784923553,
0.05450351908802986,
0.10124211013317108,
0.029170995578169823,
-0.04023382067680359,
-0.014081704430282116,
-0.16096743941307068,
0.20769844949245453,
0.011051646433770657,
-0.1115153431892395,
-0.09432114660739899,
-0.02809152565896511,
0.09402982145547867,
-0.07028061151504517,
0.0559033639729023,
-0.04817848280072212,
0.027537446469068527,
-0.06598025560379028,
-0.21828219294548035,
0.08411392569541931,
-0.0973980575799942,
-0.017777616158127785,
-0.0072177257388830185,
0.06902043521404266,
-0.08807988464832306,
-0.0005221068277023733,
0.00852283090353012,
0.025318965315818787,
-0.13202516734600067,
-0.038316626101732254,
-0.04541907086968422,
0.19857053458690643,
0.0450553297996521,
0.06750160455703735,
-0.05682804062962532,
-0.2411356419324875,
-0.003313585417345166,
-0.07322903722524643,
0.15700604021549225,
0.20975583791732788,
-0.046452973037958145,
0.0964464321732521,
0.17246299982070923,
-0.10584752261638641,
-0.22298647463321686,
-0.07632600516080856,
-0.0705769956111908,
0.04430222883820534,
-0.06957413256168365,
0.032276879996061325,
0.03493862226605415,
0.06375657767057419,
-0.013378535397350788,
-0.008298326283693314,
-0.21550406515598297,
-0.19184806942939758,
0.09251507371664047,
0.019887583330273628,
0.3544367849826813,
-0.09406761080026627,
-0.051857803016901016,
-0.1188332736492157,
-0.11744606494903564,
-0.015485783107578754,
-0.18941627442836761,
0.04739966616034508,
-0.017852239310741425,
0.030881792306900024,
-0.016062038019299507,
-0.03390178084373474,
0.14330258965492249,
-0.029187297448515892,
0.06424809992313385,
-0.10627157986164093,
-0.010460898280143738,
0.09189330041408539,
-0.06408245116472244,
0.11221546679735184,
-0.12930871546268463,
0.04127829894423485,
0.044479310512542725,
-0.01564224623143673,
-0.014566450379788876,
0.07636353373527527,
-0.021827276796102524,
-0.05399943143129349,
-0.08672983944416046,
-0.021871475502848625,
0.011513696983456612,
-0.03919269144535065,
0.1553308367729187,
-0.007392531726509333,
0.05958166345953941,
0.12826591730117798,
0.1045146957039833,
-0.16730897128582,
0.07930610328912735,
0.04548340290784836,
-0.07982729375362396,
0.07669080048799515,
-0.13759829103946686,
0.00046063505578786135,
0.08734235912561417,
-0.05434388667345047,
0.12724238634109497,
0.042188242077827454,
-0.017266687005758286,
0.0017874338664114475,
0.1111578568816185,
-0.14024293422698975,
-0.23793016374111176,
-0.04528844356536865,
-0.005458624102175236,
0.011459814384579659,
0.11629121005535126,
0.16414611041545868,
-0.06701776385307312,
-0.01437876932322979,
0.015790514647960663,
0.02334173582494259,
-0.10984039306640625,
0.0835799053311348,
-0.015319115482270718,
-0.006463075056672096,
-0.11148390173912048,
0.08005013316869736,
0.04558097943663597,
-0.10288842767477036,
0.011495865881443024,
0.09263449907302856,
-0.17180874943733215,
-0.11793898791074753,
-0.05583171918988228,
0.23905467987060547,
-0.08506491035223007,
-0.12143584340810776,
-0.07248321920633316,
-0.15217585861682892,
0.04223877564072609,
0.09008381515741348,
0.07627604156732559,
0.016552092507481575,
-0.004376022610813379,
-0.0740165188908577,
-0.018811345100402832,
0.0925430878996849,
0.03491375222802162,
0.08962104469537735,
-0.1468355655670166,
0.027143247425556183,
-0.02104761078953743,
0.09485559165477753,
-0.04915302246809006,
-0.010319074615836143,
-0.06932982802391052,
-0.015251611359417439,
-0.21321281790733337,
0.003095100400969386,
-0.16950123012065887,
-0.032116781920194626,
0.011545732617378235,
-0.06384202837944031,
-0.027932852506637573,
0.05696038901805878,
-0.028794486075639725,
-0.017364364117383957,
-0.04511673375964165,
0.04130525887012482,
-0.030219055712223053,
-0.06324677914381027,
0.001963832648470998,
-0.02454344928264618,
0.06548091769218445,
0.04773944243788719,
-0.10146269202232361,
-0.020338181406259537,
-0.10865290462970734,
-0.09280049055814743,
0.09361366182565689,
0.021693820133805275,
0.054992180317640305,
-0.10605154931545258,
-0.06235018000006676,
0.06620104610919952,
-0.03150930628180504,
-0.04802858829498291,
0.07132799923419952,
-0.010713929310441017,
0.013001282699406147,
-0.052537884563207626,
-0.018977994099259377,
-0.03423292189836502,
-0.0392972007393837,
0.031902432441711426,
0.10257746279239655,
0.1458813101053238,
-0.06557618826627731,
0.0010101477382704616,
-0.12627558410167694,
-0.020819220691919327,
-0.003671275917440653,
-0.14462994039058685,
-0.16406771540641785,
-0.14367923140525818,
-0.020307598635554314,
-0.007623574696481228,
0.16376106441020966,
0.010874126106500626,
-0.10176274180412292,
0.020689772441983223,
0.07117842137813568,
-0.0017238855361938477,
0.037581928074359894,
0.24413208663463593,
-0.03150065615773201,
0.030528787523508072,
-0.1358095407485962,
0.05804380774497986,
0.02535671927034855,
-0.02430681511759758,
0.006115555297583342,
0.035086628049612045,
0.051731254905462265,
0.08872190862894058,
0.032597482204437256,
-0.02228965424001217,
-0.05170615389943123,
-0.16837286949157715,
-0.059704434126615524,
0.06088889390230179,
0.012957505881786346,
0.1252376288175583,
0.19954438507556915,
-0.13014627993106842,
0.04920937120914459,
-0.010576333850622177,
-0.017735067754983902,
-0.08224000781774521,
-0.0845308005809784,
-0.11673063039779663,
-0.1745714545249939,
-0.06966198235750198,
-0.08056666702032089,
-0.04606088250875473,
-0.03607412055134773,
0.012847211211919785,
-0.003258444368839264,
0.1220131665468216,
0.03417804837226868,
-0.060299403965473175,
-0.03513890132308006,
-0.02518610842525959,
0.005566992796957493,
0.011835976503789425,
-0.0276175606995821,
0.04794345423579216,
-0.05912461131811142,
-0.02462632767856121,
0.03963882476091385,
0.08487626910209656,
0.07433491200208664,
-0.06915967166423798,
-0.041520506143569946,
-0.028991110622882843,
0.08703465759754181,
0.045168809592723846,
-0.058005668222904205,
0.04801785945892334,
-0.04095524176955223,
0.035774603486061096,
0.033696748316287994,
-0.029523849487304688,
-0.1692570447921753,
-0.07852458953857422,
0.2242627739906311,
-0.009577792137861252,
0.03593044355511665,
0.041194237768650055,
-0.048590730875730515,
0.020515190437436104,
0.17426177859306335,
0.36830100417137146,
0.004814733751118183,
-0.02086584083735943,
-0.04827344790101051,
0.021415045484900475,
0.012593580409884453,
0.09993328154087067,
0.042252518236637115,
0.12358443439006805,
-0.0470382496714592,
0.08246584236621857,
-0.061754047870635986,
-0.05775589495897293,
-0.08072446286678314,
0.01749020628631115,
-0.047930967062711716,
-0.08165585994720459,
0.06729404628276825,
0.08322341740131378,
-0.01257283240556717,
-0.00943269394338131,
-0.014241617172956467,
-0.08835652470588684,
-0.051224809139966965,
-0.05196461081504822,
0.18222163617610931,
-0.0018789084861055017,
0.029983827844262123,
-0.0704633966088295,
0.031196756288409233,
0.1392495334148407,
-0.00840089749544859,
-0.10502033680677414,
-0.047992512583732605,
0.08917177468538284,
-0.06740519404411316,
-0.05166534334421158,
-0.008469377644360065,
0.021859677508473396,
0.08971075713634491,
0.04577532410621643,
-0.13058672845363617,
0.02130969986319542,
-0.010197187773883343,
0.08061901479959488,
0.06848607212305069,
-0.08071178942918777,
-0.03466883674263954,
-0.07759310305118561,
0.01442883163690567,
-0.16742220520973206,
0.04007129743695259,
0.016753848642110825,
-0.07683989405632019,
-0.054727569222450256,
0.08201167732477188,
-0.03758111968636513,
0.1282273232936859,
0.08582804352045059,
-0.04888881370425224,
0.014008257538080215,
-0.04185587167739868,
-0.008064010180532932,
0.05140945315361023,
0.09488902986049652,
0.0022481780033558607,
-0.1418031007051468,
0.009751452133059502,
0.07028406113386154,
0.06310373544692993,
-0.21320904791355133,
-0.08602435886859894,
-0.11995840072631836,
-0.03235352039337158,
-0.07792564481496811,
0.13293851912021637,
0.1833440214395523,
0.02184581570327282,
-0.015084709972143173,
-0.14539748430252075,
-0.0050497460179030895,
0.08324761688709259,
-0.052847977727651596,
-0.10243066400289536
] |
null | null | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "250.25 +/- 20.72", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | luyan2007/ppo-LunarLander-v2 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-09T09:27:09+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
39,
41,
17
] | [
"passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
0.03942384943366051,
0.04900386184453964,
-0.005304091144353151,
0.026427261531352997,
0.107408307492733,
-0.026511888951063156,
0.11188238859176636,
0.0814051404595375,
0.10722193866968155,
0.04762078449130058,
0.08338645845651627,
0.06030960753560066,
0.05080918222665787,
0.2571701407432556,
0.04754156619310379,
-0.22987541556358337,
0.036159250885248184,
-0.04869936779141426,
0.12395193427801132,
0.07178173214197159,
-0.0038484656251966953,
-0.06485428661108017,
0.020415637642145157,
-0.013290755450725555,
0.05367108806967735,
0.04282612353563309,
-0.01716216839849949,
-0.08207534998655319,
0.07169748842716217,
-0.06345846503973007,
0.06986866891384125,
0.07677983492612839,
0.13218913972377777,
-0.17832116782665253,
0.029566360637545586,
0.02571309357881546,
-0.07189024239778519,
0.01342033501714468,
0.008019951172173023,
0.05120139941573143,
0.17303818464279175,
0.019879888743162155,
0.07844575494527817,
-0.0025605305563658476,
-0.15412317216396332,
-0.018950799480080605,
0.0436202734708786,
0.12546207010746002,
0.08808347582817078,
0.04605821147561073,
0.01970590092241764,
0.17503218352794647,
-0.054352790117263794,
-0.028833400458097458,
0.21759237349033356,
-0.2881564497947693,
-0.031460098922252655,
0.321048766374588,
0.06997483223676682,
0.09725230932235718,
-0.07540661096572876,
-0.03619609400629997,
0.007783263456076384,
-0.013137873262166977,
-0.028666524216532707,
-0.07447073608636856,
0.17313385009765625,
0.05152064561843872,
-0.05057951435446739,
-0.09541505575180054,
0.16948209702968597,
0.006921638268977404,
0.0018855923553928733,
-0.019282981753349304,
0.009060598909854889,
0.07402525842189789,
-0.016097044572234154,
-0.07255112379789352,
0.057438433170318604,
0.05330665782094002,
0.019649166613817215,
-0.1435653269290924,
-0.10762494057416916,
-0.022740179672837257,
-0.008012006990611553,
0.17786912620067596,
-0.009255532175302505,
0.042902372777462006,
0.003065188182517886,
0.10384012013673782,
-0.12480384111404419,
-0.03354184702038765,
-0.0454259067773819,
-0.07565800100564957,
-0.0223417766392231,
-0.02058211714029312,
-0.03580251708626747,
0.07184842973947525,
0.11971849203109741,
0.027368178591132164,
0.09350208193063736,
0.047715865075588226,
-0.03206788748502731,
0.06343851238489151,
0.05555703118443489,
0.14222665131092072,
0.05807621404528618,
0.012854371219873428,
0.13179877400398254,
0.055213116109371185,
0.033023182302713394,
-0.0613492950797081,
-0.18252409994602203,
0.07489913702011108,
-0.07031869143247604,
0.007941240444779396,
0.12051256000995636,
-0.04480670019984245,
-0.1183447614312172,
-0.037500523030757904,
-0.017392054200172424,
-0.06224250793457031,
-0.025395862758159637,
0.0547584593296051,
-0.02883218228816986,
-0.03973718360066414,
0.0011496668448671699,
0.09384800493717194,
0.00953749567270279,
-0.1752052903175354,
0.03303423151373863,
-0.025042934343218803,
-0.10782608389854431,
0.009975161403417587,
0.0022444494534283876,
0.03394931182265282,
0.04408763721585274,
-0.11822668462991714,
-0.30899152159690857,
-0.07652641832828522,
0.05490870401263237,
-0.06516939401626587,
-0.18425025045871735,
-0.13193942606449127,
0.02454492449760437,
-0.09037084132432938,
-0.044885024428367615,
-0.12759265303611755,
-0.028549788519740105,
0.01743689924478531,
0.011519349180161953,
0.10758619755506516,
-0.0106219332665205,
-0.012188062071800232,
-0.1571401208639145,
0.008273907005786896,
-0.20951123535633087,
0.0890483483672142,
-0.019150104373693466,
0.037884220480918884,
-0.032381169497966766,
-0.07404014468193054,
0.030707746744155884,
0.052499737590551376,
-0.01474119070917368,
0.13510210812091827,
-0.15592676401138306,
-0.03691192343831062,
-0.007996266707777977,
-0.13611900806427002,
-0.04786273464560509,
-0.10358831286430359,
-0.04357128217816353,
0.13354332745075226,
0.018664736300706863,
0.15356586873531342,
-0.08709818124771118,
-0.0722038671374321,
0.20489206910133362,
-0.010411538183689117,
-0.12820468842983246,
-0.076752208173275,
0.10165707021951675,
0.021510310471057892,
-0.056606587022542953,
-0.02523270808160305,
-0.1839766949415207,
-0.0152357779443264,
-0.04550420492887497,
-0.047039128839969635,
0.01796751655638218,
-0.010888241231441498,
0.13837894797325134,
0.08494598418474197,
0.05018039792776108,
-0.06086122244596481,
-0.006730288732796907,
0.10779471695423126,
0.08823856711387634,
0.008680110797286034,
0.023406028747558594,
-0.05774238705635071,
0.09552932530641556,
-0.04003755748271942,
-0.0142367510125041,
-0.08283266425132751,
-0.036246106028556824,
-0.026256313547492027,
0.17507147789001465,
0.09440762549638748,
0.2257927656173706,
0.09567736834287643,
0.039160262793302536,
0.031270865350961685,
-0.13181598484516144,
-0.1425403207540512,
-0.0017254541162401438,
0.09020978957414627,
-0.14270411431789398,
-0.04119925573468208,
-0.08974775671958923,
-0.17768175899982452,
-0.12202505767345428,
0.0006432619411498308,
-0.17960017919540405,
0.06390921026468277,
0.05408334732055664,
-0.035177867859601974,
0.03272094577550888,
0.13032332062721252,
-0.011533179320394993,
-0.03967514634132385,
0.0831870287656784,
0.0379033200442791,
-0.041234664618968964,
-0.021742934361100197,
0.11885567009449005,
0.15673065185546875,
0.13124459981918335,
-0.03511447086930275,
0.004914294462651014,
0.07076404243707657,
-0.02309088408946991,
0.06539414077997208,
0.0558244064450264,
0.20973342657089233,
0.188301220536232,
0.038996949791908264,
0.008822928182780743,
-0.07048165798187256,
0.0855446457862854,
-0.0742373839020729,
-0.14302679896354675,
-0.05579735338687897,
0.08729292452335358,
0.016605578362941742,
0.023469142615795135,
0.08711627870798111,
0.024545932188630104,
0.09132762253284454,
0.15968108177185059,
0.01990218088030815,
-0.09659269452095032,
-0.050218869000673294,
0.01175848301500082,
0.027713103219866753,
0.04794301092624664,
-0.04514073207974434,
-0.00937939714640379,
0.017020760104060173,
-0.10303554683923721,
0.031789086759090424,
-0.1413339376449585,
-0.1358717679977417,
0.044326696544885635,
0.003906996920704842,
0.010907664895057678,
0.02786896750330925,
-0.0038291432429105043,
0.019039705395698547,
0.04351753741502762,
-0.06975466758012772,
0.047416772693395615,
-0.024745507165789604,
-0.020031947642564774,
0.03340689837932587,
-0.057257164269685745,
-0.205775648355484,
-0.17696654796600342,
0.00013708483311347663,
-0.09910997003316879,
0.10194740444421768,
0.018308809027075768,
-0.12373185902833939,
0.047737859189510345,
-0.05822649225592613,
0.027574289590120316,
-0.01875593699514866,
-0.049130141735076904,
0.10507171601057053,
0.1525275856256485,
-0.016146350651979446,
0.018018173053860664,
-0.04865182936191559,
-0.10157987475395203,
-0.19632206857204437,
0.0691583976149559,
0.04680244252085686,
0.014610917307436466,
0.10669491440057755,
0.018072687089443207,
0.02367905154824257,
-0.007674071006476879,
-0.016521066427230835,
-0.011659215204417706,
-0.08781040459871292,
0.31909599900245667,
0.04510033503174782,
-0.025173069909214973,
0.02041010931134224,
-0.0043001663871109486,
-0.028083480894565582,
0.03263787180185318,
-0.0985708013176918,
-0.07548979669809341,
-0.08774089068174362,
-0.04367410019040108,
-0.09784720093011856,
0.053299110382795334,
0.05916472524404526,
0.003188040340319276,
-0.07727594673633575,
0.04221395403146744,
0.11369874328374863,
-0.0923808291554451,
-0.07137343287467957,
0.07477962225675583,
0.0972946360707283,
-0.07331304252147675,
0.00012658814375754446,
0.00874367356300354,
0.023951783776283264,
0.037102166563272476,
0.06778035312891006,
-0.03966575115919113,
0.08589404821395874,
-0.19917890429496765,
0.0372927263379097,
0.106058269739151,
0.023754918947815895,
0.0638108178973198,
0.07643651217222214,
-0.1058402881026268,
-0.008500572293996811,
-0.032518330961465836,
-0.21341575682163239,
0.1668180525302887,
0.1355515867471695,
0.06788124144077301,
-0.025637222453951836,
-0.00461410591378808,
-0.0649740919470787,
0.05773647129535675,
0.02723747305572033,
-0.14758841693401337,
0.004883295856416225,
0.06064270809292793,
0.026899009943008423,
0.01614922471344471,
0.07971042394638062,
0.014697225764393806,
-0.1801026314496994,
-0.014406266622245312,
0.10730406641960144,
0.002390873385593295,
0.0053148469887673855,
-0.03175045922398567,
-0.1755964607000351,
0.0751047357916832,
0.004285442177206278,
0.07233936339616776,
-0.1676585078239441,
0.14297930896282196,
-0.10089799761772156,
0.07726949453353882,
-0.004285062663257122,
-0.021311495453119278,
0.02507244050502777,
-0.0541163794696331,
0.15163759887218475,
0.01058570109307766,
-0.021810131147503853,
-0.1200498715043068,
-0.1717042326927185,
-0.019227758049964905,
-0.11788936704397202,
-0.11679866164922714,
0.050424277782440186,
0.062185097485780716,
0.04923136904835701,
-0.061147067695856094,
0.1518532931804657,
-0.047422297298908234,
0.060713399201631546,
-0.06893875449895859,
-0.06755045056343079,
0.03764858841896057,
-0.12588608264923096,
-0.08176055550575256,
0.05573027580976486,
0.19166934490203857,
0.15833087265491486,
-0.02816431224346161,
-0.03472423925995827,
-0.047419581562280655,
-0.006212298292666674,
-0.007802055217325687,
0.0275666993111372,
0.023223137483000755,
0.07315318286418915,
-0.07681374251842499,
-0.11649256944656372,
0.033787861466407776,
-0.06713802367448807,
-0.055589709430933,
-0.015439179725944996,
0.1513158082962036,
0.04671623185276985,
0.07720734924077988,
-0.018946662545204163,
0.03887668624520302,
-0.001724981120787561,
-0.056474871933460236,
0.16197094321250916,
0.03885216265916824,
-0.05193585529923439,
0.06837689876556396,
0.053174007683992386,
0.043745119124650955,
0.03011113777756691,
-0.026783017441630363,
0.206032395362854,
0.1980147808790207,
0.014206883497536182,
0.2175983190536499,
0.03177616000175476,
-0.03772832080721855,
-0.1300560086965561,
-0.065880686044693,
-0.006372632458806038,
0.03559038043022156,
0.08070417493581772,
-0.18207235634326935,
-0.015011128038167953,
-0.05689644813537598,
-0.034518610686063766,
-0.15059494972229004,
-0.28553900122642517,
-0.05957856774330139,
0.20075850188732147,
0.14706264436244965,
0.27519428730010986,
-0.10432573407888412,
0.035197313874959946,
0.02663275972008705,
-0.04912831634283066,
-0.006501141935586929,
0.00018665487004909664,
0.10268618166446686,
-0.15421873331069946,
0.1176437959074974,
0.08486983180046082,
-0.019002694636583328,
0.01058861706405878,
-0.1619086116552353,
0.00936629343777895,
-0.12191236019134521,
0.05354422330856323,
0.1400289237499237,
-0.048128653317689896,
-0.054873593151569366,
0.14033560454845428,
-0.024562934413552284,
-0.22685599327087402,
-0.04648222774267197,
-0.043600670993328094,
-0.010640020482242107,
0.026607351377606392,
-0.1013401448726654,
0.04101909324526787,
0.1330099105834961,
0.009380043484270573,
0.1147187277674675,
0.11749245226383209,
-0.052566803991794586,
0.10792597383260727,
0.2257719188928604,
-0.018785694614052773,
0.04689010605216026,
-0.12743118405342102,
-0.0012336712097749114,
-0.028270328417420387,
0.013657891191542149,
-0.09504974633455276,
-0.09938385337591171,
0.02366873063147068,
0.02872389927506447,
0.009118586778640747,
0.0921793207526207,
-0.029922157526016235,
0.0759170651435852,
0.06817561388015747,
-0.13014446198940277,
-0.16288450360298157,
0.015828335657715797,
-0.007344507612287998,
0.08354310691356659,
0.00027861111448146403,
0.08878035843372345,
-0.11932205408811569,
-0.018093237653374672,
-0.03153328225016594,
-0.03319635987281799,
-0.130486860871315,
-0.07138993591070175,
0.06156524643301964,
0.028095467016100883,
-0.06602972000837326,
0.1398407518863678,
0.026440169662237167,
0.15942534804344177,
0.049197953194379807,
0.012499804608523846,
0.07227300107479095,
-0.05345509201288223,
0.1283530443906784,
0.13818155229091644,
-0.00868943240493536,
-0.05460423603653908,
-0.1013643890619278,
-0.10236792266368866,
0.08925779908895493,
-0.05773641914129257,
0.07476430386304855,
-0.14885357022285461,
-0.06675903499126434,
0.015772046521306038,
0.016141414642333984,
-0.09562095999717712,
0.02571965754032135,
-0.01625603251159191,
-0.18119946122169495,
0.056570518761873245,
-0.048285093158483505,
0.0440407395362854,
-0.06347788125276566,
-0.1110161691904068,
-0.17226378619670868,
0.06091433763504028,
0.08593481779098511,
-0.053876690566539764,
-0.12229149043560028,
0.011023230850696564,
-0.00012518465518951416,
-0.06341652572154999,
-0.05023367330431938,
0.09722746908664703,
-0.11020902544260025,
0.031452205032110214,
-0.012567701749503613,
0.08853451162576675,
-0.03510405123233795,
-0.011538895778357983,
0.044220831245183945,
-0.08039166033267975,
-0.009481523185968399,
0.03534642979502678,
-0.026372017338871956,
-0.04127239063382149,
-0.2689029574394226,
0.0036654395516961813,
0.0341104120016098,
0.02497158572077751,
0.07856601476669312,
0.011906822212040424,
0.021174922585487366,
0.03993808850646019,
-0.15396519005298615,
-0.013395369984209538,
0.14574195444583893,
-0.07689505815505981,
-0.022186370566487312,
0.05703273415565491,
-0.09054436534643173,
0.013882770203053951,
-0.030287226662039757,
0.1345842480659485,
0.023923413828015327,
0.06404478847980499,
-0.0851147472858429,
0.10106813907623291,
-0.1451139897108078,
-0.04998219385743141,
-0.01244612317532301,
0.09761348366737366,
0.07019034773111343,
-0.10272270441055298,
0.014697125181555748,
0.04210108891129494,
0.19416837394237518,
0.016384804621338844,
-0.0356343574821949,
-0.03396720811724663,
0.004015897400677204,
0.22076453268527985,
0.03044266067445278,
0.10457023978233337,
0.07281364500522614,
-0.026583973318338394,
0.12624378502368927,
0.09929762035608292,
0.11280370503664017,
-0.055645186454057693,
0.13904185593128204,
0.04667386785149574,
0.038641396909952164,
0.0614289753139019,
0.06836545467376709,
0.09098632633686066,
-0.0008288522367365658,
0.1138714924454689,
0.013811973854899406,
-0.02422109805047512,
-0.021335409954190254,
0.17759373784065247,
0.10501719266176224,
-0.14769648015499115,
0.029047364369034767,
-0.01258957851678133,
0.039933037012815475,
-0.014194529503583908,
-0.15634691715240479,
-0.07240267097949982,
-0.3315149247646332,
0.1226184144616127,
-0.07119352370500565,
0.019930170848965645,
0.007913772016763687,
-0.037425633519887924,
-0.03296699747443199,
-0.04477746784687042,
0.13151589035987854,
-0.013641550205647945,
-0.006079165264964104,
-0.04815853759646416,
-0.015360191464424133,
-0.11607866734266281,
-0.11200575530529022,
-0.013207737356424332,
-0.13671602308750153,
-0.010119039565324783,
0.05595948174595833,
0.003977729007601738,
0.01821410097181797,
-0.03142618387937546,
0.0024383175186812878,
0.06541839241981506,
-0.05751744285225868,
0.056182678788900375,
0.12097269296646118,
0.08766137808561325,
-0.1058853268623352,
0.031048951670527458,
0.2011747509241104,
0.04359564557671547,
-0.12483977526426315,
0.01449228823184967,
0.1819491684436798,
0.004885740112513304,
0.017068125307559967,
-0.006097703706473112,
-0.0540788508951664,
-0.07554277032613754,
0.1251034289598465,
0.08296554535627365,
-0.09985227137804031,
0.015833314508199692,
-0.0726347416639328,
-0.01594804972410202,
-0.06374675035476685,
0.10130585730075836,
0.09538925439119339,
0.04440245032310486,
-0.10621760785579681,
-0.08487539738416672,
-0.10891728103160858,
0.040588874369859695,
-0.08629853278398514,
-0.07311757653951645,
0.09629398584365845,
-0.07057105004787445,
-0.07029950618743896,
0.025521177798509598,
-0.17978744208812714,
-0.009467960335314274,
0.1711762249469757,
-0.24654000997543335,
-0.0916430801153183,
-0.10857923328876495,
0.14477859437465668,
0.016497576609253883,
0.1013975441455841,
-0.006207061931490898,
-0.007889035157859325,
-0.20577777922153473,
0.024890204891562462,
-0.05293011665344238,
-0.02073732763528824,
0.07814782857894897,
-0.09476397186517715,
0.22629831731319427,
-0.08276885002851486,
0.020940175279974937,
0.012659613974392414,
0.0870661810040474,
-0.030675338581204414,
0.09283176809549332,
-0.03660329803824425,
-0.12576518952846527,
-0.03620953485369682,
0.03001813031733036,
0.013904244638979435,
0.10071761906147003,
0.09772487729787827,
-0.03414725139737129,
0.03389119729399681,
0.09747414290904999,
0.04172342270612717,
-0.023843804374337196,
0.0360250361263752,
-0.17077107727527618,
0.02182629331946373,
-0.018498148769140244,
-0.06935930997133255,
0.03687669709324837,
-0.06603235751390457,
0.1639697551727295,
0.04022442549467087,
0.0670473501086235,
-0.036152735352516174,
0.0073931049555540085,
-0.014454689808189869,
-0.013775371946394444,
-0.026180334389209747,
-0.17259705066680908,
-0.10422050207853317,
-0.1347656100988388,
-0.012701659463346004,
-0.034971047192811966,
0.04591470584273338,
0.023234914988279343,
-0.0003200018545612693,
-0.014577031135559082,
-0.12090865522623062,
0.04360328987240791,
0.11146783083677292,
-0.04631396010518074,
-0.026193076744675636
] |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_gpt2_qa-model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6317
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6848 | 1.0 | 3000 | 0.6317 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilgpt2", "model-index": [{"name": "my_awesome_gpt2_qa-model", "results": []}]} | text-generation | SuYee189/my_awesome_gpt2_qa-model | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T09:28:13+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-distilgpt2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| my\_awesome\_gpt2\_qa-model
===========================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6317
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-distilgpt2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
77,
98,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-distilgpt2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
-0.0986085757613182,
0.08997813612222672,
-0.0019432202680036426,
0.10657034069299698,
0.12754769623279572,
0.002050186740234494,
0.15898528695106506,
0.12115010619163513,
-0.05721743777394295,
0.048343390226364136,
0.1439472883939743,
0.11462012678384781,
0.03209193795919418,
0.1370507925748825,
-0.05934174358844757,
-0.20537205040454865,
0.022699015215039253,
0.023165011778473854,
-0.04948268458247185,
0.12572094798088074,
0.09128960967063904,
-0.10865665972232819,
0.10473152250051498,
-0.012257128953933716,
-0.1548222303390503,
-0.006865049246698618,
0.012626908719539642,
-0.04654374346137047,
0.13552750647068024,
0.040237829089164734,
0.09054035693407059,
0.03260064870119095,
0.0646696612238884,
-0.18715211749076843,
0.01168159767985344,
0.06205242872238159,
-0.0036986367776989937,
0.08396004885435104,
0.0451529286801815,
0.013070999644696712,
0.09704656153917313,
-0.07646554708480835,
0.040949251502752304,
0.026733027771115303,
-0.13272081315517426,
-0.2087511271238327,
-0.07996966689825058,
0.02951916493475437,
0.09967414289712906,
0.09639811515808105,
-0.020023969933390617,
0.12088315933942795,
-0.01611161045730114,
0.09561377018690109,
0.20711363852024078,
-0.3159831464290619,
-0.06203675642609596,
0.06184285506606102,
0.058290936052799225,
0.11044448614120483,
-0.09549741446971893,
-0.0054691256955266,
0.053875379264354706,
0.030057666823267937,
0.14208905398845673,
-0.029667221009731293,
-0.006303336936980486,
-0.0010229983599856496,
-0.14267569780349731,
-0.042605262249708176,
0.17818565666675568,
0.05868057534098625,
-0.04894421622157097,
-0.07359340786933899,
-0.07692636549472809,
-0.13748465478420258,
-0.03135791793465614,
-0.006506679579615593,
0.04015513136982918,
-0.00520437303930521,
-0.0575796514749527,
-0.050863634794950485,
-0.11139721423387527,
-0.07563009113073349,
-0.029080640524625778,
0.10289405286312103,
0.02320951782166958,
-0.0026577457319945097,
-0.01818053610622883,
0.10589214414358139,
-0.03336409479379654,
-0.14070957899093628,
0.007294094655662775,
0.02354554459452629,
0.021607697010040283,
-0.030650511384010315,
-0.044709138572216034,
-0.09083519876003265,
0.030740339308977127,
0.12300878018140793,
-0.04968364164233208,
0.05634545907378197,
0.007723817136138678,
0.04841604456305504,
-0.10419026762247086,
0.17042656242847443,
-0.03695685788989067,
-0.047616906464099884,
0.025137733668088913,
0.09838662296533585,
0.06777621805667877,
-0.022060316056013107,
-0.1410573422908783,
0.027755239978432655,
0.11432770639657974,
0.024775035679340363,
-0.037203773856163025,
0.06840837001800537,
-0.0504240021109581,
-0.01022048108279705,
0.04905206337571144,
-0.0781075730919838,
0.007674145977944136,
-0.013790631666779518,
-0.04107145220041275,
-0.07881694287061691,
0.0223829448223114,
0.027674412354826927,
-0.0004490692517720163,
0.07979056239128113,
-0.0891280546784401,
-0.006172217894345522,
-0.07176212221384048,
-0.11215268820524216,
0.017434777691960335,
-0.07846364378929138,
0.017304260283708572,
-0.10878725349903107,
-0.2022445946931839,
-0.0059638721868395805,
0.05477902665734291,
-0.03232349455356598,
-0.053558893501758575,
-0.06362339109182358,
-0.07791606336832047,
0.019150065258145332,
-0.01696525700390339,
0.09054312855005264,
-0.07367568463087082,
0.10199974477291107,
0.04401446133852005,
0.05793600156903267,
-0.06492230296134949,
0.03342027589678764,
-0.10291947424411774,
0.04742996394634247,
-0.15751589834690094,
0.03058883361518383,
-0.042913272976875305,
0.07184740155935287,
-0.09476525336503983,
-0.0676242858171463,
-0.011395659297704697,
-0.004445169121026993,
0.06729082763195038,
0.10141368955373764,
-0.15421150624752045,
-0.06383953243494034,
0.18385200202465057,
-0.08675114810466766,
-0.16694802045822144,
0.1438412219285965,
-0.035441651940345764,
0.05209752917289734,
0.0687796026468277,
0.21516083180904388,
0.05683942884206772,
-0.08797651529312134,
0.004659139085561037,
0.0072015998885035515,
0.0691656768321991,
-0.05466311424970627,
0.09619295597076416,
-0.013598361983895302,
0.008229129947721958,
0.014084527269005775,
-0.056011851876974106,
0.0641581118106842,
-0.06344559043645859,
-0.07846824824810028,
-0.04160529002547264,
-0.10455065220594406,
0.03609515354037285,
0.0418674610555172,
0.053872134536504745,
-0.11495228111743927,
-0.09864962846040726,
0.021712632849812508,
0.07408960908651352,
-0.08150456100702286,
0.021128065884113312,
-0.06712526828050613,
0.10136017203330994,
-0.09166776388883591,
-0.004821437411010265,
-0.1345142275094986,
-0.049161165952682495,
0.01882181689143181,
0.003991410601884127,
0.01099186111241579,
-0.0030247855465859175,
0.0756334587931633,
0.08238633722066879,
-0.06843774765729904,
-0.038450416177511215,
-0.008406013250350952,
0.008887898176908493,
-0.12210775166749954,
-0.18299728631973267,
-0.011837559752166271,
-0.028849883005023003,
0.16308285295963287,
-0.23040032386779785,
0.05494852736592293,
0.01048927754163742,
0.0639885663986206,
0.03160051256418228,
-0.027755899354815483,
-0.02532040886580944,
0.04409293457865715,
-0.0486636608839035,
-0.07657669484615326,
0.06752762198448181,
0.025949081405997276,
-0.11412426829338074,
-0.006173230707645416,
-0.1779642552137375,
0.18593265116214752,
0.1312311887741089,
-0.050160638988018036,
-0.05685263127088547,
0.001033496344462037,
-0.03620263189077377,
-0.029888436198234558,
-0.03443555533885956,
-0.02116522379219532,
0.12048467248678207,
-0.00002999540993187111,
0.16054846346378326,
-0.09741970151662827,
-0.04416878521442413,
0.03481191396713257,
-0.04446950927376747,
0.009285538457334042,
0.11675716191530228,
0.07253286987543106,
-0.1187894344329834,
0.1608559787273407,
0.19433993101119995,
-0.05535750836133957,
0.13885273039340973,
-0.039679620414972305,
-0.06154993548989296,
-0.027598360553383827,
0.03084191493690014,
0.016221139580011368,
0.11539244651794434,
-0.11637528240680695,
0.008047182112932205,
0.010441352613270283,
0.0291624516248703,
0.017724422737956047,
-0.2130408138036728,
-0.033685922622680664,
0.046801552176475525,
-0.06232155114412308,
-0.004946489818394184,
-0.013006789609789848,
-0.03397039696574211,
0.09060141444206238,
0.007948532700538635,
-0.06684869527816772,
0.044912926852703094,
-0.0006433524540625513,
-0.0828629806637764,
0.2041734904050827,
-0.06964494287967682,
-0.17246556282043457,
-0.14877162873744965,
-0.07075425982475281,
-0.06359158456325531,
0.0396123081445694,
0.0748039111495018,
-0.06963798403739929,
-0.03989134728908539,
-0.12735049426555634,
0.006142152473330498,
0.007793165743350983,
0.016664456576108932,
0.015876343473792076,
-0.012553391978144646,
0.07911676168441772,
-0.09796279668807983,
-0.016230907291173935,
-0.01751558668911457,
-0.04288410395383835,
0.031263720244169235,
0.011599159799516201,
0.11802542954683304,
0.1389595866203308,
0.0001004597288556397,
0.0003057665308006108,
-0.03253435343503952,
0.24390645325183868,
-0.07400620728731155,
-0.004960670135915279,
0.14732463657855988,
-0.01439247839152813,
0.06053663045167923,
0.1286834180355072,
0.043894924223423004,
-0.10532988607883453,
0.021106939762830734,
0.02136589214205742,
-0.040929701179265976,
-0.19777926802635193,
-0.024059318006038666,
-0.04870985448360443,
0.029263563454151154,
0.09324949979782104,
0.0407760888338089,
0.04642488434910774,
0.06859558075666428,
0.010484855622053146,
0.08239337056875229,
0.005928135477006435,
0.08141618221998215,
0.10963030159473419,
0.036718156188726425,
0.12796568870544434,
-0.051333725452423096,
-0.04069716855883598,
0.04421985521912575,
0.01502880547195673,
0.1931120753288269,
0.018177222460508347,
0.1868302822113037,
0.04272247105836868,
0.1468748152256012,
-0.00508419144898653,
0.060346610844135284,
-0.019454924389719963,
-0.036934416741132736,
-0.02504039742052555,
-0.049309439957141876,
-0.03054097853600979,
0.034290604293346405,
-0.085396908223629,
0.04677559807896614,
-0.09580408781766891,
0.009341024793684483,
0.054136961698532104,
0.23355084657669067,
0.05781053379178047,
-0.3353150188922882,
-0.10009034723043442,
0.03542516008019447,
-0.014255547896027565,
-0.038974352180957794,
0.02417861483991146,
0.1456148773431778,
-0.05873063951730728,
0.0337359644472599,
-0.08037111163139343,
0.08832454681396484,
-0.04028288647532463,
0.05081404000520706,
0.045843008905649185,
0.07135124504566193,
-0.013996350578963757,
0.07718134671449661,
-0.2770472466945648,
0.26396992802619934,
0.0165583323687315,
0.0659385696053505,
-0.05370263010263443,
0.006935400888323784,
0.02179216593503952,
0.07682845741510391,
0.09220673888921738,
-0.012336128391325474,
-0.05345833674073219,
-0.18545731902122498,
-0.07908392697572708,
0.03055105172097683,
0.08664367347955704,
-0.057130876928567886,
0.10348422825336456,
-0.044732410460710526,
0.00316674099303782,
0.07535240799188614,
-0.0037635252811014652,
-0.05273224040865898,
-0.10882321745157242,
0.006089311558753252,
0.04915366321802139,
-0.013639046810567379,
-0.08537305146455765,
-0.07981456071138382,
-0.11434699594974518,
0.15945492684841156,
-0.04906246066093445,
-0.048011310398578644,
-0.09663380682468414,
0.045157916843891144,
0.051024727523326874,
-0.08293487131595612,
0.039817020297050476,
0.0022176173515617847,
0.09121562540531158,
0.015776803717017174,
-0.05550328269600868,
0.12460426986217499,
-0.07136375457048416,
-0.17359425127506256,
-0.053781457245349884,
0.12969310581684113,
0.0004262069705873728,
0.04399550333619118,
-0.005460153333842754,
0.01975960284471512,
-0.04002733156085014,
-0.08131178468465805,
0.030772684141993523,
-0.009600821882486343,
0.053583625704050064,
-0.013852600939571857,
-0.02296776883304119,
0.028925659134984016,
-0.058649249374866486,
-0.056385304778814316,
0.16425959765911102,
0.30773550271987915,
-0.07121255993843079,
0.014898577705025673,
0.05717676505446434,
-0.06169425696134567,
-0.18646931648254395,
0.01139160618185997,
0.011786646209657192,
0.00046874978579580784,
0.054908134043216705,
-0.14259466528892517,
0.07412026077508926,
0.09919910877943039,
-0.03224613144993782,
0.13131670653820038,
-0.3202671408653259,
-0.13678878545761108,
0.10061778873205185,
0.15122158825397491,
0.12445180863142014,
-0.17259623110294342,
-0.05413329228758812,
-0.042793724685907364,
-0.12730692327022552,
0.10303951799869537,
-0.12363465875387192,
0.11668696254491806,
-0.005902449134737253,
0.05323614552617073,
0.003041686024516821,
-0.05654789134860039,
0.1434897631406784,
-0.030018275603652,
0.08746947348117828,
-0.0719711035490036,
0.016428649425506592,
0.0655767172574997,
-0.062481366097927094,
0.026341507211327553,
-0.15298190712928772,
0.03354492038488388,
-0.064424067735672,
-0.032704755663871765,
-0.049999695271253586,
0.02663237601518631,
-0.03231602907180786,
-0.06273030489683151,
-0.03769716992974281,
0.017473816871643066,
0.04307301342487335,
-0.008233537897467613,
0.16084985435009003,
0.000003751750227820594,
0.14680951833724976,
0.13445943593978882,
0.07620102912187576,
-0.07763958722352982,
-0.02933558076620102,
-0.012762506492435932,
-0.03541119024157524,
0.04875236004590988,
-0.1662675142288208,
0.03228503465652466,
0.11204405128955841,
0.0010226320009678602,
0.14758442342281342,
0.06815725564956665,
-0.03656364232301712,
0.020485933870077133,
0.06429087370634079,
-0.17756161093711853,
-0.14988641440868378,
-0.027867842465639114,
-0.012791399843990803,
-0.12084703892469406,
0.05361215025186539,
0.1294306069612503,
-0.06682600826025009,
0.006649704184383154,
-0.0076759872026741505,
0.025650925934314728,
-0.035347387194633484,
0.17070195078849792,
0.0462312325835228,
0.051712650805711746,
-0.07986932247877121,
0.08350653946399689,
0.04865149408578873,
-0.07757418602705002,
0.02123856544494629,
0.04953779652714729,
-0.0921073779463768,
-0.04963630437850952,
0.028247322887182236,
0.16249386966228485,
-0.031466659158468246,
-0.05108993873000145,
-0.1467975229024887,
-0.11052004247903824,
0.04875039681792259,
0.1463867574930191,
0.0861193835735321,
0.01950318180024624,
-0.027792267501354218,
0.011987894773483276,
-0.1099875420331955,
0.11682318150997162,
0.03276686742901802,
0.08745308220386505,
-0.16042377054691315,
0.09397514909505844,
-0.001570739084854722,
0.014685302972793579,
-0.0188594963401556,
0.04139997065067291,
-0.10319890081882477,
-0.005208279471844435,
-0.12760129570960999,
-0.007333205081522465,
-0.032629963010549545,
-0.004767707083374262,
-0.008736012503504753,
-0.05127716809511185,
-0.05398977920413017,
0.014803677797317505,
-0.09625692665576935,
-0.034910060465335846,
0.022823143750429153,
0.04851261153817177,
-0.1210966557264328,
-0.035694465041160583,
0.025687558576464653,
-0.07444789260625839,
0.07775205373764038,
0.012944920919835567,
0.020000094547867775,
0.04888360574841499,
-0.15217941999435425,
0.04582219198346138,
0.054251447319984436,
0.008900708518922329,
0.02812117524445057,
-0.08249510824680328,
-0.022257907316088676,
-0.0005396218621172011,
0.03493242710828781,
0.0226623322814703,
0.09227370470762253,
-0.12490155547857285,
0.0062789879739284515,
-0.021817799657583237,
-0.04874572157859802,
-0.05586865544319153,
0.03255324810743332,
0.0661555752158165,
0.019728969782590866,
0.20413607358932495,
-0.0907059982419014,
0.013383019715547562,
-0.20550821721553802,
0.011873865500092506,
0.009058005176484585,
-0.12757828831672668,
-0.1064349040389061,
-0.061249759048223495,
0.04745827987790108,
-0.05684264376759529,
0.1244746744632721,
-0.007156989071518183,
0.04189635068178177,
0.0346393883228302,
-0.014492357149720192,
0.06747417896986008,
0.012995894998311996,
0.23645110428333282,
0.020217107608914375,
-0.04130592942237854,
0.027255559340119362,
0.03415955230593681,
0.11218606680631638,
0.0828137919306755,
0.16517558693885803,
0.146519273519516,
-0.03870048746466637,
0.10414224863052368,
0.023821890354156494,
-0.0412784144282341,
-0.15997150540351868,
0.04950587823987007,
-0.04181618615984917,
0.11224900931119919,
-0.017257926985621452,
0.2299097329378128,
0.10953433811664581,
-0.15662172436714172,
0.007334217894822359,
-0.05719972401857376,
-0.07523295283317566,
-0.10134010761976242,
-0.09281603246927261,
-0.09082332253456116,
-0.14530067145824432,
-0.0097508504986763,
-0.11004457622766495,
0.020195774734020233,
0.10920416563749313,
0.0074242413975298405,
-0.024439791217446327,
0.18076041340827942,
0.01729130744934082,
0.009788292460143566,
0.04379851371049881,
-0.005645227152854204,
-0.03805626556277275,
-0.07267142832279205,
-0.08870507031679153,
0.011752394028007984,
-0.013233345001935959,
0.03547000139951706,
-0.03770700469613075,
-0.028680933639407158,
0.03932765871286392,
-0.019744887948036194,
-0.10575874149799347,
0.0003969624813180417,
0.04182983189821243,
0.048561614006757736,
0.05171000584959984,
0.010926373302936554,
-0.0018018990522250533,
0.004776674788445234,
0.2400619238615036,
-0.07924089580774307,
-0.06385770440101624,
-0.08336854726076126,
0.19960348308086395,
0.004080876242369413,
-0.006554868537932634,
0.0013956257607787848,
-0.08832401782274246,
0.0368628054857254,
0.23037618398666382,
0.1791563481092453,
-0.09122468531131744,
-0.006365812383592129,
-0.018460527062416077,
-0.004560011904686689,
-0.027731213718652725,
0.09501644968986511,
0.11094089597463608,
0.027222489938139915,
-0.0816068947315216,
-0.02731669507920742,
-0.0377996452152729,
-0.0015016410034149885,
-0.04504120722413063,
0.06612454354763031,
0.02209075354039669,
0.013346727006137371,
-0.03942617028951645,
0.06764260679483414,
-0.035434361547231674,
-0.08639258146286011,
0.0018049711361527443,
-0.193098247051239,
-0.13755665719509125,
-0.016030097380280495,
0.10916516184806824,
-0.02053230255842209,
0.04625099524855614,
-0.029813939705491066,
0.019404789432883263,
0.05186726525425911,
-0.022329727187752724,
-0.07218950241804123,
-0.0594007782638073,
0.05832713469862938,
-0.10578055679798126,
0.22984111309051514,
-0.04100139066576958,
0.02632739394903183,
0.12841790914535522,
0.030676959082484245,
-0.09443619847297668,
0.09761887788772583,
0.04576054587960243,
-0.041274867951869965,
0.038343992084264755,
0.08430037647485733,
-0.03175000101327896,
0.10633519291877747,
0.054511770606040955,
-0.11440148949623108,
0.008252149447798729,
-0.041031718254089355,
-0.061199069023132324,
-0.03953747823834419,
-0.05150630697607994,
-0.06297067552804947,
0.1423831284046173,
0.1616995632648468,
-0.04900151863694191,
-0.003505146596580744,
-0.05120489001274109,
0.019552864134311676,
0.07171519100666046,
0.04598813131451607,
-0.027269983664155006,
-0.23648391664028168,
0.0064223469235002995,
0.07800038903951645,
-0.003927840851247311,
-0.31311357021331787,
-0.08236634731292725,
-0.016802629455924034,
-0.03261573985219002,
-0.09645407646894455,
0.08700011670589447,
0.12392418086528778,
0.04112180322408676,
-0.06478431820869446,
-0.05983486399054527,
-0.07681404799222946,
0.16228260099887848,
-0.12213313579559326,
-0.0987483337521553
] |
null | null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama-2-13b-fp16-english-to-hinglish-translation
This model is a fine-tuned version of [TheBloke/Llama-2-13B-fp16](https://huggingface.co/TheBloke/Llama-2-13B-fp16) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7183
- Rouge Scores: {'rouge1': 0.9211071676780294, 'rouge2': 0.8281733381382788, 'rougeL': 0.8654973199305598, 'rougeLsum': 0.921063419066345}
- Bleu Scores: [0.9430761623222732, 0.9291834498365306, 0.9116309260224186, 0.8930302852359673]
- Gen Len: 2048.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge Scores | Bleu Scores | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------:|:-------:|
| 0.7634 | 1.0 | 500 | 0.7242 | {'rouge1': 0.9226356216479343, 'rouge2': 0.8298878258164286, 'rougeL': 0.8653820285905314, 'rougeLsum': 0.9225028041599788} | [0.9438235001535462, 0.9299872780706577, 0.9124966958220146, 0.8939299935025186] | 2048.0 |
| 0.5477 | 2.0 | 1000 | 0.7183 | {'rouge1': 0.9211071676780294, 'rouge2': 0.8281733381382788, 'rougeL': 0.8654973199305598, 'rougeLsum': 0.921063419066345} | [0.9430761623222732, 0.9291834498365306, 0.9116309260224186, 0.8930302852359673] | 2048.0 |
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.16.2.dev0
- Tokenizers 0.15.1 | {"library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "TheBloke/Llama-2-13B-fp16", "model-index": [{"name": "llama-2-13b-fp16-english-to-hinglish-translation", "results": []}]} | null | DrishtiSharma/llama-2-13b-fp16-english-to-hinglish-translation | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:TheBloke/Llama-2-13B-fp16",
"region:us"
] | 2024-02-09T09:32:15+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-TheBloke/Llama-2-13B-fp16 #region-us
| llama-2-13b-fp16-english-to-hinglish-translation
================================================
This model is a fine-tuned version of TheBloke/Llama-2-13B-fp16 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7183
* Rouge Scores: {'rouge1': 0.9211071676780294, 'rouge2': 0.8281733381382788, 'rougeL': 0.8654973199305598, 'rougeLsum': 0.921063419066345}
* Bleu Scores: [0.9430761623222732, 0.9291834498365306, 0.9116309260224186, 0.8930302852359673]
* Gen Len: 2048.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 2
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 4
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* num\_epochs: 2
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* PEFT 0.8.2
* Transformers 4.38.0.dev0
* Pytorch 2.1.0+cu118
* Datasets 2.16.2.dev0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* num\\_epochs: 2\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu118\n* Datasets 2.16.2.dev0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#peft #safetensors #generated_from_trainer #base_model-TheBloke/Llama-2-13B-fp16 #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* num\\_epochs: 2\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu118\n* Datasets 2.16.2.dev0\n* Tokenizers 0.15.1"
] | [
40,
141,
4,
47
] | [
"passage: TAGS\n#peft #safetensors #generated_from_trainer #base_model-TheBloke/Llama-2-13B-fp16 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* num\\_epochs: 2\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu118\n* Datasets 2.16.2.dev0\n* Tokenizers 0.15.1"
] | [
-0.13338612020015717,
0.08091956377029419,
-0.0023608580231666565,
0.103648841381073,
0.14716942608356476,
0.006103804800659418,
0.11834689229726791,
0.10937859117984772,
-0.12193931639194489,
0.10405924916267395,
0.11421038210391998,
0.0684882253408432,
0.04105919599533081,
0.21255676448345184,
-0.04785078391432762,
-0.24572817981243134,
0.03324825316667557,
-0.02117842435836792,
-0.060885921120643616,
0.11672268807888031,
0.08512262254953384,
-0.14496850967407227,
0.0673227533698082,
-0.02597714401781559,
-0.19295498728752136,
-0.004568083211779594,
0.005892923101782799,
-0.02971140295267105,
0.11132598668336868,
0.008216653019189835,
0.15527598559856415,
0.017189159989356995,
0.11448502540588379,
-0.20849402248859406,
0.007819360122084618,
0.08087358623743057,
0.015618127770721912,
0.07998715341091156,
0.09182532131671906,
-0.006940925028175116,
0.06362225860357285,
-0.10163672268390656,
0.062167081981897354,
0.016826845705509186,
-0.16195407509803772,
-0.24845047295093536,
-0.12187651544809341,
0.03834036737680435,
0.10294289886951447,
0.06963346153497696,
-0.017829136922955513,
0.1428915560245514,
-0.06294837594032288,
0.07957672327756882,
0.26890188455581665,
-0.26531389355659485,
-0.08749917894601822,
0.03252862021327019,
0.013943024910986423,
0.092865951359272,
-0.11716187000274658,
-0.03697945922613144,
0.0729774758219719,
0.03764658421278,
0.11107805371284485,
0.003853763686493039,
-0.04796498268842697,
-0.007435695268213749,
-0.15107464790344238,
-0.03006868064403534,
0.09827117621898651,
0.03870781138539314,
-0.041963957250118256,
-0.01595880463719368,
-0.07269436120986938,
-0.2165505439043045,
-0.04655450955033302,
-0.018763244152069092,
0.044284455478191376,
-0.04435310140252113,
-0.05725673958659172,
0.02240867353975773,
-0.07219546288251877,
-0.09209942817687988,
-0.0112563231959939,
0.1542680263519287,
0.0542430505156517,
0.0060640620067715645,
-0.021243609488010406,
0.11300498247146606,
-0.029333917424082756,
-0.15430869162082672,
-0.002865544753149152,
0.0010213633067905903,
-0.042904481291770935,
-0.05940445140004158,
-0.03832346200942993,
-0.005807520356029272,
0.01325811818242073,
0.15162211656570435,
-0.16159269213676453,
0.06099192053079605,
-0.01376202329993248,
0.014713674783706665,
-0.08646585047245026,
0.11495288461446762,
-0.08381576836109161,
0.0032991955522447824,
0.005828980356454849,
0.07982130348682404,
0.018229780718684196,
0.000724136596545577,
-0.06992834061384201,
0.031532008200883865,
0.10920374095439911,
0.043971143662929535,
-0.06554724276065826,
0.012753143906593323,
-0.0482201986014843,
0.00252565648406744,
0.07397841662168503,
-0.10691318660974503,
0.056088171899318695,
0.018102753907442093,
-0.07003577798604965,
-0.043005943298339844,
-0.0013721362920477986,
0.0071099381893873215,
0.004049985203891993,
0.12634751200675964,
-0.09360836446285248,
0.0316307507455349,
-0.10094677656888962,
-0.13663659989833832,
0.021316448226571083,
-0.05307493731379509,
-0.003393398830667138,
-0.08632244914770126,
-0.14956073462963104,
-0.0397767648100853,
0.01550991926342249,
-0.06975965946912766,
-0.010774852707982063,
-0.05887030065059662,
-0.10016211122274399,
0.022069495171308517,
-0.01823904737830162,
0.0874638706445694,
-0.0683058574795723,
0.10139457881450653,
0.027304306626319885,
0.061525654047727585,
-0.028299639001488686,
0.02473505213856697,
-0.08060161769390106,
0.05361613631248474,
-0.2537131905555725,
0.04380360618233681,
-0.07086165994405746,
0.04862326383590698,
-0.1059998944401741,
-0.09866365790367126,
-0.008855205960571766,
-0.027208000421524048,
0.1312585324048996,
0.13497589528560638,
-0.1768312007188797,
-0.04149205610156059,
0.23456482589244843,
-0.11536634713411331,
-0.11278535425662994,
0.10297992080450058,
-0.05023838207125664,
-0.012145202606916428,
0.051341086626052856,
0.1794542670249939,
0.050734393298625946,
-0.13536563515663147,
-0.011213244870305061,
-0.07918153703212738,
0.09675975888967514,
-0.008808343671262264,
0.06140735745429993,
-0.011957172304391861,
0.0139837134629488,
0.0061951205134391785,
-0.02979418821632862,
0.052944764494895935,
-0.12039141356945038,
-0.06630317121744156,
-0.03575233370065689,
-0.08551143854856491,
0.04169558733701706,
0.047429509460926056,
0.04606054723262787,
-0.11406589299440384,
-0.0779222697019577,
0.09069885313510895,
0.09274601936340332,
-0.05840831249952316,
0.031157756224274635,
-0.051036328077316284,
0.0934365838766098,
-0.08847717195749283,
-0.03418201580643654,
-0.18090105056762695,
-0.09277337044477463,
0.037307366728782654,
-0.0030942989978939295,
-0.027159538120031357,
-0.060457345098257065,
0.08681584894657135,
0.10609392076730728,
-0.07303299754858017,
-0.03460560366511345,
-0.06993909925222397,
-0.0004059566417708993,
-0.11904676258563995,
-0.23733344674110413,
-0.031701430678367615,
-0.04295288026332855,
0.10142644494771957,
-0.22557970881462097,
0.022877605631947517,
0.02581297978758812,
0.13496527075767517,
0.03485376015305519,
-0.04489391669631004,
-0.02059110626578331,
0.08484138548374176,
-0.024278176948428154,
-0.08234969526529312,
0.030428800731897354,
0.0036713401786983013,
-0.035102568566799164,
-0.016905486583709717,
-0.1444908231496811,
0.14511381089687347,
0.11197037249803543,
0.007517911493778229,
-0.11612872779369354,
-0.037831537425518036,
-0.06693270057439804,
-0.03259959816932678,
-0.07553088665008545,
0.034235529601573944,
0.10781865566968918,
0.015995632857084274,
0.13015957176685333,
-0.09200720489025116,
-0.03714601695537567,
0.044364895671606064,
-0.01442719902843237,
0.03404475003480911,
0.10795445740222931,
0.07692160457372665,
-0.04783035069704056,
0.13913050293922424,
0.11564348638057709,
-0.052921734750270844,
0.1037641391158104,
-0.061041295528411865,
-0.0962543711066246,
-0.03771655261516571,
0.024560732766985893,
0.01780862733721733,
0.15833328664302826,
-0.010710343718528748,
0.023661987856030464,
0.0018743890104815364,
0.05599711090326309,
0.00990794226527214,
-0.19903750717639923,
-0.03325631096959114,
0.027529463171958923,
-0.05243448168039322,
-0.027290552854537964,
-0.04285436123609543,
0.021236686035990715,
0.11524692922830582,
0.00019688866450451314,
-0.05543839558959007,
-0.010355156846344471,
0.008083222433924675,
-0.0836203470826149,
0.20780044794082642,
-0.10892387479543686,
-0.07273043692111969,
-0.08391738682985306,
-0.023668553680181503,
-0.02567457966506481,
0.0077275726944208145,
0.051319658756256104,
-0.10098953545093536,
-0.024191610515117645,
-0.08963193744421005,
-0.008153675124049187,
0.01490055862814188,
0.03255009651184082,
-0.0007296670810319483,
-0.015607137233018875,
0.07724850624799728,
-0.09251953661441803,
0.0073905703611671925,
-0.027593178674578667,
-0.027130650356411934,
0.0469653345644474,
0.03672144562005997,
0.12493447959423065,
0.15682737529277802,
-0.017678093165159225,
0.023045971989631653,
-0.035989467054605484,
0.22823213040828705,
-0.07470770180225372,
-0.011348335072398186,
0.06703560054302216,
-0.0019062962383031845,
0.06171347200870514,
0.1516139805316925,
0.048840489238500595,
-0.12289905548095703,
0.021163221448659897,
0.036312784999608994,
-0.03289387375116348,
-0.2317335605621338,
-0.04475461691617966,
-0.028074827045202255,
-0.026449359953403473,
0.09837330132722855,
0.027717560529708862,
-0.0008462626719847322,
0.042223963886499405,
0.002619459293782711,
0.032865576446056366,
-0.02378542535007,
0.08214002102613449,
0.025402529165148735,
0.05842418223619461,
0.11420822143554688,
-0.03165018558502197,
-0.026954490691423416,
0.04521899297833443,
-0.029851408675312996,
0.23476749658584595,
-0.021511513739824295,
0.08746788650751114,
0.046068739145994186,
0.17307882010936737,
0.00037380849244073033,
0.0821542963385582,
0.0230124369263649,
-0.03324244171380997,
0.010890422388911247,
-0.06695807725191116,
-0.008960389532148838,
0.02871149778366089,
-0.07286990433931351,
0.053440067917108536,
-0.1324053257703781,
0.014696006663143635,
0.05453076213598251,
0.2945635914802551,
0.08539090305566788,
-0.34328868985176086,
-0.08041080832481384,
-0.002455686451867223,
0.011471138335764408,
-0.04729646444320679,
0.00848125945776701,
0.15746991336345673,
-0.05702253803610802,
0.053350791335105896,
-0.0672881081700325,
0.0848143994808197,
-0.01152817066758871,
0.004451072309166193,
0.05126199126243591,
0.10326191782951355,
-0.03651663288474083,
0.028072182089090347,
-0.2411201149225235,
0.29096508026123047,
0.023248253390192986,
0.08770627528429031,
-0.01273306179791689,
-0.00811387225985527,
0.011581788770854473,
0.053462304174900055,
0.06764435023069382,
-0.005643303971737623,
-0.1298077553510666,
-0.21360015869140625,
-0.1139587014913559,
0.023596586659550667,
0.12442685663700104,
-0.011702137999236584,
0.12324468791484833,
-0.005806185305118561,
0.002571034012362361,
0.05654948204755783,
-0.06999196857213974,
-0.12099940329790115,
-0.0399557389318943,
-0.019031347706913948,
0.0023493196349591017,
0.006824766751378775,
-0.08366993814706802,
-0.1022578626871109,
-0.06642021983861923,
0.11561792343854904,
-0.034848082810640335,
-0.03201829642057419,
-0.1403864473104477,
0.10152900218963623,
0.11445114016532898,
-0.07175751030445099,
0.04791216179728508,
0.022551989182829857,
0.10940045863389969,
0.013898990117013454,
-0.019101042300462723,
0.12890185415744781,
-0.05710138753056526,
-0.20708374679088593,
-0.07323536276817322,
0.10990095883607864,
0.07243943959474564,
0.04961014539003372,
-0.012338300235569477,
0.04235895723104477,
0.03348428010940552,
-0.0999954417347908,
0.030621225014328957,
0.019146770238876343,
0.07808253169059753,
0.03474235162138939,
-0.03731471300125122,
0.057947590947151184,
-0.053379882127046585,
-0.017546482384204865,
0.11013442277908325,
0.33605194091796875,
-0.09935716539621353,
0.008455527015030384,
0.0402035266160965,
-0.06485938280820847,
-0.17050567269325256,
0.06720297038555145,
0.08100128918886185,
0.005706625524908304,
0.02121601067483425,
-0.15972378849983215,
0.07293710112571716,
0.1291353553533554,
-0.02608577534556389,
0.11911643296480179,
-0.2903199791908264,
-0.13420692086219788,
0.07229840755462646,
0.1403183937072754,
0.052274059504270554,
-0.16954033076763153,
-0.03968572989106178,
-0.011538656428456306,
-0.11325114220380783,
0.06238676607608795,
-0.14011521637439728,
0.07786742597818375,
-0.02048858068883419,
0.033696290105581284,
0.01376159768551588,
-0.0578901544213295,
0.14588187634944916,
0.00516380462795496,
0.12602880597114563,
-0.03913598507642746,
0.018596604466438293,
0.05941639840602875,
-0.0770624428987503,
0.012791838496923447,
-0.009046760387718678,
0.048046406358480453,
-0.07549974322319031,
-0.010436159558594227,
-0.0824691653251648,
0.009782484732568264,
-0.044165048748254776,
-0.04962959140539169,
-0.05125923827290535,
0.03581364452838898,
0.056021127849817276,
-0.01656828448176384,
0.12044604867696762,
0.011091342195868492,
0.18208302557468414,
0.1172824501991272,
0.03648444637656212,
-0.019121309742331505,
-0.012219618074595928,
0.03025566227734089,
-0.03198754042387009,
0.04798106104135513,
-0.1663898378610611,
0.03683679550886154,
0.1450609713792801,
0.02845681831240654,
0.11496081948280334,
0.06588660925626755,
-0.06414856761693954,
0.0181947760283947,
0.06714867055416107,
-0.1472216099500656,
-0.0994933694601059,
0.027236640453338623,
0.014518923126161098,
-0.12025874853134155,
0.029467737302184105,
0.11344577372074127,
-0.07668330520391464,
-0.01458507589995861,
-0.013840156607329845,
0.02739565260708332,
-0.03404133394360542,
0.22876638174057007,
0.06811504811048508,
0.06423183530569077,
-0.09880175441503525,
0.0846727043390274,
0.05373577028512955,
-0.08987746387720108,
0.0289505273103714,
0.08600623905658722,
-0.08034252375364304,
-0.005987904965877533,
0.12115857750177383,
0.16552039980888367,
-0.0315299890935421,
-0.04612774774432182,
-0.13996805250644684,
-0.12024527788162231,
0.0762249305844307,
0.19098390638828278,
0.05537329241633415,
0.022593384608626366,
0.02460501156747341,
0.014285856857895851,
-0.14474792778491974,
0.09815584123134613,
0.057590682059526443,
0.09766711294651031,
-0.1375201940536499,
0.15801014006137848,
-0.015359161421656609,
0.00906500592827797,
-0.01955953985452652,
0.048154231160879135,
-0.13091275095939636,
0.01166748907417059,
-0.15287576615810394,
-0.00038178812246769667,
-0.03699345886707306,
-0.014703848399221897,
-0.010044940747320652,
-0.06257184594869614,
-0.059116337448358536,
0.018881339579820633,
-0.1123090609908104,
-0.031193222850561142,
-0.002025555819272995,
0.01914927549660206,
-0.14416871964931488,
-0.02884943224489689,
0.01984735019505024,
-0.08598041534423828,
0.06581895798444748,
0.047212108969688416,
0.044420599937438965,
0.045058850198984146,
-0.12007289379835129,
0.020384185016155243,
0.06280604749917984,
-0.037700481712818146,
0.07026835530996323,
-0.12166640907526016,
-0.013980974443256855,
-0.02785925194621086,
0.04385427013039589,
0.02258085459470749,
0.05427004024386406,
-0.12641474604606628,
0.007964715361595154,
-0.024962440133094788,
-0.051113422960042953,
-0.027253296226263046,
0.024573897942900658,
0.08557676523923874,
0.01636887900531292,
0.12584659457206726,
-0.09966849535703659,
0.040702611207962036,
-0.22726580500602722,
-0.028896665200591087,
-0.022274993360042572,
-0.07104668766260147,
-0.07278493791818619,
-0.015617335215210915,
0.0880061686038971,
-0.04357773810625076,
0.10367274284362793,
-0.013565531931817532,
0.07436659187078476,
0.04232058674097061,
-0.09368940442800522,
-0.017513208091259003,
0.05752301961183548,
0.17602188885211945,
0.03071831353008747,
-0.056369997560977936,
0.060351092368364334,
0.04817986115813255,
0.1047947108745575,
0.12084222584962845,
0.22150473296642303,
0.15284664928913116,
0.025916146114468575,
0.08571140468120575,
0.0351988859474659,
-0.10852805525064468,
-0.11438973993062973,
0.04057367146015167,
-0.051378197968006134,
0.07758155465126038,
-0.013288585469126701,
0.14979928731918335,
0.10768112540245056,
-0.18448667228221893,
0.024169795215129852,
-0.04817240685224533,
-0.09247756749391556,
-0.11782120168209076,
0.03405177593231201,
-0.09291678667068481,
-0.1694285273551941,
-0.0016447912203148007,
-0.13043153285980225,
0.027088597416877747,
0.10419805347919464,
0.021398181095719337,
0.030463378876447678,
0.17554612457752228,
0.09081431478261948,
0.04476349800825119,
0.05019998550415039,
0.02699645608663559,
0.0003184415400028229,
-0.02946048602461815,
-0.10059049725532532,
0.03161568567156792,
-0.045762546360492706,
0.044312577694654465,
-0.029952513054013252,
-0.03967626020312309,
0.059488262981176376,
0.014853189699351788,
-0.10918345302343369,
0.02854679711163044,
0.033121537417173386,
0.045959122478961945,
0.06294862926006317,
0.031834959983825684,
0.007002113852649927,
-0.023655522614717484,
0.21483387053012848,
-0.06841502338647842,
-0.0396297350525856,
-0.12398987263441086,
0.27321672439575195,
0.043957632035017014,
0.009976421482861042,
0.016484493389725685,
-0.10027504712343216,
0.009236169047653675,
0.12195295840501785,
0.13532419502735138,
-0.056048642843961716,
-0.00748928589746356,
-0.020044006407260895,
-0.018164411187171936,
-0.038199130445718765,
0.10195694863796234,
0.1028391420841217,
0.007560939993709326,
-0.09721620380878448,
-0.024921713396906853,
-0.05790209025144577,
-0.029299482703208923,
-0.025529274716973305,
0.023099353536963463,
0.021074464544653893,
0.015552240423858166,
-0.06068321317434311,
0.08420824259519577,
-0.04333753138780594,
-0.08677798509597778,
0.09008116275072098,
-0.18593937158584595,
-0.17470386624336243,
-0.00762242591008544,
0.018128585070371628,
0.006350830662995577,
0.057321615517139435,
-0.030987529084086418,
-0.023461854085326195,
0.12399216741323471,
-0.03513650968670845,
-0.044672999531030655,
-0.14567427337169647,
0.06050117686390877,
-0.10691794008016586,
0.2195340245962143,
-0.029182787984609604,
0.03119388408958912,
0.12346747517585754,
0.033462800085544586,
-0.12157722562551498,
0.04439575970172882,
0.06318940967321396,
-0.07287606596946716,
-0.006136727519333363,
0.129721000790596,
-0.05777188017964363,
0.07331793010234833,
0.04707808420062065,
-0.1347009241580963,
0.002518267370760441,
-0.05324087664484978,
-0.05085523799061775,
-0.057991236448287964,
-0.04197559505701065,
-0.03675796464085579,
0.12462769448757172,
0.19411049783229828,
-0.053194064646959305,
0.04579911381006241,
-0.04247881844639778,
0.037086568772792816,
0.04302642121911049,
0.09841196238994598,
-0.01905522122979164,
-0.2664276659488678,
0.040884166955947876,
0.12327959388494492,
0.00012968531518708915,
-0.25305816531181335,
-0.07376037538051605,
0.030710823833942413,
-0.06567613780498505,
-0.08441823720932007,
0.1256631314754486,
0.04207060858607292,
0.0720604732632637,
-0.0610562227666378,
-0.12686991691589355,
-0.0690448135137558,
0.17376376688480377,
-0.12219621241092682,
-0.09123057872056961
] |
null | null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | text-generation | DrishtiSharma/llama-2-13b-fp16-english-to-hinglish-translation-merged | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2024-02-09T09:39:36+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
59,
6,
3,
82,
28,
3,
4,
9,
9,
10,
42,
20,
3,
4,
5,
9,
11,
13,
3,
12,
5,
4,
5,
3,
4,
9,
53,
9,
8,
6,
3,
14,
8,
7,
9,
4
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
] | [
-0.049007222056388855,
0.16460949182510376,
-0.005271392408758402,
0.021910345181822777,
0.09685911983251572,
0.01403510570526123,
0.07018975168466568,
0.11002060770988464,
-0.02425350993871689,
0.11399492621421814,
0.03344893455505371,
0.09780009090900421,
0.11368958652019501,
0.1498505026102066,
-0.002398149576038122,
-0.23227156698703766,
0.04924226179718971,
-0.1249755248427391,
-0.03746527433395386,
0.1159956082701683,
0.15001481771469116,
-0.10170940309762955,
0.07611104100942612,
-0.029819702729582787,
-0.008722295984625816,
-0.032589927315711975,
-0.056551046669483185,
-0.04997202008962631,
0.051094699651002884,
0.07382578402757645,
0.06793182343244553,
0.004094683099538088,
0.09450557827949524,
-0.2669448256492615,
0.0197003111243248,
0.0730973482131958,
-0.002068581758067012,
0.07547242939472198,
0.054895199835300446,
-0.07525460422039032,
0.09282654523849487,
-0.0507965162396431,
0.1469351053237915,
0.08020289987325668,
-0.09152709692716599,
-0.19188682734966278,
-0.0887833908200264,
0.10164182633161545,
0.18469172716140747,
0.045696184039115906,
-0.022488808259367943,
0.09940612316131592,
-0.08621317893266678,
0.011039474047720432,
0.05154034495353699,
-0.06937182694673538,
-0.05223534256219864,
0.06355299055576324,
0.08018788695335388,
0.07678371667861938,
-0.12301702797412872,
-0.02094447799026966,
0.008637533523142338,
0.00831096712499857,
0.08201737701892853,
0.023290244862437248,
0.1510206013917923,
0.03883988782763481,
-0.12744688987731934,
-0.050009194761514664,
0.10665731877088547,
0.041741468012332916,
-0.04784774035215378,
-0.25138479471206665,
-0.030326439067721367,
-0.027732934802770615,
-0.029999805614352226,
-0.03873695060610771,
0.04263332113623619,
-0.0072723389603197575,
0.0826614573597908,
-0.008116158656775951,
-0.07679495960474014,
-0.03798604756593704,
0.06191713735461235,
0.060809630900621414,
0.026244111359119415,
-0.011753023602068424,
0.010934822261333466,
0.1174238994717598,
0.10631082952022552,
-0.12367359548807144,
-0.051516905426979065,
-0.06431761384010315,
-0.07867198437452316,
-0.04216236248612404,
0.03455616533756256,
0.041060756891965866,
0.049376390874385834,
0.2486443817615509,
0.017620395869016647,
0.05382118001580238,
0.03803925961256027,
0.010167144238948822,
0.06406087428331375,
0.11435336619615555,
-0.061582546681165695,
-0.09715550392866135,
-0.025186026468873024,
0.08966731280088425,
0.01176387071609497,
-0.04024789482355118,
-0.05783011019229889,
0.06293477863073349,
0.016524890437722206,
0.1202789843082428,
0.09223750233650208,
0.003793274285271764,
-0.07138240337371826,
-0.06413803994655609,
0.1937950700521469,
-0.1626761257648468,
0.04747059941291809,
0.034180231392383575,
-0.038511235266923904,
-0.0016249394975602627,
0.008853171020746231,
0.024325255304574966,
-0.021725021302700043,
0.08937039971351624,
-0.05618007108569145,
-0.041590798646211624,
-0.10983981937170029,
-0.035744234919548035,
0.03192625194787979,
0.009910091757774353,
-0.03217151761054993,
-0.031847331672906876,
-0.08444786816835403,
-0.06831640005111694,
0.09424425661563873,
-0.07356466352939606,
-0.053753651678562164,
-0.016938211396336555,
-0.07437273859977722,
0.024786023423075676,
0.01960081420838833,
0.07747352123260498,
-0.02004585787653923,
0.042900070548057556,
-0.05549933388829231,
0.06014169380068779,
0.10937028378248215,
0.033117540180683136,
-0.05445994809269905,
0.0621645413339138,
-0.2418462336063385,
0.0997670441865921,
-0.06829129904508591,
0.05325306951999664,
-0.15072302520275116,
-0.02465333603322506,
0.04913770779967308,
0.008168290369212627,
-0.010590006597340107,
0.13754788041114807,
-0.21924975514411926,
-0.027699807658791542,
0.1631394773721695,
-0.09464818984270096,
-0.07676627486944199,
0.05986984074115753,
-0.052457790821790695,
0.10692904144525528,
0.04047565534710884,
-0.026259733363986015,
0.06162377819418907,
-0.13397987186908722,
0.0005626814090646803,
-0.045883387327194214,
-0.01928110048174858,
0.15731419622898102,
0.07587230950593948,
-0.06994020938873291,
0.07348526269197464,
0.023750323802232742,
-0.023168303072452545,
-0.046913031488657,
-0.017583578824996948,
-0.1088033989071846,
0.010729904286563396,
-0.061985816806554794,
0.01937699131667614,
-0.025795195251703262,
-0.09332547336816788,
-0.028493179008364677,
-0.17521639168262482,
-0.020266273990273476,
0.08516935259103775,
-0.009352635592222214,
-0.01925206556916237,
-0.11787936836481094,
0.015734510496258736,
0.03501737862825394,
0.002549536293372512,
-0.1319509893655777,
-0.05043373629450798,
0.02751830592751503,
-0.16075198352336884,
0.033688947558403015,
-0.05403051897883415,
0.0491553395986557,
0.03133281692862511,
-0.031412381678819656,
-0.028679344803094864,
0.022094380110502243,
0.004997676704078913,
-0.014611656777560711,
-0.24550160765647888,
-0.026604164391756058,
-0.02145342156291008,
0.16796952486038208,
-0.21640902757644653,
0.0374150350689888,
0.07194960117340088,
0.15254895389080048,
0.008589224889874458,
-0.038006994873285294,
0.002335198922082782,
-0.075041763484478,
-0.03255171701312065,
-0.06050482019782066,
-0.009038056246936321,
-0.03572068363428116,
-0.05482286959886551,
0.04863523691892624,
-0.16824471950531006,
-0.029467429965734482,
0.1015508770942688,
0.06473538279533386,
-0.13604550063610077,
-0.019663551822304726,
-0.03585261106491089,
-0.042308371514081955,
-0.05517838895320892,
-0.05935737490653992,
0.10260266810655594,
0.05827045813202858,
0.04566904529929161,
-0.06485172361135483,
-0.0747392401099205,
0.0017082487465813756,
-0.019673427566885948,
-0.022536588832736015,
0.09213293343782425,
0.07581926137208939,
-0.12331884354352951,
0.09213830530643463,
0.10402927547693253,
0.08686267584562302,
0.0966128259897232,
-0.023164015263319016,
-0.08361977338790894,
-0.049845483154058456,
0.02228725142776966,
0.017598064616322517,
0.13447505235671997,
-0.007804518099874258,
0.05406574159860611,
0.04160919412970543,
-0.013909573666751385,
0.009752067737281322,
-0.09242741018533707,
0.032518286257982254,
0.03427431732416153,
-0.01857241988182068,
0.041615914553403854,
-0.039849672466516495,
0.019975949078798294,
0.09018522500991821,
0.046917494386434555,
0.04021155461668968,
0.014107138849794865,
-0.04660527780652046,
-0.11187547445297241,
0.16612006723880768,
-0.12780359387397766,
-0.23512837290763855,
-0.1463187336921692,
0.0034277087543159723,
0.03630480915307999,
-0.009390040300786495,
0.0017278295708820224,
-0.06397698074579239,
-0.11876852810382843,
-0.09194197505712509,
0.010153552517294884,
0.04896695911884308,
-0.0851091742515564,
-0.0603698305785656,
0.05686335638165474,
0.04057794436812401,
-0.14546048641204834,
0.019262617453932762,
0.04933769255876541,
-0.09224124997854233,
-0.009894786402583122,
0.08289197087287903,
0.06857553124427795,
0.18091025948524475,
0.013082148507237434,
-0.02271466888487339,
0.03428078070282936,
0.21755947172641754,
-0.13586747646331787,
0.11420658230781555,
0.1426045000553131,
-0.09194567799568176,
0.08309654146432877,
0.19839057326316833,
0.04078111797571182,
-0.10157861560583115,
0.032499175518751144,
0.018653791397809982,
-0.030491048470139503,
-0.24355553090572357,
-0.07171683013439178,
0.00034942623460665345,
-0.057900771498680115,
0.07530075311660767,
0.09018687158823013,
0.09155713021755219,
0.01583298109471798,
-0.0946493074297905,
-0.07830986380577087,
0.05305508151650429,
0.10324970632791519,
0.020061472430825233,
-0.013236436992883682,
0.09051742404699326,
-0.03375976160168648,
0.017617853358387947,
0.09066354483366013,
0.0011531224008649588,
0.17065346240997314,
0.05820678174495697,
0.18275249004364014,
0.07604338973760605,
0.07338658720254898,
0.01378361415117979,
0.01180104911327362,
0.019032908603549004,
0.02708563208580017,
-0.004741039127111435,
-0.08538748323917389,
-0.01599922962486744,
0.12008915096521378,
0.07424698024988174,
0.015674617141485214,
0.014355434104800224,
-0.04089333862066269,
0.08203015476465225,
0.17435193061828613,
-0.001506963511928916,
-0.1824604868888855,
-0.06271602213382721,
0.08220411837100983,
-0.09449198096990585,
-0.10147359222173691,
-0.02445729449391365,
0.03089604340493679,
-0.17088350653648376,
0.023070847615599632,
-0.016430631279945374,
0.11182350665330887,
-0.13931094110012054,
-0.019696295261383057,
0.0640200525522232,
0.07118809968233109,
-0.00031885437783785164,
0.05944213643670082,
-0.16128569841384888,
0.10404066741466522,
0.013166810385882854,
0.06712377816438675,
-0.09715772420167923,
0.10046469420194626,
-0.006883090827614069,
-0.013416164554655552,
0.13275203108787537,
0.008256223052740097,
-0.07161599397659302,
-0.07921489328145981,
-0.09379399567842484,
-0.009093280881643295,
0.12668752670288086,
-0.14835532009601593,
0.08585991710424423,
-0.035368360579013824,
-0.04256736859679222,
0.0022144275717437267,
-0.10755012929439545,
-0.12217973172664642,
-0.1874755620956421,
0.05520224943757057,
-0.1321607530117035,
0.039849888533353806,
-0.10649667680263519,
-0.03462952747941017,
-0.029491933062672615,
0.1882491409778595,
-0.22971367835998535,
-0.06835493445396423,
-0.15157760679721832,
-0.09785088151693344,
0.14553189277648926,
-0.04969761520624161,
0.08694402873516083,
-0.005991519894450903,
0.18016821146011353,
0.022223925217986107,
-0.021585633978247643,
0.09859558939933777,
-0.09382225573062897,
-0.1963716447353363,
-0.08180448412895203,
0.15751656889915466,
0.13459575176239014,
0.03521031513810158,
-0.0027760460507124662,
0.037876322865486145,
-0.01856307126581669,
-0.12259240448474884,
0.021658578887581825,
0.17797763645648956,
0.0652514174580574,
0.02310643345117569,
-0.026529761031270027,
-0.11104881763458252,
-0.06772379577159882,
-0.033685971051454544,
0.03064778819680214,
0.18449479341506958,
-0.0722544714808464,
0.18419069051742554,
0.143813356757164,
-0.05867353826761246,
-0.1976030021905899,
0.008879725821316242,
0.03365374729037285,
0.007196295075118542,
0.03445420414209366,
-0.20255140960216522,
0.0841677114367485,
0.00034181843511760235,
-0.05190233513712883,
0.13343381881713867,
-0.17106693983078003,
-0.15042030811309814,
0.07339101284742355,
0.03619921952486038,
-0.19460853934288025,
-0.11963265389204025,
-0.08913769572973251,
-0.05391303077340126,
-0.18051348626613617,
0.10290905088186264,
0.03496568650007248,
0.008035079576075077,
0.03376363217830658,
0.028494013473391533,
0.01669638603925705,
-0.03928735852241516,
0.1920013129711151,
-0.026591487228870392,
0.029855716973543167,
-0.08456290513277054,
-0.06990274786949158,
0.04655740037560463,
-0.05482156574726105,
0.0760476216673851,
-0.027013001963496208,
0.011612839996814728,
-0.10561433434486389,
-0.042526841163635254,
-0.029051896184682846,
0.013453613966703415,
-0.0963861495256424,
-0.08940120041370392,
-0.0490599125623703,
0.09310506284236908,
0.09519506990909576,
-0.035876575857400894,
-0.03684677556157112,
-0.07069114595651627,
0.039579302072525024,
0.18676936626434326,
0.17657315731048584,
0.04523694887757301,
-0.0789421945810318,
-0.005537794437259436,
-0.011924253776669502,
0.04352729767560959,
-0.21637341380119324,
0.06442029029130936,
0.05013522133231163,
0.017847778275609016,
0.11767403781414032,
-0.02045002020895481,
-0.1556767225265503,
-0.07006701827049255,
0.06328949332237244,
-0.06132598593831062,
-0.1951322853565216,
0.005576360039412975,
0.054395273327827454,
-0.16848263144493103,
-0.048018258064985275,
0.04364382475614548,
-0.004054433200508356,
-0.0402018167078495,
0.01867259293794632,
0.08977478742599487,
0.003425614908337593,
0.0704059898853302,
0.05869606137275696,
0.08224445581436157,
-0.10246741771697998,
0.07471306622028351,
0.08622124791145325,
-0.07954994589090347,
0.026619622483849525,
0.09149482846260071,
-0.05819176882505417,
-0.02969011478126049,
0.02704544924199581,
0.0793747529387474,
0.011502381414175034,
-0.042540501803159714,
0.011518802493810654,
-0.10228829830884933,
0.06203006953001022,
0.08760257810354233,
0.03265642002224922,
0.015443529933691025,
0.03219176456332207,
0.045628782361745834,
-0.07176384329795837,
0.1219232901930809,
0.028246978297829628,
0.015991143882274628,
-0.04067446291446686,
-0.04898078367114067,
0.024271609261631966,
-0.0303955040872097,
-0.006366716232150793,
-0.03475780412554741,
-0.0729878842830658,
-0.0171539094299078,
-0.16714228689670563,
-0.016664555296301842,
-0.04662061110138893,
0.009329318068921566,
0.03086909092962742,
-0.03788549080491066,
0.008464637212455273,
0.007407912518829107,
-0.07459274679422379,
-0.06477426737546921,
-0.022905457764863968,
0.09289900958538055,
-0.16393527388572693,
0.02335011027753353,
0.08690579235553741,
-0.12064014375209808,
0.09392421692609787,
0.01837589405477047,
-0.0037578048650175333,
0.028480252251029015,
-0.14924435317516327,
0.038928523659706116,
-0.03113253228366375,
0.014821149408817291,
0.04454975947737694,
-0.2236335128545761,
0.0009650349384173751,
-0.033828526735305786,
-0.06339430809020996,
-0.009390673600137234,
-0.036760155111551285,
-0.11370383948087692,
0.10629112273454666,
0.007970798760652542,
-0.08916810154914856,
-0.031690530478954315,
0.032128699123859406,
0.08206479996442795,
-0.0239556971937418,
0.15763959288597107,
-0.0023972811177372932,
0.0736590027809143,
-0.1675432026386261,
-0.019303109496831894,
-0.011248460970818996,
0.020926566794514656,
-0.018098697066307068,
-0.01251189224421978,
0.04078914225101471,
-0.02225574664771557,
0.18437865376472473,
-0.023570427671074867,
0.023348741233348846,
0.06592654436826706,
0.027775658294558525,
-0.025002485141158104,
0.10530006885528564,
0.05339968949556351,
0.021854043006896973,
0.02036798559129238,
0.00273964018560946,
-0.04241073876619339,
-0.023610878735780716,
-0.1998770385980606,
0.06446972489356995,
0.14037446677684784,
0.09086652100086212,
-0.017234215512871742,
0.08257289230823517,
-0.1004219725728035,
-0.11521948128938675,
0.11568495631217957,
-0.05446505919098854,
-0.004037478007376194,
-0.0672159418463707,
0.12938179075717926,
0.1446845531463623,
-0.19097456336021423,
0.06995914876461029,
-0.06848131865262985,
-0.049033988267183304,
-0.11654651165008545,
-0.1963350623846054,
-0.05714293569326401,
-0.05161691829562187,
-0.01663723587989807,
-0.046969223767519,
0.07560921460390091,
0.05719533935189247,
0.007424132898449898,
-0.0017566849710419774,
0.06332923471927643,
-0.026077456772327423,
0.00009585227962816134,
0.026813751086592674,
0.06610306352376938,
0.013093758374452591,
-0.02985633723437786,
0.017491595819592476,
-0.012147722765803337,
0.042048826813697815,
0.06357792019844055,
0.04670548066496849,
-0.030032360926270485,
0.016853880137205124,
-0.03863191977143288,
-0.10680584609508514,
0.041318636387586594,
-0.028504958376288414,
-0.08043242245912552,
0.1491626501083374,
0.02454165369272232,
0.008750278502702713,
-0.0205967016518116,
0.2416755110025406,
-0.0737907737493515,
-0.09567341208457947,
-0.1479424238204956,
0.10524045675992966,
-0.04420987144112587,
0.06244929879903793,
0.045180387794971466,
-0.10425344854593277,
0.016717668622732162,
0.12817999720573425,
0.16302813589572906,
-0.044200748205184937,
0.020526019856333733,
0.027614353224635124,
0.004152800887823105,
-0.03678637370467186,
0.0514480359852314,
0.06988705694675446,
0.1595088243484497,
-0.048713311553001404,
0.09546878933906555,
-0.0016016386216506362,
-0.09618084132671356,
-0.03802286460995674,
0.11709540337324142,
-0.018092934042215347,
0.017691975459456444,
-0.055210161954164505,
0.11857418715953827,
-0.06138255074620247,
-0.2316483110189438,
0.06108921393752098,
-0.06591550260782242,
-0.13765475153923035,
-0.02143050730228424,
0.08041442185640335,
-0.013238796964287758,
0.02708347514271736,
0.07207029312849045,
-0.07533451914787292,
0.20003929734230042,
0.037636954337358475,
-0.05420409142971039,
-0.05360380560159683,
0.08255447447299957,
-0.10376271605491638,
0.27565470337867737,
0.016520937904715538,
0.04948882386088371,
0.10317612439393997,
-0.012690499424934387,
-0.13475549221038818,
0.02108365297317505,
0.09600389003753662,
-0.0946137085556984,
0.04216265305876732,
0.19903649389743805,
0.0003853837260976434,
0.1207512691617012,
0.0790785402059555,
-0.07618726044893265,
0.049590613692998886,
-0.0941753089427948,
-0.07070460170507431,
-0.09001081436872482,
0.09455035626888275,
-0.07685617357492447,
0.14261877536773682,
0.1292559802532196,
-0.053739987313747406,
0.010677514597773552,
-0.028576120734214783,
0.04638256877660751,
0.0034859003499150276,
0.1005801111459732,
0.010024284943938255,
-0.18460705876350403,
0.02157641015946865,
0.01203901320695877,
0.1056026741862297,
-0.16518552601337433,
-0.09804878383874893,
0.042120642960071564,
0.0014211505185812712,
-0.060778699815273285,
0.12909291684627533,
0.06027422100305557,
0.04478219151496887,
-0.04292554408311844,
-0.020403601229190826,
-0.009860116057097912,
0.13677826523780823,
-0.10241927951574326,
0.0014122816501185298
] |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Wav2vec_1
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-japanese](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-japanese) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0459
- Wer: 0.2213
- Cer: 0.1608
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 3.4904 | 1.0 | 120 | 3.4430 | 0.9970 | 0.9991 |
| 1.1939 | 2.0 | 240 | 1.0064 | 0.8270 | 0.6265 |
| 0.7726 | 3.0 | 360 | 0.6257 | 0.8198 | 0.5705 |
| 0.5502 | 4.0 | 480 | 0.4148 | 0.5910 | 0.3415 |
| 0.4152 | 5.0 | 600 | 0.2439 | 0.4167 | 0.2182 |
| 0.3159 | 6.0 | 720 | 0.1359 | 0.3084 | 0.1762 |
| 0.2425 | 7.0 | 840 | 0.0737 | 0.2523 | 0.1509 |
| 0.1921 | 8.0 | 960 | 0.0459 | 0.2213 | 0.1608 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "jonatasgrosman/wav2vec2-large-xlsr-53-japanese", "model-index": [{"name": "Wav2vec_1", "results": []}]} | automatic-speech-recognition | tndklab/Wav2vec_1 | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:jonatasgrosman/wav2vec2-large-xlsr-53-japanese",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-09T09:41:51+00:00 | [] | [] | TAGS
#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us
| Wav2vec\_1
==========
This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-japanese on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0459
* Wer: 0.2213
* Cer: 0.1608
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 6e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 4
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 8
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.14.6
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 8",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 8",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
] | [
80,
116,
4,
33
] | [
"passage: TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 8### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
] | [
-0.14125047624111176,
0.1521967202425003,
-0.0005216437275521457,
0.10087664425373077,
0.11841331422328949,
0.0085747130215168,
0.1750715672969818,
0.15076148509979248,
-0.039998043328523636,
0.11078555881977081,
0.11308648437261581,
0.061277713626623154,
0.05472799390554428,
0.19412781298160553,
-0.08156096190214157,
-0.2209732085466385,
0.07665301859378815,
-0.003642272436991334,
0.01002604328095913,
0.11183035373687744,
0.07049506902694702,
-0.11860526353120804,
0.09002933651208878,
-0.007718286011368036,
-0.1435917466878891,
-0.041887201368808746,
0.01623602956533432,
-0.1092299148440361,
0.10884454846382141,
0.009545153938233852,
0.066487617790699,
0.03538823872804642,
0.08978810161352158,
-0.1868763417005539,
0.0022971315775066614,
0.017065441235899925,
0.014473037794232368,
0.07409105449914932,
0.04333772137761116,
-0.0004158481606282294,
0.0036692447029054165,
-0.11297383904457092,
0.0362396202981472,
0.01531605888158083,
-0.11693057417869568,
-0.1979491263628006,
-0.07799910753965378,
0.01610075868666172,
0.09834355115890503,
0.08479040116071701,
-0.02052956447005272,
0.12368219345808029,
0.0006939045852050185,
0.07969442009925842,
0.19665373861789703,
-0.31249356269836426,
-0.05446682497859001,
-0.017059026286005974,
0.038248300552368164,
0.08281829208135605,
-0.10235965996980667,
-0.01882171630859375,
0.049454059451818466,
0.022091539576649666,
0.09008674323558807,
-0.031108636409044266,
-0.035652607679367065,
-0.01087145134806633,
-0.12023214250802994,
-0.03851096332073212,
0.18820682168006897,
0.07240341603755951,
-0.06337796896696091,
-0.07955429702997208,
-0.06460234522819519,
-0.12052233517169952,
-0.054358672350645065,
-0.006856975145637989,
0.02616422064602375,
-0.0391836017370224,
-0.09954289346933365,
-0.004578823689371347,
-0.07988310605287552,
-0.09112969785928726,
-0.017533736303448677,
0.17636540532112122,
0.01095804013311863,
0.014438606798648834,
-0.01167216431349516,
0.05478431284427643,
-0.022570885717868805,
-0.18548931181430817,
-0.022464586421847343,
0.02804272621870041,
-0.03257973864674568,
-0.013382388278841972,
-0.043355271220207214,
-0.033334966748952866,
0.04386141523718834,
0.11528920382261276,
-0.020728832110762596,
0.06600450724363327,
-0.02504425309598446,
0.0018435477977618575,
-0.08576678484678268,
0.1824333369731903,
-0.06387582421302795,
-0.06664751470088959,
0.019000805914402008,
0.12712548673152924,
0.06239994987845421,
-0.02373587340116501,
-0.09942027926445007,
-0.008812311105430126,
0.14631129801273346,
0.035189125686883926,
-0.0422135554254055,
0.05017852038145065,
-0.039090558886528015,
-0.014405746012926102,
0.058954983949661255,
-0.12133091688156128,
0.02565721422433853,
0.021783603355288506,
-0.06309547275304794,
-0.022706298157572746,
-0.011673498898744583,
0.01275449525564909,
0.01284869760274887,
0.05231533572077751,
-0.0821324959397316,
0.0038759298622608185,
-0.024013616144657135,
-0.09163149446249008,
0.027066092938184738,
-0.06701881438493729,
0.00008626643830211833,
-0.10774777084589005,
-0.17816162109375,
-0.017768649384379387,
0.024151738733053207,
-0.04990711435675621,
-0.009792771190404892,
-0.11177095770835876,
-0.09739197790622711,
0.047965534031391144,
-0.022839518263936043,
0.03518350049853325,
-0.07974667102098465,
0.10836003720760345,
0.07977335155010223,
0.08705954253673553,
-0.04152562469244003,
0.026682158932089806,
-0.09504305571317673,
0.03237894922494888,
-0.17500458657741547,
0.07430756092071533,
-0.05469497665762901,
0.033808767795562744,
-0.11950970441102982,
-0.06743356585502625,
0.020315194502472878,
-0.022087106481194496,
0.0700702965259552,
0.1418767273426056,
-0.19025950133800507,
-0.056211892515420914,
0.1955873668193817,
-0.12070746719837189,
-0.14226198196411133,
0.12860046327114105,
-0.03599442541599274,
0.03750516101717949,
0.06920826435089111,
0.22345340251922607,
0.03225436434149742,
-0.10535945743322372,
-0.040012821555137634,
-0.06335276365280151,
0.08399859815835953,
-0.037193793803453445,
0.11107689142227173,
0.005308014340698719,
-0.0010400894097983837,
0.016431936994194984,
-0.07947258651256561,
0.032004695385694504,
-0.07096727937459946,
-0.09966075420379639,
-0.04394101724028587,
-0.10554824769496918,
0.027936099097132683,
0.0168707687407732,
0.055598024278879166,
-0.09875024855136871,
-0.07047586888074875,
0.010541613213717937,
0.1084170714020729,
-0.11681826412677765,
0.012759795412421227,
-0.10438031703233719,
0.09373559802770615,
-0.1133350059390068,
-0.02046053111553192,
-0.15468262135982513,
-0.006399071775376797,
0.05417654663324356,
0.0212104395031929,
0.014459000900387764,
-0.07469739764928818,
0.08221528679132462,
0.07678280770778656,
-0.04963481426239014,
-0.07393139600753784,
-0.004338552244007587,
0.017428195104002953,
-0.06273375451564789,
-0.17349180579185486,
-0.028835861012339592,
-0.05433039739727974,
0.15927596390247345,
-0.1650363653898239,
0.0006197572220116854,
0.01044842042028904,
0.09024755656719208,
0.04400348290801048,
-0.022982385009527206,
0.019433582201600075,
0.04864801466464996,
-0.02625354193150997,
-0.07133803516626358,
0.029901552945375443,
0.015300941653549671,
-0.10354676842689514,
0.021090108901262283,
-0.1669793426990509,
0.1503065675497055,
0.138905867934227,
0.041759736835956573,
-0.05278407409787178,
0.020043937489390373,
-0.014148992486298084,
-0.042457398027181625,
-0.05368741601705551,
-0.015070006251335144,
0.10314039885997772,
0.008032898418605328,
0.12092841416597366,
-0.10258404165506363,
0.0163110364228487,
0.06485302001237869,
-0.02733452245593071,
-0.028379017487168312,
0.08152641355991364,
0.011242552660405636,
-0.1389320343732834,
0.1304362565279007,
0.11213227361440659,
-0.07129396498203278,
0.12656818330287933,
-0.061634935438632965,
-0.0843249186873436,
-0.04900871217250824,
0.0341838113963604,
0.0346684530377388,
0.13764558732509613,
-0.0809134915471077,
-0.021760324016213417,
0.02103884518146515,
0.023091109469532967,
-0.01529014203697443,
-0.19393602013587952,
-0.02015807293355465,
0.014194412156939507,
-0.09474696964025497,
-0.00848898384720087,
0.00566264009103179,
-0.017818816006183624,
0.09487912058830261,
-0.0006018521962687373,
-0.11475678533315659,
0.023607030510902405,
-0.015287446789443493,
-0.08772476017475128,
0.17192082107067108,
-0.09201274067163467,
-0.17342041432857513,
-0.13716495037078857,
-0.07231983542442322,
-0.05638223513960838,
0.038042668253183365,
0.06027974933385849,
-0.06657306104898453,
-0.04032314196228981,
-0.11548382043838501,
-0.04845133796334267,
0.03256756439805031,
0.04504414647817612,
0.05023903772234917,
-0.008885359391570091,
0.06671439856290817,
-0.08162871748209,
-0.00540919741615653,
-0.014273083768785,
-0.00821331050246954,
0.029934052377939224,
0.0001720754662528634,
0.1266886293888092,
0.12225304543972015,
0.005916896741837263,
0.024104122072458267,
-0.03683272749185562,
0.22525806725025177,
-0.06968419253826141,
-0.018952148035168648,
0.12415596842765808,
-0.02794542908668518,
0.04608072340488434,
0.1776944398880005,
0.030783915892243385,
-0.10734470933675766,
0.001995818456634879,
-0.04995163530111313,
-0.016352737322449684,
-0.18889254331588745,
-0.0337047278881073,
-0.048289019614458084,
0.013958742842078209,
0.1011514961719513,
0.030820123851299286,
0.013061054982244968,
0.04826628416776657,
0.02211691066622734,
0.04484512284398079,
0.0034997144248336554,
0.08046282827854156,
0.09577559679746628,
0.07653668522834778,
0.10800109058618546,
-0.03189811855554581,
-0.048662882298231125,
0.03292552009224892,
0.021528296172618866,
0.20165909826755524,
0.02916025184094906,
0.1926502287387848,
0.0008543370640836656,
0.1551651954650879,
0.02618327923119068,
0.07965784519910812,
0.018495982512831688,
0.009644838981330395,
-0.02124895341694355,
-0.07807683199644089,
-0.05374841392040253,
0.05487046390771866,
-0.015439837239682674,
0.06052214279770851,
-0.10545248538255692,
0.02181406132876873,
0.049377113580703735,
0.27264848351478577,
0.08786498755216599,
-0.3686057925224304,
-0.08674189448356628,
0.02079203352332115,
-0.036702532321214676,
-0.019383292645215988,
0.01700860634446144,
0.15483582019805908,
-0.06094054877758026,
0.06772451847791672,
-0.07135744392871857,
0.0633547306060791,
-0.06433292478322983,
0.019489159807562828,
0.02524326555430889,
0.04671395942568779,
0.003008174942806363,
0.03175291046500206,
-0.24116544425487518,
0.28709232807159424,
0.03566345199942589,
0.09400847554206848,
-0.05679725483059883,
-0.0035250841174274683,
0.039460960775613785,
-0.005990186706185341,
0.11716748028993607,
-0.024187443777918816,
-0.11156574636697769,
-0.1794702708721161,
-0.1345970630645752,
0.04954605549573898,
0.1056753396987915,
-0.006231397856026888,
0.116123266518116,
-0.013885954394936562,
-0.04391981661319733,
0.04482044279575348,
-0.023572290316224098,
-0.07837311178445816,
-0.0757584273815155,
0.009134619496762753,
0.11386686563491821,
0.04395563155412674,
-0.04957326501607895,
-0.09589296579360962,
-0.08766008168458939,
0.09057319909334183,
0.005352741573005915,
-0.006396668963134289,
-0.10523343831300735,
0.01761304773390293,
0.14957155287265778,
-0.09196668863296509,
0.051616035401821136,
0.009215868078172207,
0.11016096919775009,
0.027403762564063072,
-0.04938302934169769,
0.09101609885692596,
-0.06218932196497917,
-0.1795005202293396,
-0.050927381962537766,
0.1379687637090683,
-0.007417639251798391,
0.04302037134766579,
0.020595556125044823,
0.051836974918842316,
-0.005588727071881294,
-0.06723954528570175,
0.033214766532182693,
0.025605952367186546,
0.04123516380786896,
0.020016616210341454,
-0.012340972200036049,
-0.08913493901491165,
-0.09259730577468872,
-0.0236259326338768,
0.14988137781620026,
0.29785993695259094,
-0.0659012421965599,
0.017959216609597206,
0.08771614730358124,
-0.017750537022948265,
-0.15294355154037476,
-0.005074091721326113,
0.04355678707361221,
0.04403569921851158,
-0.004600462969392538,
-0.12280170619487762,
0.04581692814826965,
0.061184823513031006,
-0.04509096220135689,
0.07719814777374268,
-0.24766139686107635,
-0.12747207283973694,
0.08788204938173294,
0.13312745094299316,
0.12450616806745529,
-0.15301506221294403,
-0.06674812734127045,
-0.02323043718934059,
-0.10821424424648285,
0.10323549807071686,
-0.07171940803527832,
0.13331496715545654,
-0.0030118164140731096,
0.0654512271285057,
0.007322901859879494,
-0.05162639543414116,
0.1503182053565979,
0.02345144934952259,
0.05312151461839676,
-0.022964736446738243,
-0.017065614461898804,
0.04567818343639374,
-0.07536022365093231,
0.06807173043489456,
-0.08603296428918839,
0.05025433376431465,
-0.0616893395781517,
-0.02457711473107338,
-0.062001440674066544,
-0.0068484097719192505,
0.003835647599771619,
-0.03473997861146927,
-0.01042927522212267,
0.03641602024435997,
0.05811687558889389,
0.0030087954364717007,
0.13272708654403687,
0.01161125022917986,
0.08291979134082794,
0.14898373186588287,
0.08835546672344208,
-0.04085903614759445,
0.01567208021879196,
-0.0068923914805054665,
-0.055849168449640274,
0.05385412275791168,
-0.1318775862455368,
0.048629939556121826,
0.09727974236011505,
0.01837053708732128,
0.16015681624412537,
0.047411736100912094,
-0.049501869827508926,
0.03928621858358383,
0.06943021714687347,
-0.158866748213768,
-0.11063781380653381,
0.0033555005211383104,
-0.01168614812195301,
-0.11117006093263626,
0.04862639307975769,
0.13855622708797455,
-0.07040716707706451,
-0.0059721930883824825,
-0.017209818586707115,
0.021428875625133514,
-0.039029158651828766,
0.20003323256969452,
0.042393915355205536,
0.05175299569964409,
-0.10956566035747528,
0.08064641803503036,
0.056627534329891205,
-0.08769740909337997,
0.04879089444875717,
0.03867235779762268,
-0.11502119153738022,
-0.023336775600910187,
-0.0002505379670765251,
0.14191557466983795,
0.004371317103505135,
-0.07589147239923477,
-0.1383807212114334,
-0.08851812779903412,
0.03467753529548645,
0.1755700260400772,
0.06845434010028839,
0.03646818920969963,
-0.018278129398822784,
-0.002688067266717553,
-0.1040942370891571,
0.09375180304050446,
0.07290391623973846,
0.07562889903783798,
-0.15024402737617493,
0.08052802830934525,
-0.008509502746164799,
0.0279094185680151,
-0.020580990239977837,
0.016986852511763573,
-0.10967835038900375,
0.005353422369807959,
-0.09917356818914413,
0.057384561747312546,
-0.07772467285394669,
-0.016247663646936417,
-0.0015844986774027348,
-0.08208008110523224,
-0.061262648552656174,
0.011642390862107277,
-0.08740989863872528,
-0.026086699217557907,
0.003569004824385047,
0.04306561499834061,
-0.13561555743217468,
-0.03789588063955307,
0.022941358387470245,
-0.09773992747068405,
0.08396238088607788,
0.08583839237689972,
-0.019850773736834526,
0.04701540619134903,
-0.0945475697517395,
-0.022546639665961266,
0.08254539221525192,
0.002605593064799905,
0.050892751663923264,
-0.14291131496429443,
-0.01438264548778534,
0.03102620504796505,
0.05015599727630615,
0.02167402394115925,
0.14898386597633362,
-0.09720876067876816,
0.007087768521159887,
-0.06788776069879532,
-0.012353980913758278,
-0.05675607919692993,
0.02227788418531418,
0.1418144404888153,
0.003508957801386714,
0.1834729015827179,
-0.09546668082475662,
0.023024410009384155,
-0.19755664467811584,
0.0012935912236571312,
-0.03659328445792198,
-0.12527979910373688,
-0.14665476977825165,
-0.027101492509245872,
0.07821720838546753,
-0.062089648097753525,
0.09423313289880753,
-0.06135744974017143,
0.06844563782215118,
0.01297700498253107,
-0.059717316180467606,
-0.0008422834798693657,
0.04002208635210991,
0.24755799770355225,
0.05770957097411156,
-0.035582009702920914,
0.07774478942155838,
0.010074413381516933,
0.0937424898147583,
0.1260993331670761,
0.12403693050146103,
0.1580188274383545,
0.03274623304605484,
0.14254659414291382,
0.08444154262542725,
-0.02467517741024494,
-0.11972017586231232,
0.05830295756459236,
-0.06900274753570557,
0.08974047750234604,
0.025396181270480156,
0.20858323574066162,
0.09844253212213516,
-0.16286486387252808,
0.004348019137978554,
-0.037714190781116486,
-0.08482368290424347,
-0.09521200507879257,
-0.05900885537266731,
-0.1309724748134613,
-0.14510256052017212,
0.010881631635129452,
-0.10670696198940277,
0.034468043595552444,
0.07216524332761765,
0.014526606537401676,
0.00019886138034053147,
0.14154177904129028,
0.015501794405281544,
0.02974669262766838,
0.09494839608669281,
0.008722718805074692,
-0.03972693905234337,
0.0005849196459166706,
-0.10345478355884552,
0.024098096415400505,
0.005764060188084841,
0.056345850229263306,
-0.02080792374908924,
-0.02533102221786976,
0.06847747415304184,
-0.025696376338601112,
-0.12564626336097717,
0.010819927789270878,
0.02030128799378872,
0.06048594042658806,
0.043071966618299484,
0.05601496994495392,
-0.016744425520300865,
0.025003772228956223,
0.20785056054592133,
-0.08897324651479721,
-0.07647224515676498,
-0.13238000869750977,
0.15063908696174622,
-0.015183540992438793,
-0.00735586229711771,
0.009016456082463264,
-0.10590473562479019,
0.00167565094307065,
0.19279740750789642,
0.14934486150741577,
-0.07567431032657623,
-0.000713669287506491,
-0.026859551668167114,
-0.006173308473080397,
-0.03786028176546097,
0.06698238849639893,
0.07841470837593079,
0.03571160510182381,
-0.059858281165361404,
-0.06226013973355293,
-0.05838900804519653,
-0.04087211191654205,
-0.02270551398396492,
0.039899252355098724,
-0.03137516975402832,
-0.022226402536034584,
-0.05117225646972656,
0.0776481032371521,
-0.0825381875038147,
-0.09744510054588318,
0.007881876081228256,
-0.21809327602386475,
-0.1735389530658722,
-0.0017279572784900665,
0.07530193775892258,
0.03487732261419296,
0.026761196553707123,
-0.03431369364261627,
0.02594865672290325,
0.05500660464167595,
-0.013731813058257103,
-0.05749599635601044,
-0.057966332882642746,
0.04239138588309288,
-0.08251851052045822,
0.17460159957408905,
-0.00494897598400712,
0.06607420742511749,
0.10391218215227127,
0.08161772042512894,
-0.10787943750619888,
0.103506900370121,
0.06065499410033226,
-0.0750865787267685,
0.05503123626112938,
0.15154609084129333,
-0.0559195838868618,
0.14231190085411072,
0.05230645462870598,
-0.10396632552146912,
0.0005700048059225082,
0.009089063853025436,
-0.028599033132195473,
-0.0746016800403595,
-0.06619822233915329,
-0.04621355980634689,
0.14714044332504272,
0.13527794182300568,
-0.06596086919307709,
0.0011793436715379357,
-0.01621612347662449,
0.05594421550631523,
0.06181124597787857,
0.021533451974391937,
-0.06166063994169235,
-0.2838306725025177,
-0.0164632648229599,
0.03805575147271156,
0.021457962691783905,
-0.24440787732601166,
-0.08930613845586777,
-0.00985017605125904,
-0.04617992043495178,
-0.0741301104426384,
0.09415009617805481,
0.07881135493516922,
0.031125400215387344,
-0.05453633517026901,
-0.051254935562610626,
-0.02909942716360092,
0.17292723059654236,
-0.16358372569084167,
-0.11536269634962082
] |
null | null | fastai |
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
| {"tags": ["fastai"]} | null | edgilr/ChestXRays | [
"fastai",
"has_space",
"region:us"
] | 2024-02-09T09:46:21+00:00 | [] | [] | TAGS
#fastai #has_space #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the documentation here)!
2. Create a demo in Gradio or Streamlit using Spaces (documentation here).
3. Join the fastai community on the Fastai Discord!
Greetings fellow fastlearner ! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
| [
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---",
"# Model card",
"## Model description\nMore information needed",
"## Intended uses & limitations\nMore information needed",
"## Training and evaluation data\nMore information needed"
] | [
"TAGS\n#fastai #has_space #region-us \n",
"# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---",
"# Model card",
"## Model description\nMore information needed",
"## Intended uses & limitations\nMore information needed",
"## Training and evaluation data\nMore information needed"
] | [
13,
20,
79,
3,
6,
12,
8
] | [
"passage: TAGS\n#fastai #has_space #region-us \n# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---# Model card## Model description\nMore information needed## Intended uses & limitations\nMore information needed## Training and evaluation data\nMore information needed"
] | [
-0.048121724277734756,
-0.024616125971078873,
0.002038001548498869,
0.10439170897006989,
0.135872021317482,
0.11887997388839722,
0.07405775785446167,
0.09980081021785736,
0.07783667743206024,
0.02590852417051792,
0.08961158245801926,
-0.08088712394237518,
0.08744348585605621,
0.271692156791687,
0.06988707184791565,
-0.22761479020118713,
0.04051019623875618,
-0.00024903909070417285,
0.08053462207317352,
0.06629016250371933,
0.13507555425167084,
-0.05464952811598778,
0.14010503888130188,
-0.004088983871042728,
-0.19050447642803192,
-0.042929794639348984,
-0.01773718371987343,
-0.02527874894440174,
0.12317648530006409,
-0.04744937643408775,
0.05381017178297043,
0.015037551522254944,
0.007565062493085861,
-0.07253646105527878,
0.0623294934630394,
0.040457066148519516,
0.01740180514752865,
0.059235580265522,
-0.07249044626951218,
0.08950132131576538,
0.08404164761304855,
-0.024370938539505005,
-0.1097978875041008,
0.07827875018119812,
-0.14424212276935577,
-0.21762843430042267,
-0.1253085881471634,
-0.09017651528120041,
0.028519365936517715,
0.004388005938380957,
-0.025051530450582504,
0.12801909446716309,
-0.13558274507522583,
-0.040698226541280746,
0.20124278962612152,
-0.17012301087379456,
-0.05505548417568207,
0.034343402832746506,
0.09226689487695694,
-0.05829555168747902,
-0.06347129493951797,
0.10614984482526779,
0.09640881419181824,
-0.019833475351333618,
0.05516824126243591,
0.002579754451289773,
0.021173657849431038,
0.01370104867964983,
-0.06150497496128082,
0.04717832803726196,
-0.010183089412748814,
0.048132527619600296,
-0.09465572983026505,
-0.1303568333387375,
-0.004072192590683699,
0.01214400865137577,
-0.048744890838861465,
-0.07019646465778351,
0.07833103090524673,
-0.011118141002953053,
-0.04357248544692993,
-0.13031910359859467,
-0.09131011366844177,
-0.12358787655830383,
0.008646543137729168,
0.09500427544116974,
0.003679296001791954,
0.07374339550733566,
-0.08258994668722153,
0.06774985045194626,
-0.17329485714435577,
-0.06484591960906982,
-0.08138520270586014,
-0.11546400189399719,
0.021133482456207275,
-0.0387684591114521,
0.02668963186442852,
0.15394504368305206,
0.12983950972557068,
0.023976242169737816,
0.04388163983821869,
-0.038937073200941086,
0.051190316677093506,
0.058571770787239075,
0.03395717963576317,
0.034934818744659424,
-0.036981891840696335,
-0.1793210655450821,
-0.016702448949217796,
-0.011550825089216232,
0.07954040914773941,
-0.07523109763860703,
-0.05632320046424866,
0.013454885222017765,
-0.11071494966745377,
0.07202339172363281,
-0.03576776012778282,
-0.0032025426626205444,
0.01168301422148943,
0.018371861428022385,
0.21271461248397827,
0.03955606371164322,
0.014191740192472935,
-0.008875265717506409,
-0.13453757762908936,
-0.06874168664216995,
-0.06896194815635681,
0.03361047804355621,
0.04448792710900307,
-0.0028071461711078882,
-0.07672245055437088,
0.04325154796242714,
-0.06045534089207649,
-0.03508453071117401,
0.008032378740608692,
-0.18221288919448853,
0.007458044681698084,
-0.10049355030059814,
-0.12126200646162033,
0.05306628718972206,
0.01695440337061882,
-0.08215925842523575,
0.08141279965639114,
0.02662261202931404,
0.020931517705321312,
-0.009988143108785152,
-0.005391082260757685,
0.06874798238277435,
-0.08508864045143127,
0.029901226982474327,
0.17170792818069458,
0.13024519383907318,
-0.08046911656856537,
-0.0006887061172164977,
-0.10965746641159058,
0.04426072910428047,
-0.13325683772563934,
0.02251482754945755,
-0.09062390774488449,
0.11723794043064117,
-0.042396437376737595,
0.002038756385445595,
-0.029030200093984604,
0.0960269495844841,
0.08189879357814789,
0.16663365066051483,
-0.2419009804725647,
-0.031095001846551895,
0.13240347802639008,
-0.10711425542831421,
-0.1807439625263214,
0.18486657738685608,
-0.012035200372338295,
0.11329247802495956,
-0.047014184296131134,
0.18334640562534332,
-0.02612062357366085,
-0.13582459092140198,
-0.058872904628515244,
0.005852419883012772,
-0.2269321084022522,
-0.06286033242940903,
0.09738040715456009,
0.13425657153129578,
-0.042984943836927414,
0.007112155202776194,
0.026316028088331223,
0.13609857857227325,
-0.06715573370456696,
-0.05195777863264084,
-0.012255736626684666,
-0.10902371257543564,
0.041914235800504684,
0.018215661868453026,
0.035408079624176025,
-0.059880174696445465,
-0.02931194379925728,
-0.053190283477306366,
0.13146710395812988,
0.09760832786560059,
-0.03670211136341095,
-0.049620725214481354,
0.1689043790102005,
-0.07763876020908356,
-0.033587727695703506,
0.07560533285140991,
-0.08268500119447708,
0.03266897425055504,
0.03090597130358219,
0.055881720036268234,
0.07766123116016388,
0.08522116392850876,
0.06057543307542801,
0.00819048099219799,
0.034654274582862854,
0.12095347046852112,
-0.013591280207037926,
-0.05039411783218384,
0.021508218720555305,
0.016904234886169434,
-0.019032588228583336,
0.29030677676200867,
-0.1951042115688324,
0.024724548682570457,
-0.06477324664592743,
0.07631538063287735,
0.06136792525649071,
0.003575638635084033,
0.08580143749713898,
-0.06023019179701805,
-0.019061198458075523,
-0.04803973436355591,
0.046805646270513535,
-0.0666879191994667,
-0.04162997007369995,
0.2621194124221802,
-0.05497581139206886,
0.044914912432432175,
0.12313763797283173,
-0.05873025581240654,
-0.07091446220874786,
0.01009807363152504,
-0.00793424155563116,
0.03249288722872734,
-0.04042816907167435,
0.043721720576286316,
-0.10840129852294922,
-0.06674089282751083,
0.1573198139667511,
-0.038477856665849686,
0.06786153465509415,
0.032288823276758194,
-0.04958454892039299,
-0.0648743286728859,
0.04650486260652542,
0.13598160445690155,
-0.0875244215130806,
0.07435166835784912,
0.17612984776496887,
-0.010562662966549397,
0.168031245470047,
0.08435525000095367,
-0.07075224816799164,
-0.09465329349040985,
-0.051014289259910583,
-0.021595727652311325,
0.21222901344299316,
-0.07084725052118301,
-0.054564714431762695,
0.05911700800061226,
-0.013703816570341587,
0.07196151465177536,
-0.06009222939610481,
-0.08332337439060211,
0.03227344527840614,
-0.04517695680260658,
0.011517706327140331,
0.13512636721134186,
-0.07090822607278824,
0.04681389778852463,
0.031489867717027664,
-0.0662703812122345,
0.02217509225010872,
0.033389873802661896,
0.0068921963684260845,
0.033959709107875824,
0.07332495599985123,
-0.20893315970897675,
-0.08408680558204651,
-0.13727638125419617,
0.037881869822740555,
0.021770721301436424,
0.045787326991558075,
-0.08602345734834671,
0.02231026627123356,
-0.08954031765460968,
-0.07987114042043686,
0.029592275619506836,
-0.026350297033786774,
-0.11349643021821976,
-0.03396226093173027,
-0.009560913778841496,
-0.06662604957818985,
-0.02250705659389496,
-0.05024505779147148,
0.03983384370803833,
0.04479299485683441,
0.058377087116241455,
0.12796473503112793,
-0.013808943331241608,
-0.03839317709207535,
0.000370211957488209,
-0.022712308913469315,
0.16396735608577728,
-0.14746315777301788,
0.07954913377761841,
0.19160102307796478,
0.11742953956127167,
0.028144672513008118,
0.028885571286082268,
0.03537585213780403,
-0.06289814412593842,
-0.000050317394197918475,
0.03226194158196449,
-0.09392514824867249,
-0.05801016092300415,
-0.020014392212033272,
-0.04031052812933922,
0.17134574055671692,
-0.12160717695951462,
0.03345204517245293,
0.04098419472575188,
0.09783966839313507,
0.10073629021644592,
-0.028829937800765038,
-0.1815856397151947,
0.038818612694740295,
-0.24060091376304626,
-0.05831146240234375,
0.027899866923689842,
-0.09110201895236969,
-0.06232144311070442,
0.17409387230873108,
0.013794700615108013,
0.011769929900765419,
-0.006736889015883207,
0.07983319461345673,
0.0110100656747818,
0.1217205822467804,
0.05947643890976906,
-0.05539114400744438,
0.025202350690960884,
-0.09962950646877289,
-0.07107596844434738,
-0.04035590961575508,
-0.05832801014184952,
0.07548832893371582,
0.1409129947423935,
-0.025475580245256424,
-0.020795362070202827,
0.023489827290177345,
0.08550169318914413,
0.0423230417072773,
0.16739299893379211,
-0.16016584634780884,
-0.026555389165878296,
0.04571257904171944,
-0.03384667634963989,
-0.05433850735425949,
-0.010291114449501038,
0.1137225553393364,
-0.02820689231157303,
-0.040318265557289124,
0.021242983639240265,
0.06503437459468842,
0.01481706090271473,
0.05012747645378113,
-0.04056356102228165,
0.14796851575374603,
-0.03461192920804024,
0.019330544397234917,
-0.12413888424634933,
0.13848772644996643,
0.021095896139740944,
-0.03901609033346176,
-0.06735876202583313,
-0.05808034539222717,
0.18150931596755981,
0.0025602965615689754,
0.10535930097103119,
0.012098877690732479,
-0.12160047143697739,
-0.1359938681125641,
-0.11211287975311279,
0.005111907608807087,
0.08330471813678741,
-0.023147236555814743,
-0.022247863933444023,
0.022165266796946526,
-0.036149751394987106,
-0.0530381016433239,
0.15749511122703552,
-0.1289154291152954,
-0.001082550617866218,
0.014728817157447338,
0.06971760839223862,
-0.08223173767328262,
0.026267826557159424,
0.014071501791477203,
-0.1119147390127182,
0.10590848326683044,
0.2521335482597351,
0.10338116437196732,
-0.09591643512248993,
-0.07697287201881409,
0.03418830782175064,
-0.012184361927211285,
-0.000774814048781991,
-0.006932659074664116,
0.0495428591966629,
-0.005566445179283619,
0.006762749515473843,
0.12971895933151245,
-0.07130889594554901,
0.011540771462023258,
-0.08449850976467133,
0.05566910281777382,
-0.05276734381914139,
0.01761564053595066,
-0.002672141883522272,
-0.008124710991978645,
-0.07340748608112335,
-0.061829522252082825,
0.1609770804643631,
-0.07277000695466995,
-0.06468547880649567,
0.05801168829202652,
0.03307786211371422,
0.01431563775986433,
-0.03584568202495575,
-0.04342148080468178,
0.18088261783123016,
0.29330700635910034,
-0.08191116154193878,
0.10001859813928604,
0.09677296131849289,
0.034820813685655594,
-0.23625829815864563,
0.029798466712236404,
-0.1455078274011612,
0.04449721798300743,
0.040447335690259933,
-0.0409548319876194,
0.04191497340798378,
0.10835777968168259,
-0.06094440817832947,
0.2048867791891098,
-0.03527235612273216,
-0.07983248680830002,
-0.01788630709052086,
0.03109324350953102,
0.29443636536598206,
-0.11833466589450836,
0.006058716680854559,
-0.10420958697795868,
-0.21566011011600494,
0.06983078271150589,
-0.18948867917060852,
0.13948246836662292,
-0.05087858438491821,
0.03576415032148361,
-0.01149723306298256,
-0.07561972737312317,
0.20518061518669128,
-0.15641045570373535,
0.05273103713989258,
-0.13722458481788635,
-0.1327189952135086,
0.01617460884153843,
-0.10048147290945053,
0.1545477658510208,
-0.11024226248264313,
-0.023215843364596367,
-0.2284185290336609,
0.012587235309183598,
-0.023200806230306625,
0.10030807554721832,
0.01800704374909401,
-0.07980740070343018,
-0.08767345547676086,
0.1316242516040802,
-0.06486566364765167,
0.034810543060302734,
-0.06996636837720871,
-0.050714004784822464,
-0.010929876938462257,
-0.045061707496643066,
0.03034941293299198,
-0.07934719324111938,
0.15192505717277527,
-0.016938980668783188,
-0.04507075995206833,
0.08636019378900528,
-0.2479533851146698,
0.023727843537926674,
0.025351112708449364,
-0.03495599329471588,
0.09001832455396652,
-0.025513244792819023,
-0.06256973743438721,
0.12282291799783707,
0.1402233988046646,
-0.07322840392589569,
-0.2460673749446869,
-0.06281693279743195,
0.0076784128323197365,
0.039165716618299484,
0.06561196595430374,
0.05125982314348221,
-0.07261458039283752,
-0.011131617240607738,
-0.026896944269537926,
0.030595947057008743,
-0.11692017316818237,
-0.03854857385158539,
0.07790639251470566,
0.017095070332288742,
-0.07846562564373016,
0.07280377298593521,
0.014225782826542854,
-0.021511616185307503,
0.007357571739703417,
0.148970365524292,
0.007519228849560022,
-0.14747941493988037,
-0.06656096875667572,
0.2007484883069992,
-0.01197928935289383,
-0.07260087132453918,
-0.05383119732141495,
-0.008990069851279259,
-0.0476234145462513,
0.05585788935422897,
0.05367223918437958,
-0.013585401698946953,
0.07708586007356644,
0.06263149529695511,
-0.10210110992193222,
-0.046256959438323975,
-0.066561758518219,
0.04169114679098129,
-0.10485753417015076,
0.060470130294561386,
0.009529483504593372,
0.12185006588697433,
-0.09983488917350769,
-0.01802929677069187,
-0.10810204595327377,
-0.06766588985919952,
-0.17349553108215332,
-0.05834362283349037,
-0.041105758398771286,
-0.015651104971766472,
0.03658895567059517,
0.010445823892951012,
-0.057867538183927536,
-0.0442853718996048,
-0.07536603510379791,
0.038444988429546356,
0.06147460639476776,
0.03932281583547592,
-0.03912714496254921,
0.04001858830451965,
0.05909334123134613,
0.013087345287203789,
0.17542624473571777,
0.038768354803323746,
0.05504675209522247,
-0.05045998468995094,
-0.16491834819316864,
-0.05276111513376236,
-0.0074316514655947685,
-0.07559102028608322,
0.1224973127245903,
-0.007679440546780825,
0.007880088873207569,
-0.08065467327833176,
0.03924860805273056,
0.028234204277396202,
0.10404064506292343,
-0.0028364830650389194,
0.10070426017045975,
0.019627176225185394,
-0.07226712256669998,
-0.025392837822437286,
0.021809715777635574,
0.12809939682483673,
0.01567147858440876,
0.026090998202562332,
0.033139873296022415,
0.016619985923171043,
-0.057361043989658356,
0.033977724611759186,
-0.04997231811285019,
-0.15123651921749115,
0.02628709189593792,
-0.05165188014507294,
0.005062380339950323,
-0.016889680176973343,
0.20362506806850433,
0.07867538928985596,
-0.06474173814058304,
-0.010664013214409351,
0.015816617757081985,
-0.0168940220028162,
-0.03121885471045971,
-0.012740966863930225,
0.04592578858137131,
-0.001151384087279439,
-0.04866636544466019,
0.11825273931026459,
0.05015748366713524,
0.05386412516236305,
0.0596686452627182,
0.12528513371944427,
0.016759619116783142,
0.13257254660129547,
0.061999931931495667,
-0.03403807803988457,
-0.13461735844612122,
-0.04495539888739586,
-0.1254577934741974,
0.04646851494908333,
-0.08697032928466797,
0.09941662102937698,
0.1144254133105278,
-0.05959030240774155,
-0.030464433133602142,
-0.08851305395364761,
-0.008356761187314987,
-0.06041252240538597,
0.039516255259513855,
-0.02262675203382969,
-0.0873224213719368,
0.0481097511947155,
0.05495472997426987,
-0.022752324119210243,
0.13218675553798676,
0.015727028250694275,
-0.036317698657512665,
0.13270340859889984,
-0.07583184540271759,
0.11758984625339508,
0.061510033905506134,
-0.043043944984674454,
-0.11560922116041183,
-0.020150646567344666,
-0.06641761213541031,
-0.10098972916603088,
-0.006782987620681524,
-0.005399650428444147,
-0.07349002361297607,
-0.059971679002046585,
0.08397487550973892,
-0.03124053031206131,
-0.09979676455259323,
-0.032152675092220306,
0.0038895104080438614,
0.06054706871509552,
-0.01686914451420307,
-0.0034020058810710907,
0.04728743061423302,
0.015076374635100365,
0.1653461456298828,
-0.02208263985812664,
0.06234867498278618,
-0.13855914771556854,
0.16070103645324707,
-0.14684462547302246,
-0.029404424130916595,
-0.1890171319246292,
-0.09729582816362381,
-0.05156542733311653,
0.20326784253120422,
0.2840938866138458,
-0.19109351933002472,
-0.010187864303588867,
0.020078664645552635,
-0.014484191313385963,
-0.08961770683526993,
0.12571553885936737,
0.029420215636491776,
-0.023631498217582703,
-0.07249019294977188,
-0.02037387527525425,
0.005258576478809118,
-0.06544211506843567,
-0.026979785412549973,
0.18310695886611938,
0.001496660872362554,
0.059546373784542084,
-0.09605178982019424,
0.01754261925816536,
-0.14839904010295868,
-0.10467469692230225,
-0.02111995778977871,
-0.16156397759914398,
-0.09646477550268173,
0.006635562051087618,
0.038640011101961136,
0.08000610023736954,
0.03268849849700928,
-0.015172510407865047,
0.06479045748710632,
-0.056333884596824646,
-0.0037216036580502987,
-0.1231912299990654,
0.00034658415825106204,
0.062129102647304535,
-0.07422006875276566,
0.2545335292816162,
-0.03070417232811451,
-0.12370815873146057,
0.09026903659105301,
-0.03299184888601303,
-0.12452623248100281,
0.07951879501342773,
-0.005700904875993729,
-0.11531132459640503,
-0.057989440858364105,
0.18941475450992584,
-0.012821312062442303,
-0.1364315301179886,
0.046368811279535294,
-0.17166484892368317,
0.031349923461675644,
0.0363016203045845,
-0.001313706859946251,
-0.04714022949337959,
0.024538639932870865,
-0.008008457720279694,
0.10724439471960068,
0.1382838785648346,
0.016739921644330025,
-0.011060068383812904,
-0.05056179314851761,
0.07912429422140121,
0.056927867233753204,
-0.05218246951699257,
-0.1282637119293213,
-0.08599764108657837,
0.03429819270968437,
0.04119478166103363,
-0.08113081753253937,
-0.16903182864189148,
-0.03668912500143051,
-0.10082915425300598,
-0.004939202684909105,
0.051785312592983246,
0.06585265696048737,
0.29044589400291443,
0.06326735019683838,
0.0016605621203780174,
-0.13649453222751617,
0.050569336861371994,
0.0868251696228981,
-0.04697931930422783,
-0.07670357078313828
] |
null | null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | null | ibragim-bad/tokenizer_llamalike_52k | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-09T09:49:25+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
26,
6,
3,
82,
28,
3,
4,
9,
9,
10,
42,
20,
3,
4,
5,
9,
11,
13,
3,
12,
5,
4,
5,
3,
4,
9,
53,
9,
8,
6,
3,
14,
8,
7,
9,
4
] | [
"passage: TAGS\n#transformers #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
] | [
-0.08389580249786377,
0.19830818474292755,
-0.0013316317927092314,
0.02313883788883686,
0.11396584659814835,
0.01961737498641014,
0.053626976907253265,
0.14538456499576569,
0.0060051376931369305,
0.10656800121068954,
0.066679947078228,
0.09131570905447006,
0.09678101539611816,
0.20042605698108673,
0.04371999576687813,
-0.17659740149974823,
0.010636410675942898,
-0.06930278241634369,
-0.010073255747556686,
0.11651819199323654,
0.141214057803154,
-0.10151198506355286,
0.07627976685762405,
-0.03319970890879631,
-0.02870541252195835,
-0.0070160143077373505,
-0.07769215852022171,
-0.05755697935819626,
0.07573003321886063,
0.054863471537828445,
0.04207949340343475,
-0.0008347301045432687,
0.08447454124689102,
-0.2674994468688965,
0.013753628358244896,
0.07452993094921112,
0.010659529827535152,
0.05990942195057869,
0.07833302766084671,
-0.04036625102162361,
0.12881849706172943,
-0.06320446729660034,
0.13035163283348083,
0.0906217098236084,
-0.0681561604142189,
-0.24378153681755066,
-0.08239314705133438,
0.06505522131919861,
0.12533815205097198,
0.07694927603006363,
-0.02823091857135296,
0.16422191262245178,
-0.07247646898031235,
0.019290022552013397,
0.09481704235076904,
-0.1151006743311882,
-0.060644298791885376,
0.08318385481834412,
0.14101974666118622,
0.10340547561645508,
-0.1255619376897812,
-0.012289565056562424,
0.04275871813297272,
0.045979104936122894,
0.07389909774065018,
0.011339850723743439,
0.1143413558602333,
0.05629947781562805,
-0.13526225090026855,
-0.05700986459851265,
0.14547574520111084,
0.023872992023825645,
-0.057064127177000046,
-0.2138909548521042,
-0.002902575535699725,
-0.07730814069509506,
-0.011685127392411232,
-0.06846728920936584,
0.0291305985301733,
-0.01194276288151741,
0.060226380825042725,
-0.0496203787624836,
-0.09797755628824234,
-0.046314824372529984,
0.1015089675784111,
0.054820988327264786,
0.011354796588420868,
-0.01489334274083376,
0.03576440364122391,
0.13432876765727997,
0.04213530570268631,
-0.10012737661600113,
-0.07065672427415848,
-0.0701170489192009,
-0.09620913118124008,
-0.03947552293539047,
0.04272124543786049,
0.020167991518974304,
0.042202774435281754,
0.2283228635787964,
0.024096308276057243,
0.05459817871451378,
0.029667891561985016,
0.0026177873369306326,
0.03211980313062668,
0.1073630079627037,
-0.041210614144802094,
-0.188126802444458,
-0.03292805701494217,
0.0931866466999054,
-0.009821015410125256,
-0.028658604249358177,
-0.033444397151470184,
0.035014089196920395,
0.08379437029361725,
0.11821532249450684,
0.08875755965709686,
-0.012828069739043713,
-0.037612639367580414,
-0.03493109717965126,
0.2115669697523117,
-0.14141373336315155,
0.045799970626831055,
-0.022097334265708923,
-0.018195297569036484,
-0.06905751675367355,
0.030103791505098343,
0.01831657998263836,
-0.003142025787383318,
0.06966056674718857,
-0.061253178864717484,
-0.05794486775994301,
-0.11518853157758713,
-0.045523155480623245,
0.04711875319480896,
-0.024105608463287354,
-0.024469668045639992,
-0.07765042781829834,
-0.11219723522663116,
-0.06417357176542282,
0.06612563133239746,
-0.04156653955578804,
-0.03974827378988266,
0.005308232270181179,
-0.07131324708461761,
0.008387917652726173,
0.008993842639029026,
0.12122467905282974,
-0.030063031241297722,
0.05833350867033005,
-0.002476902212947607,
0.05916252359747887,
0.10643328726291656,
0.03227818012237549,
-0.08492200076580048,
0.057466037571430206,
-0.20633617043495178,
0.08371785283088684,
-0.11420095711946487,
0.034276340156793594,
-0.17048145830631256,
-0.024183684960007668,
0.008447963744401932,
0.023597201332449913,
0.023726604878902435,
0.1338067352771759,
-0.2097422182559967,
-0.016196569427847862,
0.14133213460445404,
-0.09649793803691864,
-0.12422871589660645,
0.07990546524524689,
-0.03459475561976433,
0.1747698187828064,
0.038475677371025085,
-0.019652999937534332,
0.09909367561340332,
-0.15559963881969452,
-0.05852397903800011,
-0.026064254343509674,
-0.008927824907004833,
0.08823978155851364,
0.07542291283607483,
-0.05844951793551445,
0.02285866066813469,
0.02562655322253704,
-0.04727208614349365,
-0.0268824752420187,
-0.05256075784564018,
-0.10127434879541397,
-0.023140445351600647,
-0.09642518311738968,
0.026515161618590355,
0.000058677000197349116,
-0.07310442626476288,
-0.028560271486639977,
-0.17347893118858337,
-0.02563360333442688,
0.10103316605091095,
0.004820956848561764,
-0.007559072691947222,
-0.08540112525224686,
0.022149885073304176,
-0.05362366884946823,
-0.006164622958749533,
-0.16996455192565918,
-0.03558015450835228,
0.051895126700401306,
-0.14917676150798798,
0.015460150316357613,
-0.07327745854854584,
0.07047311216592789,
0.02098717913031578,
-0.05859505757689476,
-0.03108096309006214,
0.0007694467785768211,
0.004292082041501999,
-0.06229274719953537,
-0.1903683841228485,
-0.058886781334877014,
-0.041500482708215714,
0.15720732510089874,
-0.24841000139713287,
0.0300158578902483,
0.03247617185115814,
0.13185922801494598,
0.007058668415993452,
-0.06344027817249298,
0.02096918225288391,
-0.04676475748419762,
-0.050621338188648224,
-0.06898977607488632,
-0.009901339188218117,
-0.014539826661348343,
-0.031393732875585556,
0.012980648316442966,
-0.14970256388187408,
-0.060514215379953384,
0.09452559798955917,
0.11224991828203201,
-0.14555825293064117,
0.00204002158716321,
-0.0460561066865921,
-0.07002599537372589,
-0.07487804442644119,
-0.0761631652712822,
0.07739497721195221,
0.044650159776210785,
0.049250341951847076,
-0.06317461282014847,
-0.06234706938266754,
0.023210179060697556,
0.005524294450879097,
-0.019023682922124863,
0.0948529988527298,
0.074309803545475,
-0.09122881293296814,
0.07973480224609375,
0.08461450785398483,
0.04414684325456619,
0.086973637342453,
0.005991141777485609,
-0.11396963149309158,
-0.03062884695827961,
0.037754856050014496,
0.024159027263522148,
0.15351562201976776,
-0.08692087233066559,
0.030462130904197693,
0.052177220582962036,
-0.03854219615459442,
0.03157065063714981,
-0.0923321321606636,
0.025362705811858177,
0.021495236083865166,
-0.006555700208991766,
0.05864228308200836,
-0.018769768998026848,
-0.01403577346354723,
0.06336429715156555,
0.05677810311317444,
0.044270504266023636,
0.02595379762351513,
-0.02093072421848774,
-0.1278371512889862,
0.16537296772003174,
-0.09028079360723495,
-0.2540280222892761,
-0.17074446380138397,
0.015454737469553947,
0.03706491366028786,
-0.021728800609707832,
0.039588842540979385,
-0.06286025792360306,
-0.10237989574670792,
-0.09417891502380371,
0.0029635571409016848,
0.023925531655550003,
-0.058347854763269424,
-0.0817074254155159,
0.060779985040426254,
0.04047083482146263,
-0.13689260184764862,
0.0349188968539238,
0.06170675903558731,
-0.03042641654610634,
0.0018567070364952087,
0.07321398705244064,
0.12743599712848663,
0.14838241040706635,
-0.006730219814926386,
-0.012446845881640911,
0.035035960376262665,
0.229813352227211,
-0.1490442156791687,
0.10630457103252411,
0.14053207635879517,
-0.021705523133277893,
0.06635113060474396,
0.1461038440465927,
0.023231739178299904,
-0.07546708732843399,
0.04147516191005707,
0.04027445614337921,
-0.04228919371962547,
-0.2589097023010254,
-0.05694316700100899,
-0.00946022942662239,
-0.07043391466140747,
0.09718906134366989,
0.09238530695438385,
0.11972260475158691,
0.0337289460003376,
-0.05568677559494972,
-0.025771914049983025,
-0.003401360474526882,
0.114128477871418,
-0.027640055865049362,
-0.004564122296869755,
0.07965842634439468,
-0.05878787487745285,
0.011684526689350605,
0.09941446036100388,
0.019347423687577248,
0.17601320147514343,
0.02533329278230667,
0.10681075602769852,
0.06725578010082245,
0.09347675740718842,
-0.0015635732561349869,
0.034774236381053925,
0.05337131395936012,
0.022044572979211807,
0.010453542694449425,
-0.09408048540353775,
-0.012431944720447063,
0.13713060319423676,
0.019816776737570763,
0.009031654335558414,
0.008926562033593655,
-0.01010479498654604,
0.03131420537829399,
0.20501568913459778,
0.0009575071162544191,
-0.22537250816822052,
-0.09500737488269806,
0.059459153562784195,
-0.06931101530790329,
-0.143676295876503,
-0.02094252221286297,
0.030270220711827278,
-0.17292405664920807,
0.016790566965937614,
-0.0316389761865139,
0.09112390875816345,
-0.07145322859287262,
-0.028050832450389862,
0.06891903281211853,
0.07569212466478348,
-0.012108199298381805,
0.07973295450210571,
-0.19069278240203857,
0.12254468351602554,
0.03037673607468605,
0.08605273067951202,
-0.11708726733922958,
0.07849059253931046,
-0.0019813794642686844,
-0.014807495288550854,
0.17999744415283203,
-0.014062200672924519,
-0.0586031936109066,
-0.08878950774669647,
-0.08704045414924622,
-0.011727320961654186,
0.10361312329769135,
-0.09322915226221085,
0.09586969763040543,
-0.02775636687874794,
-0.03705112263560295,
0.012418309226632118,
-0.10469507426023483,
-0.1636953055858612,
-0.18679304420948029,
0.06244563311338425,
-0.07802703976631165,
0.012347841635346413,
-0.11227322369813919,
-0.06334327906370163,
-0.01575082167983055,
0.23160123825073242,
-0.16648635268211365,
-0.07049825042486191,
-0.1498587429523468,
-0.03997112438082695,
0.17463743686676025,
-0.042160745710134506,
0.06849376112222672,
-0.021383514627814293,
0.1873992383480072,
-0.008081548847258091,
-0.013158116489648819,
0.06569221615791321,
-0.09637628495693207,
-0.16879262030124664,
-0.05748843029141426,
0.14160962402820587,
0.10863390564918518,
0.05731578543782234,
-0.0038195757661014795,
0.013171887956559658,
-0.03383830562233925,
-0.09896382689476013,
0.013824623078107834,
0.13817466795444489,
0.0034514935687184334,
0.00682973163202405,
-0.03995988517999649,
-0.07027145475149155,
-0.05825701728463173,
-0.07912654429674149,
0.057147104293107986,
0.187900573015213,
-0.09512355923652649,
0.1602867990732193,
0.12431421875953674,
-0.06468851119279861,
-0.2306901067495346,
0.03996593505144119,
0.04701630026102066,
0.007666614837944508,
0.022401191294193268,
-0.19138796627521515,
0.09788824617862701,
0.0009011493530124426,
-0.06807263940572739,
0.14616990089416504,
-0.16564498841762543,
-0.1461436152458191,
0.08002161979675293,
0.025075770914554596,
-0.22560662031173706,
-0.14821304380893707,
-0.1037549376487732,
-0.03735695406794548,
-0.13707835972309113,
0.048581719398498535,
0.02614329755306244,
0.019834673032164574,
0.025222565978765488,
0.005338077899068594,
0.029657263308763504,
-0.07272187620401382,
0.1870686560869217,
-0.020297454670071602,
0.0072362530045211315,
-0.050640691071748734,
-0.04617878794670105,
0.09227550774812698,
-0.06150037795305252,
0.11741586774587631,
0.018679620698094368,
0.018796883523464203,
-0.1431548148393631,
-0.049209367483854294,
-0.060803934931755066,
0.04456847906112671,
-0.07284719496965408,
-0.09393193572759628,
-0.04137463867664337,
0.08888561278581619,
0.07211937010288239,
-0.032792408019304276,
-0.0027768779546022415,
-0.07569456845521927,
0.09405932575464249,
0.184477761387825,
0.17357055842876434,
0.009977072477340698,
-0.07020942866802216,
0.024555526673793793,
-0.042279548943042755,
0.03349342197179794,
-0.24652716517448425,
0.03456863760948181,
0.066053606569767,
0.03803660348057747,
0.08509242534637451,
-0.016836483031511307,
-0.1781480610370636,
-0.04086102172732353,
0.08498652279376984,
-0.06206206604838371,
-0.19876568019390106,
-0.02703288197517395,
0.08424776047468185,
-0.20383712649345398,
-0.032998621463775635,
0.041543323546648026,
-0.03834589570760727,
-0.02396267279982567,
-0.002415500348433852,
0.06396626681089401,
-0.008327016606926918,
0.12156640738248825,
0.06747189164161682,
0.10266115516424179,
-0.09284433722496033,
0.08920657634735107,
0.10416955500841141,
-0.09140542894601822,
0.03545991703867912,
0.10264154523611069,
-0.05670900270342827,
-0.04460543021559715,
0.033935222774744034,
0.05925208330154419,
-0.028357384726405144,
-0.06409841030836105,
-0.000502707262057811,
-0.0359574519097805,
0.04993389546871185,
0.08058220148086548,
0.036113787442445755,
-0.01202210783958435,
0.06544706225395203,
0.028145326301455498,
-0.11693570017814636,
0.10949387401342392,
0.04405685141682625,
0.04509059712290764,
-0.07182393968105316,
-0.012280966155230999,
0.015999672934412956,
0.032540347427129745,
-0.019734015688300133,
-0.014576527290046215,
-0.03146412968635559,
-0.007561005651950836,
-0.1553635597229004,
-0.02064543403685093,
-0.06516171246767044,
0.006067827809602022,
0.022207623347640038,
-0.03830232471227646,
-0.012014663778245449,
0.01381110493093729,
-0.07979435473680496,
-0.07571027427911758,
-0.01700955256819725,
0.08539021760225296,
-0.1381402313709259,
0.006627439055591822,
0.07182712107896805,
-0.10980239510536194,
0.07347989827394485,
-0.0048679932951927185,
0.017079560086131096,
0.010923396795988083,
-0.11654401570558548,
0.04386281594634056,
-0.005810429807752371,
0.01551580335944891,
0.022556742653250694,
-0.171111062169075,
0.011553828604519367,
-0.038553636521101,
-0.03114982508122921,
0.011926400475203991,
-0.025060230866074562,
-0.11875922232866287,
0.08676479011774063,
-0.028097305446863174,
-0.037512701004743576,
-0.03292486071586609,
0.06296087801456451,
0.08736220002174377,
-0.011740099638700485,
0.09667140990495682,
-0.025766119360923767,
0.04818311333656311,
-0.1756584197282791,
-0.01910574547946453,
-0.050167568027973175,
0.02537350542843342,
-0.01759655587375164,
-0.0070639788173139095,
0.055272240191698074,
-0.004191063344478607,
0.20991376042366028,
-0.03921036794781685,
0.1548677533864975,
0.05199402943253517,
-0.009925156831741333,
0.010884369723498821,
0.05032730847597122,
0.06423956155776978,
0.031145188957452774,
0.00853167474269867,
0.04660189896821976,
-0.004552975296974182,
-0.020357951521873474,
-0.13699717819690704,
0.02791593410074711,
0.16117429733276367,
0.061918217688798904,
0.0392887257039547,
0.03704594820737839,
-0.1422400325536728,
-0.09538721293210983,
0.10306388139724731,
-0.0331864058971405,
0.014331420883536339,
-0.08317886292934418,
0.17621558904647827,
0.12328410148620605,
-0.1574767529964447,
0.0577850341796875,
-0.07234696298837662,
-0.05066767707467079,
-0.1024852767586708,
-0.11832084506750107,
-0.06293155997991562,
-0.06027044355869293,
-0.004747506696730852,
-0.042489297688007355,
0.05734556168317795,
0.026751231402158737,
-0.003270963439717889,
-0.006759525276720524,
0.12665949761867523,
-0.0249644722789526,
-0.004145825747400522,
0.04152364656329155,
0.0326087586581707,
0.019319625571370125,
-0.05872373282909393,
0.017997145652770996,
0.018602589145302773,
0.022180357947945595,
0.06835069507360458,
0.0260987039655447,
-0.059317342936992645,
0.044286735355854034,
0.00319746439345181,
-0.11313364654779434,
0.018146557733416557,
-0.00002245741598017048,
-0.05020225793123245,
0.13557326793670654,
0.04076748713850975,
0.01548024732619524,
-0.029270920902490616,
0.24342355132102966,
-0.07199113070964813,
-0.08681939542293549,
-0.13965600728988647,
0.11511493474245071,
-0.023563209921121597,
0.03755274787545204,
0.016542524099349976,
-0.12659503519535065,
0.011511262506246567,
0.18531471490859985,
0.12824349105358124,
0.012459068559110165,
-0.007656481582671404,
0.05736639350652695,
-0.0007639875984750688,
-0.05985576659440994,
0.05051197111606598,
0.0664999932050705,
0.16097788512706757,
-0.09069112688302994,
0.0652846097946167,
-0.008405503816902637,
-0.0831485390663147,
-0.027498632669448853,
0.11705785244703293,
-0.022675158455967903,
0.02148384228348732,
-0.03778035193681717,
0.11204422265291214,
-0.052532415837049484,
-0.2719486355781555,
0.02952493168413639,
-0.09503202140331268,
-0.13993041217327118,
-0.02591860294342041,
0.041448429226875305,
-0.03349510580301285,
0.01577647216618061,
0.06254769116640091,
-0.045389387756586075,
0.18837277591228485,
0.025987716391682625,
-0.08679025620222092,
-0.07755549252033234,
0.05874146893620491,
-0.08695939928293228,
0.2789687216281891,
0.003863075515255332,
0.04782010242342949,
0.12108923494815826,
-0.03053574077785015,
-0.18664880096912384,
0.014769754372537136,
0.11989909410476685,
-0.09114406257867813,
0.07780203968286514,
0.18139931559562683,
-0.005561648402363062,
0.12649618089199066,
0.04705416411161423,
-0.03877115994691849,
0.03976387158036232,
-0.02721380814909935,
-0.03821522742509842,
-0.12209630757570267,
0.05661242455244064,
-0.0612691193819046,
0.15957388281822205,
0.1158948540687561,
-0.05964287370443344,
0.001120698289014399,
-0.06126941740512848,
0.06300627440214157,
0.014774397015571594,
0.12115653604269028,
0.018452486023306847,
-0.2023056596517563,
0.05087360367178917,
-0.03283824771642685,
0.08166342973709106,
-0.254973828792572,
-0.08186668157577515,
0.07622263580560684,
-0.019022729247808456,
-0.04275642707943916,
0.12311509251594543,
0.06101066991686821,
0.03676839917898178,
-0.03853875398635864,
-0.08537755906581879,
-0.01412904355674982,
0.15376435220241547,
-0.14123432338237762,
-0.029574336484074593
] |
null | null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-cnn-samsum-peft
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0894
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.1055 | 1.0 | 74 | 0.1344 |
| 0.0941 | 2.0 | 148 | 0.1314 |
| 0.1007 | 3.0 | 222 | 0.1292 |
| 0.0683 | 4.0 | 296 | 0.1195 |
| 0.0647 | 5.0 | 370 | 0.1081 |
| 0.0628 | 6.0 | 444 | 0.1015 |
| 0.0611 | 7.0 | 518 | 0.0992 |
| 0.0502 | 8.0 | 592 | 0.0957 |
| 0.045 | 9.0 | 666 | 0.0920 |
| 0.0512 | 10.0 | 740 | 0.0931 |
| 0.0465 | 11.0 | 814 | 0.0926 |
| 0.0478 | 12.0 | 888 | 0.0902 |
| 0.0517 | 13.0 | 962 | 0.0902 |
| 0.0441 | 14.0 | 1036 | 0.0896 |
| 0.0484 | 15.0 | 1110 | 0.0894 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "facebook/bart-large-cnn", "model-index": [{"name": "bart-cnn-samsum-peft", "results": []}]} | null | Prakruthirai/bart-cnn-samsum-peft | [
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:facebook/bart-large-cnn",
"license:mit",
"region:us"
] | 2024-02-09T09:53:52+00:00 | [] | [] | TAGS
#tensorboard #safetensors #generated_from_trainer #base_model-facebook/bart-large-cnn #license-mit #region-us
| bart-cnn-samsum-peft
====================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0894
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Tokenizers 0.15.1"
] | [
"TAGS\n#tensorboard #safetensors #generated_from_trainer #base_model-facebook/bart-large-cnn #license-mit #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Tokenizers 0.15.1"
] | [
41,
98,
4,
27
] | [
"passage: TAGS\n#tensorboard #safetensors #generated_from_trainer #base_model-facebook/bart-large-cnn #license-mit #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Tokenizers 0.15.1"
] | [
-0.08546210080385208,
-0.027869097888469696,
-0.001021688454784453,
0.08677014708518982,
0.17671401798725128,
0.02426423318684101,
0.14679628610610962,
0.06628987938165665,
-0.08917658776044846,
0.04294268414378166,
0.1130993664264679,
0.1107880100607872,
0.0038246256299316883,
0.10468462854623795,
-0.03658110648393631,
-0.220587357878685,
-0.021830396726727486,
0.027904611080884933,
-0.08166367560625076,
0.1170005053281784,
0.0777236595749855,
-0.16098883748054504,
0.07007651031017303,
-0.008494456298649311,
-0.2792810797691345,
0.05260127782821655,
0.05013565719127655,
-0.05591883882880211,
0.1403236985206604,
-0.001420450396835804,
0.18106482923030853,
0.010886608622968197,
0.11599255353212357,
-0.16308125853538513,
0.022776931524276733,
0.0823274627327919,
0.001270068110898137,
0.0701843723654747,
0.05727463588118553,
0.0204547718167305,
0.10255252569913864,
-0.09649535268545151,
0.07689009606838226,
0.024330876767635345,
-0.14005622267723083,
-0.25889140367507935,
-0.07442004978656769,
0.007666604593396187,
0.06527945399284363,
0.08500004559755325,
-0.02029602602124214,
0.16217343509197235,
-0.08738832920789719,
0.08445747941732407,
0.24990634620189667,
-0.2697012424468994,
-0.09153302013874054,
0.06383603066205978,
0.017988011240959167,
0.09773673862218857,
-0.12405919283628464,
-0.01030153688043356,
0.09478215128183365,
0.04939694702625275,
0.1288432478904724,
-0.025734329596161842,
-0.08984515070915222,
0.017722880467772484,
-0.14554019272327423,
0.024989968165755272,
0.05241360887885094,
0.038753408938646317,
-0.02185329981148243,
-0.00004692037691711448,
-0.057488054037094116,
-0.11793527752161026,
-0.05785198509693146,
-0.05553318187594414,
0.06206812337040901,
-0.05964145064353943,
-0.11640798300504684,
-0.01585054211318493,
-0.12067726999521255,
-0.09799258410930634,
-0.05095410719513893,
0.20375756919384003,
0.03827349841594696,
0.029601216316223145,
-0.06722832471132278,
0.09659228473901749,
-0.05467798560857773,
-0.14087803661823273,
0.06253788620233536,
0.03262842819094658,
-0.00865198578685522,
-0.06786267459392548,
-0.04952070489525795,
-0.11143694072961807,
0.025639204308390617,
0.09078298509120941,
-0.11530521512031555,
0.05014055222272873,
0.023850247263908386,
0.053236521780490875,
-0.11910277605056763,
0.15845197439193726,
-0.038249801844358444,
0.01286718063056469,
-0.00045270854025147855,
0.07944425940513611,
-0.002431995002552867,
-0.0016618277877569199,
-0.0840810164809227,
0.03450020030140877,
0.08276817202568054,
-0.003593261819332838,
-0.08167406916618347,
0.04134238138794899,
-0.027855435386300087,
0.019043810665607452,
-0.008752025663852692,
-0.0872783362865448,
0.04792676866054535,
0.017692236229777336,
-0.07070991396903992,
-0.03220217674970627,
0.0004959824727848172,
0.021338988095521927,
0.035104140639305115,
0.15312029421329498,
-0.10246291011571884,
0.0523357167840004,
-0.11495411396026611,
-0.10369714349508286,
-0.002326646586880088,
-0.011427793651819229,
0.014056331478059292,
-0.10043385624885559,
-0.11667480319738388,
-0.020464850589632988,
0.05202775076031685,
-0.02108525112271309,
0.014400451444089413,
-0.033300746232271194,
-0.09294842183589935,
-0.004802382085472345,
-0.020658021792769432,
0.13823860883712769,
-0.05561702325940132,
0.10593929886817932,
0.057742662727832794,
0.059768687933683395,
-0.08288781344890594,
0.026687728241086006,
-0.10303136706352234,
0.0017200116999447346,
-0.2744758427143097,
-0.00872922595590353,
-0.06910670548677444,
0.08359674364328384,
-0.05550020933151245,
-0.09914182126522064,
-0.028606947511434555,
0.014350295066833496,
0.10182520747184753,
0.07556315511465073,
-0.20282088220119476,
-0.09259866923093796,
0.1496380716562271,
-0.09165694564580917,
-0.09812647849321365,
0.10365734249353409,
-0.05154138803482056,
0.060041651129722595,
0.08397252857685089,
0.20102928578853607,
0.004597392398864031,
-0.14309997856616974,
0.013838360086083412,
-0.041313719004392624,
0.0297140683978796,
-0.050703033804893494,
0.025006350129842758,
-0.006316162645816803,
-0.00904642604291439,
0.020457254722714424,
-0.03408903256058693,
0.049002643674612045,
-0.12527137994766235,
-0.07230950891971588,
-0.048292797058820724,
-0.10147006064653397,
0.03204675763845444,
0.08532271534204483,
0.0818113386631012,
-0.1287682205438614,
-0.06659603863954544,
0.11702503263950348,
0.050202298909425735,
-0.04886547476053238,
0.00936069805175066,
-0.049331191927194595,
0.06767001003026962,
-0.0416949987411499,
-0.03759370744228363,
-0.1589064747095108,
-0.0646926760673523,
0.00601417850703001,
0.00025710559566505253,
0.03790990263223648,
0.008129606023430824,
0.09258294850587845,
0.07669761776924133,
-0.06497877836227417,
-0.006421372760087252,
-0.0691857859492302,
0.0011104490840807557,
-0.12317626178264618,
-0.2257242649793625,
-0.001092751743271947,
-0.01671646535396576,
0.05139546096324921,
-0.23427580296993256,
0.0356222465634346,
-0.021139344200491905,
0.09625916182994843,
0.024447640404105186,
-0.01798577606678009,
-0.07079245895147324,
0.09058339893817902,
0.018534090369939804,
-0.0487956777215004,
0.04325157776474953,
-0.025668609887361526,
-0.06221523508429527,
-0.07843364775180817,
-0.11046534031629562,
0.19377794861793518,
0.15249593555927277,
-0.13777266442775726,
-0.10141300410032272,
0.021426130086183548,
-0.07039471715688705,
-0.007565587293356657,
-0.05701805278658867,
0.03698845952749252,
0.16730035841464996,
-0.01081619318574667,
0.12433981150388718,
-0.08369182050228119,
-0.04239441454410553,
0.01980479434132576,
-0.0366218127310276,
0.05654047802090645,
0.08203613013029099,
0.14333826303482056,
-0.06268984079360962,
0.11776551604270935,
0.17083291709423065,
-0.10799489170312881,
0.14634060859680176,
-0.048885323107242584,
-0.0665149837732315,
-0.015400207601487637,
-0.006026015151292086,
-0.012589339166879654,
0.17531757056713104,
-0.07305300235748291,
0.007268635556101799,
-0.02536998875439167,
0.002214472508057952,
0.032312437891960144,
-0.24586868286132812,
-0.06490416824817657,
0.0041299969889223576,
-0.03438841924071312,
-0.016441885381937027,
-0.02281707525253296,
0.023129768669605255,
0.11462494730949402,
-0.02357535809278488,
-0.06434066593647003,
0.001317216083407402,
0.00025093695148825645,
-0.07407630980014801,
0.2136210948228836,
-0.056698959320783615,
-0.05578713119029999,
-0.0867810919880867,
0.0019155868794769049,
-0.022638149559497833,
-0.012151065282523632,
0.04701562970876694,
-0.14267666637897491,
-0.016189007088541985,
-0.05383811891078949,
0.04331617429852486,
0.06927869468927383,
0.055721599608659744,
0.023073239251971245,
0.004254141356796026,
0.08953459560871124,
-0.12501084804534912,
0.002785633085295558,
-0.08915350586175919,
-0.08737853169441223,
0.029272539541125298,
0.0881534069776535,
0.107207290828228,
0.15700383484363556,
-0.042772240936756134,
-0.006373686250299215,
-0.027912311255931854,
0.24994540214538574,
-0.08823498338460922,
-0.04668066278100014,
0.09816108644008636,
-0.013775271363556385,
0.04754319787025452,
0.11244802922010422,
0.10822948068380356,
-0.11293308436870575,
0.006397610530257225,
0.053387388586997986,
-0.034928545355796814,
-0.205191507935524,
-0.015352895483374596,
-0.011331467889249325,
-0.049325451254844666,
0.058785174041986465,
0.0338619165122509,
0.013170870020985603,
0.05431753024458885,
0.046343740075826645,
0.06286745518445969,
-0.04123963415622711,
0.04092740640044212,
0.023049095645546913,
0.04651562124490738,
0.10931418836116791,
-0.047729671001434326,
-0.08665396273136139,
0.022409280762076378,
-0.05816641449928284,
0.22207555174827576,
0.012595316395163536,
0.03818565607070923,
0.0853646993637085,
0.16499803960323334,
-0.006865316536277533,
0.06831920146942139,
-0.015659725293517113,
-0.09287649393081665,
0.012514733709394932,
-0.06110954284667969,
0.027751469984650612,
0.01955942064523697,
-0.09773801267147064,
0.06609337031841278,
-0.10518879443407059,
-0.020537547767162323,
0.08005952090024948,
0.21057404577732086,
0.015312960371375084,
-0.30614253878593445,
-0.0497465580701828,
-0.014061739668250084,
-0.019399788230657578,
0.0012090455275028944,
0.018467135727405548,
0.16961202025413513,
-0.02535371109843254,
0.03777715191245079,
-0.054900988936424255,
0.07130701839923859,
0.042213842272758484,
0.06042106822133064,
0.06846647709608078,
0.14322276413440704,
-0.025841014459729195,
0.033533889800310135,
-0.28389599919319153,
0.29579222202301025,
0.021710218861699104,
0.12801170349121094,
-0.03892892971634865,
-0.027406126260757446,
0.02791740745306015,
0.0416080467402935,
0.030738281086087227,
-0.03228622302412987,
-0.050901152193546295,
-0.2084357887506485,
-0.023205852136015892,
0.05963057279586792,
0.14266912639141083,
0.007891561836004257,
0.0856163427233696,
0.003868033643811941,
0.011742300353944302,
0.10040748119354248,
-0.06681812554597855,
-0.14996619522571564,
-0.026959965005517006,
-0.06330818682909012,
0.006031861994415522,
-0.09119585156440735,
-0.080924391746521,
-0.10276854038238525,
-0.11909221112728119,
0.08773722499608994,
0.01106675062328577,
0.0040489789098501205,
-0.10355500131845474,
0.10805492848157883,
0.06889096647500992,
-0.055610816925764084,
0.03884490579366684,
0.038639020174741745,
0.050347212702035904,
0.02254517190158367,
-0.05427476018667221,
0.11452320963144302,
-0.05828852951526642,
-0.15768347680568695,
-0.06423986703157425,
0.08625209331512451,
0.05811627581715584,
0.050986357033252716,
-0.005979512818157673,
0.008004480972886086,
0.00742180272936821,
-0.10204456001520157,
0.04610969126224518,
-0.08134292811155319,
0.02940421923995018,
-0.012673371471464634,
-0.029935671016573906,
-0.03418732434511185,
-0.06250669062137604,
-0.011968893930315971,
0.11394491791725159,
0.31360405683517456,
-0.07895245403051376,
-0.0402299202978611,
0.06484225392341614,
-0.04650391638278961,
-0.17739319801330566,
0.12481813877820969,
0.06003299355506897,
-0.011917749419808388,
0.0849790871143341,
-0.1235918328166008,
0.1517241895198822,
0.12959252297878265,
-0.018166707828640938,
0.1321801096200943,
-0.2970570921897888,
-0.15033318102359772,
0.09218501299619675,
0.1964738965034485,
0.1335994452238083,
-0.16425850987434387,
-0.0233145784586668,
-0.01673210971057415,
-0.0743580088019371,
0.10818443447351456,
-0.18779867887496948,
0.08621193468570709,
-0.0027437147218734026,
0.0686340406537056,
0.00026910624001175165,
-0.06900747120380402,
0.11016950011253357,
-0.014840752817690372,
0.1549566239118576,
-0.05015502870082855,
-0.045807819813489914,
0.07058742642402649,
-0.010801797732710838,
-0.0059439013712108135,
-0.04456264153122902,
0.012387857772409916,
0.0013057191390544176,
-0.011142382398247719,
-0.0990472361445427,
0.05490688234567642,
-0.04611813277006149,
-0.06164034083485603,
-0.04926590994000435,
0.026775086298584938,
-0.01700114645063877,
-0.03055381216108799,
0.10111495107412338,
0.017652036622166634,
0.21257063746452332,
0.07159436494112015,
0.0446738637983799,
-0.07880613207817078,
-0.023117484524846077,
0.03632313013076782,
-0.02461162582039833,
0.07852251827716827,
-0.16562137007713318,
0.01100121159106493,
0.12023621797561646,
0.03245753049850464,
0.10118624567985535,
0.08156823366880417,
-0.0612005852162838,
0.030765585601329803,
0.10341201722621918,
-0.1649133861064911,
-0.11132946610450745,
0.011853144504129887,
-0.05114893615245819,
-0.07702798396348953,
0.07528955489397049,
0.07199401408433914,
-0.08260940760374069,
0.003214628202840686,
-0.01616559363901615,
-0.02425283007323742,
-0.08535165339708328,
0.21206770837306976,
0.10781469941139221,
0.041124824434518814,
-0.08764994144439697,
0.06410972774028778,
0.036294303834438324,
-0.04805601388216019,
-0.004214051645249128,
0.07855919748544693,
-0.044354792684316635,
-0.0023531983606517315,
0.13660462200641632,
0.2485618144273758,
-0.021931592375040054,
-0.0454225018620491,
-0.1731749176979065,
-0.1105731949210167,
0.0327201746404171,
0.23934084177017212,
0.08849649131298065,
-0.024263903498649597,
0.006317848339676857,
0.056861113756895065,
-0.131876140832901,
0.07350490242242813,
0.010105115361511707,
0.0770491510629654,
-0.13187314569950104,
0.1880382001399994,
0.027491655200719833,
0.00888377521187067,
-0.037599608302116394,
0.06580666452646255,
-0.14462526142597198,
0.020075028762221336,
-0.10413946211338043,
-0.06584259867668152,
0.014122292399406433,
-0.02046099305152893,
0.013102171011269093,
-0.0802871435880661,
-0.09196705371141434,
0.012172803282737732,
-0.12577903270721436,
-0.004724492318928242,
0.0635012537240982,
0.03127230331301689,
-0.15938782691955566,
-0.018960440531373024,
0.02539270929992199,
-0.04188358411192894,
0.02664162591099739,
0.034387245774269104,
0.03447168692946434,
0.10534815490245819,
-0.23900727927684784,
-0.004568199627101421,
0.07362242788076401,
-0.014438689686357975,
0.07835091650485992,
-0.011118941009044647,
-0.026718277484178543,
-0.021756527945399284,
0.12203248590230942,
0.019894009456038475,
0.0671592503786087,
-0.1276220679283142,
0.0027409458998590708,
-0.028823722153902054,
-0.08924034237861633,
-0.042333412915468216,
-0.002543429844081402,
0.0716191828250885,
-0.005052458960562944,
0.17796532809734344,
-0.09765607118606567,
0.02719258703291416,
-0.24283353984355927,
-0.01121650543063879,
-0.00393663952127099,
-0.08636075258255005,
-0.08303525298833847,
-0.03155322000384331,
0.07128267735242844,
-0.0617169626057148,
0.13331031799316406,
0.030208757147192955,
0.03897339105606079,
0.04901765659451485,
-0.041277769953012466,
-0.005768817383795977,
0.0368635430932045,
0.19569696485996246,
0.03218415379524231,
-0.023243289440870285,
0.02441137284040451,
0.0707007572054863,
0.10587064921855927,
0.04144423082470894,
0.23808297514915466,
0.1812276691198349,
-0.06560704112052917,
0.08641576021909714,
0.07881662249565125,
-0.05236867442727089,
-0.09994465857744217,
0.05849607661366463,
-0.06558109074831009,
0.050879351794719696,
-0.03526239097118378,
0.19741453230381012,
0.11914633959531784,
-0.16246247291564941,
0.03373510763049126,
-0.05512196943163872,
-0.08102915436029434,
-0.10057616978883743,
0.02615334466099739,
-0.07843129336833954,
-0.1879398673772812,
0.04049346223473549,
-0.09710881859064102,
0.00919841043651104,
0.11650938540697098,
0.008366556838154793,
-0.006457161623984575,
0.24435238540172577,
0.06776493787765503,
0.059834856539964676,
0.05420207604765892,
0.0058974167332053185,
-0.046311382204294205,
-0.1059674471616745,
-0.07222992926836014,
-0.005581922363489866,
-0.03190580755472183,
0.009678326547145844,
-0.065531425178051,
-0.11695686727762222,
0.03853515535593033,
0.006344296038150787,
-0.10216581076383591,
0.02859402447938919,
0.01621435210108757,
0.0639938935637474,
-0.014444788917899132,
0.004052076488733292,
0.025715716183185577,
-0.02197594754397869,
0.20554527640342712,
-0.06312233209609985,
-0.09015988558530807,
-0.07962536066770554,
0.21920545399188995,
0.01757854036986828,
0.01899717003107071,
-0.002958628349006176,
-0.09904111921787262,
0.009892183355987072,
0.20121565461158752,
0.16394080221652985,
-0.10989075899124146,
0.006286634132266045,
-0.029774164780974388,
-0.01697560027241707,
-0.0832410380244255,
0.13607242703437805,
0.1115267425775528,
0.03600230813026428,
-0.09947490692138672,
-0.04428798705339432,
-0.05427566170692444,
-0.007658584043383598,
-0.028576485812664032,
0.0261871125549078,
0.07023987919092178,
0.030452683568000793,
-0.06999924778938293,
0.08337438106536865,
-0.025816354900598526,
-0.13326255977153778,
0.07862424105405807,
-0.19064927101135254,
-0.14867401123046875,
-0.007869941182434559,
0.15541990101337433,
-0.03433696925640106,
0.06778404116630554,
-0.038634154945611954,
-0.007014798000454903,
-0.0026194227393716574,
-0.035349227488040924,
-0.0538727343082428,
-0.13617821037769318,
0.07964254170656204,
-0.13385671377182007,
0.24347028136253357,
-0.04727979749441147,
0.10063868761062622,
0.10811132937669754,
0.03475659340620041,
-0.04326307401061058,
0.07389865070581436,
0.040008317679166794,
-0.1277637630701065,
-0.02843696065247059,
0.1060405895113945,
-0.03303484618663788,
0.06439855694770813,
0.051556121557950974,
-0.13652759790420532,
0.03789795935153961,
-0.0658879280090332,
-0.07594909518957138,
-0.058758657425642014,
-0.05041126906871796,
-0.06443441659212112,
0.09826382994651794,
0.19523070752620697,
-0.009288071654736996,
0.0533578023314476,
-0.07123246043920517,
0.0432988665997982,
0.06989073753356934,
0.0724787712097168,
-0.06223509460687637,
-0.2665267884731293,
0.04268154501914978,
0.12354469299316406,
-0.06028193607926369,
-0.24242953956127167,
-0.08248132467269897,
0.013504584319889545,
-0.0577317513525486,
-0.07626520097255707,
0.0635475143790245,
0.1178605929017067,
0.06835264712572098,
-0.05059000477194786,
-0.17678900063037872,
-0.07805948704481125,
0.16349855065345764,
-0.14575687050819397,
-0.0950554683804512
] |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-emotions-fp16
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3051
- Accuracy: 0.9287
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 50 | 1.7679 | 0.3862 |
| No log | 2.0 | 100 | 1.4584 | 0.5375 |
| No log | 3.0 | 150 | 1.3209 | 0.5162 |
| No log | 4.0 | 200 | 1.1580 | 0.62 |
| No log | 5.0 | 250 | 0.9946 | 0.7275 |
| No log | 6.0 | 300 | 0.8519 | 0.7887 |
| No log | 7.0 | 350 | 0.7374 | 0.8325 |
| No log | 8.0 | 400 | 0.7250 | 0.815 |
| No log | 9.0 | 450 | 0.5821 | 0.88 |
| 1.1152 | 10.0 | 500 | 0.5239 | 0.8838 |
| 1.1152 | 11.0 | 550 | 0.5121 | 0.8712 |
| 1.1152 | 12.0 | 600 | 0.4444 | 0.9038 |
| 1.1152 | 13.0 | 650 | 0.3894 | 0.9137 |
| 1.1152 | 14.0 | 700 | 0.3956 | 0.9137 |
| 1.1152 | 15.0 | 750 | 0.3806 | 0.91 |
| 1.1152 | 16.0 | 800 | 0.3328 | 0.9375 |
| 1.1152 | 17.0 | 850 | 0.3076 | 0.9287 |
| 1.1152 | 18.0 | 900 | 0.3026 | 0.9363 |
| 1.1152 | 19.0 | 950 | 0.2388 | 0.96 |
| 0.3752 | 20.0 | 1000 | 0.2892 | 0.935 |
| 0.3752 | 21.0 | 1050 | 0.2539 | 0.9413 |
| 0.3752 | 22.0 | 1100 | 0.2299 | 0.9525 |
| 0.3752 | 23.0 | 1150 | 0.2131 | 0.9575 |
| 0.3752 | 24.0 | 1200 | 0.2300 | 0.9525 |
| 0.3752 | 25.0 | 1250 | 0.2393 | 0.9537 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "vit-emotions-fp16", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.92875, "name": "Accuracy"}]}]}]} | image-classification | Cithan/vit-emotions-fp16 | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-09T09:53:54+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| vit-emotions-fp16
=================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3051
* Accuracy: 0.9287
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 25
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
86,
98,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 25### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
-0.11894883960485458,
0.13836032152175903,
-0.002553703496232629,
0.11889421194791794,
0.14081858098506927,
0.00451303506270051,
0.14030911028385162,
0.1383553296327591,
-0.07248804718255997,
0.08222430944442749,
0.14902569353580475,
0.1306503415107727,
0.0314217135310173,
0.18854525685310364,
-0.04901641979813576,
-0.2227882593870163,
0.025849556550383568,
0.04794204607605934,
-0.051059916615486145,
0.12190111726522446,
0.08755414187908173,
-0.13823258876800537,
0.11721403896808624,
0.024811137467622757,
-0.20285873115062714,
-0.007572860457003117,
0.02893502451479435,
-0.057837244123220444,
0.11638261377811432,
0.037189461290836334,
0.09136750549077988,
0.027415931224822998,
0.05342970788478851,
-0.15110456943511963,
0.010080794803798199,
0.07589743286371231,
-0.008537203073501587,
0.09701698273420334,
0.05505339801311493,
0.01274188794195652,
0.013775227591395378,
-0.09529830515384674,
0.03961663320660591,
0.02569844014942646,
-0.1123657301068306,
-0.23209796845912933,
-0.08489798754453659,
0.05927921459078789,
0.07742732763290405,
0.07218093425035477,
-0.0026813875883817673,
0.14448396861553192,
-0.005519659724086523,
0.09705674648284912,
0.229448601603508,
-0.2743123173713684,
-0.07727159559726715,
0.03784169256687164,
0.017714668065309525,
0.07865413278341293,
-0.10194818675518036,
0.011826181784272194,
0.059726566076278687,
0.014024287462234497,
0.15442781150341034,
-0.0053006382659077644,
-0.012832434847950935,
-0.025878984481096268,
-0.12501466274261475,
-0.06514942646026611,
0.1929745227098465,
0.09098587185144424,
-0.04683193191885948,
-0.0820060595870018,
-0.08092482388019562,
-0.14019228518009186,
-0.04646801948547363,
-0.011420400813221931,
0.057664427906274796,
-0.03410077840089798,
-0.06435243785381317,
-0.034369174391031265,
-0.0981525257229805,
-0.06997200101613998,
-0.014131356030702591,
0.098470039665699,
0.05390840023756027,
0.01306966133415699,
-0.019967855885624886,
0.0800236389040947,
-0.03921559080481529,
-0.14447897672653198,
-0.0075427764095366,
0.01794523373246193,
0.025295840576291084,
-0.030796123668551445,
-0.02405286766588688,
-0.11334702372550964,
0.020633211359381676,
0.11024486273527145,
-0.07049229741096497,
0.056568268686532974,
-0.020230332389473915,
0.05102572590112686,
-0.11127892881631851,
0.19113096594810486,
-0.049517448991537094,
0.017495345324277878,
0.03985077515244484,
0.10378623753786087,
0.05021900683641434,
-0.0016442174091935158,
-0.10448680073022842,
0.017814213410019875,
0.11994075030088425,
0.005939898546785116,
-0.03582557290792465,
0.08036231249570847,
-0.061649493873119354,
-0.029015036299824715,
0.07859829068183899,
-0.0856136903166771,
0.025800000876188278,
-0.006142629776149988,
-0.054298851639032364,
-0.058326829224824905,
0.04585936293005943,
-0.012190929614007473,
-0.01394887175410986,
0.04172241687774658,
-0.10241660475730896,
0.012283039279282093,
-0.06691639870405197,
-0.10799840092658997,
0.01301426813006401,
-0.11765537410974503,
0.014923347160220146,
-0.12255536019802094,
-0.13697899878025055,
-0.01335061527788639,
0.058710966259241104,
-0.02927336096763611,
-0.0530887208878994,
-0.04243474826216698,
-0.08023788034915924,
0.026678010821342468,
0.0048360684886574745,
0.04465236887335777,
-0.05716675519943237,
0.08833243697881699,
0.04527464509010315,
0.07648992538452148,
-0.01990717090666294,
0.04607091099023819,
-0.08899567276239395,
0.05709858238697052,
-0.20291905105113983,
0.03662979230284691,
-0.05781305953860283,
0.08630294352769852,
-0.11984451860189438,
-0.08664550632238388,
-0.0014391695149242878,
-0.02097024954855442,
0.06496645510196686,
0.1083085760474205,
-0.13839563727378845,
-0.05825955793261528,
0.17374412715435028,
-0.10104883462190628,
-0.15580348670482635,
0.1124323308467865,
-0.029720084741711617,
0.028208564966917038,
0.055601999163627625,
0.1960398405790329,
0.07773254811763763,
-0.10816797614097595,
-0.007227069232612848,
-0.031054439023137093,
0.03471618890762329,
-0.05475877597928047,
0.07653481513261795,
-0.0011823376407846808,
-0.011294271796941757,
0.02181200496852398,
-0.09519980102777481,
0.0633440613746643,
-0.07348119467496872,
-0.08471963554620743,
-0.0666305273771286,
-0.0882546529173851,
0.042137034237384796,
0.0600552074611187,
0.06486251205205917,
-0.10092120617628098,
-0.09040726721286774,
0.02655617892742157,
0.08056612312793732,
-0.09398511052131653,
0.016381319612264633,
-0.08051761239767075,
0.1109834834933281,
-0.10999204963445663,
0.0008770604035817087,
-0.13434211909770966,
-0.03006443753838539,
0.049554165452718735,
-0.061585135757923126,
-0.008026743307709694,
-0.037178248167037964,
0.07249585539102554,
0.061721321195364,
-0.06268481910228729,
-0.07371445745229721,
-0.04135175794363022,
-0.0035437538754194975,
-0.0991089940071106,
-0.1927853524684906,
-0.023725464940071106,
-0.026530543342232704,
0.10455334186553955,
-0.21937152743339539,
0.04115571081638336,
0.05035146698355675,
0.10076897591352463,
0.05845160037279129,
-0.03194385766983032,
0.004091633949428797,
0.017925823107361794,
-0.039263613522052765,
-0.08926484733819962,
0.06160515919327736,
0.015260795131325722,
-0.0676950067281723,
0.006565677002072334,
-0.10008488595485687,
0.17498251795768738,
0.1303996443748474,
-0.03464595228433609,
-0.063439279794693,
-0.0036668276879936457,
-0.04421493783593178,
-0.03508652001619339,
-0.03734230622649193,
0.010085978545248508,
0.07951169461011887,
-0.009278498589992523,
0.16290248930454254,
-0.10530547052621841,
-0.025566572323441505,
0.0589301772415638,
-0.029879949986934662,
-0.037508416920900345,
0.0899655669927597,
0.06960181891918182,
-0.13798260688781738,
0.14779718220233917,
0.1649700254201889,
-0.0675620511174202,
0.1258879452943802,
-0.047347549349069595,
-0.06308221817016602,
-0.0239962637424469,
0.04073089733719826,
0.0314788855612278,
0.13084517419338226,
-0.11849625408649445,
-0.013405896723270416,
0.023215560242533684,
0.001988808624446392,
-0.008354234509170055,
-0.2013077288866043,
-0.008611598052084446,
0.039604105055332184,
-0.06073702871799469,
0.02434912696480751,
-0.006057247053831816,
-0.02093222178518772,
0.08440553396940231,
0.00908252689987421,
-0.04172879457473755,
0.04661141708493233,
0.011035071685910225,
-0.07112379372119904,
0.19524259865283966,
-0.08316273242235184,
-0.21739107370376587,
-0.13204528391361237,
-0.017097122967243195,
-0.07933467626571655,
0.02076839655637741,
0.057789746671915054,
-0.09398467093706131,
-0.05683926120400429,
-0.10422416031360626,
-0.01482852827757597,
0.02929008938372135,
0.03886090964078903,
0.04249925911426544,
-0.002141249133273959,
0.13101409375667572,
-0.09873750060796738,
-0.006993778049945831,
-0.010238252580165863,
-0.023733509704470634,
0.04693184420466423,
0.019388902932405472,
0.12105630338191986,
0.08663328737020493,
-0.02806549146771431,
0.03435707837343216,
-0.021417507901787758,
0.24376367032527924,
-0.07313744723796844,
-0.0036050474736839533,
0.1493687927722931,
0.01965898461639881,
0.06730625778436661,
0.13029447197914124,
0.038871098309755325,
-0.10222675651311874,
0.008112133480608463,
0.023585986346006393,
-0.02526695281267166,
-0.18700315058231354,
-0.017358293756842613,
-0.0388820506632328,
-0.0034646703861653805,
0.15270783007144928,
0.05537873134016991,
0.061657246202230453,
0.09333473443984985,
0.0001433384750271216,
0.09046798944473267,
-0.007049950305372477,
0.08770877867937088,
0.10829059034585953,
0.04736960306763649,
0.10898157954216003,
-0.044532403349876404,
-0.029316700994968414,
0.03222999349236488,
0.015243848785758018,
0.22444899380207062,
0.0016974095487967134,
0.17573051154613495,
0.04692661389708519,
0.1912885457277298,
0.01729314774274826,
0.05547479912638664,
-0.021348409354686737,
-0.026549717411398888,
-0.008400604128837585,
-0.053985267877578735,
-0.02142283134162426,
0.03651423007249832,
-0.05057244375348091,
0.06664370000362396,
-0.09377394616603851,
0.040646329522132874,
0.06333958357572556,
0.2665227949619293,
0.037390295416116714,
-0.37872421741485596,
-0.0950927585363388,
-0.006185487378388643,
-0.014697438105940819,
-0.06410276144742966,
0.0032059166114777327,
0.1460043042898178,
-0.06078622117638588,
0.05886427313089371,
-0.10430420190095901,
0.08267505466938019,
-0.05207859352231026,
0.021213967353105545,
0.0764298364520073,
0.08833130449056625,
0.008689599111676216,
0.05663774535059929,
-0.2480885088443756,
0.25886449217796326,
0.01567782275378704,
0.063437819480896,
-0.04758202284574509,
0.012067724019289017,
0.03441528603434563,
0.10466030985116959,
0.11042377352714539,
-0.0052846320904791355,
-0.015687592327594757,
-0.17788362503051758,
-0.08838820457458496,
0.007024096790701151,
0.07279884070158005,
-0.04587668552994728,
0.08099999278783798,
-0.030175358057022095,
-0.023629754781723022,
0.05146525800228119,
-0.005774283781647682,
-0.0917392447590828,
-0.09353303909301758,
-0.007867624051868916,
0.044105157256126404,
0.01582132838666439,
-0.09493948519229889,
-0.09709560871124268,
-0.10303058475255966,
0.13133561611175537,
-0.01881888508796692,
-0.03915644809603691,
-0.11889958381652832,
0.0892181321978569,
0.05812813714146614,
-0.09235568344593048,
0.08117130398750305,
-0.02872229926288128,
0.13677819073200226,
0.030860746279358864,
-0.061868879944086075,
0.1094110831618309,
-0.05959492549300194,
-0.17296168208122253,
-0.046048011630773544,
0.10442007333040237,
-0.01799250766634941,
0.024172188714146614,
0.00030530174262821674,
0.028102213516831398,
-0.010404261760413647,
-0.05913915857672691,
0.05761762708425522,
0.011682187207043171,
0.059187695384025574,
-0.013392454944550991,
-0.0199256781488657,
0.007842065766453743,
-0.06442862749099731,
-0.029927192255854607,
0.13419604301452637,
0.2427036017179489,
-0.0983722060918808,
0.0032957824878394604,
0.01940050907433033,
-0.05177154392004013,
-0.19504572451114655,
0.04897996038198471,
0.06464823335409164,
0.003512557363137603,
0.029590606689453125,
-0.15503840148448944,
0.07249337434768677,
0.08124223351478577,
-0.030149292200803757,
0.09319079667329788,
-0.26925167441368103,
-0.1328451782464981,
0.0801343023777008,
0.18313711881637573,
0.06622885912656784,
-0.14398643374443054,
-0.05521772429347038,
-0.012348608113825321,
-0.08980011940002441,
0.09502676874399185,
-0.05905781686306,
0.10439515858888626,
-0.0284890066832304,
0.0007374516571871936,
0.0062128715217113495,
-0.057737454771995544,
0.1288371980190277,
-0.03417227417230606,
0.10739613324403763,
-0.05610465258359909,
-0.009329053573310375,
0.07691963016986847,
-0.07679533213376999,
0.06498641520738602,
-0.0912962257862091,
0.06406731903553009,
-0.06172092258930206,
-0.016252834349870682,
-0.0708748996257782,
0.03251778706908226,
-0.018553614616394043,
-0.025280114263296127,
-0.051085393875837326,
0.02380780130624771,
0.05206584185361862,
-0.00065375748090446,
0.19984738528728485,
0.05000724643468857,
0.08899577707052231,
0.1373717486858368,
0.04391217231750488,
-0.07601559162139893,
-0.10033346712589264,
-0.02691355161368847,
-0.02687925286591053,
0.08764153718948364,
-0.1846550703048706,
0.049746811389923096,
0.09725037962198257,
0.011524641886353493,
0.14368487894535065,
0.04578540474176407,
-0.03192482143640518,
0.018205557018518448,
0.07071501761674881,
-0.15501420199871063,
-0.16373994946479797,
-0.03220387175679207,
-0.019900942221283913,
-0.11514656990766525,
0.06268823146820068,
0.11176924407482147,
-0.08599729835987091,
0.0030038042459636927,
-0.008408124558627605,
0.01442751195281744,
-0.002362265717238188,
0.16258504986763,
0.08126087486743927,
0.044887974858284,
-0.09093693643808365,
0.098725825548172,
0.05385074391961098,
-0.1053374856710434,
0.02308623120188713,
0.025584816932678223,
-0.10416404157876968,
-0.0371410995721817,
0.0680573359131813,
0.1449139565229416,
0.0006733408663421869,
-0.051644545048475266,
-0.14552143216133118,
-0.0925258994102478,
0.057251378893852234,
0.12803803384304047,
0.09231258928775787,
0.015306747518479824,
-0.01174720749258995,
0.0006202262593433261,
-0.10529861599206924,
0.11984246224164963,
0.032428573817014694,
0.0973435565829277,
-0.21900834143161774,
0.0604318231344223,
0.018981600180268288,
0.03203427046537399,
-0.019061140716075897,
0.02957429550588131,
-0.09848873317241669,
-0.016118109226226807,
-0.06033739447593689,
0.04195461422204971,
-0.036712925881147385,
0.004782705567777157,
-0.007054673042148352,
-0.06790725141763687,
-0.0606013759970665,
0.04087424278259277,
-0.09982536733150482,
-0.04613954946398735,
0.03589252755045891,
0.0702739730477333,
-0.10259050130844116,
-0.028737127780914307,
0.02565825544297695,
-0.07943802326917648,
0.07891366630792618,
0.01312965340912342,
0.00029780593467876315,
0.02280501089990139,
-0.09930897504091263,
0.012945673428475857,
0.08489075303077698,
0.002549972152337432,
0.02933597005903721,
-0.10325565934181213,
0.007430776953697205,
-0.0009376454981975257,
0.0019719668198376894,
-0.00872457679361105,
0.10491598397493362,
-0.1335023045539856,
-0.027062535285949707,
-0.03757190331816673,
-0.03294140100479126,
-0.05857814475893974,
0.0624234676361084,
0.08586440980434418,
-0.0040721711702644825,
0.19997569918632507,
-0.08623754233121872,
0.00022201637329999357,
-0.22387979924678802,
0.004304409958422184,
-0.004234849475324154,
-0.13640454411506653,
-0.1279314160346985,
-0.027916574850678444,
0.053475137799978256,
-0.07336326688528061,
0.09624867141246796,
0.01643064245581627,
0.006806003861129284,
0.0358128696680069,
0.0029234844259917736,
-0.0005273923161439598,
0.027310719713568687,
0.1862330436706543,
-0.007395886350423098,
-0.022235369309782982,
0.07182403653860092,
0.018963167443871498,
0.11642629653215408,
0.08331115543842316,
0.09961314499378204,
0.16315770149230957,
-0.04107492417097092,
0.1046070083975792,
0.05072840303182602,
-0.02260139212012291,
-0.1724471151828766,
0.10179220885038376,
-0.07520940154790878,
0.14462164044380188,
-0.013030881993472576,
0.16612093150615692,
0.12109090387821198,
-0.15755462646484375,
0.02822810597717762,
-0.028024055063724518,
-0.07343241572380066,
-0.07111158221960068,
-0.14401601254940033,
-0.11674758046865463,
-0.18623147904872894,
0.014876359142363071,
-0.09836778789758682,
0.006873577367514372,
0.07459866255521774,
-0.009861985221505165,
-0.02381756715476513,
0.20629765093326569,
0.05238042026758194,
-0.0024206480011343956,
0.07152590155601501,
0.0015250659780576825,
-0.06755352020263672,
-0.061908695846796036,
-0.08418524265289307,
0.03869929537177086,
-0.009296361356973648,
0.03367956727743149,
-0.030564244836568832,
-0.006204492878168821,
0.04931890219449997,
-0.00025545936659909785,
-0.10977760702371597,
0.01771249808371067,
0.015369100496172905,
0.01061437837779522,
0.003166045295074582,
0.004938668105751276,
0.0070629180409014225,
-0.009851707145571709,
0.18282608687877655,
-0.05540746450424194,
-0.006520119495689869,
-0.1185186430811882,
0.1231631264090538,
0.0278151985257864,
-0.01512550376355648,
0.029006753116846085,
-0.07982636988162994,
0.027082741260528564,
0.21879206597805023,
0.14367042481899261,
-0.01778673380613327,
-0.0016434795688837767,
-0.008162392303347588,
-0.02044287696480751,
-0.030117202550172806,
0.09256953001022339,
0.09339085221290588,
-0.044423554092645645,
-0.055459581315517426,
-0.02542763575911522,
-0.04824884235858917,
-0.01617739163339138,
-0.03662300854921341,
0.03698025643825531,
0.017825337126851082,
0.014945114962756634,
-0.06439658254384995,
0.04260869696736336,
0.020406967028975487,
-0.06954710930585861,
0.08928757160902023,
-0.19537803530693054,
-0.13857115805149078,
-0.03335585445165634,
0.09861516952514648,
-0.006465521641075611,
0.028162216767668724,
-0.02104625105857849,
0.016318529844284058,
0.07085879147052765,
-0.02368042804300785,
-0.08305905759334564,
-0.10058262944221497,
0.0497666671872139,
-0.13337257504463196,
0.245480477809906,
-0.03221895173192024,
0.007152902893722057,
0.11182404309511185,
0.019059516489505768,
-0.11803408712148666,
0.05103583261370659,
0.025600489228963852,
-0.03529244288802147,
0.02493947371840477,
0.1117299422621727,
-0.019942285493016243,
0.11038706451654434,
0.03432191163301468,
-0.08974681794643402,
-0.017703071236610413,
-0.05547734722495079,
-0.04101540520787239,
-0.057603005319833755,
-0.02375851199030876,
-0.06825660169124603,
0.12414233386516571,
0.17375953495502472,
-0.04231874272227287,
-0.026098784059286118,
-0.06346037238836288,
0.036456260830163956,
0.0909803956747055,
0.017901046201586723,
-0.01253226026892662,
-0.22883135080337524,
0.017504781484603882,
0.02403312735259533,
-0.0038431924767792225,
-0.2146744430065155,
-0.11082694679498672,
-0.016662849113345146,
-0.05348074436187744,
-0.08613599836826324,
0.08607450127601624,
0.116617351770401,
0.05069674551486969,
-0.06090410798788071,
-0.0467800609767437,
-0.06856624037027359,
0.160871222615242,
-0.12224885821342468,
-0.08594410866498947
] |
null | null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BOLETIN_8bit_27
This model is a fine-tuned version of [bertin-project/BOLETIN](https://huggingface.co/bertin-project/BOLETIN) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.41e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.1 | {"license": "openrail", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "bertin-project/BOLETIN", "model-index": [{"name": "BOLETIN_8bit_27", "results": []}]} | null | versae/BOLETIN_8bit_27 | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:bertin-project/BOLETIN",
"license:openrail",
"region:us"
] | 2024-02-09T09:54:01+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #generated_from_trainer #base_model-bertin-project/BOLETIN #license-openrail #region-us
|
# BOLETIN_8bit_27
This model is a fine-tuned version of bertin-project/BOLETIN on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.41e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.1 | [
"# BOLETIN_8bit_27\n\nThis model is a fine-tuned version of bertin-project/BOLETIN on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1.41e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.1"
] | [
"TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #base_model-bertin-project/BOLETIN #license-openrail #region-us \n",
"# BOLETIN_8bit_27\n\nThis model is a fine-tuned version of bertin-project/BOLETIN on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1.41e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.1"
] | [
44,
35,
6,
12,
8,
3,
104,
4,
39
] | [
"passage: TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #base_model-bertin-project/BOLETIN #license-openrail #region-us \n# BOLETIN_8bit_27\n\nThis model is a fine-tuned version of bertin-project/BOLETIN on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1.41e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.1"
] | [
-0.12505598366260529,
0.11091503500938416,
-0.0018214158480986953,
0.06667639315128326,
0.12946717441082,
-0.010468371212482452,
0.11432268470525742,
0.09374704211950302,
-0.08343593776226044,
0.10025238990783691,
0.09207554906606674,
-0.00333775975741446,
0.054209932684898376,
0.156844362616539,
-0.010253800079226494,
-0.28242504596710205,
0.01900818571448326,
0.02014770358800888,
-0.061409760266542435,
0.09093184769153595,
0.11407947540283203,
-0.086019366979599,
0.04722864553332329,
0.04280991107225418,
-0.15225793421268463,
0.03675396740436554,
-0.031407468020915985,
-0.06756163388490677,
0.07739603519439697,
0.039486922323703766,
0.12507925927639008,
-0.0036722503136843443,
0.09173772484064102,
-0.18626557290554047,
0.009598546661436558,
0.07716827094554901,
0.05318758636713028,
0.1027587354183197,
0.09481176733970642,
0.04777655005455017,
0.10577701777219772,
-0.09776142984628677,
0.09351012855768204,
0.03657795861363411,
-0.06841001659631729,
-0.18987847864627838,
-0.09571728110313416,
0.14627575874328613,
0.07032591849565506,
0.072323277592659,
0.004512786865234375,
0.14146855473518372,
-0.06528452783823013,
0.058064643293619156,
0.2138042151927948,
-0.2715570330619812,
-0.10155312716960907,
0.02375967986881733,
0.07912047207355499,
0.06538239121437073,
-0.11354631930589676,
-0.04512643441557884,
0.08513615280389786,
0.03136148303747177,
0.061866600066423416,
0.015068918466567993,
-0.012371175922453403,
-0.026891667395830154,
-0.16136959195137024,
-0.01354129146784544,
0.17939725518226624,
0.07577899843454361,
-0.0756068006157875,
-0.08717650920152664,
-0.030809087678790092,
-0.12804724276065826,
-0.026620788499712944,
-0.026307525113224983,
0.012540217489004135,
-0.039191991090774536,
-0.036660339683294296,
-0.06039471924304962,
-0.08692513406276703,
-0.07041903585195541,
0.02262544073164463,
0.1463364213705063,
0.0644068643450737,
0.00781179778277874,
-0.032721806317567825,
0.09374440461397171,
-0.01087842509150505,
-0.09250698238611221,
-0.004089789465069771,
-0.031832337379455566,
-0.02116990089416504,
-0.061010152101516724,
-0.031307581812143326,
-0.024695714935660362,
0.007119579240679741,
0.14397799968719482,
-0.11665503680706024,
0.07094590365886688,
-0.0005922114360146224,
0.03727343678474426,
-0.034484174102544785,
0.11958996206521988,
-0.058238737285137177,
0.060603559017181396,
0.003400081768631935,
0.10705488920211792,
0.0005876959185115993,
0.008822205476462841,
-0.07152148336172104,
-0.011614429764449596,
0.08589167892932892,
0.045273955911397934,
-0.04759013280272484,
-0.008571693673729897,
-0.04145605489611626,
-0.03175944462418556,
0.05742378160357475,
-0.1094091609120369,
0.037465810775756836,
-0.002373927040025592,
-0.07257535308599472,
0.036438606679439545,
0.025577623397111893,
0.00961990375071764,
-0.024194972589612007,
0.08444294333457947,
-0.10763716697692871,
-0.019592301920056343,
-0.10226310789585114,
-0.06718066334724426,
0.034373797476291656,
-0.020979968830943108,
-0.0045797438360750675,
-0.10656193643808365,
-0.14289714395999908,
-0.024766545742750168,
0.029285481199622154,
-0.05378148704767227,
-0.07902254164218903,
-0.006204427219927311,
-0.06478201597929001,
0.02081688679754734,
-0.0256999172270298,
0.1410066783428192,
-0.022396162152290344,
0.08969137817621231,
-0.0011088999453932047,
-0.002240576082840562,
-0.025912053883075714,
0.02905728854238987,
-0.05968691408634186,
0.05301060155034065,
-0.10085700452327728,
0.024153877049684525,
-0.12093628197908401,
0.0464455671608448,
-0.12399980425834656,
-0.1196555569767952,
-0.04030501842498779,
-0.010179685428738594,
0.10893574357032776,
0.10424020141363144,
-0.10261937230825424,
-0.0342838391661644,
0.16711747646331787,
-0.10023311525583267,
-0.10022365301847458,
0.10958249866962433,
-0.02205030992627144,
0.021946316584944725,
0.04237686097621918,
0.148911714553833,
0.10409466922283173,
-0.1513742208480835,
-0.01749049313366413,
0.01415188703685999,
0.10333949327468872,
-0.03105093166232109,
0.08472636342048645,
-0.022028908133506775,
-0.06603480875492096,
0.015492314472794533,
-0.017263008281588554,
0.02680838294327259,
-0.10510892421007156,
-0.0643935278058052,
-0.042811233550310135,
-0.09175176173448563,
0.04619112238287926,
0.02262747287750244,
0.04636308550834656,
-0.07126773148775101,
-0.1178523376584053,
0.1327817291021347,
0.14195217192173004,
-0.03344561532139778,
0.012468419037759304,
-0.07061786949634552,
0.07374775409698486,
-0.05898679047822952,
-0.045269161462783813,
-0.16659744083881378,
-0.11003757268190384,
0.048242442309856415,
-0.08250042051076889,
0.011340494267642498,
-0.007775977253913879,
0.053379084914922714,
0.0780063048005104,
-0.02694576419889927,
-0.04086442291736603,
-0.12353037297725677,
-0.01644415780901909,
-0.11583544313907623,
-0.17409899830818176,
-0.07156107574701309,
-0.032623741775751114,
0.13356268405914307,
-0.25597432255744934,
0.023209603503346443,
-0.02459779568016529,
0.15401139855384827,
0.03533177822828293,
-0.05925464630126953,
-0.013749907724559307,
0.07021428644657135,
-0.00373031385242939,
-0.06864794343709946,
0.037552833557128906,
0.027317875996232033,
-0.07680901139974594,
-0.05128458887338638,
-0.09454669803380966,
0.07116924971342087,
0.06581882387399673,
0.07664375007152557,
-0.09043005853891373,
-0.08944965153932571,
-0.10229729861021042,
-0.03852663189172745,
-0.05299462378025055,
-0.013982415199279785,
0.13026200234889984,
0.017254488542675972,
0.14740273356437683,
-0.09863481670618057,
-0.05697886645793915,
0.024594396352767944,
-0.02724153734743595,
-0.032392263412475586,
0.09754201769828796,
0.09246592968702316,
-0.04507911205291748,
0.09588712453842163,
0.06250747293233871,
-0.08687049895524979,
0.15898407995700836,
-0.0627540871500969,
-0.12351758033037186,
-0.006281708832830191,
0.056578680872917175,
0.011034701019525528,
0.14319725334644318,
-0.07681494206190109,
0.021640414372086525,
0.024467719718813896,
0.015969293192029,
0.0657503679394722,
-0.1724906861782074,
-0.013620787300169468,
0.001024985802359879,
-0.01174742728471756,
-0.012076636776328087,
0.004735365975648165,
0.013682681135833263,
0.08979964256286621,
0.0320461206138134,
-0.012037841603159904,
0.006509470287710428,
0.009425236843526363,
-0.08331695944070816,
0.18814682960510254,
-0.10496020317077637,
-0.1302333027124405,
-0.16777801513671875,
0.047208502888679504,
-0.04134882986545563,
-0.0437915101647377,
0.01729314588010311,
-0.07874841243028641,
-0.04245283454656601,
-0.0863397940993309,
-0.023795917630195618,
-0.08394984155893326,
0.002807831158861518,
0.05012379214167595,
0.008417025208473206,
0.12958353757858276,
-0.1231902614235878,
0.023090684786438942,
-0.009996629320085049,
-0.06319589167833328,
-0.01896333321928978,
0.03749988228082657,
0.0915718749165535,
0.11178959161043167,
-0.0014802945079281926,
0.018566468730568886,
-0.040629856288433075,
0.2275509238243103,
-0.04868384823203087,
-0.03199857845902443,
0.12401426583528519,
0.012300269678235054,
0.06919466704130173,
0.07459867745637894,
0.06043454259634018,
-0.07751986384391785,
0.04977615177631378,
0.0762234553694725,
-0.018677441403269768,
-0.2551930248737335,
-0.042984411120414734,
-0.031201891601085663,
-0.035868894308805466,
0.11529235541820526,
0.07064574211835861,
-0.003065674565732479,
0.06850234419107437,
-0.0282660573720932,
0.05493953451514244,
-0.05586353316903114,
0.1107313483953476,
0.0700383335351944,
0.021153099834918976,
0.08495325595140457,
-0.030924974009394646,
-0.027290482074022293,
0.0617833137512207,
0.0032178806141018867,
0.24647961556911469,
-0.007678448222577572,
0.06572794169187546,
0.06635566800832748,
0.17050762474536896,
0.007771254517138004,
0.0405643954873085,
-0.007484658155590296,
-0.018609682098031044,
0.026329727843403816,
-0.07014671713113785,
-0.007016040850430727,
0.03393974155187607,
-0.041194118559360504,
0.09125114977359772,
-0.1201479360461235,
-0.011493167839944363,
-0.02042124979197979,
0.2725021243095398,
0.02932080253958702,
-0.2932283282279968,
-0.09762760251760483,
0.0000881400701473467,
-0.05335582420229912,
-0.1037529706954956,
0.03858816251158714,
0.10814809799194336,
-0.11432494223117828,
0.026500161737203598,
-0.06605994701385498,
0.11644716560840607,
0.006256262771785259,
-0.026399722322821617,
0.04415486007928848,
0.14787408709526062,
-0.01874060370028019,
0.09018082916736603,
-0.1955447793006897,
0.22205178439617157,
0.003308819839730859,
0.09918320924043655,
-0.011577297933399677,
0.0410224013030529,
0.005087235942482948,
0.052442606538534164,
0.07974353432655334,
-0.004109594039618969,
0.01217539794743061,
-0.19993598759174347,
-0.10284168273210526,
0.029926644638180733,
0.0986487939953804,
-0.022539494559168816,
0.07569313794374466,
-0.03358308970928192,
0.030365820974111557,
0.032126061618328094,
-0.053369343280792236,
-0.219307541847229,
-0.10886585712432861,
0.015675608068704605,
0.03395539149641991,
0.003764105262234807,
-0.12765604257583618,
-0.11612223833799362,
-0.03992684558033943,
0.11911959201097488,
-0.03491459786891937,
-0.048859912902116776,
-0.13103513419628143,
0.10215969383716583,
0.13316062092781067,
-0.04855264723300934,
0.027913792058825493,
-0.010199333541095257,
0.15382397174835205,
0.007722551003098488,
-0.05348476395010948,
0.05362921953201294,
-0.08446789532899857,
-0.16672638058662415,
-0.059387508779764175,
0.1556415855884552,
0.06830190122127533,
0.034573376178741455,
-0.014242241159081459,
-0.008286180905997753,
-0.00048019588575698435,
-0.09270767122507095,
-0.0012940590968355536,
0.07196402549743652,
0.028743967413902283,
0.025064852088689804,
-0.0981488823890686,
0.07403270900249481,
-0.030842099338769913,
-0.04041208326816559,
0.10062612593173981,
0.22252832353115082,
-0.07912133634090424,
0.03783951699733734,
0.08951795101165771,
-0.07173767685890198,
-0.14007042348384857,
0.0651998221874237,
0.1672355830669403,
0.021408218890428543,
0.013197548687458038,
-0.20426778495311737,
0.10354380309581757,
0.13663442432880402,
-0.030920131132006645,
0.0915825143456459,
-0.3027168810367584,
-0.11081669479608536,
0.08639475703239441,
0.1119900718331337,
0.0172246266156435,
-0.1543622463941574,
-0.06501822918653488,
0.003338167443871498,
-0.10698448121547699,
0.08715622127056122,
-0.07523404806852341,
0.07530398666858673,
-0.010742643848061562,
0.09360716491937637,
0.023888489231467247,
-0.037605371326208115,
0.15844054520130157,
-0.03037048503756523,
0.06020798906683922,
-0.03058040328323841,
0.05252588912844658,
0.04446180909872055,
-0.06263715028762817,
0.05857197195291519,
-0.004824991337954998,
0.06460943818092346,
-0.16245576739311218,
-0.028881771489977837,
-0.07553049176931381,
0.07798297703266144,
-0.06948930025100708,
-0.05103481933474541,
-0.040753647685050964,
0.05676746740937233,
0.012384168803691864,
-0.015494146384298801,
0.07967707514762878,
0.014374885708093643,
0.15438707172870636,
0.10672100633382797,
0.06263785809278488,
-0.026131346821784973,
-0.1073397845029831,
-0.00018324059783481061,
-0.03722395375370979,
0.09240946173667908,
-0.11674538999795914,
-0.006614779122173786,
0.12137176096439362,
0.03518543764948845,
0.124346524477005,
0.034583140164613724,
-0.09524289518594742,
-0.0030172003898769617,
0.05236474797129631,
-0.09712477028369904,
-0.1296173334121704,
-0.04470536485314369,
0.14794321358203888,
-0.16323480010032654,
0.025642016902565956,
0.09687133133411407,
-0.10367992520332336,
-0.022442610934376717,
-0.006441888865083456,
-0.007852168753743172,
-0.026527702808380127,
0.1514451503753662,
0.06969135999679565,
0.06833545118570328,
-0.04860645532608032,
0.08054188638925552,
0.07760382443666458,
-0.10216844826936722,
0.03721748664975166,
0.06888435781002045,
-0.05875621736049652,
-0.005068708211183548,
0.03807714581489563,
0.1258627474308014,
-0.043205663561820984,
-0.06654104590415955,
-0.06676583737134933,
-0.09426767379045486,
0.054410047829151154,
0.09150167554616928,
0.029941244050860405,
-0.017767934128642082,
-0.017123842611908913,
0.0546497143805027,
-0.14155475795269012,
0.09548179805278778,
0.05090005695819855,
0.09177947044372559,
-0.17329992353916168,
0.10541775822639465,
0.008271911181509495,
0.019420212134718895,
-0.002459142357110977,
0.009183455258607864,
-0.10162895172834396,
-0.019546594470739365,
-0.1315581202507019,
-0.0019983062520623207,
-0.03059244342148304,
0.008247791789472103,
-0.015426001511514187,
-0.05203264206647873,
-0.052295707166194916,
0.04728315770626068,
-0.06709226965904236,
-0.04288400337100029,
0.028441110625863075,
0.07584574073553085,
-0.1068144291639328,
0.018039532005786896,
0.030897511169314384,
-0.08405441790819168,
0.05893532931804657,
0.028535891324281693,
0.043029122054576874,
0.029059888795018196,
-0.09212855249643326,
0.04277666285634041,
0.013725760392844677,
0.01408280897885561,
0.03955729305744171,
-0.07651124149560928,
-0.0030697204638272524,
-0.03298042714595795,
0.048418834805488586,
-0.0021975489798933268,
0.010572382248938084,
-0.1501413732767105,
-0.08265725523233414,
-0.012239844538271427,
-0.04108915105462074,
-0.06336430460214615,
0.040452487766742706,
0.059489648789167404,
0.06062488630414009,
0.12320014089345932,
-0.09007967263460159,
0.02222037874162197,
-0.17904132604599,
-0.014694475568830967,
-0.016260137781500816,
0.0116182342171669,
-0.024704139679670334,
-0.02269296534359455,
0.053681280463933945,
-0.0485529787838459,
0.09376228600740433,
-0.029580693691968918,
0.059472840279340744,
0.042864538729190826,
-0.10023031383752823,
-0.004629812203347683,
0.01397025678306818,
0.24259379506111145,
0.0513310581445694,
0.013182450085878372,
0.05329116806387901,
-0.018953507766127586,
0.050624869763851166,
0.0789550244808197,
0.14454533159732819,
0.15459933876991272,
-0.058779001235961914,
0.05789937824010849,
0.06534858793020248,
-0.07746029645204544,
-0.10740351676940918,
0.06970115005970001,
0.009809649549424648,
0.09596794843673706,
-0.04997801035642624,
0.18925048410892487,
0.13842859864234924,
-0.14454680681228638,
0.02178388088941574,
-0.014086890034377575,
-0.09928065538406372,
-0.11150392144918442,
-0.05246319994330406,
-0.06236456334590912,
-0.1313726156949997,
0.018326902762055397,
-0.13031920790672302,
0.010338624007999897,
0.060273125767707825,
0.02186918817460537,
0.01406258437782526,
0.1588355451822281,
0.02321847528219223,
-0.002064597327262163,
0.07933349907398224,
0.0075894310139119625,
0.00836391095072031,
-0.0885031521320343,
-0.08362134546041489,
0.058039918541908264,
-0.019572734832763672,
0.05286451429128647,
-0.04497108981013298,
-0.024202005937695503,
0.030436629429459572,
0.03081885166466236,
-0.06099950149655342,
0.02975989505648613,
0.005529554095119238,
0.033968955278396606,
0.05782013386487961,
0.028543012216687202,
-0.008597968146204948,
-0.06664914637804031,
0.26911279559135437,
-0.07223591208457947,
-0.02211807481944561,
-0.13863147795200348,
0.19809506833553314,
0.006837653927505016,
-0.01986803486943245,
0.04330914467573166,
-0.1047871932387352,
-0.0245989877730608,
0.16314490139484406,
0.08894216269254684,
-0.017866414040327072,
-0.022433767095208168,
0.004401315934956074,
-0.023324821144342422,
-0.0870983675122261,
0.13753008842468262,
0.10511178523302078,
0.06555236876010895,
-0.04809162765741348,
-0.004337175749242306,
-0.04545413702726364,
0.001059770816937089,
-0.0954332947731018,
0.060330409556627274,
0.02909296751022339,
-0.01412291545420885,
-0.05826248601078987,
0.08671409636735916,
-0.007538542151451111,
-0.13135291635990143,
0.007453967817127705,
-0.10540186613798141,
-0.18043437600135803,
-0.03742828592658043,
0.05055718868970871,
-0.0127042755484581,
0.04607642814517021,
-0.03306526318192482,
-0.0017425552941858768,
0.12747377157211304,
-0.022510677576065063,
-0.021567437797784805,
-0.13266398012638092,
0.09015729278326035,
-0.026325542479753494,
0.2439546138048172,
0.0034440732561051846,
0.06649919599294662,
0.10708251595497131,
0.011989910155534744,
-0.1375022977590561,
0.0065457578748464584,
0.07588750869035721,
-0.08575436472892761,
0.009597701020538807,
0.15157926082611084,
-0.027262063696980476,
0.11438900977373123,
0.01485814992338419,
-0.15405692160129547,
-0.04380020126700401,
-0.06326223909854889,
-0.010765890590846539,
-0.09415452182292938,
0.043289002031087875,
-0.07830916345119476,
0.13523443043231964,
0.18024536967277527,
-0.0690389946103096,
-0.047383833676576614,
-0.07179447263479233,
0.05106910690665245,
0.04798201099038124,
0.08427786827087402,
0.009700588881969452,
-0.2214175909757614,
0.008317865431308746,
-0.012936364859342575,
0.01866103895008564,
-0.2982625365257263,
-0.05212760344147682,
0.020641127601265907,
-0.03635554388165474,
-0.06365278363227844,
0.10425648093223572,
0.061674684286117554,
0.03466121479868889,
-0.039773888885974884,
-0.11265785992145538,
-0.06607659161090851,
0.14624333381652832,
-0.12264235317707062,
-0.03700531646609306
] |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 3.1364 |
### Framework versions
- Transformers 4.37.2
- Pytorch 1.13.0+cu117
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert", "results": []}]} | question-answering | Anandharaju/distilbert | [
"transformers",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-09T09:55:24+00:00 | [] | [] | TAGS
#transformers #safetensors #distilbert #question-answering #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us
| distilbert
==========
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 1.13.0+cu117
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 1.13.0+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #safetensors #distilbert #question-answering #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 1.13.0+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
61,
98,
4,
33
] | [
"passage: TAGS\n#transformers #safetensors #distilbert #question-answering #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 1.13.0+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
-0.11183685064315796,
0.07509081810712814,
-0.001273057539947331,
0.110237717628479,
0.1412845104932785,
0.009779049083590508,
0.1264915019273758,
0.10209250450134277,
-0.08885546773672104,
0.04271556809544563,
0.14071106910705566,
0.12750974297523499,
-0.014182317070662975,
0.10895468294620514,
-0.0743502601981163,
-0.18337678909301758,
0.018265927210450172,
0.021158119663596153,
-0.07119712233543396,
0.12249459326267242,
0.09694557636976242,
-0.14024275541305542,
0.08127469569444656,
-0.01615874655544758,
-0.1766848862171173,
0.020816102623939514,
0.01709921844303608,
-0.0386170968413353,
0.1316308081150055,
0.011568214744329453,
0.1234045997262001,
0.018624570220708847,
0.08889118582010269,
-0.1961088329553604,
0.011322062462568283,
0.05272924527525902,
0.007894125767052174,
0.07033248245716095,
0.017269879579544067,
-0.011867261491715908,
0.052782852202653885,
-0.08736607432365417,
0.05726761743426323,
0.03289950266480446,
-0.13305284082889557,
-0.25510701537132263,
-0.09957684576511383,
0.03204529359936714,
0.09700655937194824,
0.09924867749214172,
-0.007393489591777325,
0.14382857084274292,
-0.088978111743927,
0.08512270450592041,
0.24779893457889557,
-0.31522783637046814,
-0.06582861393690109,
0.046268120408058167,
0.02744269371032715,
0.08135057985782623,
-0.105280801653862,
-0.0327327661216259,
0.08279343694448471,
0.027036776766180992,
0.11671379208564758,
-0.04035064950585365,
-0.09933153539896011,
0.026597430929541588,
-0.14833398163318634,
-0.015553408302366734,
0.17060233652591705,
0.06959489732980728,
-0.06282955408096313,
-0.02145632915198803,
-0.0648953765630722,
-0.10260306298732758,
-0.034244779497385025,
-0.033660661429166794,
0.051474571228027344,
-0.029911436140537262,
-0.07749387621879578,
0.0026431498117744923,
-0.0890641137957573,
-0.1017269641160965,
-0.04854978621006012,
0.16628693044185638,
0.04012169688940048,
0.01735897734761238,
-0.011547123081982136,
0.0988096073269844,
-0.05294830724596977,
-0.13671614229679108,
-0.008453871123492718,
0.01667448878288269,
0.004002523608505726,
-0.05419553071260452,
-0.05927768722176552,
-0.018716786056756973,
0.04438755288720131,
0.19431115686893463,
-0.09669233858585358,
0.026244156062602997,
0.028546934947371483,
0.02634458802640438,
-0.10056404024362564,
0.15244437754154205,
-0.06069524213671684,
-0.02569088526070118,
0.016560478135943413,
0.07871094346046448,
0.03223174065351486,
0.012035960331559181,
-0.0824795812368393,
0.014602004550397396,
0.10061030834913254,
0.026786070317029953,
-0.057184237986803055,
0.05743751674890518,
-0.05460319668054581,
0.0074893212877213955,
0.020824125036597252,
-0.08626152575016022,
0.01738828793168068,
-0.0014989952323958278,
-0.06355483084917068,
-0.0561855249106884,
0.022972874343395233,
0.027171438559889793,
0.029498329386115074,
0.07818882167339325,
-0.0939735546708107,
0.012232846580445766,
-0.08544198423624039,
-0.10547969490289688,
0.006264082156121731,
-0.04968699440360069,
0.03851953148841858,
-0.09872515499591827,
-0.19047896564006805,
-0.009039666503667831,
0.04827079549431801,
-0.021111637353897095,
-0.015149527229368687,
-0.04652869701385498,
-0.0922875627875328,
-0.00975838117301464,
-0.015107030048966408,
0.07319534569978714,
-0.06635633111000061,
0.10699663311243057,
0.07273213565349579,
0.0679040402173996,
-0.04055776447057724,
0.029583808034658432,
-0.13022980093955994,
0.03829443082213402,
-0.19550460577011108,
0.016975635662674904,
-0.08517402410507202,
0.0863812118768692,
-0.08564984798431396,
-0.08969419449567795,
0.008390088565647602,
-0.0009423887240700424,
0.08681066334247589,
0.11621417105197906,
-0.14281387627124786,
-0.04449561610817909,
0.16288799047470093,
-0.09181053191423416,
-0.17899085581302643,
0.11982635408639908,
-0.05296299234032631,
0.061925455927848816,
0.0650547668337822,
0.17602504789829254,
0.0510200597345829,
-0.10976511985063553,
0.000021034193196101114,
-0.0030420254915952682,
0.05024728551506996,
-0.029352009296417236,
0.06894999742507935,
-0.014730648137629032,
-0.03639844432473183,
0.017883187159895897,
-0.08564958721399307,
0.03773287311196327,
-0.10644808411598206,
-0.08624465763568878,
-0.06237781047821045,
-0.12503162026405334,
0.055283140391111374,
0.05222048982977867,
0.05010579898953438,
-0.1313377171754837,
-0.07961540669202805,
0.07412711530923843,
0.08859588205814362,
-0.06415627896785736,
0.0161530002951622,
-0.07505738735198975,
0.07358106970787048,
-0.06946467608213425,
-0.028959909453988075,
-0.15478552877902985,
-0.04648596793413162,
0.008369873277842999,
0.016262676566839218,
0.003932406660169363,
-0.009978268295526505,
0.07689701020717621,
0.0720587894320488,
-0.07058214396238327,
-0.051298338919878006,
-0.03954792767763138,
0.021379882469773293,
-0.10993096232414246,
-0.1988888531923294,
-0.017461441457271576,
-0.029330799356102943,
0.10896150767803192,
-0.20590424537658691,
0.04487834870815277,
-0.029858596622943878,
0.07192070037126541,
0.024431629106402397,
-0.0030955765396356583,
-0.04370138421654701,
0.06812481582164764,
-0.02380423992872238,
-0.0633019283413887,
0.04254249110817909,
0.0034564866218715906,
-0.08573993295431137,
-0.0764533281326294,
-0.10574640333652496,
0.19339241087436676,
0.12370298057794571,
-0.08617204427719116,
-0.08598531782627106,
0.0010484822560101748,
-0.051923494786024094,
-0.03012337163090706,
-0.04965592548251152,
0.017095929011702538,
0.10532297939062119,
-0.020800968632102013,
0.11885149776935577,
-0.08962347358465195,
-0.026166707277297974,
0.002897208323702216,
-0.06445810198783875,
0.04032016918063164,
0.0985100194811821,
0.07502786815166473,
-0.09665481746196747,
0.14135394990444183,
0.1997121423482895,
-0.0988788902759552,
0.08525140583515167,
-0.06999070942401886,
-0.07227715104818344,
-0.03317590802907944,
0.02645309828221798,
0.002022795844823122,
0.13404497504234314,
-0.1059938594698906,
0.02288435958325863,
0.011481409892439842,
0.026570655405521393,
0.005063371732831001,
-0.21265922486782074,
-0.052859317511320114,
0.03185666352510452,
-0.05255467817187309,
-0.032521314918994904,
-0.01669021137058735,
-0.005799553822726011,
0.08369607478380203,
-0.010736346244812012,
-0.07231195271015167,
0.0460941344499588,
0.002905583241954446,
-0.07425616681575775,
0.21893706917762756,
-0.09265448153018951,
-0.07036158442497253,
-0.10178723931312561,
-0.03930284082889557,
-0.04164768010377884,
0.02204993925988674,
0.08131271600723267,
-0.0799960196018219,
-0.03719864785671234,
-0.09181992709636688,
0.0020370713900774717,
0.04505472630262375,
0.020700987428426743,
0.036271486431360245,
-0.006107644643634558,
0.10546735674142838,
-0.11696997284889221,
0.0005958355613984168,
-0.033341117203235626,
-0.07742923498153687,
0.04365970939397812,
0.03982867673039436,
0.13218435645103455,
0.12089799344539642,
-0.014632832258939743,
-0.007812775671482086,
-0.019928231835365295,
0.2719087600708008,
-0.04906720668077469,
-0.035525064915418625,
0.1421983242034912,
0.007418541703373194,
0.049075156450271606,
0.13656650483608246,
0.0636962279677391,
-0.1171124130487442,
0.03508417680859566,
0.040992479771375656,
-0.0184921957552433,
-0.21287071704864502,
-0.016916537657380104,
-0.029770931228995323,
-0.006826472468674183,
0.0731937512755394,
0.01758665218949318,
0.039031535387039185,
0.07589201629161835,
0.020147955045104027,
0.040866781026124954,
-0.023247212171554565,
0.0687548816204071,
0.0986461415886879,
0.04063514620065689,
0.11808590590953827,
-0.044881489127874374,
-0.0480462871491909,
0.02702607959508896,
-0.012240475974977016,
0.22186017036437988,
0.011415690183639526,
0.12199553102254868,
0.07763603329658508,
0.2045111209154129,
-0.020464163273572922,
0.0783115029335022,
-0.010885338298976421,
-0.06008441001176834,
-0.0045949737541377544,
-0.05285034328699112,
-0.019467487931251526,
0.0351160503923893,
-0.11360953748226166,
0.09176693856716156,
-0.11048455536365509,
0.02236635982990265,
0.0722823515534401,
0.25244346261024475,
0.050915222615003586,
-0.3081358075141907,
-0.10421177744865417,
0.012578876689076424,
-0.01716483384370804,
-0.019614722579717636,
0.03376356139779091,
0.11731532961130142,
-0.049865514039993286,
0.01145995780825615,
-0.046671077609062195,
0.0852838009595871,
0.03312571719288826,
0.04836178198456764,
0.05134354159235954,
0.09711042046546936,
-0.011075107380747795,
0.06854123622179031,
-0.2971244156360626,
0.2658396363258362,
0.014849822036921978,
0.0929926261305809,
-0.03121424838900566,
-0.015681954100728035,
0.021371863782405853,
0.09303363412618637,
0.08637533336877823,
-0.01778445765376091,
-0.039823468774557114,
-0.18763592839241028,
-0.03179602697491646,
0.043330926448106766,
0.09208974987268448,
-0.024096669629216194,
0.10875064134597778,
-0.03126715123653412,
0.013537904247641563,
0.09471160173416138,
-0.0075556435622274876,
-0.11538156121969223,
-0.06158946081995964,
-0.04115879535675049,
0.017289742827415466,
0.011988820508122444,
-0.10349082946777344,
-0.08294602483510971,
-0.11246815323829651,
0.12480878084897995,
-0.014469866640865803,
-0.02310127206146717,
-0.0973312184214592,
0.056502703577280045,
0.08393237739801407,
-0.07398057729005814,
0.048727698624134064,
0.02239709161221981,
0.046222541481256485,
0.04580415412783623,
-0.04482971504330635,
0.12137078493833542,
-0.08048433810472488,
-0.17282643914222717,
-0.05420191213488579,
0.09792061150074005,
0.03725333884358406,
0.04013451561331749,
-0.00014474100316874683,
0.008343826048076153,
-0.027876654639840126,
-0.09543126076459885,
0.011488303542137146,
-0.019565671682357788,
0.07275302708148956,
0.03530729562044144,
-0.047469861805438995,
0.03687344118952751,
-0.058952413499355316,
-0.02052317187190056,
0.1406276524066925,
0.29960373044013977,
-0.0884055644273758,
-0.012796394526958466,
0.07321150600910187,
-0.036665353924036026,
-0.18820257484912872,
0.05578232929110527,
0.025447113439440727,
-0.011220927350223064,
0.06540712714195251,
-0.1240173801779747,
0.14962562918663025,
0.11416247487068176,
-0.02440686896443367,
0.08835910260677338,
-0.30236029624938965,
-0.11945880204439163,
0.12830540537834167,
0.16410613059997559,
0.13586339354515076,
-0.16258938610553741,
-0.02551509067416191,
-0.024308621883392334,
-0.15517914295196533,
0.07419946789741516,
-0.15023300051689148,
0.09156262874603271,
-0.017185302451252937,
0.05494434013962746,
0.000548195035662502,
-0.05808328837156296,
0.14712585508823395,
0.008286923170089722,
0.13593928515911102,
-0.04916273429989815,
0.007967989891767502,
0.07171174138784409,
-0.04684184864163399,
0.03866343945264816,
-0.08955540508031845,
0.07285071909427643,
-0.03899171203374863,
-0.022472312673926353,
-0.06406261026859283,
0.04498409107327461,
-0.044416893273591995,
-0.06991421431303024,
-0.042023736983537674,
0.02547723427414894,
0.038383547216653824,
-0.018619559705257416,
0.11745508760213852,
0.02274869754910469,
0.15020036697387695,
0.10830630362033844,
0.06525050103664398,
-0.08594290167093277,
-0.035866398364305496,
0.01108234841376543,
-0.03475329279899597,
0.06697645783424377,
-0.15060974657535553,
0.04897216707468033,
0.1208873838186264,
0.03415193036198616,
0.13589639961719513,
0.0680881142616272,
-0.02454277314245701,
0.00897021871060133,
0.048712193965911865,
-0.1619674265384674,
-0.11971896886825562,
0.008944901637732983,
-0.043579909950494766,
-0.12960101664066315,
0.0856587216258049,
0.08941322565078735,
-0.06231668218970299,
0.0033963103778660297,
-0.018172333016991615,
0.005326727405190468,
-0.06432978808879852,
0.18950146436691284,
0.08754538744688034,
0.05384911596775055,
-0.08572117239236832,
0.08472277969121933,
0.02558952197432518,
-0.05367996171116829,
-0.005065029487013817,
0.01611078903079033,
-0.0801796242594719,
-0.043783366680145264,
0.07958602160215378,
0.17895324528217316,
-0.0471854992210865,
-0.04779958352446556,
-0.13907171785831451,
-0.11431056261062622,
0.045609816908836365,
0.17269647121429443,
0.10816528648138046,
0.011865333653986454,
-0.0019146426348015666,
0.020818982273340225,
-0.11859235167503357,
0.10775873064994812,
0.041508227586746216,
0.08295073360204697,
-0.15736331045627594,
0.10063884407281876,
-0.013196907937526703,
0.013813658617436886,
-0.020542513579130173,
0.05208507552742958,
-0.11683099716901779,
0.008588239550590515,
-0.17931827902793884,
-0.027495086193084717,
-0.04370627924799919,
0.012247799895703793,
0.013033898547291756,
-0.08126012980937958,
-0.06481299549341202,
0.030893323943018913,
-0.10263198614120483,
-0.015415881760418415,
0.061710137873888016,
0.0539582297205925,
-0.1516515016555786,
-0.04179812967777252,
0.041872963309288025,
-0.06633003056049347,
0.05519958585500717,
0.04536423459649086,
0.01819918304681778,
0.05442165583372116,
-0.18622055649757385,
0.006595405284315348,
0.059979844838380814,
0.0009018214186653495,
0.05762210860848427,
-0.09720979630947113,
-0.029172664508223534,
0.005996914114803076,
0.05237002298235893,
0.012634138576686382,
0.05920102819800377,
-0.12553341686725616,
-0.014783765189349651,
-0.038508445024490356,
-0.056834083050489426,
-0.05590476840734482,
-0.0055146366357803345,
0.11596325784921646,
-0.000014820935575698968,
0.21156376600265503,
-0.08655818551778793,
0.017232557758688927,
-0.20929662883281708,
-0.004801836796104908,
-0.012346768751740456,
-0.09166603535413742,
-0.13835394382476807,
-0.04320288822054863,
0.04840676486492157,
-0.055478330701589584,
0.13235890865325928,
-0.04684416577219963,
0.05220752954483032,
0.029928896576166153,
-0.060085345059633255,
0.06875813007354736,
0.026457853615283966,
0.2770983874797821,
0.01982911303639412,
-0.03212719038128853,
0.024102574214339256,
0.04700165241956711,
0.09883904457092285,
0.05356805399060249,
0.16359105706214905,
0.19455008208751678,
-0.054315630346536636,
0.07971448451280594,
0.03587774559855461,
-0.054386626929044724,
-0.1069716066122055,
0.04916549474000931,
-0.013093571178615093,
0.051523175090551376,
-0.010522131808102131,
0.21039146184921265,
0.10689374059438705,
-0.16334664821624756,
0.01978929527103901,
-0.060883451253175735,
-0.08445033431053162,
-0.09649374336004257,
-0.007786097936332226,
-0.08784451335668564,
-0.1953769475221634,
0.009022224694490433,
-0.12783373892307281,
0.005854851566255093,
0.10440151393413544,
-0.0011862805113196373,
-0.0145388999953866,
0.21180683374404907,
0.023332398384809494,
0.05090140178799629,
0.02219432033598423,
-0.01720026321709156,
-0.043545544147491455,
-0.05436594784259796,
-0.08479278534650803,
0.02857123874127865,
-0.03788129985332489,
0.023290952667593956,
-0.05428886041045189,
-0.06372924149036407,
0.038629308342933655,
-0.019833102822303772,
-0.09722483903169632,
0.008505062200129032,
0.04026463255286217,
0.0396905392408371,
0.051714491099119186,
0.02394782193005085,
0.01184902060776949,
0.000533741433173418,
0.23786033689975739,
-0.08468709141016006,
-0.10126019269227982,
-0.11600013077259064,
0.23487143218517303,
0.03971864655613899,
0.01902075856924057,
0.01836708001792431,
-0.0967102125287056,
0.04248747229576111,
0.19808156788349152,
0.15012907981872559,
-0.09005001187324524,
-0.00022861105389893055,
-0.026902340352535248,
-0.017497725784778595,
-0.06891487538814545,
0.061787933111190796,
0.1329432725906372,
-0.008845934644341469,
-0.0807860940694809,
-0.050059299916028976,
-0.05082076042890549,
-0.014487894251942635,
-0.055435068905353546,
0.02790037728846073,
0.03047294169664383,
0.0004314643156249076,
-0.046179987490177155,
0.06219051405787468,
-0.03009379282593727,
-0.11467890441417694,
0.07111714780330658,
-0.15378418564796448,
-0.13131900131702423,
-0.029096219688653946,
0.10567522794008255,
-0.0013063478982076049,
0.049395810812711716,
-0.0430574044585228,
-0.0013778667198494077,
0.0804399773478508,
-0.025011027231812477,
-0.05232388153672218,
-0.09792521595954895,
0.0794183686375618,
-0.08586379140615463,
0.21780242025852203,
-0.023448938503861427,
0.0654626339673996,
0.13475193083286285,
0.03517073392868042,
-0.09331618249416351,
0.10530301928520203,
0.05804574862122536,
-0.0952521562576294,
0.01517803966999054,
0.060675669461488724,
-0.030499696731567383,
0.1218089610338211,
0.05062233284115791,
-0.15895621478557587,
0.0056443107314407825,
-0.014648244716227055,
-0.06991437822580338,
-0.08081354945898056,
-0.04764086380600929,
-0.055470578372478485,
0.12968960404396057,
0.1798020750284195,
-0.05564955621957779,
0.031937599182128906,
-0.04502016678452492,
0.05295601859688759,
0.07505203783512115,
0.02970193326473236,
-0.033503349870443344,
-0.22947396337985992,
0.05336650833487511,
0.08880496770143509,
-0.027791300788521767,
-0.23249982297420502,
-0.09315017610788345,
0.01186178158968687,
-0.05751008540391922,
-0.06851352751255035,
0.07252593338489532,
0.12508071959018707,
0.06315668672323227,
-0.053082119673490524,
-0.11362546682357788,
-0.093072310090065,
0.17539148032665253,
-0.12029724568128586,
-0.10056403279304504
] |
null | null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | null | gsstein/model-1 | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-09T09:57:57+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
31,
6,
3,
82,
28,
3,
4,
9,
9,
10,
42,
20,
3,
4,
5,
9,
11,
13,
3,
12,
5,
4,
5,
3,
4,
9,
53,
9,
8,
6,
3,
14,
8,
7,
9,
4
] | [
"passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
] | [
-0.06646376848220825,
0.2168014943599701,
-0.00225935154594481,
0.023818302899599075,
0.1271018385887146,
-0.001635765191167593,
0.04218708351254463,
0.13324736058712006,
-0.020175931975245476,
0.11144465953111649,
0.046588581055402756,
0.09377603232860565,
0.09928803145885468,
0.18404334783554077,
0.04859916493296623,
-0.2059975117444992,
0.007056170143187046,
-0.09090408682823181,
0.014076028019189835,
0.1116579994559288,
0.13719257712364197,
-0.10291384905576706,
0.08272874355316162,
-0.04045208916068077,
-0.02019004337489605,
0.00012576708104461432,
-0.09259183704853058,
-0.07032395154237747,
0.06885425746440887,
0.06264153122901917,
0.051234472543001175,
0.001456156256608665,
0.09140396863222122,
-0.2864592671394348,
0.017265573143959045,
0.08406311273574829,
0.0027674848679453135,
0.06290827691555023,
0.07236549258232117,
-0.07389893382787704,
0.11328595131635666,
-0.08021481335163116,
0.13019037246704102,
0.08625296503305435,
-0.062064990401268005,
-0.23071379959583282,
-0.07525765895843506,
0.0963398814201355,
0.12251301854848862,
0.06215599179267883,
-0.022921854630112648,
0.15455181896686554,
-0.06248689442873001,
0.012971068732440472,
0.1294165402650833,
-0.11526761949062347,
-0.05572471022605896,
0.061741601675748825,
0.11775490641593933,
0.10740239918231964,
-0.14110268652439117,
-0.0017287094378843904,
0.04900608956813812,
0.029121357947587967,
0.08589313924312592,
0.022661056369543076,
0.12003941088914871,
0.04652795568108559,
-0.13695219159126282,
-0.04037507623434067,
0.12011898308992386,
0.038862764835357666,
-0.06446044892072678,
-0.2168138176202774,
-0.006778308190405369,
-0.0601806715130806,
-0.014732478186488152,
-0.07019448280334473,
0.039128515869379044,
-0.02470310963690281,
0.07317749410867691,
-0.04465159401297569,
-0.1063927412033081,
-0.0421026237308979,
0.0892222449183464,
0.07748593389987946,
0.011527054943144321,
-0.02519804798066616,
0.04627908393740654,
0.13455867767333984,
0.05402068421244621,
-0.10399353504180908,
-0.07017925381660461,
-0.06942764669656754,
-0.09420394152402878,
-0.04035796597599983,
0.056760527193546295,
0.031942449510097504,
0.02665667235851288,
0.22703726589679718,
0.016653569415211678,
0.04155244305729866,
0.0224777739495039,
0.01032855175435543,
0.043662428855895996,
0.0955500528216362,
-0.05303520709276199,
-0.15660029649734497,
-0.04072032496333122,
0.09077946096658707,
-0.0027527001220732927,
-0.036689214408397675,
-0.03966725245118141,
0.03849169611930847,
0.06843466311693192,
0.13122352957725525,
0.07552056759595871,
-0.017929591238498688,
-0.04813180863857269,
-0.030096933245658875,
0.23523783683776855,
-0.1493375599384308,
0.04426715523004532,
-0.02271856553852558,
-0.01804111897945404,
-0.03908449783921242,
0.03597262129187584,
0.022118929773569107,
-0.000004518366949923802,
0.09706240892410278,
-0.058981191366910934,
-0.05378659814596176,
-0.10168042778968811,
-0.03272576630115509,
0.04088849574327469,
-0.013975566253066063,
-0.010589460842311382,
-0.09025166928768158,
-0.09490354359149933,
-0.04766594246029854,
0.05537205561995506,
-0.05123869329690933,
-0.03770573064684868,
0.009465423412621021,
-0.08151785284280777,
-0.005444355774670839,
-0.005417742300778627,
0.10699385404586792,
-0.03222226724028587,
0.04445803165435791,
-0.027600755915045738,
0.05225523188710213,
0.09919606149196625,
0.031576547771692276,
-0.0773419588804245,
0.0561848059296608,
-0.22559374570846558,
0.07503069192171097,
-0.11481974273920059,
0.04335082694888115,
-0.1704932004213333,
-0.042439818382263184,
0.005444696638733149,
0.0139949731528759,
0.013206101022660732,
0.12720820307731628,
-0.19255615770816803,
-0.01654396951198578,
0.13260798156261444,
-0.09212633967399597,
-0.118110790848732,
0.07884611934423447,
-0.029701577499508858,
0.1624738723039627,
0.04682036489248276,
-0.027025915682315826,
0.09224298596382141,
-0.16434773802757263,
-0.07092688232660294,
-0.00949116237461567,
-0.01727987825870514,
0.12109188735485077,
0.07512219995260239,
-0.05991523340344429,
0.046571120619773865,
0.02832140028476715,
-0.038078423589468,
-0.04424772411584854,
-0.050857074558734894,
-0.10884185880422592,
-0.01070026308298111,
-0.08987759798765182,
0.04065500199794769,
-0.01250192429870367,
-0.07916021347045898,
-0.029885273426771164,
-0.18612512946128845,
-0.0030564051121473312,
0.10038342326879501,
0.0035033065360039473,
-0.005652366206049919,
-0.08666291832923889,
0.026358824223279953,
-0.03112892620265484,
-0.008404186926782131,
-0.16764774918556213,
-0.04399421438574791,
0.046902090311050415,
-0.16094985604286194,
0.020117372274398804,
-0.06413903087377548,
0.06334125250577927,
0.03641495108604431,
-0.05590536445379257,
-0.0248766727745533,
-0.01730942726135254,
0.011945613659918308,
-0.05083848536014557,
-0.18994836509227753,
-0.056277405470609665,
-0.037882111966609955,
0.149809330701828,
-0.25956398248672485,
0.032966937869787216,
0.051140617579221725,
0.14649195969104767,
0.00406361510977149,
-0.05115427449345589,
0.01429014839231968,
-0.05360214412212372,
-0.054652128368616104,
-0.06746816635131836,
-0.006135428790003061,
-0.027576493099331856,
-0.05147203803062439,
0.019243421033024788,
-0.1755700707435608,
-0.021410830318927765,
0.09424154460430145,
0.12876708805561066,
-0.1486445665359497,
-0.018640631809830666,
-0.048725154250860214,
-0.06339836865663528,
-0.0715010017156601,
-0.07038594037294388,
0.10712739825248718,
0.0513901449739933,
0.04796046018600464,
-0.07435787469148636,
-0.07092321664094925,
0.02726263552904129,
0.006906150374561548,
-0.03382374346256256,
0.08727246522903442,
0.05199531093239784,
-0.09209315478801727,
0.0756213590502739,
0.1092359870672226,
0.07177663594484329,
0.09363535046577454,
0.01574566215276718,
-0.11756632477045059,
-0.028492970392107964,
0.036266472190618515,
0.02740776725113392,
0.1465986967086792,
-0.05952361226081848,
0.04016614332795143,
0.04494241625070572,
-0.04170418903231621,
0.022319864481687546,
-0.08787637203931808,
0.024075502529740334,
0.025203049182891846,
-0.0034381982404738665,
0.06284574419260025,
-0.02525499276816845,
-0.0050758360885083675,
0.07016654312610626,
0.047779910266399384,
0.04621000960469246,
0.009655474685132504,
-0.01720241829752922,
-0.1047825813293457,
0.16950392723083496,
-0.0951867327094078,
-0.269941508769989,
-0.17632324993610382,
0.026197833940386772,
0.04035249724984169,
-0.022378476336598396,
0.031619444489479065,
-0.07056326419115067,
-0.10630585998296738,
-0.1060405746102333,
-0.002429972169920802,
0.01714223250746727,
-0.06364088505506516,
-0.0741225928068161,
0.07348573952913284,
0.04382912442088127,
-0.14902326464653015,
0.038552410900592804,
0.055694397538900375,
-0.057955220341682434,
-0.0233661737293005,
0.09118817001581192,
0.12397737801074982,
0.14583967626094818,
-0.021366750821471214,
-0.028626007959246635,
0.029004426673054695,
0.19620531797409058,
-0.13469526171684265,
0.10371150821447372,
0.13814030587673187,
-0.04545360431075096,
0.08360563963651657,
0.1560150384902954,
0.029186224564909935,
-0.08317049592733383,
0.05044832453131676,
0.04082648828625679,
-0.043159641325473785,
-0.2666129767894745,
-0.0534592866897583,
0.012832709588110447,
-0.06255637854337692,
0.09786593168973923,
0.10183793306350708,
0.11542957276105881,
0.034910861402750015,
-0.07166364789009094,
-0.043925940990448,
-0.0058974819257855415,
0.11737963557243347,
-0.05490213260054588,
-0.012639665976166725,
0.07686592638492584,
-0.05086168646812439,
0.005355054512619972,
0.10266812145709991,
0.02973790094256401,
0.17442677915096283,
0.020399179309606552,
0.11231429129838943,
0.06195578724145889,
0.08633565157651901,
0.0007386076031252742,
0.02951662428677082,
0.05147615820169449,
0.017203815281391144,
-0.002300140680745244,
-0.10421168059110641,
-0.006156572140753269,
0.1449710875749588,
0.028103826567530632,
0.029669636860489845,
-0.0018948549404740334,
-0.005003341939300299,
0.05121048167347908,
0.1746254414319992,
-0.011592294089496136,
-0.22072425484657288,
-0.0845772922039032,
0.06936841458082199,
-0.06218599155545235,
-0.12968985736370087,
-0.026130788028240204,
0.045467354357242584,
-0.17519839107990265,
0.026703642681241035,
-0.027433741837739944,
0.0919293761253357,
-0.09345759451389313,
-0.02221956104040146,
0.03687324374914169,
0.084866963326931,
-0.014529162086546421,
0.08703910559415817,
-0.14498743414878845,
0.11886418610811234,
0.02978132851421833,
0.09024628251791,
-0.11081171780824661,
0.07909037172794342,
-0.007550720125436783,
0.009180475026369095,
0.19379350543022156,
-0.011335089802742004,
-0.03514958545565605,
-0.08774717897176743,
-0.11210042238235474,
-0.013537433929741383,
0.12687496840953827,
-0.1243172138929367,
0.08773399889469147,
-0.015198243781924248,
-0.044079482555389404,
0.00937260314822197,
-0.12100647389888763,
-0.17273177206516266,
-0.19628387689590454,
0.05585884302854538,
-0.09575839340686798,
0.025643249973654747,
-0.11914430558681488,
-0.07089093327522278,
-0.02952558360993862,
0.241120383143425,
-0.1745356321334839,
-0.06510113179683685,
-0.1468164622783661,
-0.046294767409563065,
0.1662203073501587,
-0.04437198117375374,
0.0718095526099205,
-0.0208172257989645,
0.20345525443553925,
0.005988610442727804,
-0.004939318168908358,
0.06724198162555695,
-0.08892562240362167,
-0.16873881220817566,
-0.06771010160446167,
0.1510489284992218,
0.11680185794830322,
0.04907919466495514,
-0.002248800592496991,
0.0011772146681323647,
-0.016943959519267082,
-0.1137804463505745,
-0.0033210667315870523,
0.16037839651107788,
0.03878779336810112,
0.025986969470977783,
-0.05243593826889992,
-0.08797456324100494,
-0.06899320334196091,
-0.06853509694337845,
0.06221301481127739,
0.19590823352336884,
-0.10376439243555069,
0.1700313836336136,
0.147536963224411,
-0.07305635511875153,
-0.23175598680973053,
0.035342130810022354,
0.04983805492520332,
0.0014306638622656465,
0.04886869341135025,
-0.18252557516098022,
0.10521943867206573,
0.019543392583727837,
-0.05505957826972008,
0.13485197722911835,
-0.1557481735944748,
-0.1552847921848297,
0.0722852572798729,
0.03904085233807564,
-0.22423844039440155,
-0.1354004591703415,
-0.09622503817081451,
-0.05825018882751465,
-0.14065024256706238,
0.06054598465561867,
-0.002136280992999673,
0.015948504209518433,
0.03500790148973465,
-0.0015643214574083686,
0.027123261243104935,
-0.058935679495334625,
0.18609118461608887,
-0.004065449349582195,
0.020676052197813988,
-0.060264769941568375,
-0.0478842556476593,
0.09839435666799545,
-0.06130504235625267,
0.12208222597837448,
0.004057085141539574,
0.01594383642077446,
-0.10362856835126877,
-0.048314861953258514,
-0.04328322783112526,
0.05154227837920189,
-0.07548051327466965,
-0.10070807486772537,
-0.043625857681035995,
0.08841723203659058,
0.07005169242620468,
-0.03383097052574158,
0.00549331633374095,
-0.07189501076936722,
0.10019614547491074,
0.17795267701148987,
0.17573626339435577,
0.009926567785441875,
-0.07241068035364151,
0.01677953451871872,
-0.04142116755247116,
0.044231921434402466,
-0.2513144314289093,
0.03756171092391014,
0.06098250672221184,
0.029438555240631104,
0.09217222779989243,
-0.020435843616724014,
-0.1820858269929886,
-0.04050002992153168,
0.08094815909862518,
-0.05452597141265869,
-0.22617179155349731,
-0.019085140898823738,
0.0954197570681572,
-0.2020406424999237,
-0.007372708059847355,
0.03995226323604584,
-0.048725228756666183,
-0.023169852793216705,
0.00010950004070764408,
0.06317184865474701,
0.002471912419423461,
0.09773622453212738,
0.0735151618719101,
0.09715340286493301,
-0.08337292820215225,
0.10562895983457565,
0.10150538384914398,
-0.09572599828243256,
0.03605884686112404,
0.06754924356937408,
-0.05300498008728027,
-0.043293699622154236,
0.03665391728281975,
0.033023297786712646,
0.005234600510448217,
-0.060321882367134094,
0.013913018628954887,
-0.036497246474027634,
0.044923391193151474,
0.08326134830713272,
0.03754979372024536,
-0.013354414142668247,
0.06462216377258301,
0.03401726484298706,
-0.10898099094629288,
0.10366570204496384,
0.01731540448963642,
0.04105307161808014,
-0.08384523540735245,
-0.019968897104263306,
0.035425446927547455,
0.030576206743717194,
-0.01765924133360386,
-0.02306121215224266,
-0.02860277332365513,
-0.01614218018949032,
-0.14299540221691132,
-0.023106401786208153,
-0.07243485748767853,
0.006181265693157911,
0.014656842686235905,
-0.031884219497442245,
-0.011233693920075893,
0.02475680410861969,
-0.06979699432849884,
-0.07426341623067856,
-0.006949664559215307,
0.09833318740129471,
-0.15115703642368317,
0.008848577737808228,
0.06907843053340912,
-0.11088496446609497,
0.08190931379795074,
-0.008411259390413761,
0.016245156526565552,
0.022527478635311127,
-0.15448406338691711,
0.05601610988378525,
0.0008648968650959432,
0.01916889287531376,
0.025886621326208115,
-0.16471809148788452,
0.004104440100491047,
-0.04661374166607857,
-0.02149827405810356,
-0.00004464812809601426,
-0.02647159807384014,
-0.12325995415449142,
0.06858719140291214,
-0.015622655861079693,
-0.035931166261434555,
-0.02701525390148163,
0.0539589487016201,
0.07888586074113846,
-0.027474910020828247,
0.10445091128349304,
-0.008690856397151947,
0.04941811040043831,
-0.16801609098911285,
-0.02470702864229679,
-0.04982255399227142,
0.019377702847123146,
0.009884213097393513,
-0.007693959400057793,
0.04183054715394974,
-0.00976533442735672,
0.21883612871170044,
-0.05075952783226967,
0.1607085019350052,
0.05847611650824547,
-0.017352959141135216,
-0.0007513365126214921,
0.06180921941995621,
0.05997028574347496,
0.04658793285489082,
0.009480604901909828,
0.023740366101264954,
-0.022450892254710197,
-0.006695089396089315,
-0.15932634472846985,
0.01890849508345127,
0.14999441802501678,
0.06301083415746689,
0.024745315313339233,
0.05866100639104843,
-0.12775006890296936,
-0.12135478109121323,
0.09311001747846603,
-0.026755332946777344,
0.00928465835750103,
-0.08245618641376495,
0.1358020007610321,
0.14980104565620422,
-0.14000412821769714,
0.05256148427724838,
-0.06134212389588356,
-0.05217423290014267,
-0.10388828068971634,
-0.12032219022512436,
-0.05887215584516525,
-0.053666237741708755,
0.002330566756427288,
-0.03760887682437897,
0.054546963423490524,
0.03344334661960602,
-0.009351172484457493,
-0.00022941511997487396,
0.13597318530082703,
-0.019751882180571556,
-0.0028988157864660025,
0.048313532024621964,
0.03693558648228645,
0.02373051457107067,
-0.05275435373187065,
0.02940409444272518,
0.02539868652820587,
0.032232340425252914,
0.06546790152788162,
0.033412106335163116,
-0.047448933124542236,
0.03804153576493263,
-0.0025254099164158106,
-0.11207924783229828,
0.019641218706965446,
-0.00460948096588254,
-0.0742158442735672,
0.1268945336341858,
0.0407399944961071,
0.010224059224128723,
-0.03741471841931343,
0.24361543357372284,
-0.06653323769569397,
-0.06378097087144852,
-0.13251738250255585,
0.10491154342889786,
-0.0027236645109951496,
0.06476365029811859,
0.023412218317389488,
-0.1284150779247284,
0.005243356805294752,
0.13858191668987274,
0.12181595712900162,
0.0045748427510261536,
0.009228081442415714,
0.0518609918653965,
0.0025186820421367884,
-0.06998204439878464,
0.054019294679164886,
0.06992026418447495,
0.12919506430625916,
-0.07847554981708527,
0.07680778950452805,
0.0006860480643808842,
-0.08370215445756912,
-0.02947772853076458,
0.11312682181596756,
-0.0409729965031147,
0.03491825982928276,
-0.047444481402635574,
0.10916327685117722,
-0.05787910893559456,
-0.29412412643432617,
0.02350960113108158,
-0.09588567912578583,
-0.15202060341835022,
-0.018367812037467957,
0.05944539234042168,
-0.02624768204987049,
0.018029648810625076,
0.06971040368080139,
-0.06011629104614258,
0.20098382234573364,
0.0335683599114418,
-0.07864278554916382,
-0.0664360448718071,
0.04837050288915634,
-0.06564252078533173,
0.2949807047843933,
0.008418165147304535,
0.02863333560526371,
0.10770907253026962,
-0.03253700211644173,
-0.18271861970424652,
0.010723991319537163,
0.1133992001414299,
-0.08056149631738663,
0.08200647681951523,
0.19000613689422607,
-0.012578671798110008,
0.1209007054567337,
0.05294662341475487,
-0.047376248985528946,
0.04217283055186272,
-0.03389401361346245,
-0.051268599927425385,
-0.10752558708190918,
0.058453381061553955,
-0.05909625440835953,
0.15447644889354706,
0.10152646154165268,
-0.05671518296003342,
-0.004550917539745569,
-0.05555408447980881,
0.04875178262591362,
0.01804669201374054,
0.12263146042823792,
0.02951994352042675,
-0.1865430772304535,
0.032826557755470276,
-0.01144319772720337,
0.10186848044395447,
-0.25588861107826233,
-0.08421015739440918,
0.08833149075508118,
-0.011924264021217823,
-0.05105875805020332,
0.10560628771781921,
0.057650718837976456,
0.04243382066488266,
-0.043439045548439026,
-0.10480839014053345,
-0.02186836116015911,
0.14663739502429962,
-0.1469624787569046,
-0.025013303384184837
] |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper_snall - Devansh Jain
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4313
- Wer: 33.3827
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0817 | 2.44 | 1000 | 0.2951 | 35.2789 |
| 0.0197 | 4.89 | 2000 | 0.3452 | 33.6536 |
| 0.0017 | 7.33 | 3000 | 0.4126 | 33.4716 |
| 0.0005 | 9.78 | 4000 | 0.4313 | 33.3827 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
| {"language": ["hi"], "license": "apache-2.0", "tags": ["hf-asr-leaderboard", "generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_11_0"], "metrics": ["wer"], "base_model": "openai/whisper-small", "model-index": [{"name": "Whisper_snall - Devansh Jain", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 11.0", "type": "mozilla-foundation/common_voice_11_0", "config": "hi", "split": "None", "args": "config: hi, split: test"}, "metrics": [{"type": "wer", "value": 33.382713959197496, "name": "Wer"}]}]}]} | automatic-speech-recognition | Devanshj7/whisper-small-new | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"hi",
"dataset:mozilla-foundation/common_voice_11_0",
"base_model:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2024-02-09T10:02:44+00:00 | [] | [
"hi"
] | TAGS
#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #hi #dataset-mozilla-foundation/common_voice_11_0 #base_model-openai/whisper-small #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Whisper\_snall - Devansh Jain
=============================
This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4313
* Wer: 33.3827
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 4000
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.2.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #hi #dataset-mozilla-foundation/common_voice_11_0 #base_model-openai/whisper-small #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
104,
130,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #hi #dataset-mozilla-foundation/common_voice_11_0 #base_model-openai/whisper-small #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2"
] | [
-0.11952745914459229,
0.11941242963075638,
-0.0034983286168426275,
0.04692809283733368,
0.09798111766576767,
0.0060584768652915955,
0.1288139671087265,
0.14827460050582886,
-0.043524064123630524,
0.08948429673910141,
0.08115120977163315,
0.06395590305328369,
0.07923804223537445,
0.16324268281459808,
-0.031108615919947624,
-0.28583917021751404,
0.017502352595329285,
-0.029275206848978996,
-0.1038767620921135,
0.09745732694864273,
0.097342848777771,
-0.10451850295066833,
0.032644402235746384,
0.00688188336789608,
-0.07390320301055908,
0.002600488718599081,
-0.020681682974100113,
-0.05890017747879028,
0.09682659059762955,
0.03471076488494873,
0.05035526677966118,
0.038123611360788345,
0.08046657592058182,
-0.25796082615852356,
0.01534019410610199,
0.045544300228357315,
0.04349525272846222,
0.06084654480218887,
0.08028247207403183,
-0.01722591184079647,
0.07676748186349869,
-0.08122553676366806,
0.06774035841226578,
0.05936288833618164,
-0.09004805982112885,
-0.28519579768180847,
-0.07825398445129395,
0.04210852459073067,
0.13024745881557465,
0.06603112071752548,
-0.03357609733939171,
0.06465145945549011,
-0.05802129581570625,
0.09395456314086914,
0.18617649376392365,
-0.21816898882389069,
-0.06402730941772461,
-0.03493266925215721,
0.05131533741950989,
0.0749332457780838,
-0.10691995918750763,
-0.012593217194080353,
0.017313674092292786,
0.018831145018339157,
0.09526355564594269,
-0.004454630892723799,
0.021339988335967064,
-0.010821346193552017,
-0.13076823949813843,
-0.03430645167827606,
0.11370992660522461,
0.07261544466018677,
-0.02663978934288025,
-0.14259254932403564,
-0.028363145887851715,
-0.12987786531448364,
-0.06374671310186386,
0.014203444123268127,
0.024891313165426254,
-0.035851072520017624,
-0.06452930718660355,
-0.003598109120503068,
-0.05644652247428894,
-0.08200107514858246,
0.060559891164302826,
0.14118337631225586,
0.035056062042713165,
-0.03819194436073303,
0.0075057861395180225,
0.08459623903036118,
0.05496944114565849,
-0.15436875820159912,
-0.011375181376934052,
0.03292703256011009,
-0.0949220210313797,
-0.017533864825963974,
-0.01440479326993227,
-0.021164191886782646,
0.05282386764883995,
0.13765040040016174,
-0.008906918577849865,
0.10261900722980499,
0.014567142352461815,
0.01708231121301651,
-0.09227313101291656,
0.15572156012058258,
-0.04268523305654526,
-0.08205948024988174,
-0.027877457439899445,
0.14562711119651794,
0.014405537396669388,
-0.016903985291719437,
-0.06588205695152283,
0.0302223339676857,
0.08297321945428848,
0.05567732825875282,
-0.006089586764574051,
0.02845030277967453,
-0.06372176110744476,
-0.020995760336518288,
-0.011534272693097591,
-0.131720632314682,
0.040582675486803055,
0.047146085649728775,
-0.044086843729019165,
-0.03653584420681,
0.0028848021756857634,
0.02877398394048214,
-0.017224594950675964,
0.06498377025127411,
-0.044540904462337494,
-0.008623589761555195,
-0.07434704154729843,
-0.08681794255971909,
0.021690359339118004,
-0.020298663526773453,
-0.002197478199377656,
-0.0648069828748703,
-0.10264565050601959,
-0.05966245383024216,
0.05116072669625282,
-0.03918933868408203,
-0.056476179510354996,
-0.0798715204000473,
-0.07135006785392761,
0.04199925437569618,
-0.018104400485754013,
0.13484030961990356,
-0.048864640295505524,
0.08869440853595734,
0.020535755902528763,
0.05614147707819939,
0.0712602287530899,
0.055699750781059265,
-0.030800791457295418,
0.059460971504449844,
-0.15392880141735077,
0.10911240428686142,
-0.1226733848452568,
0.07272999733686447,
-0.1368824690580368,
-0.08588013797998428,
0.004145994316786528,
0.008730090223252773,
0.08712982386350632,
0.13220174610614777,
-0.17641796171665192,
-0.09463862329721451,
0.1747668832540512,
-0.07852226495742798,
-0.09228803217411041,
0.14350375533103943,
-0.02241562306880951,
0.00523065309971571,
0.04351940378546715,
0.2072439044713974,
0.12873324751853943,
-0.08468225598335266,
0.01467787567526102,
-0.04006185755133629,
0.10662805289030075,
0.046200066804885864,
0.08311737328767776,
-0.05424147844314575,
0.014157935976982117,
0.00042649111128412187,
-0.031753648072481155,
0.06749869138002396,
-0.06618921458721161,
-0.09281827509403229,
-0.014023403637111187,
-0.0872659981250763,
0.0167353805154562,
0.05001756176352501,
0.016330284997820854,
-0.08653146773576736,
-0.11281269043684006,
0.015116813592612743,
0.11439769715070724,
-0.1089588850736618,
0.017349163070321083,
-0.08983778208494186,
0.03312992304563522,
-0.004220901522785425,
-0.006095947232097387,
-0.1309293806552887,
0.009179343469440937,
0.035098493099212646,
-0.07182370126247406,
0.022384634241461754,
-0.05025791376829147,
0.09348874539136887,
0.04603251442313194,
-0.05060623213648796,
-0.07353019714355469,
-0.017907744273543358,
0.013746016658842564,
-0.07527130842208862,
-0.24055416882038116,
-0.0636473149061203,
-0.03487027436494827,
0.2021082192659378,
-0.22024983167648315,
0.02507866732776165,
0.0521056093275547,
0.1395055055618286,
0.04216594249010086,
-0.04012947902083397,
0.03554844483733177,
0.0591169074177742,
0.0048338077031075954,
-0.08569404482841492,
0.029870986938476562,
0.0022893387358635664,
-0.15210577845573425,
0.007095719687640667,
-0.17089305818080902,
0.0710691437125206,
0.08799991756677628,
0.03455221280455589,
-0.07010524719953537,
-0.052359189838171005,
-0.05160144343972206,
-0.05835094302892685,
-0.01420415099710226,
-0.012856307439506054,
0.17147696018218994,
0.010983424261212349,
0.11628621816635132,
-0.06803790479898453,
-0.04312071576714516,
0.0176822692155838,
-0.002559351036325097,
-0.007079341448843479,
0.15680916607379913,
-0.008110545575618744,
-0.07149000465869904,
0.09618992358446121,
0.06229818984866142,
-0.06282979249954224,
0.17279301583766937,
-0.07949286699295044,
-0.07784535735845566,
-0.025266867130994797,
0.04544786363840103,
0.04071880131959915,
0.10456791520118713,
-0.1346634328365326,
-0.021541133522987366,
0.018186118453741074,
-0.0013034805888310075,
0.02121780626475811,
-0.18438908457756042,
0.0007059398922137916,
0.03505101054906845,
-0.07508032023906708,
0.009286429733037949,
-0.0028789446223527193,
-0.0065664928406476974,
0.07456853985786438,
-0.001122213900089264,
-0.06141354516148567,
-0.003072938183322549,
-0.04502532631158829,
-0.09310977160930634,
0.1746501326560974,
-0.10071238875389099,
-0.14682719111442566,
-0.12029477208852768,
-0.012908092699944973,
0.004265648778527975,
-0.013425689190626144,
0.035222377628088,
-0.10793486982584,
-0.033076781779527664,
-0.08370888978242874,
-0.004785699304193258,
-0.011117681860923767,
0.0200131107121706,
0.02421337552368641,
0.005548682063817978,
0.09302026033401489,
-0.0947633683681488,
0.012924120761454105,
-0.006686903070658445,
-0.01832933910191059,
0.0022130825091153383,
0.026665352284908295,
0.07487990707159042,
0.15370598435401917,
0.040944699198007584,
0.026151590049266815,
-0.03408077731728554,
0.18954290449619293,
-0.12446358054876328,
0.0031510915141552687,
0.12005428224802017,
-0.020065180957317352,
0.047518983483314514,
0.16621968150138855,
0.043602362275123596,
-0.07364882528781891,
0.016232283785939217,
0.0178935918956995,
-0.013181803748011589,
-0.21044516563415527,
-0.025328248739242554,
-0.06327281892299652,
-0.018009979277849197,
0.09575854241847992,
0.03991356119513512,
0.004842280875891447,
0.025078535079956055,
-0.03248609974980354,
-0.018755190074443817,
0.042884934693574905,
0.05858403071761131,
0.05861319229006767,
0.026003599166870117,
0.09817096590995789,
-0.01868632808327675,
-0.04464186728000641,
0.019282745197415352,
0.01648535393178463,
0.2136167585849762,
0.01610110141336918,
0.19078713655471802,
0.03922203183174133,
0.12886427342891693,
0.00791970081627369,
0.023703839629888535,
0.010112499818205833,
-0.008174440823495388,
0.02548724040389061,
-0.06650281697511673,
-0.02919403277337551,
0.0606008879840374,
0.0787060484290123,
0.04225388169288635,
-0.09098447114229202,
0.016420481726527214,
0.03135128691792488,
0.32727938890457153,
0.06055638939142227,
-0.2645769715309143,
-0.08430121093988419,
0.03708885982632637,
-0.09060091525316238,
-0.03232046589255333,
0.01756691187620163,
0.14349518716335297,
-0.08296804875135422,
0.0709259957075119,
-0.07496126741170883,
0.07497856765985489,
-0.06194079667329788,
0.003530541667714715,
0.04905394837260246,
0.1087394580245018,
-0.005427693482488394,
0.04753420501947403,
-0.25647714734077454,
0.2814826965332031,
0.0012574215652421117,
0.09416768699884415,
-0.03950008377432823,
0.039533521980047226,
0.05074671655893326,
-0.023528438061475754,
0.08773477375507355,
-0.007422709371894598,
-0.1512431502342224,
-0.172977015376091,
-0.09395820647478104,
0.01990201696753502,
0.11999990791082382,
-0.04609869420528412,
0.11030023545026779,
-0.032308585941791534,
-0.03815151005983353,
0.04238434135913849,
-0.08888746052980423,
-0.11528032273054123,
-0.1063641831278801,
0.03209308162331581,
0.057535480707883835,
0.058663610368967056,
-0.11520875245332718,
-0.08785652369260788,
-0.05179084464907646,
0.12484003603458405,
-0.12684881687164307,
-0.03403575345873833,
-0.13523773849010468,
0.04349049553275108,
0.14749790728092194,
-0.06583154946565628,
0.04047336429357529,
0.02987254410982132,
0.1238592341542244,
0.017669573426246643,
-0.011919243261218071,
0.10430090874433517,
-0.0801929235458374,
-0.2123517245054245,
-0.047822389751672745,
0.184900164604187,
0.037627071142196655,
0.06925957649946213,
-0.017945967614650726,
0.026492862030863762,
-0.00434005307033658,
-0.06266047805547714,
0.0764547809958458,
0.0642702579498291,
-0.02657286450266838,
0.06182205304503441,
-0.025088230147957802,
-0.03514308109879494,
-0.08188898116350174,
-0.05828098952770233,
0.13007205724716187,
0.26730045676231384,
-0.07294762134552002,
0.07500497251749039,
0.07002048939466476,
-0.06241129711270332,
-0.15735425055027008,
0.0020471944008022547,
0.1078544482588768,
0.04633590579032898,
0.00989038310945034,
-0.19295376539230347,
0.025038868188858032,
0.0520709864795208,
-0.03151359409093857,
0.061676025390625,
-0.3151063919067383,
-0.13907687366008759,
0.10902129858732224,
0.07561812549829483,
-0.040071964263916016,
-0.15286508202552795,
-0.0708455964922905,
-0.025727465748786926,
-0.04762274771928787,
0.014978566206991673,
-0.05368592217564583,
0.1275627762079239,
0.006688409019261599,
0.0465836375951767,
0.02939966879785061,
-0.05198580026626587,
0.14436952769756317,
-0.02187541127204895,
0.05658009275794029,
-0.01618662104010582,
0.017672833055257797,
-0.019115546718239784,
-0.06864836066961288,
0.011848270893096924,
-0.11059471219778061,
0.02423982135951519,
-0.1126566156744957,
-0.028107086196541786,
-0.08116959035396576,
0.021997852250933647,
-0.034752022475004196,
-0.028026659041643143,
-0.003531903261318803,
0.05418132245540619,
0.08487168699502945,
0.015320220962166786,
0.08837055414915085,
-0.08150123804807663,
0.14069586992263794,
0.11806732416152954,
0.14798295497894287,
-0.0016591933090239763,
-0.07908724248409271,
-0.006160065531730652,
-0.012616157531738281,
0.04963330551981926,
-0.11831887066364288,
0.051184944808483124,
0.12540899217128754,
0.04064278304576874,
0.15315593779087067,
0.04342269152402878,
-0.0917094275355339,
0.010783716104924679,
0.05866837501525879,
-0.07964034378528595,
-0.18852826952934265,
-0.023612936958670616,
0.04788045585155487,
-0.1501951515674591,
-0.0021728600841015577,
0.11855264753103256,
-0.04768458753824234,
-0.006174104288220406,
0.004083594307303429,
0.042014021426439285,
-0.036044999957084656,
0.21577829122543335,
0.039509065449237823,
0.08655931800603867,
-0.09385907649993896,
0.0700959786772728,
0.04145458713173866,
-0.10082066804170609,
0.054334934800863266,
0.11779406666755676,
-0.0475216843187809,
-0.02110002189874649,
0.01193252857774496,
0.09968695044517517,
0.08000069111585617,
-0.049252986907958984,
-0.11791063100099564,
-0.1422557383775711,
0.05794129893183708,
0.1054307073354721,
0.02129416912794113,
0.012261711061000824,
-0.01966511830687523,
0.03198307380080223,
-0.0944933369755745,
0.12007220834493637,
0.07800702005624771,
0.054624807089567184,
-0.13073956966400146,
0.14232595264911652,
0.00026228424394503236,
-0.013811721466481686,
-0.004594823811203241,
0.00043840508442372084,
-0.11224035173654556,
0.017569249495863914,
-0.10774071514606476,
-0.009511743672192097,
-0.05439676716923714,
0.005507384426891804,
0.011010124348104,
-0.05715755745768547,
-0.04469361528754234,
0.03201301768422127,
-0.10805429518222809,
-0.04408301040530205,
-0.015437453053891659,
0.07198168337345123,
-0.07643314450979233,
-0.03289058431982994,
0.03383709117770195,
-0.11493521928787231,
0.08498129993677139,
0.032620128244161606,
-0.004714885260909796,
0.008412191644310951,
-0.1308978796005249,
-0.011289605870842934,
0.0261381883174181,
-0.0033505787141621113,
0.02391890063881874,
-0.17079317569732666,
-0.02935093082487583,
-0.030705606564879417,
0.017189864069223404,
-0.007500909734517336,
0.04458881914615631,
-0.10950363427400589,
-0.006346797104924917,
-0.03632097318768501,
-0.04714008793234825,
-0.062272388488054276,
0.05484534427523613,
0.06847908347845078,
0.014928136952221394,
0.14510565996170044,
-0.10225963592529297,
0.06237776204943657,
-0.2226281464099884,
0.0037912314292043447,
-0.01671564392745495,
-0.0813775509595871,
-0.08159793168306351,
-0.022236304357647896,
0.097804956138134,
-0.06533510982990265,
0.08396703749895096,
-0.05204255133867264,
0.02872321754693985,
0.03026866912841797,
-0.12108452618122101,
0.044529128819704056,
0.05738179758191109,
0.19506876170635223,
0.04115875065326691,
-0.025684624910354614,
0.08791419118642807,
-0.02174193225800991,
0.04689950868487358,
0.1305496245622635,
0.11388595402240753,
0.1744554340839386,
0.056860923767089844,
0.082044318318367,
0.08216549456119537,
-0.10002688318490982,
-0.12451065331697464,
0.13418014347553253,
-0.044968560338020325,
0.1275157779455185,
-0.04301357641816139,
0.19970309734344482,
0.11693760007619858,
-0.18135394155979156,
0.06502195447683334,
-0.04571789875626564,
-0.0880185216665268,
-0.10237839818000793,
-0.08493196219205856,
-0.08575645089149475,
-0.1610981822013855,
0.004628344904631376,
-0.09629598259925842,
0.0428316555917263,
0.04176415130496025,
0.030830593779683113,
0.03512853384017944,
0.12840282917022705,
0.041157808154821396,
0.019879555329680443,
0.11446329951286316,
-0.0018007741309702396,
-0.007585086394101381,
-0.04532283544540405,
-0.1095205768942833,
0.07011307030916214,
-0.025091502815485,
0.04256945103406906,
-0.04892434552311897,
-0.0905938372015953,
0.055070482194423676,
0.0038477580528706312,
-0.11337634921073914,
0.033142756670713425,
-0.014428764581680298,
0.06827457249164581,
0.06839388608932495,
0.045860935002565384,
-0.023039689287543297,
-0.017512336373329163,
0.2131844162940979,
-0.09777319431304932,
-0.07876671105623245,
-0.13243259489536285,
0.21583561599254608,
-0.023301532492041588,
-0.012594071216881275,
0.009333155117928982,
-0.07680629938840866,
0.008916276507079601,
0.16775500774383545,
0.14913442730903625,
-0.02725561521947384,
-0.004771735053509474,
-0.006881669629365206,
-0.013627194799482822,
-0.050715651363134384,
0.06777065992355347,
0.10699573904275894,
0.024944420903921127,
-0.04143504798412323,
-0.012454831041395664,
-0.00484080333262682,
-0.07995137572288513,
-0.05092564970254898,
0.08926763385534286,
0.02399688959121704,
0.004930112045258284,
-0.020483432337641716,
0.11330273002386093,
-0.04446711018681526,
-0.14298978447914124,
0.01734940893948078,
-0.18030744791030884,
-0.17254313826560974,
-0.03737930208444595,
0.07484807074069977,
0.05463893339037895,
0.03486495837569237,
-0.0009617824107408524,
-0.017259210348129272,
0.08951011300086975,
-0.0011857241624966264,
-0.025341644883155823,
-0.08526486903429031,
0.07554655522108078,
-0.14774759113788605,
0.1780402809381485,
-0.039346035569906235,
0.02149898372590542,
0.1241479218006134,
0.03904791921377182,
-0.08549077808856964,
0.037659887224435806,
0.07576432079076767,
-0.14129512012004852,
0.04975307360291481,
0.21355071663856506,
-0.04222874343395233,
0.15331335365772247,
0.038730427622795105,
-0.10163275897502899,
0.014846576377749443,
-0.08536198735237122,
-0.0686468780040741,
-0.0572323203086853,
-0.0017801770009100437,
-0.04190419614315033,
0.14094145596027374,
0.20114079117774963,
-0.07954412698745728,
-0.012017423287034035,
-0.04253046214580536,
0.004786417819559574,
0.04667959734797478,
0.10046125948429108,
-0.04529635235667229,
-0.2652556896209717,
0.009671885520219803,
0.0037308107130229473,
0.018098467960953712,
-0.21087907254695892,
-0.09487006813287735,
0.002774630207568407,
-0.04550875350832939,
-0.06071397289633751,
0.11716031283140182,
0.09901489317417145,
0.0434233695268631,
-0.04709358513355255,
-0.12909695506095886,
-0.017609814181923866,
0.17705214023590088,
-0.1672070473432541,
-0.04565240442752838
] |
null | null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | text-generation | JoshVictor/TinyLlama-1.1B-MEDCODE | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T10:03:01+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
60,
6,
3,
82,
28,
3,
4,
9,
9,
10,
42,
20,
3,
4,
5,
9,
11,
13,
3,
12,
5,
4,
5,
3,
4,
9,
53,
9,
8,
6,
3,
14,
8,
7,
9,
4
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
] | [
-0.04654794931411743,
0.16618601977825165,
-0.005445904564112425,
0.01853804849088192,
0.0981811136007309,
0.011998992413282394,
0.06433123350143433,
0.11398410052061081,
-0.0230073444545269,
0.11406639218330383,
0.03047988750040531,
0.10172267258167267,
0.11317981779575348,
0.14841650426387787,
-0.002152352826669812,
-0.22403094172477722,
0.050844956189394,
-0.12105348706245422,
-0.033293843269348145,
0.11749980598688126,
0.1483822613954544,
-0.09928343445062637,
0.07274559140205383,
-0.029687678441405296,
-0.012143402360379696,
-0.030057786032557487,
-0.05890674889087677,
-0.046214159578084946,
0.04651786759495735,
0.06640566885471344,
0.06770290434360504,
0.0071083661168813705,
0.09012923389673233,
-0.2696533799171448,
0.018959321081638336,
0.07145345956087112,
-0.002759667346253991,
0.06957992166280746,
0.06404146552085876,
-0.07107418030500412,
0.10337356477975845,
-0.05106033384799957,
0.14650006592273712,
0.08365883678197861,
-0.09081148356199265,
-0.1895141303539276,
-0.08866965025663376,
0.09882009029388428,
0.17572562396526337,
0.04925641790032387,
-0.02320658043026924,
0.09761467576026917,
-0.08769196271896362,
0.015438909642398357,
0.04981724172830582,
-0.07620415836572647,
-0.05378096550703049,
0.05986575037240982,
0.07907199114561081,
0.06627275794744492,
-0.12434766441583633,
-0.02885502204298973,
0.005009706597775221,
0.010980482213199139,
0.0769270583987236,
0.01728810742497444,
0.146672785282135,
0.0338633768260479,
-0.12615777552127838,
-0.04880760237574577,
0.09869225323200226,
0.03395522013306618,
-0.04422314465045929,
-0.24749068915843964,
-0.03152675926685333,
-0.030810698866844177,
-0.029386121779680252,
-0.03716538846492767,
0.04340358078479767,
-0.007673026993870735,
0.08638741075992584,
-0.0060646249912679195,
-0.07403432577848434,
-0.03937075287103653,
0.06169692054390907,
0.0672287791967392,
0.02999979443848133,
-0.013745363801717758,
0.010938193649053574,
0.11620724946260452,
0.1095694974064827,
-0.12054188549518585,
-0.05555335059762001,
-0.06393084675073624,
-0.08656639605760574,
-0.040790557861328125,
0.034162238240242004,
0.03456587344408035,
0.05349370837211609,
0.25305667519569397,
0.015654386952519417,
0.059652652591466904,
0.034477248787879944,
0.007892133668065071,
0.05848940089344978,
0.11044429242610931,
-0.06018859148025513,
-0.10444226115942001,
-0.02648012898862362,
0.08843598514795303,
0.008199662901461124,
-0.03287925571203232,
-0.05088530853390694,
0.06019928678870201,
0.01946467161178589,
0.11926145106554031,
0.09061790257692337,
0.010536285117268562,
-0.07121123373508453,
-0.061038948595523834,
0.1891259253025055,
-0.16544590890407562,
0.04322727024555206,
0.035097137093544006,
-0.03903156518936157,
0.00019933005387429148,
0.013914269395172596,
0.016625655815005302,
-0.025983380153775215,
0.09017423540353775,
-0.054113563150167465,
-0.04145489260554314,
-0.11186197400093079,
-0.03383193537592888,
0.033762916922569275,
0.008953776210546494,
-0.035059962421655655,
-0.033713940531015396,
-0.08351044356822968,
-0.07577689737081528,
0.09320491552352905,
-0.07346344739198685,
-0.04878907650709152,
-0.01804324984550476,
-0.07530532777309418,
0.022395428270101547,
0.019394835457205772,
0.07707412540912628,
-0.02362251654267311,
0.04399976506829262,
-0.05189276114106178,
0.05863580107688904,
0.11207318305969238,
0.03570080175995827,
-0.05736649036407471,
0.06062258034944534,
-0.23834340274333954,
0.09552820026874542,
-0.07409077137708664,
0.05591456592082977,
-0.153293639421463,
-0.024439791217446327,
0.04788333550095558,
0.008784620091319084,
-0.009650949388742447,
0.13416339457035065,
-0.21702027320861816,
-0.02536402828991413,
0.1717337965965271,
-0.10057014971971512,
-0.07069246470928192,
0.05619903281331062,
-0.04835370555520058,
0.10988964140415192,
0.03825836628675461,
-0.025690359994769096,
0.06171267107129097,
-0.1267417073249817,
0.003717758459970355,
-0.05005312338471413,
-0.017048977315425873,
0.1548657864332199,
0.07182947546243668,
-0.07217690348625183,
0.07399354875087738,
0.025708531960844994,
-0.0246540866792202,
-0.04625825211405754,
-0.015164627693593502,
-0.10536660254001617,
0.014689887873828411,
-0.06369215250015259,
0.014470234513282776,
-0.020807426422834396,
-0.09071163833141327,
-0.027962757274508476,
-0.17504668235778809,
-0.03014434315264225,
0.08651752024888992,
-0.008693269453942776,
-0.01803150773048401,
-0.1178668737411499,
0.009341353550553322,
0.04177580401301384,
0.0061247628182172775,
-0.13462838530540466,
-0.04812471568584442,
0.02780051715672016,
-0.1600649207830429,
0.034652888774871826,
-0.05392369255423546,
0.04932025074958801,
0.025790516287088394,
-0.028889117762446404,
-0.026493212208151817,
0.021633783355355263,
0.005992184858769178,
-0.011999987065792084,
-0.24343903362751007,
-0.028118690475821495,
-0.024888472631573677,
0.1682123839855194,
-0.20917098224163055,
0.03546025976538658,
0.07867541164159775,
0.15366052091121674,
0.011240328662097454,
-0.04177491366863251,
0.005974748637527227,
-0.06935794651508331,
-0.02736494317650795,
-0.05875484645366669,
-0.0047869328409433365,
-0.03310677409172058,
-0.04545191675424576,
0.04568447172641754,
-0.16510973870754242,
-0.032636504620313644,
0.09776268899440765,
0.06289951503276825,
-0.13922683894634247,
-0.020621931180357933,
-0.03630133345723152,
-0.049253206700086594,
-0.04911839962005615,
-0.0605199858546257,
0.10893940925598145,
0.05891856551170349,
0.04574795812368393,
-0.05928509309887886,
-0.07568105310201645,
-0.001827909960411489,
-0.013898161239922047,
-0.017864689230918884,
0.09759635478258133,
0.0751434788107872,
-0.13251115381717682,
0.09224759042263031,
0.09603385627269745,
0.07919023185968399,
0.09113933145999908,
-0.02355697751045227,
-0.08261934667825699,
-0.045987509191036224,
0.031442027539014816,
0.020124373957514763,
0.13039541244506836,
-0.024294709786772728,
0.04352088272571564,
0.042134687304496765,
-0.019369594752788544,
0.014752166345715523,
-0.08687400817871094,
0.033972494304180145,
0.028472330421209335,
-0.016721390187740326,
0.050190530717372894,
-0.03876714035868645,
0.02440318465232849,
0.08830609917640686,
0.045322712510824203,
0.03507532551884651,
0.015493292361497879,
-0.05206458270549774,
-0.1083620935678482,
0.16405931115150452,
-0.12714070081710815,
-0.22483378648757935,
-0.13936103880405426,
0.0037376401014626026,
0.035628627985715866,
-0.015835661441087723,
0.002417160663753748,
-0.059374887496232986,
-0.12220635265111923,
-0.08858037739992142,
0.015140829607844353,
0.04942670464515686,
-0.09028962254524231,
-0.06437795609235764,
0.058117836713790894,
0.03889724239706993,
-0.14560972154140472,
0.017612040042877197,
0.04854894429445267,
-0.09789852797985077,
-0.006774199660867453,
0.08094939589500427,
0.0698540136218071,
0.1770169734954834,
0.017703235149383545,
-0.021850809454917908,
0.032354529947042465,
0.20614571869373322,
-0.13538233935832977,
0.11083246022462845,
0.13607586920261383,
-0.09041404724121094,
0.08072979003190994,
0.19951270520687103,
0.03932560607790947,
-0.10153959691524506,
0.031980328261852264,
0.02283124253153801,
-0.0284719280898571,
-0.24526868760585785,
-0.07212468236684799,
-0.004402178805321455,
-0.058010730892419815,
0.07660572230815887,
0.09286724030971527,
0.08215958625078201,
0.012304253876209259,
-0.09310996532440186,
-0.08154371380805969,
0.05942574888467789,
0.10367169976234436,
0.024584239348769188,
-0.010839897207915783,
0.08998730033636093,
-0.034100502729415894,
0.019626356661319733,
0.0853661298751831,
0.005239574704319239,
0.17840281128883362,
0.05159219726920128,
0.18830420076847076,
0.07925192266702652,
0.07219027727842331,
0.009912233799695969,
0.013080619275569916,
0.018877580761909485,
0.03300119563937187,
-0.002769160782918334,
-0.08440786600112915,
-0.02248465269804001,
0.11566436290740967,
0.06668911874294281,
0.010815348476171494,
0.015172341838479042,
-0.04104290530085564,
0.07965951412916183,
0.1831512451171875,
-0.007656289264559746,
-0.1783534437417984,
-0.057547420263290405,
0.07553383708000183,
-0.09879875183105469,
-0.09854305535554886,
-0.013454320840537548,
0.03072015568614006,
-0.17046253383159637,
0.023390959948301315,
-0.02239842526614666,
0.1106182336807251,
-0.14194999635219574,
-0.020490378141403198,
0.07218493521213531,
0.07199500501155853,
0.004729843698441982,
0.05758659541606903,
-0.16417601704597473,
0.10671813786029816,
0.008950476534664631,
0.06779605895280838,
-0.09610627591609955,
0.1008887067437172,
-0.004196076653897762,
-0.02063460275530815,
0.1393408179283142,
0.002700034761801362,
-0.06884108483791351,
-0.0763031542301178,
-0.08754398673772812,
-0.009632662869989872,
0.12754282355308533,
-0.1419651061296463,
0.08767123520374298,
-0.037212442606687546,
-0.0424150750041008,
-0.0017086371080949903,
-0.10206665843725204,
-0.11638247221708298,
-0.18888559937477112,
0.06001543253660202,
-0.13492922484874725,
0.03152317553758621,
-0.10799519717693329,
-0.032371897250413895,
-0.030304040759801865,
0.19337286055088043,
-0.23447458446025848,
-0.07199826091527939,
-0.1475764364004135,
-0.10233612358570099,
0.1443224400281906,
-0.0501345656812191,
0.08485390990972519,
-0.007241467013955116,
0.16846685111522675,
0.019060896709561348,
-0.02531743235886097,
0.0971490666270256,
-0.09173708409070969,
-0.19302815198898315,
-0.07869284600019455,
0.15662524104118347,
0.13260218501091003,
0.031680017709732056,
-0.002461588243022561,
0.036563750356435776,
-0.015421539545059204,
-0.11935004591941833,
0.015969349071383476,
0.1787186712026596,
0.06237189099192619,
0.02331034652888775,
-0.027346095070242882,
-0.11273157596588135,
-0.06900003552436829,
-0.028530338779091835,
0.03054865077137947,
0.17762407660484314,
-0.07057618349790573,
0.18207968771457672,
0.14163152873516083,
-0.05922834202647209,
-0.20400173962116241,
0.010538800619542599,
0.03055560030043125,
0.0009220078936778009,
0.02591954916715622,
-0.20123432576656342,
0.08688826113939285,
0.004683020059019327,
-0.05110127478837967,
0.13194532692432404,
-0.17217805981636047,
-0.14451217651367188,
0.0765485092997551,
0.038384392857551575,
-0.19559739530086517,
-0.12913893163204193,
-0.09174312651157379,
-0.045869920402765274,
-0.18591414391994476,
0.09569250047206879,
0.0305706188082695,
0.010893458500504494,
0.03030681423842907,
0.029179483652114868,
0.019487828016281128,
-0.0418255440890789,
0.18391458690166473,
-0.024792250245809555,
0.026594700291752815,
-0.08539514988660812,
-0.06927408277988434,
0.03743394836783409,
-0.052842434495687485,
0.07349982857704163,
-0.023486759513616562,
0.007861839607357979,
-0.10348054021596909,
-0.042148489505052567,
-0.03735732287168503,
0.015448716469109058,
-0.09657872468233109,
-0.08514349907636642,
-0.045032672584056854,
0.09675803780555725,
0.09690850973129272,
-0.033646680414676666,
-0.028050623834133148,
-0.07533035427331924,
0.04412057250738144,
0.19926515221595764,
0.1785389482975006,
0.042153384536504745,
-0.08034496754407883,
-0.004150947090238333,
-0.010121207684278488,
0.04310847446322441,
-0.20463712513446808,
0.06283636391162872,
0.05450061708688736,
0.01973269321024418,
0.11436162889003754,
-0.019565396010875702,
-0.15359151363372803,
-0.07263088971376419,
0.06303015351295471,
-0.060181066393852234,
-0.19620554149150848,
0.00867035984992981,
0.060603946447372437,
-0.16371412575244904,
-0.04535605385899544,
0.04643881320953369,
-0.005620351992547512,
-0.038163937628269196,
0.021896906197071075,
0.09194854646921158,
0.0026654244866222143,
0.07427921891212463,
0.05387866869568825,
0.0827430784702301,
-0.10537070035934448,
0.08090532571077347,
0.08839722722768784,
-0.08452684432268143,
0.023530138656497,
0.10478579998016357,
-0.059433579444885254,
-0.03440561518073082,
0.020135708153247833,
0.08153781294822693,
0.01775863952934742,
-0.040019966661930084,
0.013229827396571636,
-0.10452935844659805,
0.05954122915863991,
0.08839859813451767,
0.032507482916116714,
0.016702456399798393,
0.03425082191824913,
0.04607953503727913,
-0.07238735258579254,
0.12142276018857956,
0.031868141144514084,
0.017129309475421906,
-0.036505792289972305,
-0.040896978229284286,
0.019542274996638298,
-0.03214648738503456,
-0.005015232600271702,
-0.03023446537554264,
-0.07695909589529037,
-0.014793801121413708,
-0.1626158058643341,
-0.011131818406283855,
-0.05648450180888176,
0.010329355485737324,
0.03204665705561638,
-0.032609567046165466,
0.008124498650431633,
0.009250079281628132,
-0.07695289701223373,
-0.0663459524512291,
-0.020460480824112892,
0.09540658444166183,
-0.16213038563728333,
0.022481130436062813,
0.08244425803422928,
-0.12187694013118744,
0.09281346201896667,
0.016204802319407463,
-0.006236857734620571,
0.025038830935955048,
-0.1475188434123993,
0.034843120723962784,
-0.03386561945080757,
0.010836300440132618,
0.04373383894562721,
-0.21569781005382538,
-0.00004886732858722098,
-0.033673107624053955,
-0.06639216095209122,
-0.009451326914131641,
-0.03672455996274948,
-0.11508306115865707,
0.1058407872915268,
0.007236586883664131,
-0.08753558248281479,
-0.03186136856675148,
0.029325377196073532,
0.0838974118232727,
-0.021959776058793068,
0.15145497024059296,
-0.008370938710868359,
0.07429654151201248,
-0.16209737956523895,
-0.018623165786266327,
-0.006028574425727129,
0.022658247500658035,
-0.01664556935429573,
-0.01111356820911169,
0.044031109660863876,
-0.022746501490473747,
0.17925859987735748,
-0.030318550765514374,
0.02272745408117771,
0.06815794110298157,
0.019072026014328003,
-0.030184008181095123,
0.10406795144081116,
0.04094860330224037,
0.02014910988509655,
0.018591465428471565,
0.003289656015112996,
-0.04647882282733917,
-0.03173251822590828,
-0.19407226145267487,
0.07288651913404465,
0.15608493983745575,
0.09729263186454773,
-0.016707008704543114,
0.07954329252243042,
-0.10199416428804398,
-0.1109243705868721,
0.12477338314056396,
-0.04797708988189697,
-0.002418199321255088,
-0.07150927931070328,
0.13247236609458923,
0.1437523066997528,
-0.1859612911939621,
0.07269313186407089,
-0.0699717253446579,
-0.04708027467131615,
-0.10980689525604248,
-0.19441905617713928,
-0.05561789125204086,
-0.049456022679805756,
-0.016053348779678345,
-0.04698808491230011,
0.07504211366176605,
0.054538097232580185,
0.006766852922737598,
-0.0023397188633680344,
0.06506035476922989,
-0.031050674617290497,
-0.0037882844917476177,
0.032597362995147705,
0.06591679900884628,
0.012734474614262581,
-0.030802709981799126,
0.016619903966784477,
-0.013545602560043335,
0.045626189559698105,
0.06578011065721512,
0.04976864159107208,
-0.02938537672162056,
0.014603170566260815,
-0.038539156317710876,
-0.10249634087085724,
0.043612558394670486,
-0.024421939626336098,
-0.0789753645658493,
0.15477414429187775,
0.023680059239268303,
0.007779473438858986,
-0.020137663930654526,
0.23901568353176117,
-0.0738423764705658,
-0.0964353010058403,
-0.14737580716609955,
0.10557299107313156,
-0.038081806153059006,
0.05800395458936691,
0.04625935107469559,
-0.10226529091596603,
0.018044332042336464,
0.1338089406490326,
0.16182038187980652,
-0.039008259773254395,
0.020095856860280037,
0.031135575845837593,
0.00566398398950696,
-0.03622615709900856,
0.04847532883286476,
0.06906453520059586,
0.16569648683071136,
-0.04632584750652313,
0.09100406616926193,
0.0019041687482967973,
-0.09579581767320633,
-0.038361791521310806,
0.11069868505001068,
-0.016052277758717537,
0.019335128366947174,
-0.05818064883351326,
0.11742528527975082,
-0.06386786699295044,
-0.23783175647258759,
0.06453443318605423,
-0.0684293657541275,
-0.13765870034694672,
-0.02378307841718197,
0.08207765966653824,
-0.012955902144312859,
0.027587108314037323,
0.0730307325720787,
-0.07240920513868332,
0.201939657330513,
0.03798431158065796,
-0.05499868467450142,
-0.055047210305929184,
0.0805421993136406,
-0.10008571296930313,
0.2739645540714264,
0.01557221356779337,
0.04601577669382095,
0.10384146869182587,
-0.009341772645711899,
-0.13838784396648407,
0.019836371764540672,
0.09581108391284943,
-0.10502193123102188,
0.04196618124842644,
0.19815568625926971,
-0.0014755994779989123,
0.12389086186885834,
0.07657600939273834,
-0.07551808655261993,
0.0478031262755394,
-0.08054235577583313,
-0.06760486960411072,
-0.09260394424200058,
0.09703279286623001,
-0.07772123068571091,
0.14251399040222168,
0.13876807689666748,
-0.05074559152126312,
0.012724342755973339,
-0.031311117112636566,
0.044293127954006195,
-0.00010600237874314189,
0.10321761667728424,
0.004272161517292261,
-0.1832672357559204,
0.024692710489034653,
0.005650998093187809,
0.10749758034944534,
-0.16033467650413513,
-0.09566054493188858,
0.042343202978372574,
0.003505636239424348,
-0.0672195628285408,
0.1290110945701599,
0.05665452033281326,
0.04342988133430481,
-0.03997718170285225,
-0.03521440550684929,
-0.0060732318088412285,
0.13561366498470306,
-0.10713256150484085,
0.0009933578548952937
] |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# shakespearean-lm
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.4100
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.9075 | 0.53 | 500 | 4.6423 |
| 4.6274 | 1.06 | 1000 | 4.5136 |
| 4.4057 | 1.59 | 1500 | 4.4582 |
| 4.3596 | 2.12 | 2000 | 4.4350 |
| 4.2589 | 2.65 | 2500 | 4.4163 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilgpt2", "model-index": [{"name": "shakespearean-lm", "results": []}]} | text-generation | perceptron-743/shakespearean-lm | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T10:10:13+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-distilgpt2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| shakespearean-lm
================
This model is a fine-tuned version of distilgpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 4.4100
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-distilgpt2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
77,
97,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-distilgpt2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
-0.1033276841044426,
0.09521856158971786,
-0.0019958498887717724,
0.10558190196752548,
0.12683963775634766,
-0.0013844871427863836,
0.16743405163288116,
0.12321116775274277,
-0.06815411895513535,
0.04630738124251366,
0.14123684167861938,
0.11519799381494522,
0.029274150729179382,
0.13133087754249573,
-0.06387747824192047,
-0.2042749971151352,
0.02458903007209301,
0.02826322242617607,
-0.04811926558613777,
0.12225412577390671,
0.093136265873909,
-0.10798031091690063,
0.1054435521364212,
-0.009785925038158894,
-0.1487124264240265,
-0.0004416943993419409,
0.015774419531226158,
-0.0469854511320591,
0.1370145082473755,
0.041207533329725266,
0.09231843054294586,
0.03109368495643139,
0.0634625181555748,
-0.18975350260734558,
0.012524260208010674,
0.06450165808200836,
-0.0063437046483159065,
0.0818186029791832,
0.040610797703266144,
0.004734691232442856,
0.10466066002845764,
-0.0763949528336525,
0.04025273397564888,
0.02526632696390152,
-0.1371115893125534,
-0.21775487065315247,
-0.08149553835391998,
0.03263584151864052,
0.1011502593755722,
0.09939917922019958,
-0.017947422340512276,
0.13046859204769135,
-0.01642494648694992,
0.09757290780544281,
0.21743036806583405,
-0.3172791600227356,
-0.0591190867125988,
0.06634489446878433,
0.05579744279384613,
0.0993443951010704,
-0.09741019457578659,
-0.005615434143692255,
0.053670771420001984,
0.02858695760369301,
0.13820607960224152,
-0.031117070466279984,
-0.003293041605502367,
-0.003527879249304533,
-0.14026567339897156,
-0.04316394776105881,
0.18211859464645386,
0.05782010033726692,
-0.05328962206840515,
-0.07221653312444687,
-0.07429132610559464,
-0.1227358728647232,
-0.03014320135116577,
-0.0042559318244457245,
0.040069542825222015,
-0.007567097432911396,
-0.06269621849060059,
-0.046420931816101074,
-0.11349499970674515,
-0.07548203319311142,
-0.029450098052620888,
0.11882875859737396,
0.02790258452296257,
-0.0011203755857422948,
-0.02472790703177452,
0.11241995543241501,
-0.029252255335450172,
-0.1409459114074707,
0.0038655425887554884,
0.02730620838701725,
0.02831389009952545,
-0.034657709300518036,
-0.05030185729265213,
-0.09213144332170486,
0.02710602805018425,
0.13733883202075958,
-0.06912094354629517,
0.05586264282464981,
0.009607996791601181,
0.046205103397369385,
-0.10878053307533264,
0.16450217366218567,
-0.03716516122221947,
-0.04015567526221275,
0.029594387859106064,
0.09659314900636673,
0.07606421411037445,
-0.016191953793168068,
-0.1416049599647522,
0.022228935733437538,
0.11225295811891556,
0.02711070515215397,
-0.0428205244243145,
0.07016235589981079,
-0.045914191752672195,
-0.007011271547526121,
0.04988915100693703,
-0.0780830904841423,
0.008459505625069141,
-0.01229159813374281,
-0.041088495403528214,
-0.07996315509080887,
0.029256341978907585,
0.029078403487801552,
-0.0028440721798688173,
0.07550986111164093,
-0.08499204367399216,
-0.002343385014683008,
-0.07251109182834625,
-0.10899855941534042,
0.015104522928595543,
-0.07086595147848129,
0.02285625971853733,
-0.11046254634857178,
-0.2109549194574356,
-0.005046474747359753,
0.054923176765441895,
-0.02843550778925419,
-0.046056222170591354,
-0.06269539892673492,
-0.07422980666160583,
0.01639058068394661,
-0.01760314777493477,
0.08639116585254669,
-0.07091373205184937,
0.10277746617794037,
0.054206252098083496,
0.05619547516107559,
-0.06561994552612305,
0.027919892221689224,
-0.10495783388614655,
0.04700882360339165,
-0.15460136532783508,
0.028458382934331894,
-0.03373069688677788,
0.06464200466871262,
-0.0893004834651947,
-0.06546667218208313,
-0.013738994486629963,
0.00174518337007612,
0.07084771990776062,
0.10685407370328903,
-0.14755214750766754,
-0.06558728963136673,
0.19393643736839294,
-0.09230750054121017,
-0.17128485441207886,
0.13980825245380402,
-0.039268895983695984,
0.05904391035437584,
0.07328221201896667,
0.20290935039520264,
0.06103036180138588,
-0.09257430583238602,
0.0012095447164028883,
0.004495304077863693,
0.06634689122438431,
-0.056518878787755966,
0.09289905428886414,
-0.010534952394664288,
0.009911597706377506,
0.012904894538223743,
-0.06438415497541428,
0.06450321525335312,
-0.06174224987626076,
-0.07661619037389755,
-0.04385092481970787,
-0.10513588786125183,
0.03480272740125656,
0.044316548854112625,
0.0583384744822979,
-0.116841159760952,
-0.09977155923843384,
0.02876749262213707,
0.07716885954141617,
-0.08540081977844238,
0.02807169035077095,
-0.06784851104021072,
0.10176122188568115,
-0.08842965215444565,
-0.005354571156203747,
-0.13335466384887695,
-0.04700455069541931,
0.014837216585874557,
0.006071412470191717,
0.009142273105680943,
-0.00722633209079504,
0.08450993150472641,
0.08541995286941528,
-0.07724910974502563,
-0.04640708118677139,
-0.007535684388130903,
0.008168239146471024,
-0.12388598918914795,
-0.17981797456741333,
-0.011950070969760418,
-0.030675841495394707,
0.16406495869159698,
-0.22686909139156342,
0.058979183435440063,
0.006234527565538883,
0.07568962872028351,
0.030611658468842506,
-0.02665330469608307,
-0.02650504745543003,
0.042034126818180084,
-0.05031490698456764,
-0.0789603665471077,
0.06689595431089401,
0.02818327210843563,
-0.11432936787605286,
-0.005950375460088253,
-0.17968879640102386,
0.18234209716320038,
0.13261106610298157,
-0.04751068353652954,
-0.059112340211868286,
-0.001993319019675255,
-0.03567981719970703,
-0.028289033100008965,
-0.0371166467666626,
-0.021512454375624657,
0.12651877105236053,
-0.0012284911936149001,
0.16156047582626343,
-0.09860344231128693,
-0.04501728713512421,
0.028878599405288696,
-0.0498027428984642,
0.010571472346782684,
0.11579510569572449,
0.061584580689668655,
-0.11411363631486893,
0.1592264175415039,
0.18813014030456543,
-0.06423446536064148,
0.1345091164112091,
-0.04377041384577751,
-0.062310438603162766,
-0.0279258880764246,
0.035441815853118896,
0.02198277972638607,
0.11241793632507324,
-0.1066889688372612,
0.01269453763961792,
0.006245365832000971,
0.02723965421319008,
0.01828855276107788,
-0.21595610678195953,
-0.03314408287405968,
0.04949251189827919,
-0.060559630393981934,
0.00003357714376761578,
-0.011353685520589352,
-0.03558845818042755,
0.08785092085599899,
0.00120290566701442,
-0.06819859892129898,
0.04911991208791733,
0.0021202052012085915,
-0.08472120016813278,
0.20665742456912994,
-0.06560717523097992,
-0.16917063295841217,
-0.15288414061069489,
-0.06517935544252396,
-0.06433635950088501,
0.040831308811903,
0.07316456735134125,
-0.06673496961593628,
-0.04092647507786751,
-0.13055004179477692,
0.003747403621673584,
0.004265883006155491,
0.015314646065235138,
0.014092398807406425,
-0.01041058823466301,
0.08013083040714264,
-0.09874777495861053,
-0.013663889840245247,
-0.016561754047870636,
-0.043912239372730255,
0.030703267082571983,
0.006807544268667698,
0.11054911464452744,
0.12988492846488953,
-0.0013263969449326396,
0.003122587688267231,
-0.034241802990436554,
0.23638759553432465,
-0.07205566018819809,
-0.006333988159894943,
0.15757998824119568,
-0.007951616309583187,
0.058055948466062546,
0.11793281883001328,
0.04064124822616577,
-0.10540236532688141,
0.02143518440425396,
0.023480966687202454,
-0.04008165001869202,
-0.19671961665153503,
-0.01768297515809536,
-0.04855809733271599,
0.023018235340714455,
0.09280341118574142,
0.03718733415007591,
0.050683651119470596,
0.0687958374619484,
0.006237296387553215,
0.08018636703491211,
0.00808852631598711,
0.08615809679031372,
0.10818672180175781,
0.03907134011387825,
0.12525750696659088,
-0.05289396643638611,
-0.04353765770792961,
0.040055740624666214,
0.01533424761146307,
0.1859285533428192,
0.014379225671291351,
0.17612144351005554,
0.04022933542728424,
0.1407570093870163,
-0.009556708857417107,
0.0638096034526825,
-0.023496849462389946,
-0.041197508573532104,
-0.020045733079314232,
-0.05047227814793587,
-0.03168053552508354,
0.034974683076143265,
-0.08870384842157364,
0.05311429128050804,
-0.0989535003900528,
0.011615701019763947,
0.05012277513742447,
0.22630919516086578,
0.06238775700330734,
-0.33729639649391174,
-0.102507583796978,
0.038664281368255615,
-0.009542838670313358,
-0.04118262231349945,
0.02548457495868206,
0.14064884185791016,
-0.059706784784793854,
0.03772140294313431,
-0.07975733280181885,
0.08491027355194092,
-0.040893349796533585,
0.045602381229400635,
0.03142109140753746,
0.07386785745620728,
-0.015777966007590294,
0.07391753792762756,
-0.28137755393981934,
0.260945200920105,
0.018460705876350403,
0.0728016272187233,
-0.04957715794444084,
0.0084843123331666,
0.02421787567436695,
0.08853897452354431,
0.09249535202980042,
-0.012518334202468395,
-0.0569688081741333,
-0.17985504865646362,
-0.07610658556222916,
0.029013821855187416,
0.0846957191824913,
-0.05530023202300072,
0.10571274906396866,
-0.041580405086278915,
0.004618077538907528,
0.07947076112031937,
-0.005996992811560631,
-0.05672165006399155,
-0.10927633941173553,
-0.0000416701368521899,
0.0588286817073822,
-0.009190788492560387,
-0.0895802229642868,
-0.08344268053770065,
-0.11753012239933014,
0.1577446460723877,
-0.046554096043109894,
-0.04751481115818024,
-0.09670931100845337,
0.04484214261174202,
0.04730517044663429,
-0.08363600820302963,
0.035766422748565674,
0.0018001386197283864,
0.09078112989664078,
0.01106167770922184,
-0.05702691525220871,
0.12051737308502197,
-0.0724426805973053,
-0.16800110042095184,
-0.05509166046977043,
0.1341862678527832,
0.0008932521450333297,
0.0387740321457386,
-0.004277298226952553,
0.020756268873810768,
-0.03780873492360115,
-0.07810886204242706,
0.029939398169517517,
-0.0164894200861454,
0.06310679763555527,
-0.006576564162969589,
-0.028318217024207115,
0.029752368107438087,
-0.05315686762332916,
-0.055319927632808685,
0.1610298454761505,
0.308118611574173,
-0.06711490452289581,
0.007342552300542593,
0.05452953651547432,
-0.05848097428679466,
-0.18387718498706818,
0.010947087779641151,
0.00805364828556776,
0.002286080038174987,
0.05485859140753746,
-0.14407551288604736,
0.07296427339315414,
0.09192876517772675,
-0.02911691553890705,
0.1285625845193863,
-0.3158254027366638,
-0.13638992607593536,
0.10870005935430527,
0.15664780139923096,
0.12851209938526154,
-0.17294859886169434,
-0.05213764309883118,
-0.04563562199473381,
-0.13051430881023407,
0.09841879457235336,
-0.13360832631587982,
0.11742702126502991,
-0.007711050100624561,
0.05458776652812958,
0.001198252779431641,
-0.058471716940402985,
0.14603173732757568,
-0.032127343118190765,
0.09422972798347473,
-0.07073274999856949,
0.023102104663848877,
0.0757845789194107,
-0.06619935482740402,
0.031164146959781647,
-0.15883299708366394,
0.03675650805234909,
-0.06594482064247131,
-0.03237467259168625,
-0.05138818174600601,
0.03199228271842003,
-0.033552784472703934,
-0.06309552490711212,
-0.03841857612133026,
0.01819639466702938,
0.04389483481645584,
-0.007955959066748619,
0.16783250868320465,
0.0003206528490409255,
0.153097003698349,
0.13559214770793915,
0.07735070586204529,
-0.073285773396492,
-0.029441744089126587,
-0.013638464733958244,
-0.03598160669207573,
0.04842744767665863,
-0.1613275706768036,
0.033531446009874344,
0.10530512779951096,
0.0033350002486258745,
0.14612244069576263,
0.06732867658138275,
-0.03840560093522072,
0.01569654606282711,
0.060417793691158295,
-0.17649780213832855,
-0.14624042809009552,
-0.02851891703903675,
-0.003929600119590759,
-0.11861301213502884,
0.06708686798810959,
0.1324509233236313,
-0.06806997954845428,
0.005800989456474781,
-0.010679291561245918,
0.022458963096141815,
-0.037026457488536835,
0.16752251982688904,
0.047893837094306946,
0.04724087193608284,
-0.07829032093286514,
0.08255384862422943,
0.041171036660671234,
-0.06741016358137131,
0.024331288412213326,
0.04363130405545235,
-0.09037178754806519,
-0.0479615144431591,
0.03385937213897705,
0.17140810191631317,
-0.04054047912359238,
-0.05956658348441124,
-0.15701965987682343,
-0.11045173555612564,
0.04507920891046524,
0.14724470674991608,
0.08627328276634216,
0.021154973655939102,
-0.02666495367884636,
0.009898040443658829,
-0.11426297575235367,
0.11314242333173752,
0.03358123451471329,
0.0896313413977623,
-0.15852771699428558,
0.10280182212591171,
0.000696204777341336,
0.004497079644352198,
-0.015783846378326416,
0.03934076800942421,
-0.1033598780632019,
-0.0015789164463058114,
-0.132105752825737,
-0.009908043779432774,
-0.03254484385251999,
-0.0026546998415142298,
-0.008271236903965473,
-0.04796382412314415,
-0.05119578167796135,
0.02113998308777809,
-0.09471085667610168,
-0.03146317973732948,
0.02448657527565956,
0.0472317673265934,
-0.12465570122003555,
-0.0351434126496315,
0.023084862157702446,
-0.07734142243862152,
0.07235359400510788,
0.015370186418294907,
0.022615555673837662,
0.0484594888985157,
-0.16041843593120575,
0.045435309410095215,
0.052626535296440125,
0.005492215044796467,
0.023006070405244827,
-0.09067021310329437,
-0.018391931429505348,
0.005185992922633886,
0.036817871034145355,
0.02406749501824379,
0.08956749737262726,
-0.12142685800790787,
0.009444558061659336,
-0.021708020940423012,
-0.04741758108139038,
-0.05394233390688896,
0.03736795112490654,
0.06506025791168213,
0.017532067373394966,
0.20516589283943176,
-0.09422017633914948,
0.006931031588464975,
-0.2032826691865921,
0.011815742589533329,
0.008297058753669262,
-0.12799988687038422,
-0.1129942238330841,
-0.05917743965983391,
0.04677991196513176,
-0.05140988156199455,
0.11966682970523834,
-0.010265897959470749,
0.04254021495580673,
0.034491293132305145,
-0.01910431869328022,
0.07578037679195404,
0.011138860136270523,
0.24081926047801971,
0.017811045050621033,
-0.04232403635978699,
0.030476072803139687,
0.03406931087374687,
0.11359462141990662,
0.0794057697057724,
0.1637905091047287,
0.1455911099910736,
-0.051280803978443146,
0.11202750355005264,
0.022110827267169952,
-0.03752245381474495,
-0.15138466656208038,
0.03736921027302742,
-0.040957387536764145,
0.09994804114103317,
-0.017205823212862015,
0.2301923930644989,
0.11746285855770111,
-0.15666691958904266,
0.009109796956181526,
-0.054411351680755615,
-0.07346251606941223,
-0.1023799404501915,
-0.09602107107639313,
-0.09065889567136765,
-0.14484350383281708,
-0.007542016450315714,
-0.10705454647541046,
0.0156270582228899,
0.1025112047791481,
0.005846773739904165,
-0.025755533948540688,
0.1826050877571106,
0.023018913343548775,
0.009919784031808376,
0.03908051177859306,
-0.008708232082426548,
-0.03507836535573006,
-0.06407511234283447,
-0.09384032338857651,
0.015391964465379715,
-0.021107880398631096,
0.034702662378549576,
-0.040465887635946274,
-0.021167393773794174,
0.04344907030463219,
-0.014486140571534634,
-0.10363961011171341,
0.002198021626099944,
0.04048170521855354,
0.05336381494998932,
0.05814381316304207,
0.013022132217884064,
0.003361644223332405,
0.004605115856975317,
0.23741132020950317,
-0.0812743529677391,
-0.06672695279121399,
-0.08400095999240875,
0.21344654262065887,
0.01093436498194933,
-0.005828700494021177,
0.0010129064321517944,
-0.09278301149606705,
0.03992851823568344,
0.2270059734582901,
0.1718991994857788,
-0.08888807147741318,
-0.006771116051822901,
-0.022147512063384056,
-0.005369687452912331,
-0.024882936850190163,
0.08952189236879349,
0.10743040591478348,
0.02172127738595009,
-0.08003348857164383,
-0.028926562517881393,
-0.03328961506485939,
-0.001962429378181696,
-0.047536514699459076,
0.06523574143648148,
0.012995625846087933,
0.011329153552651405,
-0.03892996162176132,
0.06306423246860504,
-0.028632089495658875,
-0.09128297120332718,
0.008703877218067646,
-0.19078285992145538,
-0.13208910822868347,
-0.017271241173148155,
0.11064161360263824,
-0.019752001389861107,
0.049217693507671356,
-0.028373833745718002,
0.02321934886276722,
0.05501510202884674,
-0.02068614587187767,
-0.07098007947206497,
-0.06505750864744186,
0.05950227007269859,
-0.10683431476354599,
0.22785675525665283,
-0.04147013649344444,
0.02974424883723259,
0.1294884979724884,
0.029117705300450325,
-0.09244970977306366,
0.10329536348581314,
0.04276980832219124,
-0.04421164467930794,
0.037289682775735855,
0.08307967334985733,
-0.03255017474293709,
0.10714507848024368,
0.04750456288456917,
-0.11407536268234253,
0.010414649732410908,
-0.029517972841858864,
-0.06460503488779068,
-0.0457562692463398,
-0.04924934357404709,
-0.06338930875062943,
0.1409616768360138,
0.15939395129680634,
-0.04884187877178192,
-0.00461670383810997,
-0.051545366644859314,
0.02245466038584709,
0.07915132492780685,
0.03324801102280617,
-0.02532804012298584,
-0.2344360649585724,
0.011211401782929897,
0.07870617508888245,
-0.005657391157001257,
-0.32182082533836365,
-0.08303361386060715,
-0.015962908044457436,
-0.03964393958449364,
-0.09550055116415024,
0.08332137018442154,
0.1254855990409851,
0.046314530074596405,
-0.06353364139795303,
-0.060715869069099426,
-0.07665644586086273,
0.15991398692131042,
-0.11743154376745224,
-0.10144305229187012
] |
null | null | diffusers | base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: [CB] bottle photo
license: openrail++
---
# SDXL LoRA DreamBooth - erikhsos/cbbier_LoRA
<Gallery />
## Model description
These are erikhsos/campusbiernew_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use [CB] bottle photo to trigger the image generation.
## Training Settings
#!/usr/bin/env bash
!accelerate launch train_dreambooth_lora_sdxl.py \
--pretrained_model_name_or_path="stabilityai/stable-diffusion-xl-base-1.0" \
--pretrained_vae_model_name_or_path="madebyollin/sdxl-vae-fp16-fix" \
--dataset_name="cbbier" \
--output_dir="cbbier_LoRA" \
--caption_column="prompt"\
--mixed_precision="fp16" \
--instance_prompt="[CB] bottle photo" \
--resolution=1024 \
--train_batch_size=1 \
--gradient_accumulation_steps=3 \
--gradient_checkpointing \
--learning_rate=1e-4 \
--snr_gamma=5.0 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--mixed_precision="fp16" \
--use_8bit_adam \
--max_train_steps=100 \
--checkpointing_steps=717 \
--seed="0"
## Download model
Weights for this model are available in Safetensors format.
[Download](erikhsos/cbbier_LoRA/tree/main) them in the Files & versions tab. | {"tags": ["text-to-image", "dreambooth", "lora", "diffusers"], "datasets": ["erikhsos/cbbier"], "pipeline_tag": "text-to-image"} | text-to-image | erikhsos/cbbier_LoRA | [
"diffusers",
"text-to-image",
"dreambooth",
"lora",
"dataset:erikhsos/cbbier",
"region:us"
] | 2024-02-09T10:11:19+00:00 | [] | [] | TAGS
#diffusers #text-to-image #dreambooth #lora #dataset-erikhsos/cbbier #region-us
| base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: [CB] bottle photo
license: openrail++
---
# SDXL LoRA DreamBooth - erikhsos/cbbier_LoRA
<Gallery />
## Model description
These are erikhsos/campusbiernew_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using DreamBooth.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use [CB] bottle photo to trigger the image generation.
## Training Settings
#!/usr/bin/env bash
!accelerate launch train_dreambooth_lora_sdxl.py \
--pretrained_model_name_or_path="stabilityai/stable-diffusion-xl-base-1.0" \
--pretrained_vae_model_name_or_path="madebyollin/sdxl-vae-fp16-fix" \
--dataset_name="cbbier" \
--output_dir="cbbier_LoRA" \
--caption_column="prompt"\
--mixed_precision="fp16" \
--instance_prompt="[CB] bottle photo" \
--resolution=1024 \
--train_batch_size=1 \
--gradient_accumulation_steps=3 \
--gradient_checkpointing \
--learning_rate=1e-4 \
--snr_gamma=5.0 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--mixed_precision="fp16" \
--use_8bit_adam \
--max_train_steps=100 \
--checkpointing_steps=717 \
--seed="0"
## Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab. | [
"# SDXL LoRA DreamBooth - erikhsos/cbbier_LoRA\n\n<Gallery />",
"## Model description\n\nThese are erikhsos/campusbiernew_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.",
"## Trigger words\n\nYou should use [CB] bottle photo to trigger the image generation.",
"## Training Settings",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
"TAGS\n#diffusers #text-to-image #dreambooth #lora #dataset-erikhsos/cbbier #region-us \n",
"# SDXL LoRA DreamBooth - erikhsos/cbbier_LoRA\n\n<Gallery />",
"## Model description\n\nThese are erikhsos/campusbiernew_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.",
"## Trigger words\n\nYou should use [CB] bottle photo to trigger the image generation.",
"## Training Settings",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
34,
24,
90,
18,
4,
28
] | [
"passage: TAGS\n#diffusers #text-to-image #dreambooth #lora #dataset-erikhsos/cbbier #region-us \n# SDXL LoRA DreamBooth - erikhsos/cbbier_LoRA\n\n<Gallery />## Model description\n\nThese are erikhsos/campusbiernew_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.## Trigger words\n\nYou should use [CB] bottle photo to trigger the image generation.## Training Settings## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
-0.05616731196641922,
0.14344191551208496,
-0.0013897137250751257,
0.10608816891908646,
0.15407998859882355,
0.024999696761369705,
0.10923335701227188,
0.06885208934545517,
0.050802867859601974,
0.05770106986165047,
0.09776225686073303,
0.017947621643543243,
0.05277254804968834,
0.13586068153381348,
-0.020260430872440338,
-0.17508773505687714,
0.028519855812191963,
-0.030963800847530365,
-0.007208670023828745,
0.027603579685091972,
0.009169846773147583,
-0.06206812337040901,
0.1540774405002594,
-0.07059279084205627,
-0.16069136559963226,
0.019280336797237396,
-0.015197589993476868,
-0.010609294287860394,
0.03268393129110336,
0.054686930030584335,
0.11104697734117508,
0.022674042731523514,
0.03359119966626167,
-0.20312917232513428,
0.040654901415109634,
0.01139065157622099,
-0.03644748032093048,
0.03710014000535011,
-0.049020782113075256,
-0.07252762466669083,
0.11684814840555191,
-0.13147683441638947,
0.04477575048804283,
0.013661150820553303,
-0.08256121724843979,
-0.10695839673280716,
0.006017918698489666,
0.015861205756664276,
0.07030458003282547,
0.08022911846637726,
-0.002950454829260707,
0.004896833561360836,
0.029081203043460846,
0.062560074031353,
0.21038754284381866,
-0.057263560593128204,
-0.059291303157806396,
0.24223299324512482,
-0.023867817595601082,
0.07360073179006577,
-0.0467265360057354,
0.056602656841278076,
0.07575009018182755,
0.028180742636322975,
-0.002097158459946513,
-0.07740800827741623,
-0.14913184940814972,
-0.09060059487819672,
-0.0417034737765789,
0.008759123273193836,
0.25921985507011414,
-0.01222686655819416,
-0.07550925761461258,
-0.07606770843267441,
-0.052596963942050934,
0.09727192670106888,
-0.015074281021952629,
-0.001079265377484262,
-0.005816171411424875,
-0.00267059076577425,
0.016555478796362877,
-0.07392016053199768,
-0.06566967815160751,
-0.05338550731539726,
0.03713567182421684,
0.06924136728048325,
-0.011212565004825592,
0.041380874812603,
-0.023895446211099625,
0.09849146753549576,
-0.030393412336707115,
-0.12862622737884521,
-0.0014503864804282784,
-0.07501009851694107,
-0.010633875615894794,
0.0607372485101223,
0.014584912918508053,
-0.0892132893204689,
0.04310717061161995,
-0.05175371095538139,
0.06997494399547577,
0.020361166447401047,
-0.014894718304276466,
0.04181750863790512,
-0.02801813930273056,
0.07152346521615982,
0.0663342997431755,
-0.047291893512010574,
0.007547144778072834,
0.00016276394308079034,
0.055539317429065704,
-0.07651851326227188,
-0.15130694210529327,
-0.0342494361102581,
-0.09902290999889374,
0.0703463926911354,
-0.06252524256706238,
-0.023079201579093933,
-0.0700024664402008,
-0.03071943297982216,
0.15885029733181,
-0.018044637516140938,
0.019751833751797676,
-0.06563980877399445,
-0.031705696135759354,
0.05410504713654518,
0.18795964121818542,
-0.0031413722317665815,
-0.012577870860695839,
0.05067937821149826,
-0.06586921215057373,
0.044220179319381714,
-0.0831160619854927,
-0.07966767251491547,
0.028987322002649307,
-0.1497267782688141,
0.05093071609735489,
-0.1314486414194107,
-0.22002868354320526,
-0.054780807346105576,
0.039425868541002274,
-0.03653712570667267,
0.027159258723258972,
-0.12611395120620728,
-0.09790500998497009,
-0.044018588960170746,
0.05894620344042778,
0.04479611665010452,
0.0026012484449893236,
0.06715890765190125,
-0.041141778230667114,
0.11870209872722626,
-0.1151818037033081,
0.001714377198368311,
-0.12295962125062943,
0.04767029359936714,
-0.02660483494400978,
0.1382448971271515,
-0.04350465163588524,
0.10684225708246231,
-0.04516727104783058,
-0.03171674162149429,
-0.0010313186794519424,
0.024232828989624977,
-0.018546968698501587,
0.16116219758987427,
-0.3115833103656769,
-0.019729748368263245,
0.1579321324825287,
-0.17400266230106354,
-0.11482322961091995,
0.02258186973631382,
-0.06855287402868271,
0.14098158478736877,
0.11883235722780228,
0.1301797777414322,
0.1409866362810135,
-0.22283121943473816,
-0.06458159536123276,
-0.05776918679475784,
-0.036611150950193405,
-0.043029673397541046,
0.01995036192238331,
0.08288504183292389,
-0.031815048307180405,
0.07441230118274689,
-0.06156197935342789,
0.059299033135175705,
0.003190586343407631,
-0.08033818751573563,
-0.026124799624085426,
-0.06903976947069168,
-0.031743746250867844,
0.01148297730833292,
-0.019788140431046486,
-0.017264418303966522,
0.011475290171802044,
0.060876719653606415,
0.08662385493516922,
-0.03694533556699753,
-0.013103817589581013,
-0.010920047760009766,
-0.020038826391100883,
-0.08594179898500443,
-0.015505097806453705,
-0.02487955056130886,
-0.1360301971435547,
0.023583628237247467,
0.10798783600330353,
0.08865073323249817,
-0.0373188816010952,
0.05951915308833122,
0.10732396692037582,
-0.0696898028254509,
-0.0428299754858017,
0.03426620736718178,
-0.03746568784117699,
-0.009430071339011192,
-0.12759889662265778,
0.012076054699718952,
-0.06989695131778717,
0.07306120544672012,
-0.2511097192764282,
0.035574786365032196,
0.05724044516682625,
0.1084970086812973,
0.08705274015665054,
-0.029224399477243423,
0.05607229098677635,
0.05594794824719429,
-0.009709752164781094,
-0.07887547463178635,
0.013275008648633957,
-0.0331643670797348,
-0.15974050760269165,
0.06701774150133133,
-0.12394669651985168,
-0.06103633716702461,
0.12097721546888351,
0.12687352299690247,
-0.03686397522687912,
-0.08838102966547012,
0.022150108590722084,
-0.007708359509706497,
-0.07390885055065155,
0.010293682105839252,
0.15539054572582245,
-0.006253512110561132,
0.07762474566698074,
-0.04976047948002815,
0.02477150782942772,
0.036853909492492676,
-0.04521610960364342,
-0.09070957452058792,
0.10408354550600052,
0.01784759759902954,
0.03760555014014244,
0.06587284803390503,
0.016286175698041916,
-0.051200296729803085,
0.27059826254844666,
0.03762928396463394,
-0.015154997818171978,
-0.03618529811501503,
0.013999114744365215,
0.03164121136069298,
0.15844853222370148,
0.02791811339557171,
0.01652020588517189,
0.018232479691505432,
-0.04842674359679222,
-0.00493044825270772,
-0.14777208864688873,
-0.08656352013349533,
0.005015935283154249,
-0.05764782428741455,
0.09743103384971619,
0.05292123183608055,
-0.0680517703294754,
0.01717589609324932,
-0.08073893189430237,
0.012943731620907784,
0.03508869931101799,
-0.02864096499979496,
-0.04351286217570305,
0.1071397066116333,
-0.08586470782756805,
-0.2646331489086151,
-0.17985495924949646,
0.0451565720140934,
-0.04002309590578079,
0.013135379180312157,
0.046823106706142426,
-0.11495503038167953,
-0.051363393664360046,
-0.07099531590938568,
0.10024949163198471,
0.007067717611789703,
-0.016393588855862617,
-0.03294144943356514,
0.011480655521154404,
-0.030534489080309868,
-0.06611371040344238,
-0.00659504160284996,
-0.022465761750936508,
-0.021026575937867165,
0.06929120421409607,
-0.07272100448608398,
0.1523916870355606,
0.08662353456020355,
0.01321191806346178,
0.026694832369685173,
0.0009806824382394552,
0.2395034283399582,
-0.07857438176870346,
0.09722830355167389,
0.18157033622264862,
0.07132671028375626,
0.06609698385000229,
0.15907105803489685,
0.0025864746421575546,
-0.10830668359994888,
0.07632983475923538,
0.013245939277112484,
-0.16268475353717804,
-0.1204189732670784,
-0.11429005116224289,
-0.022188158705830574,
-0.05541863664984703,
0.07146914303302765,
0.07903418689966202,
0.1489626169204712,
0.16887566447257996,
0.01292367558926344,
0.05322394147515297,
0.010911871679127216,
0.09179157763719559,
-0.0511048324406147,
-0.013667477294802666,
-0.00014006787387188524,
-0.06954643130302429,
-0.05983936786651611,
0.09898879379034042,
0.048470281064510345,
0.20178543031215668,
-0.07844489067792892,
-0.023423761129379272,
0.04195611551403999,
0.01799766905605793,
0.07013039290904999,
0.040984779596328735,
-0.03517523035407066,
-0.014457725919783115,
-0.06727367639541626,
-0.09834068268537521,
0.014614077284932137,
0.1550123691558838,
-0.04439351707696915,
0.053917065262794495,
0.03059256635606289,
0.061481282114982605,
0.03672740235924721,
-0.016902590170502663,
0.0541691929101944,
-0.27785810828208923,
-0.02596166357398033,
0.08092544972896576,
0.10393642634153366,
-0.0799081027507782,
-0.010766657069325447,
0.26529890298843384,
-0.06540198624134064,
0.07541690021753311,
-0.03506721556186676,
0.0689551904797554,
0.025400960817933083,
-0.061630360782146454,
-0.0777183249592781,
0.10962327569723129,
-0.010789471678435802,
0.034850019961595535,
-0.23935124278068542,
0.018061820417642593,
-0.007500629406422377,
0.07297603785991669,
-0.06219162419438362,
-0.006678288336843252,
0.0328153632581234,
0.040477752685546875,
0.15870513021945953,
0.027259893715381622,
-0.053877633064985275,
-0.1060316190123558,
-0.09167982637882233,
0.01733281835913658,
0.017497416585683823,
-0.03889398276805878,
0.05868713930249214,
0.07374992966651917,
-0.010567420162260532,
-0.007150596007704735,
0.024664828553795815,
-0.05852947011590004,
-0.1167776882648468,
-0.05731870234012604,
0.1383207142353058,
0.09653165191411972,
-0.03030354343354702,
-0.04510403051972389,
0.09244733303785324,
-0.02727363631129265,
-0.04172757640480995,
-0.11832297593355179,
-0.07528501749038696,
-0.08453793823719025,
0.09991218894720078,
-0.05794982984662056,
0.008562372997403145,
-0.003880535252392292,
0.1182810589671135,
-0.13815423846244812,
-0.082963727414608,
-0.007657601032406092,
-0.031244641169905663,
-0.11139288544654846,
-0.07539360970258713,
0.10800456255674362,
0.032049357891082764,
0.017048059031367302,
-0.0024505748879164457,
0.031660981476306915,
-0.003464680165052414,
-0.11047086119651794,
0.04426996037364006,
0.15465675294399261,
-0.08796005696058273,
0.15973228216171265,
0.006193868350237608,
-0.013347862288355827,
-0.08978267759084702,
-0.016332179307937622,
0.0572589635848999,
0.25468266010284424,
-0.08424660563468933,
0.11031195521354675,
0.01166196446865797,
-0.10638435930013657,
-0.22668327391147614,
-0.0090198814868927,
-0.00011218954023206607,
0.024913514032959938,
-0.02854352816939354,
-0.1595858931541443,
0.1775224804878235,
0.03787941485643387,
0.008505748584866524,
0.19024762511253357,
-0.29946500062942505,
-0.1307918280363083,
-0.02094286121428013,
0.1722995489835739,
0.1879262775182724,
-0.1339971423149109,
-0.024935582652688026,
-0.10497678071260452,
0.00544956186786294,
0.2548232078552246,
-0.06842412054538727,
0.09296970069408417,
-0.015769708901643753,
0.10928548127412796,
0.02225073240697384,
-0.016500480473041534,
0.1971692144870758,
0.03798014298081398,
0.1374499350786209,
-0.0492437370121479,
-0.11479367315769196,
0.08085627853870392,
-0.05048412084579468,
0.10047295689582825,
-0.11230681836605072,
0.00719112902879715,
-0.02296685241162777,
-0.03958677873015404,
-0.05723554268479347,
0.04832994565367699,
-0.04902450367808342,
-0.09441215544939041,
-0.07934387028217316,
0.0843939334154129,
0.08240854740142822,
0.01039175409823656,
-0.04942121356725693,
-0.05906786397099495,
-0.06103019788861275,
0.10679703205823898,
-0.02722959592938423,
0.0780177041888237,
0.007211134769022465,
0.015633774921298027,
-0.049948304891586304,
0.1205521821975708,
-0.15983633697032928,
-0.008924907073378563,
0.11772545427083969,
0.025908267125487328,
0.15372003614902496,
0.0032233770471066236,
-0.08249342441558838,
0.0655379369854927,
0.08021992444992065,
-0.06823714822530746,
-0.13767167925834656,
-0.08285444974899292,
-0.08207646757364273,
0.011747680604457855,
-0.11613718420267105,
0.129966601729393,
-0.12279296666383743,
0.02875307947397232,
-0.03477795422077179,
0.013327576220035553,
-0.03647524490952492,
0.0547601655125618,
0.04346407204866409,
-0.002205135067924857,
-0.09509030729532242,
0.06026553362607956,
0.02982761152088642,
-0.020271852612495422,
0.05003373697400093,
0.13244378566741943,
-0.061710163950920105,
-0.01652425155043602,
-0.041371263563632965,
0.23721566796302795,
-0.10285580158233643,
-0.05749550089240074,
-0.06272045522928238,
-0.09625929594039917,
-0.018444128334522247,
0.11539950221776962,
0.058919377624988556,
0.021947117522358894,
-0.0012955828569829464,
-0.027569474652409554,
-0.11687732487916946,
0.07075436413288116,
0.038531478494405746,
0.0650387853384018,
-0.23110061883926392,
0.029588956385850906,
0.037184517830610275,
-0.007992836646735668,
-0.062344323843717575,
-0.033744536340236664,
-0.09291014820337296,
0.02180946245789528,
-0.03997161611914635,
0.17619110643863678,
-0.06595072150230408,
0.009808583185076714,
-0.016883503645658493,
-0.009563717059791088,
-0.014323204755783081,
0.04401222616434097,
-0.03849974274635315,
-0.005788062233477831,
0.0023593269288539886,
0.0319821797311306,
-0.0491696372628212,
-0.1267291009426117,
0.03726731613278389,
-0.03454841300845146,
-0.02219439670443535,
-0.009272157214581966,
-0.08981652557849884,
0.0784447118639946,
-0.26379337906837463,
0.08200192451477051,
0.17193682491779327,
0.003876582020893693,
0.01010480709373951,
-0.007928584702312946,
-0.04630441963672638,
-0.04096376523375511,
0.032112009823322296,
0.0017437333008274436,
0.013624712824821472,
-0.0942234918475151,
-0.06234252452850342,
-0.0008421525708399713,
-0.07832363247871399,
-0.0531572550535202,
0.0880766287446022,
0.14124058187007904,
0.10337410122156143,
0.1152215451002121,
-0.10766837745904922,
0.10946676880121231,
-0.1154281347990036,
-0.022576140239834785,
-0.01105480082333088,
-0.06189946457743645,
-0.08711837232112885,
-0.05783756822347641,
0.0031962415669113398,
0.006850251462310553,
0.0893157571554184,
0.059935592114925385,
-0.0671454668045044,
-0.05887721851468086,
0.01412946730852127,
-0.02388700656592846,
-0.002095314906910062,
0.16856658458709717,
0.034101348370313644,
0.015852177515625954,
0.007707949262112379,
0.06044409051537514,
0.09939491748809814,
0.22561541199684143,
0.021592779085040092,
0.0786895602941513,
0.12574662268161774,
0.10708648711442947,
0.03422904387116432,
0.06121449917554855,
0.03778219595551491,
0.07133539766073227,
-0.026480425149202347,
0.0045575546100735664,
-0.07475020736455917,
-0.05501556396484375,
0.08476630598306656,
-0.14381693303585052,
0.04917681962251663,
0.09395336359739304,
-0.0946493148803711,
-0.05660915747284889,
-0.17444536089897156,
-0.10232818871736526,
-0.07751724123954773,
0.028344688937067986,
-0.0996321439743042,
-0.03552529588341713,
0.09041225910186768,
-0.028841765597462654,
0.005891901906579733,
0.19227944314479828,
-0.05258722975850105,
-0.01604929007589817,
0.07101783901453018,
-0.027333196252584457,
-0.017628369852900505,
0.02088523656129837,
0.02952101081609726,
0.09206856787204742,
0.018501078709959984,
-0.008088420145213604,
0.05133581534028053,
0.046451546251773834,
0.0203392431139946,
-0.028363492339849472,
-0.08394739776849747,
-0.02012675069272518,
0.027587004005908966,
-0.00440935930237174,
0.13709211349487305,
0.10830656439065933,
-0.03386393189430237,
-0.02888425998389721,
0.13993459939956665,
-0.03456508368253708,
-0.06015949323773384,
-0.13900481164455414,
0.07618465274572372,
-0.07285692542791367,
0.02983783930540085,
-0.01770637184381485,
-0.05953759700059891,
-0.0456748902797699,
0.0799928605556488,
0.15879127383232117,
-0.020908832550048828,
0.01684960164129734,
-0.08397383242845535,
0.017161047086119652,
-0.08576758950948715,
0.08449514955282211,
0.007899289019405842,
0.19254866242408752,
-0.09633762389421463,
0.06384187936782837,
-0.054641686379909515,
-0.07175052165985107,
-0.012320801615715027,
-0.09481459110975266,
-0.012364372611045837,
-0.02970539964735508,
-0.06754985451698303,
0.05822191759943962,
-0.20994067192077637,
-0.07951731234788895,
0.23687689006328583,
-0.16645292937755585,
-0.035375457257032394,
-0.07159031182527542,
-0.0428178645670414,
0.0745164006948471,
0.014179027639329433,
-0.07423365116119385,
0.004957372788339853,
0.07949725538492203,
-0.004632108844816685,
-0.20242781937122345,
-0.036678384989500046,
0.015301135368645191,
-0.1657804399728775,
0.1935335397720337,
-0.0339248962700367,
-0.0029792035929858685,
0.027525637298822403,
-0.004935700912028551,
-0.060495130717754364,
0.08023054897785187,
-0.028370892629027367,
0.015307124704122543,
-0.048430465161800385,
0.17430801689624786,
-0.06328640133142471,
0.15047518908977509,
0.03725586459040642,
-0.12510281801223755,
-0.023192603141069412,
0.14116257429122925,
-0.019546110183000565,
-0.06570795178413391,
0.03661857545375824,
-0.13138358294963837,
0.08207430690526962,
0.16026872396469116,
0.005778615828603506,
0.016694089397788048,
-0.0222797654569149,
0.04224088042974472,
0.024697521701455116,
0.03786968067288399,
0.038900893181562424,
0.016111861914396286,
-0.06160234287381172,
0.050111860036849976,
0.0015087570063769817,
-0.2984440326690674,
-0.0636427029967308,
-0.18425524234771729,
-0.049545060843229294,
-0.00027426829910837114,
0.0806632861495018,
0.12370897829532623,
0.0362410694360733,
-0.011450475081801414,
-0.15605193376541138,
0.028900252655148506,
0.08381733298301697,
-0.04576284438371658,
-0.07206633687019348
] |
null | null | null | #test read | {} | null | Joppiee/llama-2-7b-usa | [
"region:us"
] | 2024-02-09T10:11:45+00:00 | [] | [] | TAGS
#region-us
| #test read | [] | [
"TAGS\n#region-us \n"
] | [
6
] | [
"passage: TAGS\n#region-us \n"
] | [
0.024608636274933815,
-0.026205500587821007,
-0.009666500613093376,
-0.10395516455173492,
0.08638657629489899,
0.059816278517246246,
0.01882290467619896,
0.020661840215325356,
0.23975107073783875,
-0.005599027033895254,
0.1219947561621666,
0.0015615287702530622,
-0.037353623658418655,
0.03733762726187706,
-0.0035912662278860807,
-0.17583473026752472,
0.03876631706953049,
-0.018274923786520958,
0.01843859627842903,
0.026470553129911423,
-0.07776834815740585,
-0.07564429938793182,
0.015296397730708122,
-0.10247814655303955,
-0.083692267537117,
0.11002834886312485,
0.031466204673051834,
-0.019670886918902397,
0.10779199749231339,
-0.04243955761194229,
0.18699054419994354,
-0.011512263678014278,
-0.11213519424200058,
-0.2536850869655609,
0.021806683391332626,
-0.01765260472893715,
-0.08747660368680954,
0.01506110467016697,
0.0665089413523674,
-0.09014441072940826,
-0.0588928684592247,
0.0795099288225174,
-0.01132340170443058,
0.04246443510055542,
-0.27593839168548584,
-0.12684126198291779,
-0.05297930911183357,
-0.1421966552734375,
0.08651168644428253,
0.04035491496324539,
0.008764253929257393,
0.15506891906261444,
-0.20897391438484192,
0.004104613792151213,
0.08255259692668915,
-0.2538507878780365,
0.05591634660959244,
0.17671173810958862,
0.03623908758163452,
0.18037272989749908,
0.0060391901060938835,
0.11029672622680664,
0.0716743916273117,
-0.024263937026262283,
-0.17590197920799255,
-0.08127854019403458,
-0.04696211963891983,
0.16642488539218903,
-0.06727185100317001,
-0.14248386025428772,
0.34701237082481384,
0.00015008423360995948,
0.009657775051891804,
0.16921205818653107,
-0.059524230659008026,
-0.09972117841243744,
0.07259953022003174,
0.016484731808304787,
0.018492350354790688,
0.1471305936574936,
0.16307872533798218,
-0.0458691343665123,
-0.13837823271751404,
-0.018630273640155792,
-0.22798998653888702,
0.17510560154914856,
-0.03248048573732376,
0.13137903809547424,
-0.27447956800460815,
0.01684025302529335,
-0.2570667266845703,
0.0032130838371813297,
0.04178816080093384,
-0.06004921346902847,
-0.0226522795855999,
-0.013265985064208508,
-0.08018817007541656,
0.004899587947875261,
0.06192673370242119,
0.1266920566558838,
-0.06128726154565811,
0.06128238886594772,
-0.09319206327199936,
0.141696035861969,
0.07166698575019836,
0.07868369668722153,
0.13037432730197906,
0.041205424815416336,
-0.07187089323997498,
-0.21872246265411377,
-0.0026476888451725245,
-0.06275863200426102,
-0.09502086788415909,
-0.0020165652967989445,
-0.11606067419052124,
0.17244569957256317,
-0.030802514404058456,
-0.09825427830219269,
-0.11208184063434601,
0.09148659557104111,
-0.032992321997880936,
-0.03437839448451996,
-0.03552987426519394,
-0.020977836102247238,
0.019381176680326462,
0.04704452306032181,
-0.1548958420753479,
-0.005131472367793322,
0.07039852440357208,
0.11502562463283539,
-0.1346137970685959,
-0.003783059772104025,
-0.07908964157104492,
0.03039063885807991,
0.07654735445976257,
-0.16510222852230072,
0.03158547356724739,
-0.1124754324555397,
-0.07531405985355377,
0.002912673633545637,
-0.015710093080997467,
-0.016202643513679504,
0.166526660323143,
-0.0020451415330171585,
0.0714716836810112,
-0.026345307007431984,
-0.05890209600329399,
-0.11243434250354767,
-0.08489254862070084,
0.05390460044145584,
0.03670717030763626,
0.03266148269176483,
-0.2193479984998703,
0.014805203303694725,
-0.12762966752052307,
0.1360815018415451,
-0.10566820204257965,
-0.04705966264009476,
-0.022842247039079666,
0.20562705397605896,
0.037286072969436646,
0.08762791007757187,
-0.22171171009540558,
0.039756543934345245,
-0.05404696613550186,
0.18480908870697021,
-0.1502426266670227,
-0.0799463614821434,
0.20813211798667908,
-0.07964949309825897,
-0.10115210711956024,
0.021235812455415726,
0.020391687750816345,
0.026287272572517395,
0.0766737088561058,
0.4564172327518463,
-0.09766800701618195,
-0.09146861732006073,
0.10178250074386597,
0.17055274546146393,
-0.12427149713039398,
-0.1827561855316162,
0.06446871906518936,
-0.16666454076766968,
-0.1973118633031845,
0.0018917324487119913,
0.09222044050693512,
0.038269978016614914,
-0.07875611633062363,
-0.020746968686580658,
0.06325206160545349,
-0.0007678253459744155,
0.09095914661884308,
0.03755716234445572,
0.09034032374620438,
-0.08716782182455063,
0.11115926504135132,
-0.05017651244997978,
0.004037132486701012,
0.1343354731798172,
0.027325427159667015,
-0.03223329409956932,
0.08694463223218918,
-0.0485352948307991,
0.05295134335756302,
-0.1662379503250122,
-0.15068690478801727,
0.03398871049284935,
0.06283251196146011,
0.03186952322721481,
0.1280253529548645,
0.08141885697841644,
-0.10732853412628174,
0.022690722718834877,
-0.004228927195072174,
0.058398615568876266,
0.03891623765230179,
0.006107209715992212,
0.008764320984482765,
0.0961301177740097,
-0.10607069730758667,
-0.13589619100093842,
-0.07336436957120895,
-0.014715781435370445,
0.14371353387832642,
-0.0302802175283432,
0.07690227776765823,
-0.004240254405885935,
0.00013200697139836848,
0.06930823624134064,
0.08137880265712738,
0.016412746161222458,
0.08971183747053146,
-0.05237193778157234,
-0.05160155147314072,
0.10863113403320312,
-0.13533565402030945,
0.17837053537368774,
0.14053137600421906,
-0.20532016456127167,
0.029453208670020103,
-0.06838275492191315,
0.03670361638069153,
-0.008162540383636951,
0.0975119024515152,
-0.08272241055965424,
-0.02106042578816414,
0.013134466484189034,
0.0052274600602686405,
-0.013007243163883686,
0.017682146281003952,
-0.07295988500118256,
-0.07787393033504486,
-0.10233919322490692,
0.08436838537454605,
0.11562882363796234,
-0.10282530635595322,
0.14214380085468292,
0.4384984076023102,
0.11495281755924225,
0.21582984924316406,
-0.09581480920314789,
-0.0412987545132637,
0.007486371789127588,
0.0001535322517156601,
-0.04476691037416458,
0.08031861484050751,
-0.15973517298698425,
-0.038901735097169876,
0.027348900213837624,
0.07128690183162689,
0.11475157737731934,
-0.14959022402763367,
-0.09639324247837067,
-0.00793045200407505,
0.0022841424215584993,
-0.1249532699584961,
0.023905446752905846,
-0.03974650055170059,
0.04015624523162842,
0.07232289016246796,
-0.021535737439990044,
0.13939237594604492,
-0.04166141897439957,
-0.0639561116695404,
0.07585346698760986,
-0.2017085999250412,
-0.23179671168327332,
-0.12309670448303223,
-0.14680525660514832,
0.04366797208786011,
0.05154111236333847,
0.01726446859538555,
-0.17635835707187653,
-0.015074856579303741,
0.07706750929355621,
0.07820965349674225,
-0.20886357128620148,
-0.022814949974417686,
-0.004290030337870121,
0.0895976573228836,
-0.10227091610431671,
-0.0017130117630586028,
-0.04419664293527603,
-0.10150232166051865,
0.0017003051470965147,
0.07279510796070099,
-0.137485533952713,
0.13807645440101624,
0.21589438617229462,
0.07225540280342102,
0.07359948754310608,
-0.019093448296189308,
0.09936179965734482,
-0.10856141895055771,
-0.16549113392829895,
0.08348225057125092,
-0.06234746053814888,
0.047262318432331085,
0.17534415423870087,
0.03307317942380905,
-0.13904969394207,
-0.015682822093367577,
-0.0402069091796875,
-0.15603256225585938,
-0.238995760679245,
-0.09178274869918823,
-0.1182505264878273,
0.16442428529262543,
0.0009358620154671371,
0.06651917099952698,
0.08258313685655594,
-0.022042419761419296,
0.16447891294956207,
-0.07379321753978729,
-0.07578866183757782,
-0.006978808436542749,
0.12375060468912125,
-0.056660156697034836,
-0.03080669604241848,
-0.10566964000463486,
-0.008295975625514984,
0.1151021271944046,
0.15304014086723328,
0.12214863300323486,
0.2957419455051422,
0.08268889784812927,
0.026645636186003685,
0.08958091586828232,
0.17622539401054382,
0.09495089203119278,
0.07838419824838638,
-0.045413073152303696,
-0.014814783819019794,
0.014317171648144722,
-0.04022889584302902,
0.010141594335436821,
0.14683100581169128,
-0.2679629921913147,
-0.006678564939647913,
-0.2710230350494385,
0.0965198427438736,
-0.10913380235433578,
0.11837165057659149,
-0.01015760749578476,
0.10194015502929688,
0.11082887649536133,
0.03233652561903,
-0.03858073800802231,
0.16613617539405823,
0.08450309932231903,
-0.11277695000171661,
0.001758623169735074,
0.03737903758883476,
0.09715615212917328,
-0.02818971499800682,
0.12721189856529236,
-0.11048974841833115,
-0.1464834064245224,
0.013753619976341724,
0.07152791321277618,
-0.15373679995536804,
0.3138748109340668,
0.012069208547472954,
-0.13481520116329193,
-0.01481647603213787,
-0.09957809001207352,
-0.006440147757530212,
0.1254177987575531,
0.09333524852991104,
0.07935678958892822,
-0.2185502052307129,
-0.13339371979236603,
0.05872276425361633,
-0.00575496768578887,
0.22408108413219452,
-0.034034017473459244,
-0.11356475204229355,
-0.027013886719942093,
0.04241163283586502,
-0.06043251231312752,
0.08524788916110992,
0.023536119610071182,
-0.08113526552915573,
-0.032957352697849274,
0.05323701351881027,
0.012368366122245789,
0.00524376705288887,
0.09360801428556442,
0.020107939839363098,
-0.0009265501867048442,
0.01785753294825554,
0.047885000705718994,
-0.0675911232829094,
-0.1984109878540039,
0.09357594698667526,
-0.05215044692158699,
0.0015536568826064467,
-0.08013670891523361,
-0.15122665464878082,
-0.08837161958217621,
-0.16009655594825745,
0.12540200352668762,
-0.034406669437885284,
0.12700119614601135,
-0.06619787961244583,
0.17341409623622894,
-0.07871770113706589,
0.04481020197272301,
-0.047349292784929276,
0.050332702696323395,
-0.007268077693879604,
-0.07756082713603973,
0.16585899889469147,
-0.15564003586769104,
0.01809087023139,
0.19572502374649048,
-0.018915493041276932,
0.07177707552909851,
0.021322092041373253,
-0.0636206790804863,
0.23147478699684143,
0.3014698624610901,
0.008138049393892288,
0.1665448248386383,
0.3018903136253357,
-0.07466315478086472,
-0.2642788887023926,
-0.05505012720823288,
-0.2841376066207886,
-0.05371501296758652,
0.10716094076633453,
-0.22523896396160126,
0.06986407935619354,
0.14383509755134583,
-0.06471995264291763,
0.30228954553604126,
-0.21825523674488068,
0.012589273042976856,
0.15434536337852478,
-0.08868814259767532,
0.5515313148498535,
-0.1133413165807724,
-0.17677772045135498,
-0.008122089318931103,
-0.08741296827793121,
0.10602109134197235,
-0.0340677872300148,
0.06877441704273224,
0.013465235009789467,
0.04797380417585373,
0.048932258039712906,
-0.03111894056200981,
0.22701001167297363,
0.008710170164704323,
0.09015397727489471,
-0.07378865778446198,
-0.18624304234981537,
0.11639340221881866,
-0.04359482601284981,
-0.08891059458255768,
0.0849778801202774,
-0.05942516401410103,
-0.11078983545303345,
0.04663389176130295,
-0.07950539886951447,
-0.024862350896000862,
0.08423490077257156,
-0.04678233340382576,
-0.042606171220541,
-0.008054176345467567,
-0.1618063747882843,
-0.0002289071271661669,
0.31360217928886414,
-0.07096036523580551,
0.16695955395698547,
0.03677211329340935,
0.00038613268407061696,
-0.11027684062719345,
0.030288029462099075,
-0.05203165486454964,
-0.021576624363660812,
0.09578979015350342,
-0.11096979677677155,
0.03204701095819473,
0.14160704612731934,
-0.04864364117383957,
0.05846960097551346,
0.09256096184253693,
-0.0849417969584465,
0.007583672646433115,
0.17753590643405914,
-0.17537221312522888,
-0.1273445188999176,
-0.006135711446404457,
-0.09862716495990753,
0.14055661857128143,
0.04394126310944557,
0.05191568285226822,
0.16669964790344238,
0.03967129811644554,
-0.029474308714270592,
-0.02817419543862343,
-0.1153380498290062,
-0.0201893113553524,
0.040153320878744125,
0.00045633706031367183,
-0.08791285753250122,
0.2262638509273529,
0.06409153342247009,
-0.1328488290309906,
-0.051157206296920776,
0.2161225974559784,
-0.06805316358804703,
-0.04911920800805092,
-0.223562553524971,
0.10752306133508682,
-0.07112517952919006,
-0.0965060144662857,
0.05453834682703018,
-0.02270081453025341,
0.005106312222778797,
0.181985542178154,
0.03941008821129799,
0.11070270836353302,
0.03738937899470329,
-0.02448922023177147,
0.15798696875572205,
-0.142850860953331,
-0.14191335439682007,
-0.025354057550430298,
-0.08757315576076508,
-0.13844476640224457,
-0.026804137974977493,
0.1617041826248169,
-0.09177309274673462,
-0.14772607386112213,
-0.2621181011199951,
0.10968475043773651,
-0.16432365775108337,
-0.10192688554525375,
-0.03469514101743698,
-0.08968492597341537,
0.0696166530251503,
0.030301768332719803,
-0.03093348816037178,
-0.06706760823726654,
-0.18593791127204895,
0.0816768929362297,
0.06349513679742813,
0.045533183962106705,
-0.017847947776317596,
0.0067379772663116455,
0.1720137596130371,
0.025955144315958023,
0.10040043294429779,
0.16762186586856842,
0.011397695168852806,
0.2246655523777008,
-0.1671202927827835,
-0.11496317386627197,
0.1336962729692459,
-0.026543032377958298,
0.06762003898620605,
0.16792191565036774,
-0.0772583931684494,
0.015526676550507545,
-0.028136352077126503,
0.07066910713911057,
-0.11003983020782471,
-0.105624258518219,
0.007937257178127766,
0.02567129209637642,
-0.2755882740020752,
-0.005599735304713249,
-0.19717298448085785,
0.14788752794265747,
0.02579621411859989,
0.03297143429517746,
0.10257530212402344,
0.10404334217309952,
0.08312062919139862,
-0.0017710148822516203,
0.03226327523589134,
-0.1176818460226059,
0.02753005363047123,
-0.059239376336336136,
-0.020663779228925705,
0.017624232918024063,
0.36952024698257446,
-0.03603357449173927,
-0.046802736818790436,
0.003710439894348383,
0.1307835876941681,
-0.02139742486178875,
0.017395347356796265,
0.13209912180900574,
0.12607666850090027,
-0.08595693111419678,
-0.1504845917224884,
0.04888554662466049,
-0.04565655067563057,
-0.02836887165904045,
0.1464131623506546,
0.05905961990356445,
0.1050296202301979,
0.0908031314611435,
-0.014463032595813274,
-0.00318976235575974,
0.012856799177825451,
-0.15486004948616028,
0.06223496049642563,
-0.010558074340224266,
0.012565906159579754,
0.017934376373887062,
0.15238402783870697,
-0.005540105979889631,
0.07739730179309845,
-0.09889880567789078,
0.004208535887300968,
-0.13498884439468384,
-0.07913459837436676,
0.03617347031831741,
-0.13393273949623108,
0.04141177982091904,
-0.01871878281235695,
0.029611799865961075,
0.30386561155319214,
0.02558239921927452,
-0.020639164373278618,
0.12512871623039246,
-0.1214587539434433,
-0.12050267308950424,
-0.001594188273884356,
-0.029960084706544876,
0.0791488066315651,
-0.02633434161543846,
-0.0997740775346756,
-0.1001306027173996,
-0.15166029334068298,
-0.09759195148944855,
0.05182836204767227,
-0.04993441700935364,
-0.059362251311540604,
-0.17634081840515137,
-0.05707859992980957,
-0.05147340148687363,
0.14025864005088806,
-0.12263951450586319,
0.15159130096435547,
-0.014490418136119843,
0.004084470681846142,
0.04405883327126503,
0.1950942426919937,
-0.03644494712352753,
0.08714226633310318,
0.0154351145029068,
0.1522706001996994,
-0.05119588226079941,
0.14720745384693146,
-0.10931728035211563,
-0.04014137014746666,
-0.06710435450077057,
0.21513493359088898,
0.25630924105644226,
-0.06136954948306084,
-0.008937356993556023,
-0.012760217301547527,
0.058654606342315674,
0.1073930487036705,
0.16049085557460785,
0.002326392102986574,
0.2802925705909729,
-0.03133585304021835,
0.04815128445625305,
0.02901598811149597,
0.013607407920062542,
-0.06336209923028946,
0.03397751972079277,
0.07539387792348862,
-0.035039983689785004,
-0.1412304788827896,
0.15837742388248444,
-0.21980468928813934,
0.18157227337360382,
0.11640069633722305,
-0.19996967911720276,
-0.013728445395827293,
-0.04882071167230606,
0.1689416468143463,
-0.0856364443898201,
0.1637246012687683,
-0.0903693437576294,
-0.2108195722103119,
-0.2056000679731369,
0.03867346793413162,
-0.34623071551322937,
-0.254462867975235,
0.10422009229660034,
0.1488201916217804,
0.04015883058309555,
-0.018507536500692368,
-0.019967829808592796,
-0.018367022275924683,
0.04877542704343796,
-0.0067357709631323814,
0.06014643982052803,
0.031397558748722076,
-0.02988368645310402,
-0.24127542972564697,
-0.029804671183228493,
0.023964406922459602,
-0.07093082368373871,
0.07464958727359772,
-0.06874357163906097,
-0.022495782002806664,
0.08059766888618469,
-0.03066304884850979,
0.03298592567443848,
-0.035373736172914505,
-0.16326889395713806,
0.027529051527380943,
0.03900543600320816,
0.036012712866067886,
0.00634160777553916,
0.0008072225609794259,
-0.03455270454287529,
0.0644603744149208,
-0.16716794669628143,
-0.16015739738941193,
0.14140215516090393,
-0.06745140254497528,
0.2779497504234314,
-0.05812826007604599,
-0.0809100940823555,
0.04766704887151718,
-0.03426874056458473,
0.1807648241519928,
-0.07756473124027252,
0.047254521399736404,
0.12766779959201813,
0.011127962730824947,
0.03121316432952881,
-0.3092964291572571,
0.11082969605922699,
-0.000795336440205574,
-0.006093299947679043,
-0.07581598311662674
] |
null | null | ml-agents |
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: sanchitkalra/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy"]} | reinforcement-learning | sanchitkalra/ppo-Huggy | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | 2024-02-09T10:18:18+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us
|
# ppo Agent playing Huggy
This is a trained model of a ppo agent playing Huggy
using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your
browser: URL
- A *longer tutorial* to understand how works ML-Agents:
URL
### Resume the training
### Watch your Agent play
You can watch your agent playing directly in your browser
1. If the environment is part of ML-Agents official environments, go to URL
2. Step 1: Find your model_id: sanchitkalra/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play
| [
"# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: sanchitkalra/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
"TAGS\n#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us \n",
"# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: sanchitkalra/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
44,
200
] | [
"passage: TAGS\n#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us \n# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: sanchitkalra/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
0.013912866823375225,
0.01949082314968109,
-0.004278897773474455,
0.03808801621198654,
0.14358673989772797,
-0.006284553091973066,
0.15263475477695465,
0.12422651052474976,
0.12307265400886536,
0.0893474593758583,
0.06703846156597137,
0.04859704524278641,
0.05647590756416321,
0.20734277367591858,
0.07505964487791061,
-0.22941924631595612,
0.0018432973884046078,
-0.08304885029792786,
0.030567629262804985,
0.08987820893526077,
0.052021827548742294,
-0.03823164850473404,
0.07440099865198135,
0.026085730642080307,
-0.01841052435338497,
-0.014532335102558136,
-0.08144309371709824,
-0.03368021920323372,
0.04705658555030823,
-0.010414289310574532,
-0.015609931200742722,
-0.030841344967484474,
0.05994049832224846,
-0.214448481798172,
0.03364216536283493,
0.05619799718260765,
-0.0037382710725069046,
0.0008309264667332172,
0.09782186150550842,
0.027245935052633286,
0.11603809893131256,
-0.06758823245763779,
0.06904544681310654,
0.057410869747400284,
-0.07601271569728851,
-0.01681382581591606,
-0.11480643600225449,
0.04366299510002136,
0.22081884741783142,
0.10185931622982025,
0.0028396283742040396,
0.09715381264686584,
-0.0909823551774025,
0.03183278813958168,
0.18644584715366364,
-0.21058997511863708,
-0.07308952510356903,
0.09151746332645416,
0.07248207181692123,
-0.008119042031466961,
-0.049379657953977585,
0.03416718915104866,
-0.020889678969979286,
0.04118472710251808,
0.07804784923791885,
-0.03700399026274681,
0.23354585468769073,
-0.01153772696852684,
-0.07181819528341293,
-0.07904809713363647,
0.053138796240091324,
0.05876197665929794,
-0.06263764947652817,
-0.221560999751091,
0.028172558173537254,
0.12601231038570404,
-0.021050704643130302,
0.014908489771187305,
0.06655712425708771,
-0.020322008058428764,
-0.038744959980249405,
-0.10375691205263138,
-0.05862076207995415,
-0.06207014620304108,
0.09027054905891418,
0.1592814028263092,
0.0024327016435563564,
-0.03528968617320061,
0.07559290528297424,
0.06907778978347778,
0.03665388748049736,
-0.03861984610557556,
-0.026482632383704185,
-0.03525818884372711,
-0.10859525948762894,
0.00906895287334919,
-0.014381630346179008,
0.05308223143219948,
0.05512067303061485,
0.1428605020046234,
0.015403726138174534,
0.021928563714027405,
0.03801997750997543,
0.04294993728399277,
-0.009389469400048256,
0.1267632097005844,
0.016258783638477325,
0.04086808115243912,
0.03279869630932808,
0.06092468649148941,
0.07087454944849014,
-0.060742031782865524,
-0.09382610023021698,
0.08222337067127228,
-0.11864317953586578,
0.10226163268089294,
0.07377005368471146,
0.02799413539469242,
-0.08680537343025208,
-0.031075110659003258,
0.022948939353227615,
-0.1356847882270813,
0.07201523333787918,
0.042046092450618744,
-0.0369640551507473,
-0.09459969401359558,
-0.009367725811898708,
0.007041499949991703,
-0.0739312469959259,
0.03858998045325279,
-0.021130969747900963,
0.04003886133432388,
-0.015946567058563232,
-0.028418047353625298,
0.09499817341566086,
-0.08346141129732132,
-0.0245100986212492,
-0.1613524854183197,
-0.10201451182365417,
-0.05632864683866501,
0.049031440168619156,
-0.05690383538603783,
-0.12043663114309311,
-0.04124511405825615,
0.018433229997754097,
-0.09155789017677307,
-0.005774117074906826,
-0.04375855624675751,
-0.06553103029727936,
-0.009590285830199718,
-0.03401520103216171,
0.07921557873487473,
0.16480670869350433,
0.03597531095147133,
-0.026453016325831413,
0.08479084074497223,
-0.20279091596603394,
0.09825469553470612,
-0.11535017192363739,
0.1751791536808014,
-0.05829249694943428,
0.010657528415322304,
0.02299174852669239,
0.017820168286561966,
0.018300510942935944,
0.17632953822612762,
-0.05271182209253311,
-0.10775750875473022,
0.14208510518074036,
-0.04476003348827362,
-0.11615825444459915,
0.05413508042693138,
0.03317848965525627,
0.07674980908632278,
0.033200349658727646,
0.25217604637145996,
0.09525452554225922,
-0.28119346499443054,
0.060772184282541275,
0.02927757054567337,
-0.12429622560739517,
0.021137133240699768,
0.14901207387447357,
-0.07003846764564514,
0.016057590022683144,
0.0010865814983844757,
-0.1546744406223297,
0.07981036603450775,
-0.003383189206942916,
-0.034131359308958054,
0.05155700817704201,
-0.03773357346653938,
-0.022087952122092247,
-0.008406432345509529,
0.0010424575302749872,
-0.049140091985464096,
-0.0919540598988533,
-0.058058422058820724,
0.0887422189116478,
-0.0188469085842371,
0.0754549503326416,
-0.0653240904211998,
0.13759565353393555,
0.03581603616476059,
0.0478316992521286,
-0.09203452616930008,
-0.1011326014995575,
0.0164390429854393,
0.014197946526110172,
0.08620816469192505,
-0.1056629940867424,
0.05580069497227669,
0.06499490886926651,
0.0011455488856881857,
-0.07759148627519608,
-0.09847576916217804,
-0.00933445431292057,
-0.062423862516880035,
-0.1031726747751236,
-0.062236085534095764,
-0.06397195160388947,
0.140730082988739,
-0.07763312757015228,
0.06123964488506317,
-0.09701194614171982,
0.04473888874053955,
-0.012301203794777393,
-0.03593873977661133,
0.06347624957561493,
0.007879387587308884,
0.031134750694036484,
-0.07012004405260086,
0.10565385222434998,
0.03143167123198509,
-0.08809154480695724,
0.08684271574020386,
-0.05376902595162392,
-0.0738421231508255,
0.09223692864179611,
0.054588232189416885,
-0.02346559800207615,
-0.03621990606188774,
-0.08002440631389618,
0.010873363353312016,
-0.08895037323236465,
-0.0024624173529446125,
0.14117534458637238,
0.10031202435493469,
0.11761818826198578,
-0.07753627002239227,
-0.05894411727786064,
-0.01958172768354416,
-0.1106799989938736,
-0.05656024441123009,
0.15491467714309692,
0.02096433751285076,
0.08495879173278809,
0.05047125369310379,
0.0719786137342453,
0.07631471008062363,
0.0953005701303482,
0.01344170980155468,
-0.1215502917766571,
-0.017365656793117523,
0.07310621440410614,
0.05626581609249115,
0.0071006761863827705,
0.029030276462435722,
-0.008518705144524574,
0.021484937518835068,
-0.03661421313881874,
-0.011757876724004745,
-0.12819893658161163,
-0.07982992380857468,
0.010631637647747993,
-0.03388748690485954,
0.03204934298992157,
-0.002719224663451314,
-0.04166882485151291,
0.0545722134411335,
0.09695333987474442,
0.030084898695349693,
0.001271768705919385,
-0.046886567026376724,
-0.10178258270025253,
0.07902075350284576,
-0.08341789245605469,
-0.3121103346347809,
-0.12382375448942184,
-0.11522714048624039,
-0.05511033535003662,
0.03198058530688286,
0.05627104640007019,
-0.16223682463169098,
-0.027320237830281258,
-0.11539069563150406,
-0.04380467161536217,
0.06030948832631111,
-0.07564616203308105,
0.17278358340263367,
0.10081960260868073,
0.022245371714234352,
-0.06923381984233856,
-0.023631839081645012,
0.01668277010321617,
-0.04505609720945358,
0.03928403928875923,
0.0210224948823452,
0.06617482751607895,
0.11713612824678421,
0.0763215571641922,
0.04417125880718231,
-0.03088906593620777,
0.0729726180434227,
-0.074139304459095,
-0.012980621308088303,
0.13679179549217224,
-0.023730751127004623,
0.08033578842878342,
0.047791119664907455,
0.017641352489590645,
-0.03155747428536415,
0.05260641872882843,
0.009822766296565533,
-0.07815835624933243,
-0.1900395005941391,
-0.09831155091524124,
-0.03363991156220436,
0.23229697346687317,
0.0870399922132492,
0.08967014402151108,
-0.05560557171702385,
-0.0254431813955307,
-0.0023977241944521666,
-0.060066141188144684,
0.14204753935337067,
0.12347196042537689,
-0.03281702101230621,
-0.07347887754440308,
0.0018852775683626533,
-0.04485497996211052,
0.01976105198264122,
0.09626205265522003,
0.005344674456864595,
0.06863952428102493,
0.022899003699421883,
0.028698360547423363,
0.037636928260326385,
-0.039484746754169464,
-0.06795700639486313,
0.06001844257116318,
0.030823567882180214,
-0.0023363735526800156,
-0.04282291233539581,
-0.08267582952976227,
-0.034100405871868134,
0.10401097685098648,
0.12421873211860657,
-0.06864720582962036,
-0.09435471147298813,
0.0627637431025505,
0.09943517297506332,
0.10659271478652954,
0.023363469168543816,
-0.13374653458595276,
-0.04951421171426773,
0.01879873499274254,
-0.1196146309375763,
0.02644534595310688,
-0.003154805162921548,
0.0306418277323246,
-0.19851462543010712,
0.08038870990276337,
0.017967822030186653,
0.11781266331672668,
0.06082659214735031,
0.005345495417714119,
0.028723159804940224,
0.07045045495033264,
-0.015355011448264122,
0.06998579204082489,
-0.17029868066310883,
0.07030192017555237,
-0.020733539015054703,
0.07538870722055435,
-0.0491526760160923,
0.001359339221380651,
0.08990743011236191,
-0.037842340767383575,
0.1792023926973343,
0.037012774497270584,
0.053881701081991196,
-0.09770984202623367,
-0.17304296791553497,
-0.048873212188482285,
-0.002746864687651396,
-0.09968961775302887,
0.06881223618984222,
-0.0054172007367014885,
-0.03697158023715019,
-0.10796613991260529,
0.14740680158138275,
0.022381817921996117,
-0.06682277470827103,
-0.0013361794408410788,
-0.056336432695388794,
0.00901131983846426,
-0.06438561528921127,
-0.031967297196388245,
-0.025635989382863045,
0.19282802939414978,
0.1404501348733902,
-0.024091484025120735,
-0.09813282638788223,
-0.06171920523047447,
-0.026166822761297226,
-0.030996134504675865,
-0.0003331196785438806,
-0.015648646280169487,
0.1277134120464325,
-0.0748479962348938,
-0.030144838616251945,
-0.011899829842150211,
-0.10049006342887878,
-0.11882750689983368,
-0.003450408112257719,
0.24202309548854828,
-0.011385969817638397,
0.09070654958486557,
-0.022002026438713074,
0.024260690435767174,
-0.000850543612614274,
-0.07936982065439224,
0.1410362273454666,
0.16656875610351562,
0.026260020211338997,
0.0651647225022316,
-0.09656745940446854,
0.04997217655181885,
-0.10802606493234634,
-0.03780430927872658,
0.18275576829910278,
0.31086593866348267,
-0.024676380679011345,
0.21574705839157104,
0.06622890383005142,
-0.05439082160592079,
-0.21985037624835968,
-0.08508211374282837,
0.03857145830988884,
-0.0064392476342618465,
0.15239101648330688,
-0.1474023014307022,
0.031680379062891006,
0.029195938259363174,
-0.01632710173726082,
-0.004067187663167715,
-0.15045739710330963,
-0.09642613679170609,
-0.011540835723280907,
0.0626259595155716,
0.009990934282541275,
-0.10128402709960938,
-0.05465618148446083,
-0.03603252023458481,
-0.0898377001285553,
0.056582335382699966,
-0.15178121626377106,
0.08793234080076218,
0.003594930050894618,
0.03440571203827858,
0.04358251765370369,
-0.030986029654741287,
0.14115259051322937,
-0.06353185325860977,
-0.03589347377419472,
-0.09135019034147263,
-0.006245157681405544,
0.0041479747742414474,
-0.11549984663724899,
0.08058501780033112,
-0.06003414839506149,
-0.05414252355694771,
-0.18429994583129883,
-0.04738378897309303,
-0.03751663863658905,
0.04884665831923485,
-0.007320301607251167,
-0.018880484625697136,
-0.014845091849565506,
0.07868388295173645,
0.08143512159585953,
0.04031652957201004,
0.0724465623497963,
-0.029854847118258476,
0.00028156585176475346,
0.09331642836332321,
0.08512724936008453,
0.03315239027142525,
-0.08384794741868973,
-0.04751488193869591,
-0.041811469942331314,
-0.024532850831747055,
-0.0935908779501915,
0.00048397600767202675,
0.024565307423472404,
0.013281944207847118,
0.054970938712358475,
0.050560299307107925,
-0.09902767091989517,
-0.003808935871347785,
0.08305138349533081,
-0.09417006373405457,
-0.12829427421092987,
-0.042467501014471054,
-0.09568573534488678,
-0.04711597040295601,
-0.06587748229503632,
0.04331240803003311,
-0.025825852528214455,
-0.004827001132071018,
0.04182383418083191,
0.04152008891105652,
-0.0657409280538559,
0.042383063584566116,
-0.01380169577896595,
0.021127229556441307,
-0.05984218791127205,
0.15415605902671814,
0.015970006585121155,
-0.06426183879375458,
0.032508913427591324,
0.1980218142271042,
-0.06761644035577774,
-0.07044593244791031,
-0.044916898012161255,
0.03502626717090607,
0.15184251964092255,
-0.0311757642775774,
-0.04486960917711258,
-0.07459413260221481,
0.07658905535936356,
-0.09645078331232071,
0.009423159062862396,
-0.09430725872516632,
0.027084626257419586,
0.0903988778591156,
-0.10535555332899094,
0.09689707309007645,
0.020365258678793907,
-0.06071978062391281,
-0.11294043809175491,
0.07432376593351364,
0.052691083401441574,
0.1651841104030609,
-0.019026705995202065,
-0.048916954547166824,
-0.15137501060962677,
0.011945275589823723,
-0.02569970302283764,
-0.00790596753358841,
-0.17547371983528137,
-0.02109973132610321,
-0.02612914890050888,
0.050343044102191925,
-0.01056208461523056,
0.03985007852315903,
-0.05138487368822098,
-0.0699538141489029,
-0.05842532590031624,
0.0930938869714737,
-0.04935208335518837,
-0.03826834261417389,
0.0325402170419693,
-0.07969501614570618,
0.09476234018802643,
0.07600145787000656,
-0.022649001330137253,
-0.053711727261543274,
-0.06960244476795197,
-0.030822088941931725,
0.023101652041077614,
-0.046016767621040344,
0.0323469452559948,
-0.18278191983699799,
0.010969621129333973,
-0.04088293015956879,
-0.10292091220617294,
0.014050162397325039,
0.10498400032520294,
-0.07706627994775772,
0.06502266228199005,
0.011922108009457588,
-0.1392570585012436,
-0.07784201949834824,
0.006621953099966049,
0.011523645371198654,
0.0682699903845787,
0.08274282515048981,
-0.07265239953994751,
0.17531941831111908,
-0.11964938044548035,
-0.009246448054909706,
-0.0013763748575001955,
0.013741952367126942,
-0.01858046092092991,
-0.09397637099027634,
0.03130004182457924,
-0.011612001806497574,
0.1238425001502037,
0.07595572620630264,
-0.021839866414666176,
0.031425874680280685,
0.02520223893225193,
0.1320616900920868,
0.00986271258443594,
0.030503222718834877,
-0.019268406555056572,
-0.002040766179561615,
0.050973858684301376,
-0.012364058755338192,
0.05770988389849663,
-0.11409693211317062,
0.08780261874198914,
0.09607450664043427,
0.12560780346393585,
0.05841410905122757,
0.06280624121427536,
-0.08959707617759705,
-0.17728932201862335,
-0.04899268224835396,
0.002273079939186573,
0.030711088329553604,
-0.059758201241493225,
0.23091484606266022,
0.10145152360200882,
-0.22351878881454468,
0.06298269331455231,
-0.010716341435909271,
0.015637952834367752,
-0.08656303584575653,
-0.14005525410175323,
-0.0001828094245865941,
-0.2057436853647232,
0.07406117767095566,
-0.05353718623518944,
0.008718542754650116,
-0.010622356086969376,
-0.024192510172724724,
-0.006092938128858805,
0.09632956236600876,
-0.13205909729003906,
-0.05549517646431923,
0.08664805442094803,
-0.036908894777297974,
0.012522386386990547,
0.008033870719373226,
-0.01969698816537857,
-0.049084655940532684,
-0.07323008030653,
0.05397481843829155,
0.05626896023750305,
0.0044862511567771435,
0.045594166964292526,
-0.06531810760498047,
-0.0662725567817688,
0.036717914044857025,
-0.0058953845873475075,
0.02079104818403721,
0.13203662633895874,
0.04687240719795227,
-0.10004469007253647,
0.0024303505197167397,
0.21512019634246826,
-0.04912292957305908,
-0.00157128123100847,
-0.09774290770292282,
0.1459764838218689,
-0.018051035702228546,
-0.06270567327737808,
-0.043903205543756485,
-0.0987202599644661,
-0.0867447480559349,
0.239289328455925,
0.12312336266040802,
-0.05420450121164322,
0.01805422268807888,
-0.038637664169073105,
0.028307680040597916,
0.006949485279619694,
0.12787899374961853,
0.06505098193883896,
0.13559570908546448,
-0.06299645453691483,
-0.01385438907891512,
-0.00541959423571825,
-0.07888318598270416,
-0.1812218725681305,
-0.006192502565681934,
0.021824723109602928,
-0.036356259137392044,
-0.0349930040538311,
0.06706788390874863,
-0.12007372826337814,
-0.11014141142368317,
0.09754876792430878,
-0.09393119812011719,
-0.07367090880870819,
-0.01786600612103939,
0.011479104869067669,
0.029084932059049606,
0.12572608888149261,
0.05414829030632973,
0.029192183166742325,
0.10287461429834366,
-0.02105443738400936,
-0.05306048318743706,
0.02117631770670414,
0.09175841510295868,
-0.0664481595158577,
0.19176597893238068,
-0.04529353231191635,
0.0321197472512722,
0.051943808794021606,
0.023049550130963326,
-0.1363917589187622,
0.0677260085940361,
0.03781493753194809,
-0.1590750515460968,
0.010416089557111263,
0.07061144709587097,
-0.07263916730880737,
-0.03391150385141373,
0.07443561404943466,
-0.028789615258574486,
0.008370332419872284,
0.1250256896018982,
-0.00044676722609438,
-0.05516013130545616,
0.0806371420621872,
-0.1702018529176712,
0.10109170526266098,
0.14556628465652466,
-0.06329818069934845,
-0.016308486461639404,
-0.048967111855745316,
0.04044068977236748,
0.035925742238759995,
0.04423607513308525,
-0.010898809880018234,
-0.14326155185699463,
0.0060302140191197395,
0.0038463419768959284,
0.03577153757214546,
-0.30258384346961975,
-0.11684641242027283,
-0.05294342339038849,
-0.03548569232225418,
-0.0468159057199955,
0.11105988919734955,
0.08205538243055344,
-0.010700567625463009,
-0.0056922705844044685,
-0.16824021935462952,
0.04431147500872612,
0.16516534984111786,
-0.08254995197057724,
-0.008064123801887035
] |
null | null | transformers |
## Exllama v2 Quantizations of Pasta-Lake-7b
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.13">turboderp's ExLlamaV2 v0.0.13</a> for quantization.
<b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Original model: https://huggingface.co/Test157t/Pasta-Lake-7b
| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description |
| ----- | ---- | ------- | ------ | ------ | ------ | ------------ |
| [8_0](https://huggingface.co/bartowski/Pasta-Lake-7b-exl2/tree/8_0) | 8.0 | 8.0 | 8.4 GB | 9.8 GB | 11.8 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
| [6_5](https://huggingface.co/bartowski/Pasta-Lake-7b-exl2/tree/6_5) | 6.5 | 8.0 | 7.2 GB | 8.6 GB | 10.6 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. |
| [5_0](https://huggingface.co/bartowski/Pasta-Lake-7b-exl2/tree/5_0) | 5.0 | 6.0 | 6.0 GB | 7.4 GB | 9.4 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. |
| [4_25](https://huggingface.co/bartowski/Pasta-Lake-7b-exl2/tree/4_25) | 4.25 | 6.0 | 5.3 GB | 6.7 GB | 8.7 GB | GPTQ equivalent bits per weight, slightly higher quality. |
| [3_5](https://huggingface.co/bartowski/Pasta-Lake-7b-exl2/tree/3_5) | 3.5 | 6.0 | 4.7 GB | 6.1 GB | 8.1 GB | Lower quality, only use if you have to. |
## Download instructions
With git:
```shell
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Pasta-Lake-7b-exl2 Pasta-Lake-7b-exl2-6_5
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `Pasta-Lake-7b-exl2`:
```shell
mkdir Pasta-Lake-7b-exl2
huggingface-cli download bartowski/Pasta-Lake-7b-exl2 --local-dir Pasta-Lake-7b-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
Linux:
```shell
mkdir Pasta-Lake-7b-exl2-6_5
huggingface-cli download bartowski/Pasta-Lake-7b-exl2 --revision 6_5 --local-dir Pasta-Lake-7b-exl2-6_5 --local-dir-use-symlinks False
```
Windows (which apparently doesn't like _ in folders sometimes?):
```shell
mkdir Pasta-Lake-7b-exl2-6.5
huggingface-cli download bartowski/Pasta-Lake-7b-exl2 --revision 6_5 --local-dir Pasta-Lake-7b-exl2-6.5 --local-dir-use-symlinks False
```
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski | {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Test157t/Pasta-PrimaMaid-7b", "macadeliccc/WestLake-7B-v2-laser-truthy-dpo"], "quantized_by": "bartowski", "pipeline_tag": "text-generation"} | text-generation | bartowski/Pasta-Lake-7b-exl2 | [
"transformers",
"mergekit",
"merge",
"text-generation",
"base_model:Test157t/Pasta-PrimaMaid-7b",
"base_model:macadeliccc/WestLake-7B-v2-laser-truthy-dpo",
"endpoints_compatible",
"region:us"
] | 2024-02-09T10:20:59+00:00 | [] | [] | TAGS
#transformers #mergekit #merge #text-generation #base_model-Test157t/Pasta-PrimaMaid-7b #base_model-macadeliccc/WestLake-7B-v2-laser-truthy-dpo #endpoints_compatible #region-us
| Exllama v2 Quantizations of Pasta-Lake-7b
-----------------------------------------
Using <a href="URL ExLlamaV2 v0.0.13 for quantization.
**The "main" branch only contains the URL, download one of the other branches for the model (see below)**
Each branch contains an individual bits per weight, with the main one containing only the URL for further conversions.
Original model: URL
Download instructions
---------------------
With git:
With huggingface hub (credit to TheBloke for instructions):
To download the 'main' (only useful if you only care about URL) branch to a folder called 'Pasta-Lake-7b-exl2':
To download from a different branch, add the '--revision' parameter:
Linux:
Windows (which apparently doesn't like \_ in folders sometimes?):
Want to support my work? Visit my ko-fi page here: URL
| [] | [
"TAGS\n#transformers #mergekit #merge #text-generation #base_model-Test157t/Pasta-PrimaMaid-7b #base_model-macadeliccc/WestLake-7B-v2-laser-truthy-dpo #endpoints_compatible #region-us \n"
] | [
73
] | [
"passage: TAGS\n#transformers #mergekit #merge #text-generation #base_model-Test157t/Pasta-PrimaMaid-7b #base_model-macadeliccc/WestLake-7B-v2-laser-truthy-dpo #endpoints_compatible #region-us \n"
] | [
-0.09780821204185486,
-0.069078229367733,
-0.003744260873645544,
-0.050525180995464325,
0.04958468675613403,
0.06778597831726074,
0.10447453707456589,
0.09251422435045242,
0.014875766821205616,
-0.02696623094379902,
0.09215288609266281,
0.13908573985099792,
-0.0012943365145474672,
0.13036395609378815,
-0.09671859443187714,
-0.17224259674549103,
0.059353116899728775,
0.11753425002098083,
-0.16876055300235748,
0.07041360437870026,
0.12377424538135529,
-0.05577472597360611,
0.07599803060293198,
-0.02990739420056343,
-0.1753530204296112,
0.10239629447460175,
-0.005896438844501972,
-0.03892625868320465,
0.08362735062837601,
0.06175504997372627,
0.13306273519992828,
0.058376945555210114,
-0.07393993437290192,
-0.13140349090099335,
0.0319225937128067,
0.0014022720279172063,
-0.03105125203728676,
0.03772391751408577,
0.08066142350435257,
0.03690038248896599,
0.09048214554786682,
-0.041353560984134674,
0.020365845412015915,
0.07254264503717422,
-0.1072360947728157,
-0.07331688702106476,
-0.09094680100679398,
0.08557041734457016,
0.07522705942392349,
0.020037150010466576,
0.024749567732214928,
0.0625394880771637,
-0.006098716054111719,
0.07519673556089401,
0.08203385025262833,
-0.2752642333507538,
-0.009399482049047947,
0.17511136829853058,
0.11585263907909393,
0.008014246821403503,
0.058002769947052,
0.06041422113776207,
0.04778342321515083,
-0.014859536662697792,
-0.060269709676504135,
-0.07944741100072861,
0.09356638789176941,
0.02574477717280388,
-0.07788649201393127,
-0.03058101236820221,
0.27281832695007324,
0.004098537843674421,
0.0488140694797039,
-0.01715117134153843,
-0.09797127544879913,
-0.02214938960969448,
-0.014643276110291481,
-0.041412804275751114,
-0.0036958970595151186,
0.037034109234809875,
0.02874820865690708,
-0.0736822634935379,
-0.04019816964864731,
-0.0928628072142601,
-0.16557982563972473,
0.22557087242603302,
-0.020931532606482506,
0.0388985350728035,
-0.14901165664196014,
0.0757470354437828,
-0.12401527911424637,
-0.12717491388320923,
-0.007294852286577225,
-0.09853515028953552,
-0.04008849337697029,
-0.0135300038382411,
-0.1298241764307022,
-0.05270344391465187,
0.09316616505384445,
0.19008782505989075,
0.030493758618831635,
0.014259740710258484,
0.03269791230559349,
0.0748329758644104,
-0.026669170707464218,
0.05161469802260399,
-0.04155620187520981,
-0.13760636746883392,
-0.0026128191966563463,
-0.07309537380933762,
0.016818750649690628,
0.008733183145523071,
-0.08100038021802902,
-0.11215449124574661,
-0.033877160400152206,
0.032257795333862305,
0.06214486062526703,
0.02731114625930786,
-0.032226644456386566,
-0.03831823915243149,
0.12140930444002151,
-0.06261559575796127,
-0.056290581822395325,
-0.04305623471736908,
0.03310231491923332,
0.15225091576576233,
0.044052526354789734,
0.018992317840456963,
0.03614672273397446,
0.026920625939965248,
-0.046312522143125534,
-0.0519990548491478,
-0.08058642596006393,
-0.029451997950673103,
0.03967968374490738,
-0.09425671398639679,
0.028592688962817192,
-0.14537875354290009,
-0.19969558715820312,
0.01923278719186783,
0.011272808536887169,
-0.043043140321969986,
-0.02688094787299633,
0.011487390846014023,
0.027165016159415245,
0.012476303614675999,
-0.0043633803725242615,
0.040668681263923645,
-0.009400865994393826,
0.02407195046544075,
0.07948525995016098,
0.06282878667116165,
-0.17618779838085175,
0.05498815327882767,
-0.042196791619062424,
0.08695659041404724,
-0.19652046263217926,
0.032640356570482254,
-0.05185733363032341,
0.1595853865146637,
-0.04945686459541321,
-0.005690572317689657,
-0.11845827847719193,
0.016342516988515854,
0.021283317357301712,
0.13827228546142578,
-0.12651941180229187,
-0.05999777093529701,
0.06629367917776108,
-0.10661064833402634,
-0.15943004190921783,
0.019882982596755028,
0.04018298164010048,
0.024182792752981186,
0.048580728471279144,
0.16789859533309937,
0.12503066658973694,
-0.015815962105989456,
0.0514422282576561,
0.09128960967063904,
-0.08478115499019623,
-0.1543213278055191,
0.08715822547674179,
-0.05307330563664436,
-0.1692095547914505,
0.042048461735248566,
0.016903851181268692,
0.05511602759361267,
-0.042986731976270676,
-0.033741362392902374,
-0.10865170508623123,
-0.08108603209257126,
0.07782526314258575,
-0.007523993495851755,
0.05823313817381859,
-0.09471738338470459,
0.00506375590339303,
0.08886300772428513,
0.09690282493829727,
-0.014952313154935837,
-0.0026248011272400618,
-0.013567734509706497,
0.12578387558460236,
-0.1709108054637909,
0.03723969683051109,
-0.15965190529823303,
-0.0598423033952713,
-0.03385280817747116,
0.052031442523002625,
-0.024365397170186043,
0.13170400261878967,
0.08574098348617554,
-0.09751980006694794,
-0.04318012669682503,
-0.005037767346948385,
0.05323109030723572,
0.07269325107336044,
-0.017618879675865173,
-0.0660022422671318,
-0.03212478384375572,
-0.06636181473731995,
0.04309610277414322,
-0.022487537935376167,
0.020843418315052986,
0.01507044117897749,
0.18799376487731934,
-0.012505005113780499,
0.05450977757573128,
-0.00999751128256321,
0.05403700843453407,
0.016176840290427208,
0.00122327811550349,
0.06733236461877823,
0.003469193587079644,
-0.10195156931877136,
0.10253069549798965,
-0.01488927099853754,
0.1939259022474289,
0.1348356455564499,
-0.20326215028762817,
0.014501765370368958,
-0.055175378918647766,
-0.025670047849416733,
-0.0047971708700060844,
0.08722887188196182,
-0.11442513763904572,
-0.008643516339361668,
-0.03209836408495903,
0.10048501193523407,
-0.043572522699832916,
-0.05538717657327652,
-0.008350258693099022,
0.010026500560343266,
-0.05946638062596321,
0.07491879910230637,
0.05104135721921921,
-0.1816856861114502,
0.11005557328462601,
0.27954885363578796,
0.03615143522620201,
0.11464624106884003,
-0.05236231908202171,
-0.062845878303051,
-0.0256518442183733,
-0.02135619707405567,
-0.03714796155691147,
0.07459017634391785,
-0.1891370266675949,
0.028469843789935112,
0.08241038024425507,
-0.006887061055749655,
0.12139745056629181,
-0.06671544164419174,
-0.02647239901125431,
0.04242978245019913,
-0.0009249084978364408,
-0.022908756509423256,
0.12821440398693085,
0.04024997353553772,
0.08896910399198532,
0.044225968420505524,
0.002571440301835537,
0.08538398891687393,
-0.008928160183131695,
-0.05294494330883026,
0.15161898732185364,
-0.12382762134075165,
-0.17081795632839203,
-0.18597428500652313,
-0.03173695132136345,
-0.06037318333983421,
0.015803929418325424,
0.04273558408021927,
-0.06149812042713165,
-0.03283148258924484,
0.000003643624268079293,
0.06258422881364822,
-0.06168905645608902,
0.03945881500840187,
0.029443610459566116,
0.07501660287380219,
0.00073896290268749,
-0.08593278378248215,
-0.04785341024398804,
0.025821607559919357,
0.06379421055316925,
0.03562644124031067,
-0.11178940534591675,
0.11041772365570068,
0.1174585148692131,
0.00454928632825613,
0.020946431905031204,
-0.0024743734393268824,
0.1240980476140976,
-0.08171941339969635,
-0.01117681059986353,
0.20362454652786255,
-0.03145629167556763,
0.014283567667007446,
0.13485033810138702,
0.028038185089826584,
-0.052411191165447235,
0.004817667882889509,
-0.06403674930334091,
-0.04381876438856125,
-0.2067832350730896,
-0.12108250707387924,
-0.13978169858455658,
-0.0205667894333601,
-0.021163668483495712,
0.040411870926618576,
0.02429870329797268,
0.059433985501527786,
0.005160878412425518,
-0.005741556640714407,
-0.06522214412689209,
0.03822782263159752,
0.313052773475647,
-0.024676064029335976,
0.08691533654928207,
-0.1248110979795456,
-0.036118436604738235,
0.05753597617149353,
0.1061011329293251,
0.08867456018924713,
0.1277688443660736,
0.06141453981399536,
0.0513572059571743,
0.03457298502326012,
0.10130384564399719,
0.1316363662481308,
0.04319968819618225,
-0.04164142534136772,
-0.001481347600929439,
-0.058248069137334824,
0.031378038227558136,
0.017418531700968742,
-0.03137708455324173,
-0.02088385447859764,
-0.06017298996448517,
-0.1528427004814148,
0.05294302478432655,
0.12467305362224579,
0.07543667405843735,
-0.22814945876598358,
-0.0337909460067749,
0.06005725637078285,
0.015369427390396595,
-0.050903115421533585,
0.0029278264846652746,
-0.03148452937602997,
-0.040545642375946045,
0.13410604000091553,
-0.006735123693943024,
0.10985276848077774,
0.05882936343550682,
0.028237367048859596,
0.039890941232442856,
-0.01575145684182644,
0.037077896296978,
0.07125159353017807,
-0.24290946125984192,
0.20280218124389648,
0.01990380510687828,
-0.042664967477321625,
0.005913256201893091,
0.0195302814245224,
-0.011392459273338318,
0.1977715641260147,
0.059084657579660416,
-0.011163630522787571,
-0.008618032559752464,
-0.057557739317417145,
-0.0036056700628250837,
0.03363170474767685,
0.06753603368997574,
-0.017714079469442368,
0.025995461270213127,
-0.024469388648867607,
-0.02094854973256588,
-0.006350186187773943,
0.017993515357375145,
-0.13419967889785767,
-0.11618190258741379,
0.09856180101633072,
-0.02603641152381897,
0.051783137023448944,
-0.050081007182598114,
-0.0063112410716712475,
-0.09810726344585419,
0.22545841336250305,
-0.029121674597263336,
-0.05446186289191246,
-0.1114591509103775,
-0.09906286746263504,
0.16456660628318787,
-0.03439302742481232,
0.09344906359910965,
-0.03239572048187256,
0.0216753538697958,
-0.06855082511901855,
-0.21136026084423065,
0.08874733000993729,
-0.09770946204662323,
-0.019003648310899734,
-0.009260307997465134,
0.1456129550933838,
-0.12352616339921951,
0.0344601608812809,
0.015816207975149155,
0.014650316908955574,
-0.0920068621635437,
-0.037504442036151886,
-0.007771965581923723,
0.07633385807275772,
-0.00353956688195467,
0.0725380927324295,
-0.0073034316301345825,
0.043607767671346664,
0.030187275260686874,
-0.012180648744106293,
0.14933298528194427,
0.18429796397686005,
-0.028604110702872276,
0.03840787336230278,
0.03591519966721535,
-0.035924479365348816,
-0.19300056993961334,
-0.004735605325549841,
-0.11942603439092636,
0.02447851002216339,
-0.07020016759634018,
-0.017603542655706406,
0.08261454850435257,
0.10227279365062714,
0.00029931130120530725,
0.10073075443506241,
-0.25182539224624634,
-0.1074429452419281,
0.10893529653549194,
-0.005762513726949692,
0.2874453663825989,
-0.12135133892297745,
-0.09906584769487381,
-0.10852136462926865,
-0.3009507954120636,
0.02855869010090828,
-0.05303230509161949,
0.061786651611328125,
-0.07334264367818832,
-0.004247260745614767,
0.012274552136659622,
-0.05741491541266441,
0.16956450045108795,
-0.031497471034526825,
0.011170492507517338,
-0.08686114102602005,
0.0055634137243032455,
0.07985100150108337,
-0.031209591776132584,
0.028567098081111908,
-0.008941112086176872,
0.05469532683491707,
-0.16951067745685577,
-0.04645393788814545,
-0.0314822643995285,
0.04222229868173599,
-0.003568322630599141,
-0.02354085072875023,
-0.024104949086904526,
-0.009876931086182594,
-0.09983306378126144,
-0.033241644501686096,
0.14709627628326416,
-0.07404127717018127,
0.09563467651605606,
0.05179554969072342,
0.10534129291772842,
-0.1583550125360489,
-0.05132430046796799,
0.029648538678884506,
-0.0629914179444313,
0.07953869551420212,
-0.11177222430706024,
0.006927947048097849,
0.12746179103851318,
-0.015219055116176605,
0.013706727884709835,
0.08305917680263519,
0.02936234325170517,
-0.02369103580713272,
0.14014947414398193,
-0.18025599420070648,
-0.07105471193790436,
-0.07168269157409668,
-0.06572465598583221,
-0.0008575544925406575,
0.10335411131381989,
0.1281665414571762,
0.025294464081525803,
0.005422347690910101,
-0.0036290334537625313,
-0.0669899433851242,
-0.18390260636806488,
0.09704125672578812,
0.07013896852731705,
0.04941244423389435,
-0.08669400215148926,
0.07953569293022156,
-0.03681275248527527,
-0.19247667491436005,
-0.07864165306091309,
0.056637242436409,
-0.07387725263834,
-0.0895569697022438,
-0.03367815539240837,
0.16445867717266083,
-0.09830399602651596,
-0.054859910160303116,
-0.13933105766773224,
-0.14470575749874115,
0.0885324701666832,
0.1781768798828125,
0.09733062982559204,
0.050844911485910416,
-0.004376672673970461,
-0.050918176770210266,
0.008639387786388397,
-0.011463736183941364,
-0.009846345521509647,
0.09180355072021484,
-0.08005142211914062,
-0.00782565213739872,
-0.08839277923107147,
0.10924512892961502,
-0.06439501792192459,
0.009655158035457134,
-0.11633383482694626,
0.009905418381094933,
-0.156230628490448,
-0.11547616124153137,
-0.09575392305850983,
-0.05088644474744797,
-0.007033531554043293,
-0.024721046909689903,
-0.0541190430521965,
-0.003977376502007246,
-0.05247578024864197,
-0.014747089706361294,
0.01192124467343092,
0.0234355591237545,
-0.02970716543495655,
0.019258689135313034,
0.07247976958751678,
-0.049654439091682434,
0.044601913541555405,
0.05237840116024017,
-0.017644206061959267,
0.0007200171239674091,
-0.07260927557945251,
-0.10911969095468521,
0.04321997985243797,
0.019281545653939247,
0.03710692375898361,
-0.031285472214221954,
0.026774883270263672,
0.06211167573928833,
0.0021967552602291107,
0.03278660774230957,
0.025418631732463837,
-0.08921591192483902,
-0.04986586049199104,
-0.053051769733428955,
-0.09038487821817398,
-0.05066337063908577,
-0.09132982790470123,
0.15183737874031067,
0.09513717144727707,
0.05283929035067558,
-0.014794277027249336,
0.028817083686590195,
-0.1088835597038269,
-0.010802199132740498,
-0.0030928172636777163,
-0.12461792677640915,
0.09729794412851334,
-0.07001923024654388,
0.01629054546356201,
0.020369580015540123,
0.26154232025146484,
-0.09120888262987137,
0.008663917891681194,
0.05678221955895424,
0.010323031805455685,
0.08405633270740509,
0.05176901817321777,
0.391896516084671,
0.10744881629943848,
0.025841543450951576,
-0.09565956890583038,
0.1105111837387085,
0.01469792053103447,
0.06447698175907135,
0.07241282612085342,
0.20957154035568237,
-0.054118577390909195,
0.1060459092259407,
0.1145630031824112,
0.03386647626757622,
0.037937138229608536,
-0.12949490547180176,
0.039751097559928894,
0.014939915388822556,
-0.0018120198510587215,
0.08703629672527313,
0.11620248109102249,
-0.13513779640197754,
0.04140246659517288,
0.014979420229792595,
-0.02285427786409855,
-0.06454294919967651,
-0.019865013659000397,
-0.08517157286405563,
-0.19365189969539642,
0.008804689161479473,
-0.07615190744400024,
-0.0007352085667662323,
0.09920714050531387,
0.020307818427681923,
-0.05764952674508095,
0.1354781985282898,
-0.009341353550553322,
-0.03134867176413536,
0.0664215236902237,
-0.00944740790873766,
0.007977747358381748,
-0.030306072905659676,
-0.043833062052726746,
-0.008693858049809933,
-0.055970996618270874,
-0.06987790018320084,
0.008374182507395744,
-0.06803426146507263,
-0.004596308339387178,
-0.04258345812559128,
-0.07339239120483398,
-0.029834523797035217,
0.0524834468960762,
0.014185377396643162,
0.08260801434516907,
0.000045280565245775506,
0.016601217910647392,
0.010943952016532421,
0.10222625732421875,
-0.045605577528476715,
-0.12065605074167252,
-0.07400122284889221,
0.0025207276921719313,
0.020822126418352127,
0.08749648183584213,
0.004768725484609604,
-0.04187891259789467,
-0.02602667547762394,
0.23582223057746887,
0.2581375241279602,
0.005596891976892948,
0.024523574858903885,
0.05983324721455574,
0.029609844088554382,
0.07812772691249847,
0.07733986526727676,
0.052225563675165176,
0.12954893708229065,
-0.03809205815196037,
-0.05645047128200531,
-0.022765187546610832,
-0.018989114090800285,
-0.029695890843868256,
0.013630948960781097,
0.07633236795663834,
-0.10749029368162155,
0.037792861461639404,
0.14782746136188507,
-0.04732389748096466,
0.16474011540412903,
0.06164073199033737,
-0.14505602419376373,
-0.07045605033636093,
-0.08901821821928024,
0.10380618274211884,
-0.09633578360080719,
0.034363724291324615,
-0.07726065814495087,
-0.06113545224070549,
0.15604302287101746,
-0.010407560504972935,
-0.15720073878765106,
-0.15875612199306488,
0.04864909499883652,
-0.002940947422757745,
-0.09735381603240967,
-0.02773979678750038,
0.07683499902486801,
0.10760451853275299,
0.05717603117227554,
-0.08625160902738571,
0.04104660823941231,
0.013301047496497631,
-0.0761217549443245,
0.03143325448036194,
0.009697198867797852,
-0.008398481644690037,
-0.019613346084952354,
0.03707127645611763,
-0.14404763281345367,
0.04599294066429138,
-0.002912394469603896,
-0.0038830654229968786,
-0.07944092154502869,
0.04633224755525589,
-0.06287375092506409,
0.12104247510433197,
0.11934344470500946,
-0.023943357169628143,
0.018765367567539215,
-0.030642256140708923,
0.04639357328414917,
0.07145103067159653,
0.033985432237386703,
-0.08483563363552094,
-0.08959005773067474,
-0.05080737546086311,
0.06576461344957352,
-0.04452744498848915,
-0.152644544839859,
-0.08628394454717636,
-0.10801523178815842,
0.0061040716245770454,
-0.021694432944059372,
0.05896664783358574,
0.13174554705619812,
0.010141704231500626,
-0.016046520322561264,
-0.1681833267211914,
0.002613153075799346,
0.09363018721342087,
-0.15733174979686737,
-0.08937591314315796
] |
null | null | transformers |
# MedRoBERTa.nl finetuned for experiencer
## Description
This model is a finetuned RoBERTa-based model pre-trained from scratch
on Dutch hospital notes sourced from Electronic Health Records.
All code used for the creation of MedRoBERTa.nl
can be found at https://github.com/cltl-students/verkijk_stella_rma_thesis_dutch_medical_language_model.
The publication associated with the negation detection task can be found at https://arxiv.org/abs/2209.00470.
The code for finetuning the model can be found at https://github.com/umcu/negation-detection.
## Minimal example
```python
tokenizer = AutoTokenizer\
.from_pretrained("UMCU/MedRoBERTa.nl_Experiencer")
model = AutoModelForTokenClassification\
.from_pretrained("UMCU/MedRoBERTa.nl_Experiencer")
some_text = "De patient was niet aanspreekbaar en hij zag er grauw uit. \
Hij heeft de inspanningstest echter goed doorstaan. \
De broer heeft onlangs een operatie ondergaan."
inputs = tokenizer(some_text, return_tensors='pt')
output = model.forward(inputs)
probas = torch.nn.functional.softmax(output.logits[0]).detach().numpy()
# associate with tokens
input_tokens = tokenizer.convert_ids_to_tokens(inputs['input_ids'][0])
target_map = {0: 'B-Patient', 1:'B-Other',2:'I-Patient',3:'I-Other'}
results = [{'token': input_tokens[idx],
'proba_patient': proba_arr[0]+proba_arr[2],
'proba_other': proba_arr[1]+proba_arr[3]
}
for idx,proba_arr in enumerate(probas)]
```
The medical entity classifiers are (being) integrated in the opensource library [clinlp](https://github.com/umcu/clinlp), feel free to contact
us for access, either through Huggingface or through git.
It is perhaps good to note that we assume the [Inside-Outside-Beginning](https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)) format.
## Intended use
The model is finetuned for experiencer detection on Dutch clinical text.
Since it is a domain-specific model trained on medical data,
it is meant to be used on medical NLP tasks for Dutch.
This particular model is trained on a 64-max token windows surrounding the concept-to-be labeled.
## Data
The pre-trained model was trained on nearly 10 million hospital notes from the Amsterdam University Medical Centres.
The training data was anonymized before starting the pre-training procedure.
The finetuning was performed on the Erasmus Dutch Clinical Corpus (EDCC), which was synthetically upsampled for the minority classses.
The EDCC can be obtained through Jan Kors ([email protected]).
The EDCC is described here: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-014-0373-3
## Authors
MedRoBERTa.nl: Stella Verkijk, Piek Vossen,
Finetuning: Bram van Es
## Contact
If you are having problems with this model please add an issue on our git: https://github.com/umcu/negation-detection/issues
## Usage
If you use the model in your work please use the following referral; https://doi.org/10.1186/s12859-022-05130-x
## References
Paper: Verkijk, S. & Vossen, P. (2022) MedRoBERTa.nl: A Language Model for Dutch Electronic Health Records. Computational Linguistics in the Netherlands Journal, 11.
Paper: Bram van Es, Leon C. Reteig, Sander C. Tan, Marijn Schraagen, Myrthe M. Hemker, Sebastiaan R.S. Arends, Miguel A.R. Rios, Saskia Haitjema (2022): Negation detection in Dutch clinical texts: an evaluation of rule-based and machine learning methods, Arxiv | {"language": ["nl"], "license": "gpl-3.0", "tags": ["medical"], "pipeline_tag": "token-classification"} | token-classification | UMCU/MedRoBERTa.nl_Experiencer | [
"transformers",
"safetensors",
"roberta",
"token-classification",
"medical",
"nl",
"arxiv:2209.00470",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-09T10:21:49+00:00 | [
"2209.00470"
] | [
"nl"
] | TAGS
#transformers #safetensors #roberta #token-classification #medical #nl #arxiv-2209.00470 #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
# URL finetuned for experiencer
## Description
This model is a finetuned RoBERTa-based model pre-trained from scratch
on Dutch hospital notes sourced from Electronic Health Records.
All code used for the creation of URL
can be found at URL
The publication associated with the negation detection task can be found at URL
The code for finetuning the model can be found at URL
## Minimal example
The medical entity classifiers are (being) integrated in the opensource library clinlp, feel free to contact
us for access, either through Huggingface or through git.
It is perhaps good to note that we assume the Inside-Outside-Beginning) format.
## Intended use
The model is finetuned for experiencer detection on Dutch clinical text.
Since it is a domain-specific model trained on medical data,
it is meant to be used on medical NLP tasks for Dutch.
This particular model is trained on a 64-max token windows surrounding the concept-to-be labeled.
## Data
The pre-trained model was trained on nearly 10 million hospital notes from the Amsterdam University Medical Centres.
The training data was anonymized before starting the pre-training procedure.
The finetuning was performed on the Erasmus Dutch Clinical Corpus (EDCC), which was synthetically upsampled for the minority classses.
The EDCC can be obtained through Jan Kors (j.kors@URL).
The EDCC is described here: URL
## Authors
URL: Stella Verkijk, Piek Vossen,
Finetuning: Bram van Es
## Contact
If you are having problems with this model please add an issue on our git: URL
## Usage
If you use the model in your work please use the following referral; URL
## References
Paper: Verkijk, S. & Vossen, P. (2022) URL: A Language Model for Dutch Electronic Health Records. Computational Linguistics in the Netherlands Journal, 11.
Paper: Bram van Es, Leon C. Reteig, Sander C. Tan, Marijn Schraagen, Myrthe M. Hemker, Sebastiaan R.S. Arends, Miguel A.R. Rios, Saskia Haitjema (2022): Negation detection in Dutch clinical texts: an evaluation of rule-based and machine learning methods, Arxiv | [
"# URL finetuned for experiencer",
"## Description\nThis model is a finetuned RoBERTa-based model pre-trained from scratch \non Dutch hospital notes sourced from Electronic Health Records. \nAll code used for the creation of URL \ncan be found at URL\nThe publication associated with the negation detection task can be found at URL\nThe code for finetuning the model can be found at URL",
"## Minimal example\n\n\n\nThe medical entity classifiers are (being) integrated in the opensource library clinlp, feel free to contact\nus for access, either through Huggingface or through git.\n\nIt is perhaps good to note that we assume the Inside-Outside-Beginning) format.",
"## Intended use\nThe model is finetuned for experiencer detection on Dutch clinical text.\nSince it is a domain-specific model trained on medical data, \nit is meant to be used on medical NLP tasks for Dutch. \nThis particular model is trained on a 64-max token windows surrounding the concept-to-be labeled.",
"## Data\nThe pre-trained model was trained on nearly 10 million hospital notes from the Amsterdam University Medical Centres. \nThe training data was anonymized before starting the pre-training procedure. \n\nThe finetuning was performed on the Erasmus Dutch Clinical Corpus (EDCC), which was synthetically upsampled for the minority classses.\nThe EDCC can be obtained through Jan Kors (j.kors@URL). \nThe EDCC is described here: URL",
"## Authors\n\nURL: Stella Verkijk, Piek Vossen,\nFinetuning: Bram van Es",
"## Contact\n\nIf you are having problems with this model please add an issue on our git: URL",
"## Usage\n\nIf you use the model in your work please use the following referral; URL",
"## References\nPaper: Verkijk, S. & Vossen, P. (2022) URL: A Language Model for Dutch Electronic Health Records. Computational Linguistics in the Netherlands Journal, 11.\n\nPaper: Bram van Es, Leon C. Reteig, Sander C. Tan, Marijn Schraagen, Myrthe M. Hemker, Sebastiaan R.S. Arends, Miguel A.R. Rios, Saskia Haitjema (2022): Negation detection in Dutch clinical texts: an evaluation of rule-based and machine learning methods, Arxiv"
] | [
"TAGS\n#transformers #safetensors #roberta #token-classification #medical #nl #arxiv-2209.00470 #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# URL finetuned for experiencer",
"## Description\nThis model is a finetuned RoBERTa-based model pre-trained from scratch \non Dutch hospital notes sourced from Electronic Health Records. \nAll code used for the creation of URL \ncan be found at URL\nThe publication associated with the negation detection task can be found at URL\nThe code for finetuning the model can be found at URL",
"## Minimal example\n\n\n\nThe medical entity classifiers are (being) integrated in the opensource library clinlp, feel free to contact\nus for access, either through Huggingface or through git.\n\nIt is perhaps good to note that we assume the Inside-Outside-Beginning) format.",
"## Intended use\nThe model is finetuned for experiencer detection on Dutch clinical text.\nSince it is a domain-specific model trained on medical data, \nit is meant to be used on medical NLP tasks for Dutch. \nThis particular model is trained on a 64-max token windows surrounding the concept-to-be labeled.",
"## Data\nThe pre-trained model was trained on nearly 10 million hospital notes from the Amsterdam University Medical Centres. \nThe training data was anonymized before starting the pre-training procedure. \n\nThe finetuning was performed on the Erasmus Dutch Clinical Corpus (EDCC), which was synthetically upsampled for the minority classses.\nThe EDCC can be obtained through Jan Kors (j.kors@URL). \nThe EDCC is described here: URL",
"## Authors\n\nURL: Stella Verkijk, Piek Vossen,\nFinetuning: Bram van Es",
"## Contact\n\nIf you are having problems with this model please add an issue on our git: URL",
"## Usage\n\nIf you use the model in your work please use the following referral; URL",
"## References\nPaper: Verkijk, S. & Vossen, P. (2022) URL: A Language Model for Dutch Electronic Health Records. Computational Linguistics in the Netherlands Journal, 11.\n\nPaper: Bram van Es, Leon C. Reteig, Sander C. Tan, Marijn Schraagen, Myrthe M. Hemker, Sebastiaan R.S. Arends, Miguel A.R. Rios, Saskia Haitjema (2022): Negation detection in Dutch clinical texts: an evaluation of rule-based and machine learning methods, Arxiv"
] | [
60,
8,
73,
65,
75,
99,
22,
19,
19,
130
] | [
"passage: TAGS\n#transformers #safetensors #roberta #token-classification #medical #nl #arxiv-2209.00470 #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n# URL finetuned for experiencer## Description\nThis model is a finetuned RoBERTa-based model pre-trained from scratch \non Dutch hospital notes sourced from Electronic Health Records. \nAll code used for the creation of URL \ncan be found at URL\nThe publication associated with the negation detection task can be found at URL\nThe code for finetuning the model can be found at URL## Minimal example\n\n\n\nThe medical entity classifiers are (being) integrated in the opensource library clinlp, feel free to contact\nus for access, either through Huggingface or through git.\n\nIt is perhaps good to note that we assume the Inside-Outside-Beginning) format.## Intended use\nThe model is finetuned for experiencer detection on Dutch clinical text.\nSince it is a domain-specific model trained on medical data, \nit is meant to be used on medical NLP tasks for Dutch. \nThis particular model is trained on a 64-max token windows surrounding the concept-to-be labeled.## Data\nThe pre-trained model was trained on nearly 10 million hospital notes from the Amsterdam University Medical Centres. \nThe training data was anonymized before starting the pre-training procedure. \n\nThe finetuning was performed on the Erasmus Dutch Clinical Corpus (EDCC), which was synthetically upsampled for the minority classses.\nThe EDCC can be obtained through Jan Kors (j.kors@URL). \nThe EDCC is described here: URL## Authors\n\nURL: Stella Verkijk, Piek Vossen,\nFinetuning: Bram van Es## Contact\n\nIf you are having problems with this model please add an issue on our git: URL## Usage\n\nIf you use the model in your work please use the following referral; URL"
] | [
-0.0659211128950119,
0.03561000898480415,
-0.003195290919393301,
0.0062140957452356815,
0.048467960208654404,
-0.026864176616072655,
0.084188312292099,
0.06957506388425827,
0.0497644804418087,
0.13257314264774323,
0.053680334240198135,
-0.03647536039352417,
0.028847230598330498,
0.11867167800664902,
0.12368742376565933,
-0.2200777530670166,
0.10223070532083511,
-0.03558749705553055,
0.07104773074388504,
0.06184397265315056,
0.0740814134478569,
-0.06140659376978874,
0.05653941258788109,
0.04250684753060341,
-0.004312039352953434,
-0.0039343987591564655,
-0.02418285794556141,
-0.03162813559174538,
0.11709198355674744,
-0.02159680239856243,
0.08098570257425308,
0.00012637690815608948,
0.1147688701748848,
-0.1645716279745102,
-0.014298141933977604,
0.06387927383184433,
-0.02892245166003704,
0.005640406161546707,
0.019664620980620384,
-0.013275842182338238,
0.29345110058784485,
-0.15851643681526184,
0.07493577152490616,
-0.02574692852795124,
-0.09515976160764694,
-0.17673927545547485,
-0.14559714496135712,
0.14017502963542938,
0.04531806707382202,
0.11166706681251526,
-0.0035757524892687798,
0.1369510143995285,
-0.04355683550238609,
0.06097450852394104,
0.11226499825716019,
-0.17359328269958496,
-0.014499776996672153,
0.11472899466753006,
0.11524724215269089,
-0.008277786895632744,
-0.10387475043535233,
0.038396917283535004,
-0.009977857582271099,
0.05115224048495293,
0.0714997872710228,
-0.03254125267267227,
0.0077551426365971565,
-0.04245501756668091,
-0.1080969050526619,
-0.0433758907020092,
0.0893242210149765,
-0.03573199734091759,
-0.06272771954536438,
-0.09184322506189346,
0.0009061826858669519,
0.015071711502969265,
0.04506941884756088,
-0.09468407928943634,
0.031145215034484863,
-0.02194126322865486,
0.03509092703461647,
-0.0449371412396431,
-0.07449167221784592,
0.003512009745463729,
-0.07108286768198013,
0.06402942538261414,
0.005836882162839174,
0.025664402171969414,
0.030062014237046242,
0.10072077810764313,
-0.1305166631937027,
-0.014689523726701736,
-0.0898982584476471,
0.003983783535659313,
-0.09440137445926666,
-0.07641278207302094,
0.021245867013931274,
-0.027834570035338402,
0.03593839332461357,
0.13095076382160187,
-0.09345720708370209,
-0.024494729936122894,
0.018735360354185104,
-0.01331000030040741,
0.02770589292049408,
0.019603852182626724,
-0.15923579037189484,
-0.027342285960912704,
-0.007955781184136868,
-0.024811450392007828,
0.0819942057132721,
0.0450933575630188,
-0.011627335101366043,
-0.008848489262163639,
0.07323519140481949,
0.01921922340989113,
-0.09247919172048569,
0.04248703271150589,
0.008629120886325836,
-0.011228193528950214,
0.16227692365646362,
-0.03555945307016373,
0.0026731728576123714,
-0.020379414781928062,
0.04567955806851387,
0.007566843181848526,
0.10215441882610321,
0.0019401099998503923,
-0.06508871167898178,
0.08067626506090164,
-0.03850799426436424,
-0.029772799462080002,
-0.06488395482301712,
-0.11541906744241714,
0.05462055280804634,
-0.07142401486635208,
-0.028880208730697632,
-0.12461487948894501,
-0.18083076179027557,
-0.08771125972270966,
0.026264900341629982,
-0.0045527126640081406,
0.10326533019542694,
-0.04278586804866791,
-0.04863738641142845,
-0.006632179021835327,
0.032648906111717224,
0.007682148367166519,
-0.008219735696911812,
-0.0174875445663929,
-0.1399131715297699,
0.06748519092798233,
-0.08868606388568878,
-0.02574937976896763,
-0.10688181221485138,
0.05384736880660057,
-0.10160408169031143,
0.029086969792842865,
-0.05064889416098595,
-0.05631453916430473,
-0.11771250516176224,
-0.039953719824552536,
-0.07755547016859055,
0.01506615150719881,
0.09850634634494781,
0.08656127005815506,
-0.1032305657863617,
-0.013753049075603485,
0.18089669942855835,
-0.14033055305480957,
-0.05403509363532066,
0.10490996390581131,
-0.023908933624625206,
0.11851156502962112,
0.08399923145771027,
0.1401570588350296,
0.05004270002245903,
-0.17463068664073944,
-0.07802172750234604,
-0.04903675615787506,
-0.07514603435993195,
0.019323572516441345,
0.0485055074095726,
-0.04509829729795456,
0.04937922582030296,
0.02438175678253174,
-0.11581862717866898,
-0.01575213484466076,
-0.039618946611881256,
0.019763950258493423,
0.00429560337215662,
-0.048421863466501236,
-0.08015242218971252,
0.000889598682988435,
-0.023398566991090775,
-0.0805668979883194,
-0.015664657577872276,
0.09364809095859528,
0.14487847685813904,
-0.05290963873267174,
-0.01251305267214775,
-0.06525032222270966,
-0.0661134421825409,
0.0077824373729527,
-0.005167804192751646,
-0.10940832644701004,
-0.026078561320900917,
-0.009816867299377918,
-0.06956865638494492,
0.00034334693918935955,
0.030810097232460976,
0.02124260738492012,
0.0840449333190918,
-0.026038050651550293,
-0.0037109593395143747,
-0.006146986037492752,
0.010885010473430157,
-0.09245257824659348,
-0.08557832986116409,
-0.015464391559362411,
-0.0701013058423996,
0.019821666181087494,
-0.14798147976398468,
0.013237133622169495,
0.015374792739748955,
0.10239315778017044,
0.0811779722571373,
-0.0744413360953331,
0.05276549607515335,
0.04084487631917,
0.030407486483454704,
-0.055210985243320465,
-0.005868740379810333,
-0.008788065053522587,
0.013080422766506672,
0.07424269616603851,
-0.0896400436758995,
-0.1302483081817627,
-0.0012821569107472897,
0.005354959983378649,
-0.08550506830215454,
-0.015049849636852741,
-0.011163045652210712,
-0.0160172451287508,
-0.16753345727920532,
-0.12172252684831619,
0.09968039393424988,
0.010154872201383114,
0.058570608496665955,
0.0032681389711797237,
-0.011158239096403122,
-0.004298369400203228,
-0.0484638512134552,
-0.05269438400864601,
0.10946303606033325,
-0.04211505129933357,
-0.11036066710948944,
0.02393503114581108,
0.01904900185763836,
-0.021626494824886322,
0.05849504470825195,
0.03137848898768425,
-0.013813285157084465,
-0.05054299533367157,
-0.026158148422837257,
0.04409748688340187,
0.06880993396043777,
-0.17741511762142181,
-0.04863898083567619,
0.04170970618724823,
0.054832179099321365,
0.028661221265792847,
-0.027160733938217163,
0.025838246569037437,
0.014063553884625435,
-0.008975786156952381,
0.031944695860147476,
0.05658693239092827,
-0.08994539827108383,
0.10854398459196091,
-0.012258923612535,
0.006775626912713051,
-0.0276715736836195,
-0.02245710976421833,
-0.15602754056453705,
0.16005201637744904,
-0.05532289668917656,
-0.21152721345424652,
-0.09900058060884476,
0.046152617782354355,
0.00688537722453475,
0.005435733590275049,
0.026776855811476707,
-0.03867514431476593,
-0.08372235298156738,
-0.1492232084274292,
-0.001681555644609034,
0.0587938129901886,
-0.047022003680467606,
-0.05748039856553078,
0.034075409173965454,
0.005249071400612593,
-0.087242990732193,
-0.01939251646399498,
-0.06814615428447723,
-0.08496742695569992,
0.08669600635766983,
-0.06782045215368271,
0.0869910940527916,
0.09199044853448868,
0.007478479295969009,
-0.035049375146627426,
0.03408147767186165,
0.14603465795516968,
-0.0621316134929657,
0.12913744151592255,
0.12656347453594208,
0.09588805586099625,
-0.008854410611093044,
0.10683659464120865,
0.03454706445336342,
-0.06566860526800156,
0.07067088782787323,
0.06002955883741379,
-0.039004649966955185,
-0.1731543093919754,
-0.10684800893068314,
-0.045776452869176865,
-0.1715218722820282,
0.001418009283952415,
0.030435552820563316,
0.005827015731483698,
0.02205732837319374,
-0.004190589766949415,
0.07650236040353775,
0.0113858412951231,
0.08393970876932144,
0.07240813225507736,
0.01659124530851841,
0.08527540415525436,
-0.014664683490991592,
-0.00933119561523199,
0.07082958519458771,
-0.037641461938619614,
0.21681714057922363,
-0.04002000764012337,
0.1142197847366333,
0.1401495784521103,
-0.025708656758069992,
0.019257139414548874,
0.06964626163244247,
0.016267070546746254,
0.025096772238612175,
-0.012803085148334503,
-0.06871969252824783,
-0.019434161484241486,
0.0107225077226758,
-0.19100406765937805,
-0.0032385913655161858,
-0.05285579338669777,
-0.0071341414004564285,
0.07148801535367966,
0.09939579665660858,
0.11370882391929626,
-0.13272953033447266,
-0.09869560599327087,
0.01706007681787014,
-0.054907847195863724,
-0.06369295716285706,
-0.01796024665236473,
0.1614619791507721,
-0.13094964623451233,
0.012799650430679321,
-0.009907863102853298,
0.07325094938278198,
-0.11683928966522217,
0.04919107258319855,
-0.02502286061644554,
0.01280062086880207,
-0.06345190107822418,
0.06779743731021881,
-0.18998585641384125,
0.183879554271698,
-0.020289959385991096,
0.12095538526773453,
-0.05990900844335556,
-0.0455198772251606,
0.012125537730753422,
0.21628694236278534,
0.16604851186275482,
0.07239449769258499,
-0.11703428626060486,
-0.010742388665676117,
-0.0292028971016407,
0.03257432579994202,
0.023865079507231712,
-0.04567093402147293,
0.043073490262031555,
0.028636576607823372,
0.008612333796918392,
-0.05121030658483505,
-0.010892065241932869,
-0.22491733729839325,
-0.05807936564087868,
0.05954217165708542,
0.0009948920924216509,
0.0409170426428318,
-0.06497309356927872,
-0.03251964971423149,
-0.027751322835683823,
0.10053101181983948,
-0.1503618061542511,
-0.012835775502026081,
-0.13712215423583984,
0.0964009165763855,
0.06604456156492233,
-0.08293116837739944,
-0.018170464783906937,
0.019801031798124313,
0.02441546693444252,
-0.041652001440525055,
-0.028090186417102814,
0.024343503639101982,
-0.13339397311210632,
-0.13985185325145721,
-0.036638882011175156,
0.012799035757780075,
0.16049478948116302,
0.008482891134917736,
0.03328382596373558,
0.05398380011320114,
0.016178768128156662,
-0.09247356653213501,
0.08711183816194534,
0.2299637496471405,
0.004838378168642521,
0.031126031652092934,
-0.06973862648010254,
-0.08394090086221695,
-0.015897268429398537,
-0.056929729878902435,
-0.008951053954660892,
0.13339611887931824,
-0.0179450586438179,
0.08618844300508499,
0.1917782425880432,
-0.13498590886592865,
-0.3023015856742859,
0.03795091435313225,
0.009741476736962795,
0.03991727903485298,
-0.04073478281497955,
-0.12593968212604523,
0.13583318889141083,
0.10816794633865356,
0.0025434631388634443,
-0.08338132500648499,
-0.15971006453037262,
-0.11114199459552765,
0.08626025170087814,
0.017337821424007416,
0.08282144367694855,
-0.03027554228901863,
-0.03781142458319664,
0.028743434697389603,
-0.06696361303329468,
0.04622670263051987,
-0.1835373044013977,
0.040272872895002365,
-0.03875252977013588,
-0.014063720591366291,
0.04742085561156273,
-0.037998493760824203,
0.10370087623596191,
0.09929317235946655,
0.10877810418605804,
-0.0677715465426445,
-0.01717507466673851,
0.07464438676834106,
-0.03864647448062897,
0.15987637639045715,
0.07322856783866882,
0.006057120859622955,
-0.08822225779294968,
-0.03743014112114906,
-0.10522259771823883,
0.10284381359815598,
-0.013359024189412594,
-0.07169049233198166,
-0.09711195528507233,
0.13671694695949554,
0.052936673164367676,
0.022311333566904068,
0.03651559352874756,
-0.08187059313058853,
-0.06552419811487198,
0.09805265814065933,
0.15839071571826935,
0.006774120964109898,
0.04502858594059944,
0.07660020142793655,
-0.02994135208427906,
0.06401717662811279,
-0.033876411616802216,
-0.00007442716741934419,
0.09175903350114822,
0.04016822949051857,
0.10069695115089417,
0.005446247756481171,
-0.16626964509487152,
-0.005221245810389519,
0.04756954684853554,
-0.08450485020875931,
-0.08632566779851913,
-0.006205898243933916,
0.06739824265241623,
-0.06055612117052078,
0.060433533042669296,
0.12747763097286224,
-0.08838973194360733,
0.008849995210766792,
-0.07057222723960876,
0.07663663476705551,
-0.057074908167123795,
0.1504860669374466,
0.01872311532497406,
0.00993774551898241,
-0.032477617263793945,
0.02442830801010132,
0.08163971453905106,
-0.13455049693584442,
0.04669443145394325,
0.05030813440680504,
-0.16416896879673004,
-0.09721401333808899,
0.04606480523943901,
0.15489163994789124,
-0.05760518088936806,
-0.115848109126091,
0.06575646251440048,
-0.036543600261211395,
-0.008926467970013618,
0.10053931176662445,
0.026715656742453575,
0.017078058794140816,
-0.01047824788838625,
0.006510521285235882,
-0.09219058603048325,
0.09135035425424576,
0.00001504415922681801,
0.06250808387994766,
0.05385427176952362,
0.09741385281085968,
-0.007722540758550167,
0.008755228482186794,
-0.03421392664313316,
0.0034514032304286957,
-0.10557951778173447,
-0.05492006242275238,
-0.07680398225784302,
-0.010415665805339813,
-0.0801084116101265,
-0.0461549274623394,
0.016932863742113113,
-0.004060355480760336,
0.02358314022421837,
0.032949838787317276,
0.004904594738036394,
-0.03175201639533043,
-0.009726828895509243,
0.05505611002445221,
-0.08367079496383667,
-0.04244859516620636,
0.04839612543582916,
-0.047945212572813034,
0.10208279639482498,
-0.04432632029056549,
0.013159913942217827,
-0.026583531871438026,
-0.17596381902694702,
0.058381784707307816,
-0.023638369515538216,
0.052337825298309326,
0.02649366483092308,
-0.1276400238275528,
-0.03244461864233017,
0.0038416730239987373,
-0.04422670230269432,
0.03692816570401192,
0.054986998438835144,
0.005915384739637375,
0.06904550641775131,
-0.002261452842503786,
-0.04769878834486008,
-0.07923109829425812,
0.11213994026184082,
0.025676680728793144,
0.006549681071192026,
0.08219458162784576,
-0.10276860743761063,
-0.015578041784465313,
-0.10470973700284958,
-0.036953192204236984,
0.02986850030720234,
0.020545829087495804,
-0.07913199812173843,
0.0034821131266653538,
0.05529355630278587,
0.03895868733525276,
0.09316819161176682,
0.03481569141149521,
-0.057307131588459015,
0.00227840687148273,
-0.033605124801397324,
0.00017573602963238955,
0.005406206473708153,
0.018956702202558517,
-0.029351118952035904,
-0.022645961493253708,
-0.09682919085025787,
-0.024313708767294884,
-0.03691490739583969,
0.018556686118245125,
0.17425750195980072,
0.1280396580696106,
0.0774192214012146,
0.005218023434281349,
-0.04599153250455856,
-0.029010387137532234,
-0.12160949409008026,
-0.09603646397590637,
0.02443821355700493,
0.013401919975876808,
-0.033889561891555786,
-0.06911110877990723,
0.1203601285815239,
-0.17497225105762482,
0.08597950637340546,
-0.007603561971336603,
-0.07715695351362228,
-0.05548866093158722,
-0.24102386832237244,
-0.007248378824442625,
-0.026093611493706703,
-0.022943153977394104,
-0.09752778708934784,
0.03104235976934433,
0.03044622391462326,
0.04509983956813812,
0.016886239871382713,
0.12164530158042908,
-0.16696181893348694,
0.01780986227095127,
0.05550655722618103,
-0.011477463878691196,
0.016607414931058884,
-0.04665172100067139,
0.08218266814947128,
0.008898290805518627,
0.008520696312189102,
0.05216498672962189,
0.0844683051109314,
0.09941305220127106,
-0.030842360109090805,
0.012923302128911018,
-0.052620865404605865,
0.04351605847477913,
-0.0540127232670784,
-0.020802872255444527,
0.12546077370643616,
0.15377411246299744,
-0.07892048358917236,
0.02995377779006958,
0.16246062517166138,
-0.01723906770348549,
-0.06850575655698776,
-0.128053680062294,
0.12223777174949646,
-0.009106502868235111,
0.033246006816625595,
-0.041324492543935776,
-0.08484754711389542,
0.07230118662118912,
0.23456919193267822,
0.1042826771736145,
0.06035542115569115,
0.00014314167492557317,
-0.05375673621892929,
-0.01754704862833023,
0.045427240431308746,
0.08506085723638535,
0.01541853230446577,
0.21414749324321747,
-0.06619486957788467,
0.06827519088983536,
-0.05548367649316788,
-0.053688228130340576,
-0.08861122280359268,
-0.01557283103466034,
-0.012733418494462967,
-0.02663790062069893,
-0.05448174849152565,
0.10622482746839523,
-0.04487156495451927,
-0.17349094152450562,
0.11474932730197906,
0.030385298654437065,
-0.019656512886285782,
-0.028242990374565125,
-0.07035301625728607,
0.03275883197784424,
0.02899918146431446,
-0.014190612360835075,
0.051207784563302994,
0.1719445288181305,
0.025161946192383766,
-0.04791812226176262,
-0.08268752694129944,
0.06673523783683777,
0.09618362784385681,
0.18534588813781738,
-0.006084615830332041,
0.10205111652612686,
0.09909109771251678,
-0.014367722906172276,
-0.10440976917743683,
0.10415562987327576,
-0.062237463891506195,
0.06852491945028305,
0.0471147820353508,
0.08900570124387741,
0.014397605322301388,
0.03231532499194145,
0.044376078993082047,
-0.13272921741008759,
0.022319085896015167,
0.10760115087032318,
-0.003018643008545041,
-0.12290143966674805,
0.14530478417873383,
-0.03683135285973549,
0.14956225454807281,
0.10846211016178131,
-0.013236741535365582,
0.05623959004878998,
-0.05160985514521599,
0.02456800825893879,
-0.017655331641435623,
0.06625769287347794,
0.060424938797950745,
-0.11425067484378815,
0.10525885224342346,
-0.08262640982866287,
0.02960280328989029,
-0.24121108651161194,
-0.04862750321626663,
0.007135597988963127,
-0.029731446877121925,
0.04001817852258682,
0.06287308037281036,
0.03949683532118797,
0.02639826387166977,
-0.061391040682792664,
0.049096811562776566,
0.0332731232047081,
0.11350925266742706,
-0.035995420068502426,
-0.0723649188876152
] |
null | null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | null | sohug/opt-6.7b-lora | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-09T10:23:52+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
31,
6,
3,
82,
28,
3,
4,
9,
9,
10,
42,
20,
3,
4,
5,
9,
11,
13,
3,
12,
5,
4,
5,
3,
4,
9,
53,
9,
8,
6,
3,
14,
8,
7,
9,
4
] | [
"passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
] | [
-0.06646376848220825,
0.2168014943599701,
-0.00225935154594481,
0.023818302899599075,
0.1271018385887146,
-0.001635765191167593,
0.04218708351254463,
0.13324736058712006,
-0.020175931975245476,
0.11144465953111649,
0.046588581055402756,
0.09377603232860565,
0.09928803145885468,
0.18404334783554077,
0.04859916493296623,
-0.2059975117444992,
0.007056170143187046,
-0.09090408682823181,
0.014076028019189835,
0.1116579994559288,
0.13719257712364197,
-0.10291384905576706,
0.08272874355316162,
-0.04045208916068077,
-0.02019004337489605,
0.00012576708104461432,
-0.09259183704853058,
-0.07032395154237747,
0.06885425746440887,
0.06264153122901917,
0.051234472543001175,
0.001456156256608665,
0.09140396863222122,
-0.2864592671394348,
0.017265573143959045,
0.08406311273574829,
0.0027674848679453135,
0.06290827691555023,
0.07236549258232117,
-0.07389893382787704,
0.11328595131635666,
-0.08021481335163116,
0.13019037246704102,
0.08625296503305435,
-0.062064990401268005,
-0.23071379959583282,
-0.07525765895843506,
0.0963398814201355,
0.12251301854848862,
0.06215599179267883,
-0.022921854630112648,
0.15455181896686554,
-0.06248689442873001,
0.012971068732440472,
0.1294165402650833,
-0.11526761949062347,
-0.05572471022605896,
0.061741601675748825,
0.11775490641593933,
0.10740239918231964,
-0.14110268652439117,
-0.0017287094378843904,
0.04900608956813812,
0.029121357947587967,
0.08589313924312592,
0.022661056369543076,
0.12003941088914871,
0.04652795568108559,
-0.13695219159126282,
-0.04037507623434067,
0.12011898308992386,
0.038862764835357666,
-0.06446044892072678,
-0.2168138176202774,
-0.006778308190405369,
-0.0601806715130806,
-0.014732478186488152,
-0.07019448280334473,
0.039128515869379044,
-0.02470310963690281,
0.07317749410867691,
-0.04465159401297569,
-0.1063927412033081,
-0.0421026237308979,
0.0892222449183464,
0.07748593389987946,
0.011527054943144321,
-0.02519804798066616,
0.04627908393740654,
0.13455867767333984,
0.05402068421244621,
-0.10399353504180908,
-0.07017925381660461,
-0.06942764669656754,
-0.09420394152402878,
-0.04035796597599983,
0.056760527193546295,
0.031942449510097504,
0.02665667235851288,
0.22703726589679718,
0.016653569415211678,
0.04155244305729866,
0.0224777739495039,
0.01032855175435543,
0.043662428855895996,
0.0955500528216362,
-0.05303520709276199,
-0.15660029649734497,
-0.04072032496333122,
0.09077946096658707,
-0.0027527001220732927,
-0.036689214408397675,
-0.03966725245118141,
0.03849169611930847,
0.06843466311693192,
0.13122352957725525,
0.07552056759595871,
-0.017929591238498688,
-0.04813180863857269,
-0.030096933245658875,
0.23523783683776855,
-0.1493375599384308,
0.04426715523004532,
-0.02271856553852558,
-0.01804111897945404,
-0.03908449783921242,
0.03597262129187584,
0.022118929773569107,
-0.000004518366949923802,
0.09706240892410278,
-0.058981191366910934,
-0.05378659814596176,
-0.10168042778968811,
-0.03272576630115509,
0.04088849574327469,
-0.013975566253066063,
-0.010589460842311382,
-0.09025166928768158,
-0.09490354359149933,
-0.04766594246029854,
0.05537205561995506,
-0.05123869329690933,
-0.03770573064684868,
0.009465423412621021,
-0.08151785284280777,
-0.005444355774670839,
-0.005417742300778627,
0.10699385404586792,
-0.03222226724028587,
0.04445803165435791,
-0.027600755915045738,
0.05225523188710213,
0.09919606149196625,
0.031576547771692276,
-0.0773419588804245,
0.0561848059296608,
-0.22559374570846558,
0.07503069192171097,
-0.11481974273920059,
0.04335082694888115,
-0.1704932004213333,
-0.042439818382263184,
0.005444696638733149,
0.0139949731528759,
0.013206101022660732,
0.12720820307731628,
-0.19255615770816803,
-0.01654396951198578,
0.13260798156261444,
-0.09212633967399597,
-0.118110790848732,
0.07884611934423447,
-0.029701577499508858,
0.1624738723039627,
0.04682036489248276,
-0.027025915682315826,
0.09224298596382141,
-0.16434773802757263,
-0.07092688232660294,
-0.00949116237461567,
-0.01727987825870514,
0.12109188735485077,
0.07512219995260239,
-0.05991523340344429,
0.046571120619773865,
0.02832140028476715,
-0.038078423589468,
-0.04424772411584854,
-0.050857074558734894,
-0.10884185880422592,
-0.01070026308298111,
-0.08987759798765182,
0.04065500199794769,
-0.01250192429870367,
-0.07916021347045898,
-0.029885273426771164,
-0.18612512946128845,
-0.0030564051121473312,
0.10038342326879501,
0.0035033065360039473,
-0.005652366206049919,
-0.08666291832923889,
0.026358824223279953,
-0.03112892620265484,
-0.008404186926782131,
-0.16764774918556213,
-0.04399421438574791,
0.046902090311050415,
-0.16094985604286194,
0.020117372274398804,
-0.06413903087377548,
0.06334125250577927,
0.03641495108604431,
-0.05590536445379257,
-0.0248766727745533,
-0.01730942726135254,
0.011945613659918308,
-0.05083848536014557,
-0.18994836509227753,
-0.056277405470609665,
-0.037882111966609955,
0.149809330701828,
-0.25956398248672485,
0.032966937869787216,
0.051140617579221725,
0.14649195969104767,
0.00406361510977149,
-0.05115427449345589,
0.01429014839231968,
-0.05360214412212372,
-0.054652128368616104,
-0.06746816635131836,
-0.006135428790003061,
-0.027576493099331856,
-0.05147203803062439,
0.019243421033024788,
-0.1755700707435608,
-0.021410830318927765,
0.09424154460430145,
0.12876708805561066,
-0.1486445665359497,
-0.018640631809830666,
-0.048725154250860214,
-0.06339836865663528,
-0.0715010017156601,
-0.07038594037294388,
0.10712739825248718,
0.0513901449739933,
0.04796046018600464,
-0.07435787469148636,
-0.07092321664094925,
0.02726263552904129,
0.006906150374561548,
-0.03382374346256256,
0.08727246522903442,
0.05199531093239784,
-0.09209315478801727,
0.0756213590502739,
0.1092359870672226,
0.07177663594484329,
0.09363535046577454,
0.01574566215276718,
-0.11756632477045059,
-0.028492970392107964,
0.036266472190618515,
0.02740776725113392,
0.1465986967086792,
-0.05952361226081848,
0.04016614332795143,
0.04494241625070572,
-0.04170418903231621,
0.022319864481687546,
-0.08787637203931808,
0.024075502529740334,
0.025203049182891846,
-0.0034381982404738665,
0.06284574419260025,
-0.02525499276816845,
-0.0050758360885083675,
0.07016654312610626,
0.047779910266399384,
0.04621000960469246,
0.009655474685132504,
-0.01720241829752922,
-0.1047825813293457,
0.16950392723083496,
-0.0951867327094078,
-0.269941508769989,
-0.17632324993610382,
0.026197833940386772,
0.04035249724984169,
-0.022378476336598396,
0.031619444489479065,
-0.07056326419115067,
-0.10630585998296738,
-0.1060405746102333,
-0.002429972169920802,
0.01714223250746727,
-0.06364088505506516,
-0.0741225928068161,
0.07348573952913284,
0.04382912442088127,
-0.14902326464653015,
0.038552410900592804,
0.055694397538900375,
-0.057955220341682434,
-0.0233661737293005,
0.09118817001581192,
0.12397737801074982,
0.14583967626094818,
-0.021366750821471214,
-0.028626007959246635,
0.029004426673054695,
0.19620531797409058,
-0.13469526171684265,
0.10371150821447372,
0.13814030587673187,
-0.04545360431075096,
0.08360563963651657,
0.1560150384902954,
0.029186224564909935,
-0.08317049592733383,
0.05044832453131676,
0.04082648828625679,
-0.043159641325473785,
-0.2666129767894745,
-0.0534592866897583,
0.012832709588110447,
-0.06255637854337692,
0.09786593168973923,
0.10183793306350708,
0.11542957276105881,
0.034910861402750015,
-0.07166364789009094,
-0.043925940990448,
-0.0058974819257855415,
0.11737963557243347,
-0.05490213260054588,
-0.012639665976166725,
0.07686592638492584,
-0.05086168646812439,
0.005355054512619972,
0.10266812145709991,
0.02973790094256401,
0.17442677915096283,
0.020399179309606552,
0.11231429129838943,
0.06195578724145889,
0.08633565157651901,
0.0007386076031252742,
0.02951662428677082,
0.05147615820169449,
0.017203815281391144,
-0.002300140680745244,
-0.10421168059110641,
-0.006156572140753269,
0.1449710875749588,
0.028103826567530632,
0.029669636860489845,
-0.0018948549404740334,
-0.005003341939300299,
0.05121048167347908,
0.1746254414319992,
-0.011592294089496136,
-0.22072425484657288,
-0.0845772922039032,
0.06936841458082199,
-0.06218599155545235,
-0.12968985736370087,
-0.026130788028240204,
0.045467354357242584,
-0.17519839107990265,
0.026703642681241035,
-0.027433741837739944,
0.0919293761253357,
-0.09345759451389313,
-0.02221956104040146,
0.03687324374914169,
0.084866963326931,
-0.014529162086546421,
0.08703910559415817,
-0.14498743414878845,
0.11886418610811234,
0.02978132851421833,
0.09024628251791,
-0.11081171780824661,
0.07909037172794342,
-0.007550720125436783,
0.009180475026369095,
0.19379350543022156,
-0.011335089802742004,
-0.03514958545565605,
-0.08774717897176743,
-0.11210042238235474,
-0.013537433929741383,
0.12687496840953827,
-0.1243172138929367,
0.08773399889469147,
-0.015198243781924248,
-0.044079482555389404,
0.00937260314822197,
-0.12100647389888763,
-0.17273177206516266,
-0.19628387689590454,
0.05585884302854538,
-0.09575839340686798,
0.025643249973654747,
-0.11914430558681488,
-0.07089093327522278,
-0.02952558360993862,
0.241120383143425,
-0.1745356321334839,
-0.06510113179683685,
-0.1468164622783661,
-0.046294767409563065,
0.1662203073501587,
-0.04437198117375374,
0.0718095526099205,
-0.0208172257989645,
0.20345525443553925,
0.005988610442727804,
-0.004939318168908358,
0.06724198162555695,
-0.08892562240362167,
-0.16873881220817566,
-0.06771010160446167,
0.1510489284992218,
0.11680185794830322,
0.04907919466495514,
-0.002248800592496991,
0.0011772146681323647,
-0.016943959519267082,
-0.1137804463505745,
-0.0033210667315870523,
0.16037839651107788,
0.03878779336810112,
0.025986969470977783,
-0.05243593826889992,
-0.08797456324100494,
-0.06899320334196091,
-0.06853509694337845,
0.06221301481127739,
0.19590823352336884,
-0.10376439243555069,
0.1700313836336136,
0.147536963224411,
-0.07305635511875153,
-0.23175598680973053,
0.035342130810022354,
0.04983805492520332,
0.0014306638622656465,
0.04886869341135025,
-0.18252557516098022,
0.10521943867206573,
0.019543392583727837,
-0.05505957826972008,
0.13485197722911835,
-0.1557481735944748,
-0.1552847921848297,
0.0722852572798729,
0.03904085233807564,
-0.22423844039440155,
-0.1354004591703415,
-0.09622503817081451,
-0.05825018882751465,
-0.14065024256706238,
0.06054598465561867,
-0.002136280992999673,
0.015948504209518433,
0.03500790148973465,
-0.0015643214574083686,
0.027123261243104935,
-0.058935679495334625,
0.18609118461608887,
-0.004065449349582195,
0.020676052197813988,
-0.060264769941568375,
-0.0478842556476593,
0.09839435666799545,
-0.06130504235625267,
0.12208222597837448,
0.004057085141539574,
0.01594383642077446,
-0.10362856835126877,
-0.048314861953258514,
-0.04328322783112526,
0.05154227837920189,
-0.07548051327466965,
-0.10070807486772537,
-0.043625857681035995,
0.08841723203659058,
0.07005169242620468,
-0.03383097052574158,
0.00549331633374095,
-0.07189501076936722,
0.10019614547491074,
0.17795267701148987,
0.17573626339435577,
0.009926567785441875,
-0.07241068035364151,
0.01677953451871872,
-0.04142116755247116,
0.044231921434402466,
-0.2513144314289093,
0.03756171092391014,
0.06098250672221184,
0.029438555240631104,
0.09217222779989243,
-0.020435843616724014,
-0.1820858269929886,
-0.04050002992153168,
0.08094815909862518,
-0.05452597141265869,
-0.22617179155349731,
-0.019085140898823738,
0.0954197570681572,
-0.2020406424999237,
-0.007372708059847355,
0.03995226323604584,
-0.048725228756666183,
-0.023169852793216705,
0.00010950004070764408,
0.06317184865474701,
0.002471912419423461,
0.09773622453212738,
0.0735151618719101,
0.09715340286493301,
-0.08337292820215225,
0.10562895983457565,
0.10150538384914398,
-0.09572599828243256,
0.03605884686112404,
0.06754924356937408,
-0.05300498008728027,
-0.043293699622154236,
0.03665391728281975,
0.033023297786712646,
0.005234600510448217,
-0.060321882367134094,
0.013913018628954887,
-0.036497246474027634,
0.044923391193151474,
0.08326134830713272,
0.03754979372024536,
-0.013354414142668247,
0.06462216377258301,
0.03401726484298706,
-0.10898099094629288,
0.10366570204496384,
0.01731540448963642,
0.04105307161808014,
-0.08384523540735245,
-0.019968897104263306,
0.035425446927547455,
0.030576206743717194,
-0.01765924133360386,
-0.02306121215224266,
-0.02860277332365513,
-0.01614218018949032,
-0.14299540221691132,
-0.023106401786208153,
-0.07243485748767853,
0.006181265693157911,
0.014656842686235905,
-0.031884219497442245,
-0.011233693920075893,
0.02475680410861969,
-0.06979699432849884,
-0.07426341623067856,
-0.006949664559215307,
0.09833318740129471,
-0.15115703642368317,
0.008848577737808228,
0.06907843053340912,
-0.11088496446609497,
0.08190931379795074,
-0.008411259390413761,
0.016245156526565552,
0.022527478635311127,
-0.15448406338691711,
0.05601610988378525,
0.0008648968650959432,
0.01916889287531376,
0.025886621326208115,
-0.16471809148788452,
0.004104440100491047,
-0.04661374166607857,
-0.02149827405810356,
-0.00004464812809601426,
-0.02647159807384014,
-0.12325995415449142,
0.06858719140291214,
-0.015622655861079693,
-0.035931166261434555,
-0.02701525390148163,
0.0539589487016201,
0.07888586074113846,
-0.027474910020828247,
0.10445091128349304,
-0.008690856397151947,
0.04941811040043831,
-0.16801609098911285,
-0.02470702864229679,
-0.04982255399227142,
0.019377702847123146,
0.009884213097393513,
-0.007693959400057793,
0.04183054715394974,
-0.00976533442735672,
0.21883612871170044,
-0.05075952783226967,
0.1607085019350052,
0.05847611650824547,
-0.017352959141135216,
-0.0007513365126214921,
0.06180921941995621,
0.05997028574347496,
0.04658793285489082,
0.009480604901909828,
0.023740366101264954,
-0.022450892254710197,
-0.006695089396089315,
-0.15932634472846985,
0.01890849508345127,
0.14999441802501678,
0.06301083415746689,
0.024745315313339233,
0.05866100639104843,
-0.12775006890296936,
-0.12135478109121323,
0.09311001747846603,
-0.026755332946777344,
0.00928465835750103,
-0.08245618641376495,
0.1358020007610321,
0.14980104565620422,
-0.14000412821769714,
0.05256148427724838,
-0.06134212389588356,
-0.05217423290014267,
-0.10388828068971634,
-0.12032219022512436,
-0.05887215584516525,
-0.053666237741708755,
0.002330566756427288,
-0.03760887682437897,
0.054546963423490524,
0.03344334661960602,
-0.009351172484457493,
-0.00022941511997487396,
0.13597318530082703,
-0.019751882180571556,
-0.0028988157864660025,
0.048313532024621964,
0.03693558648228645,
0.02373051457107067,
-0.05275435373187065,
0.02940409444272518,
0.02539868652820587,
0.032232340425252914,
0.06546790152788162,
0.033412106335163116,
-0.047448933124542236,
0.03804153576493263,
-0.0025254099164158106,
-0.11207924783229828,
0.019641218706965446,
-0.00460948096588254,
-0.0742158442735672,
0.1268945336341858,
0.0407399944961071,
0.010224059224128723,
-0.03741471841931343,
0.24361543357372284,
-0.06653323769569397,
-0.06378097087144852,
-0.13251738250255585,
0.10491154342889786,
-0.0027236645109951496,
0.06476365029811859,
0.023412218317389488,
-0.1284150779247284,
0.005243356805294752,
0.13858191668987274,
0.12181595712900162,
0.0045748427510261536,
0.009228081442415714,
0.0518609918653965,
0.0025186820421367884,
-0.06998204439878464,
0.054019294679164886,
0.06992026418447495,
0.12919506430625916,
-0.07847554981708527,
0.07680778950452805,
0.0006860480643808842,
-0.08370215445756912,
-0.02947772853076458,
0.11312682181596756,
-0.0409729965031147,
0.03491825982928276,
-0.047444481402635574,
0.10916327685117722,
-0.05787910893559456,
-0.29412412643432617,
0.02350960113108158,
-0.09588567912578583,
-0.15202060341835022,
-0.018367812037467957,
0.05944539234042168,
-0.02624768204987049,
0.018029648810625076,
0.06971040368080139,
-0.06011629104614258,
0.20098382234573364,
0.0335683599114418,
-0.07864278554916382,
-0.0664360448718071,
0.04837050288915634,
-0.06564252078533173,
0.2949807047843933,
0.008418165147304535,
0.02863333560526371,
0.10770907253026962,
-0.03253700211644173,
-0.18271861970424652,
0.010723991319537163,
0.1133992001414299,
-0.08056149631738663,
0.08200647681951523,
0.19000613689422607,
-0.012578671798110008,
0.1209007054567337,
0.05294662341475487,
-0.047376248985528946,
0.04217283055186272,
-0.03389401361346245,
-0.051268599927425385,
-0.10752558708190918,
0.058453381061553955,
-0.05909625440835953,
0.15447644889354706,
0.10152646154165268,
-0.05671518296003342,
-0.004550917539745569,
-0.05555408447980881,
0.04875178262591362,
0.01804669201374054,
0.12263146042823792,
0.02951994352042675,
-0.1865430772304535,
0.032826557755470276,
-0.01144319772720337,
0.10186848044395447,
-0.25588861107826233,
-0.08421015739440918,
0.08833149075508118,
-0.011924264021217823,
-0.05105875805020332,
0.10560628771781921,
0.057650718837976456,
0.04243382066488266,
-0.043439045548439026,
-0.10480839014053345,
-0.02186836116015911,
0.14663739502429962,
-0.1469624787569046,
-0.025013303384184837
] |
null | null | null | <table align="center" border="0">
<tr><td colspan=2 align="center">
# DeepFaceLab
<a href="https://arxiv.org/abs/2005.05535">
<img src="https://static.arxiv.org/static/browse/0.3.0/images/icons/favicon.ico" width=14></img>
https://arxiv.org/abs/2005.05535</a>
### the leading software for creating deepfakes
<img src="doc/DFL_welcome.png" align="center">
</td></tr>
<tr><td colspan=2 align="center">
<p align="center">
</p>
More than 95% of deepfake videos are created with DeepFaceLab.
DeepFaceLab is used by such popular youtube channels as
| [deeptomcruise](https://www.tiktok.com/@deeptomcruise)| [1facerussia](https://www.tiktok.com/@1facerussia)| [arnoldschwarzneggar](https://www.tiktok.com/@arnoldschwarzneggar)
|---|---|---|
| [mariahcareyathome?](https://www.tiktok.com/@mariahcareyathome?)| [diepnep](https://www.tiktok.com/@diepnep)| [mr__heisenberg](https://www.tiktok.com/@mr__heisenberg)| [deepcaprio](https://www.tiktok.com/@deepcaprio)
|---|---|---|---|
| [VFXChris Ume](https://www.youtube.com/channel/UCGf4OlX_aTt8DlrgiH3jN3g/videos)| [Sham00k](https://www.youtube.com/channel/UCZXbWcv7fSZFTAZV4beckyw/videos)|
|---|---|
| [Collider videos](https://www.youtube.com/watch?v=A91P2qtPT54&list=PLayt6616lBclvOprvrC8qKGCO-mAhPRux)| [iFake](https://www.youtube.com/channel/UCC0lK2Zo2BMXX-k1Ks0r7dg/videos)| [NextFace](https://www.youtube.com/channel/UCFh3gL0a8BS21g-DHvXZEeQ/videos)|
|---|---|---|
| [Futuring Machine](https://www.youtube.com/channel/UCC5BbFxqLQgfnWPhprmQLVg)| [RepresentUS](https://www.youtube.com/channel/UCRzgK52MmetD9aG8pDOID3g)| [Corridor Crew](https://www.youtube.com/c/corridorcrew/videos)|
|---|---|---|
| [DeepFaker](https://www.youtube.com/channel/UCkHecfDTcSazNZSKPEhtPVQ)| [DeepFakes in movie](https://www.youtube.com/c/DeepFakesinmovie/videos)|
|---|---|
| [DeepFakeCreator](https://www.youtube.com/channel/UCkNFhcYNLQ5hr6A6lZ56mKA)| [Jarkan](https://www.youtube.com/user/Jarkancio/videos)|
|---|---|
</td></tr>
<tr><td colspan=2 align="center">
# What can I do using DeepFaceLab?
</td></tr>
<tr><td colspan=2 align="center">
## Replace the face
<img src="doc/replace_the_face.jpg" align="center">
</td></tr>
<tr><td colspan=2 align="center">
## De-age the face
</td></tr>
<tr><td align="center" width="50%">
<img src="doc/deage_0_1.jpg" align="center">
</td>
<td align="center" width="50%">
<img src="doc/deage_0_2.jpg" align="center">
</td></tr>
<tr><td colspan=2 align="center">
 https://www.youtube.com/watch?v=Ddx5B-84ebo
</td></tr>
<tr><td colspan=2 align="center">
## Replace the head
</td></tr>
<tr><td align="center" width="50%">
<img src="doc/head_replace_0_1.jpg" align="center">
</td>
<td align="center" width="50%">
<img src="doc/head_replace_0_2.jpg" align="center">
</td></tr>
<tr><td colspan=2 align="center">
 https://www.youtube.com/watch?v=xr5FHd0AdlQ
</td></tr>
<tr><td align="center" width="50%">
<img src="doc/head_replace_1_1.jpg" align="center">
</td>
<td align="center" width="50%">
<img src="doc/head_replace_1_2.jpg" align="center">
</td></tr>
<tr><td colspan=2 align="center">
 https://www.youtube.com/watch?v=RTjgkhMugVw
</td></tr>
<tr><td align="center" width="50%">
<img src="doc/head_replace_2_1.jpg" align="center">
</td>
<td align="center" width="50%">
<img src="doc/head_replace_2_2.jpg" align="center">
</td></tr>
<tr><td colspan=2 align="center">
 https://www.youtube.com/watch?v=R9f7WD0gKPo
</td></tr>
<tr><td colspan=2 align="center">
## Manipulate politicians lips
(voice replacement is not included!)
(also requires a skill in video editors such as *Adobe After Effects* or *Davinci Resolve*)
<img src="doc/political_speech2.jpg" align="center">
 https://www.youtube.com/watch?v=IvY-Abd2FfM
<img src="doc/political_speech3.jpg" align="center">
 https://www.youtube.com/watch?v=ERQlaJ_czHU
</td></tr>
<tr><td colspan=2 align="center">
# Deepfake native resolution progress
</td></tr>
<tr><td colspan=2 align="center">
<img src="doc/deepfake_progress.png" align="center">
</td></tr>
<tr><td colspan=2 align="center">
<img src="doc/make_everything_ok.png" align="center">
Unfortunately, there is no "make everything ok" button in DeepFaceLab. You should spend time studying the workflow and growing your skills. A skill in programs such as *AfterEffects* or *Davinci Resolve* is also desirable.
</td></tr>
<tr><td colspan=2 align="center">
## Mini tutorial
<a href="https://www.youtube.com/watch?v=kOIMXt8KK8M">
<img src="doc/mini_tutorial.jpg" align="center">
</a>
</td></tr>
<tr><td colspan=2 align="center">
## Releases
</td></tr>
<tr><td align="right">
<a href="https://tinyurl.com/2p9cvt25">Windows (magnet link)</a>
</td><td align="center">Last release. Use torrent client to download.</td></tr>
<tr><td align="right">
<a href="https://mega.nz/folder/Po0nGQrA#dbbttiNWojCt8jzD4xYaPw">Windows (Mega.nz)</a>
</td><td align="center">Contains new and prev releases.</td></tr>
<tr><td align="right">
<a href="https://disk.yandex.ru/d/7i5XTKIKVg5UUg">Windows (yandex.ru)</a>
</td><td align="center">Contains new and prev releases.</td></tr>
<tr><td align="right">
<a href="https://github.com/chervonij/DFL-Colab">Google Colab (github)</a>
</td><td align="center">by @chervonij . You can train fakes for free using Google Colab.</td></tr>
<tr><td align="right">
<a href="https://github.com/nagadit/DeepFaceLab_Linux">Linux (github)</a>
</td><td align="center">by @nagadit</td></tr>
<tr><td align="right">
<a href="https://github.com/elemantalcode/dfl">CentOS Linux (github)</a>
</td><td align="center">May be outdated. By @elemantalcode</td></tr>
</table>
<table align="center" border="0">
<tr><td colspan=2 align="center">
## Links
</td></tr>
<tr><td colspan=2 align="center">
### Guides and tutorials
</td></tr>
<tr><td align="right">
<a href="https://mrdeepfakes.com/forums/thread-guide-deepfacelab-2-0-guide">DeepFaceLab guide</a>
</td><td align="center">Main guide</td></tr>
<tr><td align="right">
<a href="https://mrdeepfakes.com/forums/thread-guide-deepfacelab-2-0-guide?pid=18459#pid18459">Faceset creation guide</a>
</td><td align="center">How to create the right faceset</td></tr>
<tr><td align="right">
<a href="https://mrdeepfakes.com/forums/thread-guide-deepfacelab-google-colab-tutorial">Google Colab guide</a>
</td><td align="center">Guide how to train the fake on Google Colab</td></tr>
<tr><td align="right">
<a href="https://mrdeepfakes.com/forums/thread-deepfacelab-2-0-compositing-in-davinci-resolve-vegas-pro-and-after-effects">Compositing</a>
</td><td align="center">To achieve the highest quality, compose deepfake manually in video editors such as Davinci Resolve or Adobe AfterEffects</td></tr>
<tr><td align="right">
<a href="https://mrdeepfakes.com/forums/thread-deepfacelab-2-0-discussion-tips-suggestions">Discussion and suggestions</a>
</td><td align="center"></td></tr>
<tr><td colspan=2 align="center">
### Supplementary material
</td></tr>
<tr><td align="right">
<a href="https://mrdeepfakes.com/forums/forum-celebrity-facesets">Ready to work facesets</a>
</td><td align="center">Celebrity facesets made by community</td></tr>
<tr><td align="right">
<a href="https://mrdeepfakes.com/forums/forum-trained-models">Pretrained models</a>
</td><td align="center">Pretrained models made by community</td></tr>
<tr><td colspan=2 align="center">
### Communication groups
</td></tr>
<tr><td align="right">
<a href="https://discord.gg/rxa7h9M6rH">Discord</a>
</td><td align="center">Official discord channel. English / Russian.</td></tr>
<tr><td align="right">
<a href="https://t.me/joinchat/ElkhqlgJ0I5HhdJyFar80w">Telegram group</a>
</td><td align="center">Official telegram group. English / Russian. For anonymous communication. Don't forget to hide your phone number</td></tr>
<tr><td align="right">
<a href="https://mrdeepfakes.com/forums/forum-russian-community">Русский форум</a>
</td><td align="center"></td></tr>
<tr><td align="right">
<a href="https://mrdeepfakes.com/forums/">mrdeepfakes</a>
</td><td align="center">the biggest NSFW English community</td></tr>
<tr><td align="right">
<a href="https://www.reddit.com/r/DeepFakesSFW/new/">reddit r/DeepFakesSFW/</a>
</td><td align="center">Post your deepfakes there !</td></tr>
<tr><td align="right">
<a href="https://www.reddit.com/r/RUdeepfakes/new/">reddit r/RUdeepfakes/</a>
</td><td align="center">Постим русские дипфейки сюда !</td></tr>
<tr><td align="right">
QQ群124500433
</td><td align="center">中文交流QQ群，商务合作找群主</td></tr>
<tr><td align="right">
<a href="https://www.dfldata.cc">dfldata.cc</a>
</td><td align="center">中文交流论坛，免费软件教程、模型、人脸数据</td></tr>
<tr><td align="right">
<a href="https://www.deepfaker.xyz/">deepfaker.xyz</a>
</td><td align="center">中文学习站（非官方)</td></tr>
<tr><td colspan=2 align="center">
## Related works
</td></tr>
<tr><td align="right">
<a href="https://github.com/iperov/DeepFaceLive">DeepFaceLive</a>
</td><td align="center">Real-time face swap for PC streaming or video calls</td></tr>
<tr><td align="right">
<a href="https://github.com/neuralchen/SimSwap">neuralchen/SimSwap</a>
</td><td align="center">Swapping face using ONE single photo 一张图免训练换脸</td></tr>
<tr><td align="right">
<a href="https://github.com/deepfakes/faceswap">deepfakes/faceswap</a>
</td><td align="center">Something that was before DeepFaceLab and still remains in the past</td></tr>
</td></tr>
</table>
<table align="center" border="0">
<tr><td colspan=2 align="center">
## How I can help the project?
</td></tr>
<tr><td colspan=2 align="center">
### Sponsor deepfake research and DeepFaceLab development.
</td></tr>
<!--
<tr><td colspan=2 align="center">
<a href="https://www.paypal.com/paypalme/DeepFaceLab">Donate via Paypal</a>
</td></tr>
-->
<tr><td colspan=2 align="center">
<a href="https://yoomoney.ru/to/41001142318065">Donate via Yoomoney</a>
</td></tr>
<tr><td colspan=2 align="center">
bitcoin:bc1qkhh7h0gwwhxgg6h6gpllfgstkd645fefrd5s6z
</td></tr>
<tr><td colspan=2 align="center">
### Collect facesets
</td></tr>
<tr><td colspan=2 align="center">
You can collect faceset of any celebrity that can be used in DeepFaceLab and share it <a href="https://mrdeepfakes.com/forums/forum-celebrity-facesets">in the community</a>
</td></tr>
<tr><td colspan=2 align="center">
### Star this repo
</td></tr>
<tr><td colspan=2 align="center">
Register github account and push "Star" button.
</td></tr>
</table>
<table align="center" border="0">
<tr><td colspan=2 align="center">
## Meme zone
</td></tr>
<tr><td align="center" width="50%">
<img src="doc/meme1.jpg" align="center">
</td>
<td align="center" width="50%">
<img src="doc/meme2.jpg" align="center">
</td></tr>
<tr><td colspan=2 align="center">
<sub>#deepfacelab #deepfakes #faceswap #face-swap #deep-learning #deeplearning #deep-neural-networks #deepface #deep-face-swap #fakeapp #fake-app #neural-networks #neural-nets #tensorflow #cuda #nvidia</sub>
</td></tr>
</table>
| {} | null | Jatin7860/DF | [
"arxiv:2005.05535",
"region:us"
] | 2024-02-09T10:31:24+00:00 | [
"2005.05535"
] | [] | TAGS
#arxiv-2005.05535 #region-us
|
| DeepFaceLab
<a href="URL
<img src="URL width=14>
URL
the leading software for creating deepfakes
|
|
More than 95% of deepfake videos are created with DeepFaceLab.
DeepFaceLab is used by such popular youtube channels as
|
| What can I do using DeepFaceLab? |
| Replace the face
|
| De-age the face |
| | |
| |
| Replace the head |
| | |
| |
| | |
| |
| | |
| |
| Manipulate politicians lips
(voice replacement is not included!)
(also requires a skill in video editors such as *Adobe After Effects* or *Davinci Resolve*)
|
| Deepfake native resolution progress |
| |
|
Unfortunately, there is no "make everything ok" button in DeepFaceLab. You should spend time studying the workflow and growing your skills. A skill in programs such as *AfterEffects* or *Davinci Resolve* is also desirable. |
| Mini tutorial
<a href="URL
|
| Releases |
| [Last release. Use torrent client to download.](URL (magnet link)</a>
</td><td align=) |
| [Contains new and prev releases.](URL (URL)</a>
</td><td align=) |
| [Contains new and prev releases.](URL (URL)</a>
</td><td align=) |
| [by @chervonij . You can train fakes for free using Google Colab.](URL Colab (github)</a>
</td><td align=) |
| [by @nagadit](URL (github)</a>
</td><td align=) |
| [May be outdated. By @elemantalcode](URL Linux (github)</a>
</td><td align=) |
| |
| --- |
| Links |
| Guides and tutorials |
| [Main guide](URL guide</a>
</td><td align=) |
| [How to create the right faceset](URL creation guide</a>
</td><td align=) |
| [Guide how to train the fake on Google Colab](URL Colab guide</a>
</td><td align=) |
| [To achieve the highest quality, compose deepfake manually in video editors such as Davinci Resolve or Adobe AfterEffects](URL
</td><td align=) |
| |
| Supplementary material |
| [Celebrity facesets made by community](URL to work facesets</a>
</td><td align=) |
| [Pretrained models made by community](URL models</a>
</td><td align=) |
| Communication groups |
| [Official discord channel. English / Russian.](URL
</td><td align=) |
| [Telegram group](https://t.me/joinchat/ElkhqlgJ0I5HhdJyFar80w) | Official telegram group. English / Russian. For anonymous communication. Don't forget to hide your phone number |
| |
| [the biggest NSFW English community](URL
</td><td align=) |
| [Post your deepfakes there !](URL r/DeepFakesSFW/</a>
</td><td align=) |
| [Постим русские дипфейки сюда !](URL r/RUdeepfakes/</a>
</td><td align=) |
|
QQ群124500433
| 中文交流QQ群，商务合作找群主 |
| <URL> | 中文交流论坛，免费软件教程、模型、人脸数据 |
| [中文学习站（非官方)](URL
</td><td align=) |
| Related works |
| [Real-time face swap for PC streaming or video calls](URL
</td><td align=) |
| [Swapping face using ONE single photo 一张图免训练换脸](URL
</td><td align=) |
| [Something that was before DeepFaceLab and still remains in the past](URL
</td><td align=) |
| |
| --- |
| How I can help the project? |
| Sponsor deepfake research and DeepFaceLab development. |
| [bitcoin:bc1qkhh7h0gwwhxgg6h6gpllfgstkd645fefrd5s6z](URL via Yoomoney</a>
</td></tr>
<tr><td colspan=2 align=) |
| Collect facesets |
| You can collect faceset of any celebrity that can be used in DeepFaceLab and share it <a href="URL the community |
| Star this repo |
| Register github account and push "Star" button. |
| |
| --- |
| Meme zone |
| | |
| #deepfacelab #deepfakes #faceswap #face-swap #deep-learning #deeplearning #deep-neural-networks #deepface #deep-face-swap #fakeapp #fake-app #neural-networks #neural-nets #tensorflow #cuda #nvidia |
| [] | [
"TAGS\n#arxiv-2005.05535 #region-us \n"
] | [
14
] | [
"passage: TAGS\n#arxiv-2005.05535 #region-us \n"
] | [
-0.0361945815384388,
0.08070804178714752,
-0.009317569434642792,
-0.032977085560560226,
0.059838924556970596,
0.056394532322883606,
0.040054574608802795,
0.011007708497345448,
0.18688297271728516,
0.0344153493642807,
0.17516891658306122,
0.02718847244977951,
0.003750127274543047,
0.060694072395563126,
-0.03633967041969299,
-0.08529523015022278,
0.03916061669588089,
-0.0034718455281108618,
0.0386529415845871,
0.05958084389567375,
-0.006707379594445229,
-0.13791102170944214,
0.0259186290204525,
-0.06925008445978165,
-0.10979804396629333,
0.06794173270463943,
0.03654919192194939,
-0.06291544437408447,
0.10750600695610046,
0.0031657794024795294,
0.16482451558113098,
0.02626962773501873,
-0.06240175664424896,
-0.23016943037509918,
0.015022809617221355,
-0.0468030646443367,
-0.09237533807754517,
0.09088359773159027,
0.11816862225532532,
0.00911617185920477,
0.030402710661292076,
0.09944642335176468,
-0.04039612039923668,
0.050266560167074203,
-0.2896064221858978,
-0.1809055358171463,
-0.06292254477739334,
-0.007804215885698795,
0.1012558564543724,
0.07500376552343369,
0.030327938497066498,
0.11717578768730164,
-0.0969177708029747,
0.016842350363731384,
0.16880759596824646,
-0.32326582074165344,
0.027274273335933685,
0.15063923597335815,
-0.03218543902039528,
0.14303016662597656,
-0.0047964355908334255,
0.09483878314495087,
0.08998885750770569,
-0.05276181921362877,
-0.10059129446744919,
-0.05571906268596649,
-0.1022321954369545,
0.15637871623039246,
-0.06921141594648361,
-0.12008926272392273,
0.3953889310359955,
0.04910250008106232,
0.035384781658649445,
0.1746806651353836,
-0.06441498547792435,
-0.11957429349422455,
0.05548030138015747,
-0.026462316513061523,
0.008611883036792278,
0.08352836221456528,
0.21383784711360931,
-0.02713082917034626,
-0.1738862842321396,
-0.022290362045168877,
-0.21542604267597198,
0.12019070982933044,
-0.04734328016638756,
0.09012603759765625,
-0.22858813405036926,
-0.022086599841713905,
-0.23962396383285522,
-0.005568524356931448,
0.047809503972530365,
-0.09126129746437073,
0.03110506199300289,
0.012670212425291538,
-0.02364633046090603,
-0.028323130682110786,
0.06434588134288788,
0.07965026050806046,
0.0001489308924647048,
0.07643017172813416,
-0.0401180274784565,
0.1342546045780182,
0.06374584883451462,
0.05153541639447212,
0.07549528032541275,
-0.043452490121126175,
-0.015660235658288002,
-0.09552968293428421,
-0.009351629763841629,
-0.03393187373876572,
-0.09925008565187454,
-0.0567099004983902,
-0.06819865107536316,
0.11616764962673187,
0.009319727309048176,
-0.14172926545143127,
-0.11223709583282471,
0.06473320722579956,
0.02264087088406086,
-0.01586362160742283,
-0.0876762643456459,
-0.003685810137540102,
0.003733726916834712,
-0.026315957307815552,
-0.11039861291646957,
0.0032874946482479572,
0.0643308162689209,
0.08295171707868576,
-0.15990397334098816,
0.004368763417005539,
-0.023588206619024277,
-0.04229258745908737,
0.06582394987344742,
-0.08252089470624924,
0.05053876340389252,
-0.12276634573936462,
0.028916537761688232,
-0.003257830161601305,
-0.019045431166887283,
-0.024220898747444153,
0.10156732052564621,
0.017319509759545326,
0.07385384291410446,
-0.03246154263615608,
-0.06359868496656418,
-0.07993479818105698,
-0.05918407440185547,
0.03975691273808479,
0.07576186954975128,
0.020341835916042328,
-0.09468993544578552,
-0.013889582827687263,
-0.08506517112255096,
0.05461118742823601,
-0.018270980566740036,
-0.12592409551143646,
-0.055126555263996124,
0.1728840470314026,
-0.06345111131668091,
0.04551677778363228,
-0.16952459514141083,
-0.03290921077132225,
0.022421488538384438,
0.21823590993881226,
-0.03884198144078255,
-0.04337730258703232,
0.12982624769210815,
-0.09406740218400955,
-0.13247434794902802,
-0.035193175077438354,
0.040503665804862976,
-0.032615575939416885,
0.049570370465517044,
0.3411255180835724,
-0.15238800644874573,
-0.12764786183834076,
0.02938236854970455,
0.13309592008590698,
-0.07461310178041458,
-0.1922551542520523,
0.11124980449676514,
-0.12975719571113586,
-0.23184460401535034,
0.002008850686252117,
0.038502342998981476,
0.048858609050512314,
-0.07288233935832977,
-0.025992074981331825,
0.05652032792568207,
-0.023520376533269882,
0.07149981707334518,
0.03568482771515846,
0.09747587144374847,
-0.05276390537619591,
0.10289853066205978,
-0.0864991843700409,
0.02068762108683586,
0.1694433093070984,
-0.023942997679114342,
-0.027397504076361656,
0.048823900520801544,
-0.1855916678905487,
0.020235799252986908,
-0.1636289656162262,
-0.10400273650884628,
0.0027763359248638153,
0.011325208470225334,
0.0524434894323349,
0.11710386723279953,
0.0975751206278801,
-0.10969352722167969,
0.029582181945443153,
-0.01577892154455185,
0.022112395614385605,
0.04689696058630943,
0.008577428758144379,
-0.05837026610970497,
0.08086919784545898,
-0.05159999802708626,
-0.11102919280529022,
-0.14925386011600494,
-0.03997756168246269,
0.09785236418247223,
-0.05903686583042145,
0.08138909935951233,
-0.005827105138450861,
0.056433215737342834,
0.0017140177078545094,
0.06423845887184143,
0.05355699732899666,
0.08762014657258987,
-0.009906573221087456,
0.010032444261014462,
0.06545626372098923,
-0.07251373678445816,
0.3438440263271332,
0.1061357706785202,
-0.1858396977186203,
0.016300121322274208,
-0.1257944107055664,
0.031188461929559708,
0.0071264407597482204,
0.09688626229763031,
0.006960070692002773,
-0.07897350192070007,
-0.008415745571255684,
0.03508220985531807,
-0.01673070527613163,
0.05129863694310188,
0.0020499974489212036,
-0.07283781468868256,
-0.07675431668758392,
0.14415080845355988,
0.101730577647686,
-0.19352205097675323,
0.12038624286651611,
0.37348291277885437,
0.09346306324005127,
0.12170832604169846,
-0.021227087825536728,
-0.07642695307731628,
-0.02993960492312908,
-0.003005102975293994,
-0.05026811733841896,
0.1412065476179123,
-0.10438504070043564,
-0.029896868392825127,
0.06008191779255867,
0.03523274511098862,
0.07672372460365295,
-0.13740286231040955,
-0.0883474051952362,
0.023019589483737946,
0.031868718564510345,
-0.2141328901052475,
0.024012114852666855,
-0.0685967355966568,
0.08362346142530441,
0.09427396208047867,
0.01085941307246685,
0.05403808876872063,
-0.01454460620880127,
-0.06501485407352448,
0.11280491948127747,
-0.16245123744010925,
-0.20428603887557983,
-0.12812739610671997,
-0.06289443373680115,
0.00239161285571754,
0.032728418707847595,
0.010554894804954529,
-0.19533278048038483,
-0.06502170860767365,
0.0745186135172844,
0.03936871513724327,
-0.2092825472354889,
-0.009289194829761982,
0.1337864100933075,
0.07189606130123138,
-0.06037312000989914,
-0.01751389354467392,
-0.03404434025287628,
-0.13518719375133514,
0.07374633103609085,
0.058657772839069366,
-0.08440916240215302,
0.15331916511058807,
0.12745656073093414,
0.07627340406179428,
0.06461873650550842,
0.007304435130208731,
0.18072696030139923,
-0.06581666320562363,
-0.13869968056678772,
0.13040852546691895,
-0.07154236733913422,
0.03242583945393562,
0.13977867364883423,
0.05133260786533356,
-0.10743147134780884,
-0.02221147157251835,
-0.06456288695335388,
-0.07925876975059509,
-0.28753742575645447,
-0.03938576951622963,
-0.1259259283542633,
0.15492409467697144,
0.05674968659877777,
0.04164053499698639,
0.09588749706745148,
0.017935913056135178,
0.14240720868110657,
-0.07486918568611145,
-0.049508824944496155,
0.006282872520387173,
0.26130354404449463,
-0.0630442351102829,
-0.022305503487586975,
-0.12091747671365738,
0.04549244046211243,
0.09438947588205338,
0.08062511682510376,
0.15483185648918152,
0.2663668394088745,
0.061832625418901443,
0.012844066135585308,
0.09137307107448578,
0.1422014832496643,
0.1317858099937439,
0.04591590538620949,
-0.06860345602035522,
-0.009808099828660488,
0.004494659136980772,
-0.017311185598373413,
0.05153629928827286,
0.0010580933885648847,
-0.19611254334449768,
0.002585421781986952,
-0.2608073949813843,
0.025724610313773155,
-0.11351382732391357,
0.12305855751037598,
-0.052464839071035385,
0.037325646728277206,
0.028429269790649414,
0.06899461150169373,
-0.0704515278339386,
0.1128714382648468,
0.00987987406551838,
-0.029221154749393463,
-0.016162371262907982,
0.0035392732825130224,
0.06421107053756714,
-0.014253388158977032,
0.09242257475852966,
-0.17797046899795532,
-0.11444204300642014,
-0.0059941913932561874,
0.07695012539625168,
-0.20137935876846313,
0.37824511528015137,
0.025472471490502357,
-0.14199186861515045,
0.011030173860490322,
-0.08145734667778015,
-0.039539117366075516,
0.07765264064073563,
0.15633414685726166,
0.053617965430021286,
-0.21120929718017578,
-0.14160171151161194,
0.01886998862028122,
0.013047797605395317,
0.16237491369247437,
-0.007149722892791033,
-0.10629186034202576,
-0.030592411756515503,
0.03697685897350311,
-0.028938382863998413,
0.06370620429515839,
-0.010636367835104465,
-0.05779951065778732,
0.05720369517803192,
0.028783520683646202,
-0.0006757258670404553,
0.04044470563530922,
0.061059217900037766,
-0.017934951931238174,
-0.028837382793426514,
-0.12795241177082062,
0.0351082980632782,
-0.08102936297655106,
-0.14340536296367645,
0.13260719180107117,
-0.09901455044746399,
0.04371161013841629,
-0.08128044009208679,
-0.1418417990207672,
-0.05074688047170639,
-0.1352306306362152,
0.1685456931591034,
-0.08833351731300354,
0.09856866300106049,
-0.05080605670809746,
0.11117275804281235,
-0.07168934494256973,
0.04020480439066887,
-0.08338567614555359,
0.0825553834438324,
-0.08030803501605988,
-0.05118189752101898,
0.17254336178302765,
-0.13228563964366913,
0.033465076237916946,
0.035513997077941895,
0.012858176603913307,
0.01686857081949711,
-0.002168675186112523,
-0.07998886704444885,
0.1999884694814682,
0.372465580701828,
-0.02746749110519886,
0.12925991415977478,
0.26442310214042664,
-0.042949989438056946,
-0.18443353474140167,
-0.033256664872169495,
-0.21935932338237762,
-0.04448288306593895,
0.10644763708114624,
-0.23635037243366241,
0.05340580642223358,
0.14266207814216614,
-0.09354225546121597,
0.2840172350406647,
-0.2056029736995697,
0.008951726369559765,
0.15359361469745636,
-0.055758364498615265,
0.6228697896003723,
-0.09811294823884964,
-0.11474091559648514,
0.03844425827264786,
-0.20467457175254822,
0.1050923690199852,
0.09274432808160782,
0.018498122692108154,
-0.02245788834989071,
-0.0607604905962944,
0.0569564625620842,
-0.0418996699154377,
0.19466851651668549,
-0.08162032067775726,
0.072541743516922,
-0.08953412622213364,
-0.2239852249622345,
0.0027900326531380415,
-0.034762926399707794,
-0.038137223571538925,
0.08503538370132446,
-0.015869710594415665,
-0.14929445087909698,
0.029474087059497833,
-0.0545186921954155,
-0.022088946774601936,
0.08783071488142014,
-0.05231399089097977,
-0.050519831478595734,
-0.04167227819561958,
-0.13881006836891174,
-0.00038236132240854204,
0.3983941674232483,
-0.07810436934232712,
0.17415694892406464,
0.0682244673371315,
-0.0010068581905215979,
-0.06187347695231438,
-0.00478910468518734,
-0.03130677342414856,
-0.044147491455078125,
0.11308471113443375,
-0.21136368811130524,
0.03320954740047455,
0.17254990339279175,
0.0006985623040236533,
0.047288961708545685,
0.04791208356618881,
-0.03229806572198868,
0.001426452537998557,
0.1767122447490692,
-0.20432525873184204,
-0.07460568100214005,
-0.026829438284039497,
-0.04434642940759659,
0.1181185394525528,
0.012760872952640057,
0.07419467717409134,
0.06853406876325607,
0.021064989268779755,
-0.004581437911838293,
-0.029139433056116104,
-0.0592942051589489,
0.057595714926719666,
0.03455119580030441,
-0.0051660737954080105,
-0.06887641549110413,
0.20589667558670044,
0.09024892002344131,
-0.10969152301549911,
-0.04269750788807869,
0.14779283106327057,
-0.06438031792640686,
-0.0482223741710186,
-0.24233262240886688,
0.04290800541639328,
-0.1799597144126892,
-0.05655548349022865,
-0.010393211618065834,
-0.0007873614085838199,
-0.003529631532728672,
0.18933777511119843,
0.02418946474790573,
0.04858563840389252,
0.039914146065711975,
-0.019931945949792862,
0.09600332379341125,
-0.07520871609449387,
-0.11694066971540451,
-0.04265177622437477,
-0.10267753154039383,
-0.1647249162197113,
-0.005224933847784996,
0.0941980630159378,
-0.06410302221775055,
-0.08413144946098328,
-0.19465456902980804,
0.06650127470493317,
-0.057107407599687576,
-0.06441336870193481,
-0.05127812922000885,
-0.04920831322669983,
-0.0038395756855607033,
-0.025887111201882362,
-0.07222626358270645,
-0.0031808980274945498,
-0.1615312248468399,
0.08842813223600388,
0.021541420370340347,
0.06828681379556656,
-0.0045395502820611,
-0.012976881116628647,
0.136039599776268,
0.062355101108551025,
0.10918517410755157,
0.14011353254318237,
0.03511720150709152,
0.19851471483707428,
-0.11732681840658188,
-0.025057265534996986,
0.11816973239183426,
-0.030918972566723824,
0.021104102954268456,
0.08127411454916,
-0.048171766102313995,
0.009103674441576004,
-0.06345102936029434,
0.030304081737995148,
-0.0785653218626976,
-0.08175269514322281,
-0.08136635273694992,
0.0018010780913755298,
-0.20177507400512695,
0.03895021975040436,
-0.17887137830257416,
0.14261211454868317,
0.003030810970813036,
0.037459421902894974,
0.09381139278411865,
0.038357872515916824,
0.004094145726412535,
0.03502272069454193,
-0.006999715231359005,
-0.13762564957141876,
-0.08925067633390427,
-0.068879134953022,
-0.0006595947197638452,
-0.025382496416568756,
0.25582242012023926,
-0.10304231196641922,
-0.03706187382340431,
0.023369360715150833,
0.12243190407752991,
-0.08210866153240204,
-0.008707369677722454,
0.10959010571241379,
0.10034394264221191,
-0.1031704843044281,
-0.13310891389846802,
0.10976613312959671,
-0.05767694488167763,
0.009065394289791584,
0.13346539437770844,
0.09792068600654602,
0.07288062572479248,
0.05172249674797058,
-0.01585603691637516,
-0.07667164504528046,
0.01746072992682457,
-0.03642899543046951,
-0.009307351894676685,
0.030387364327907562,
0.03059200383722782,
0.06886853277683258,
0.2760474681854248,
0.03903459012508392,
0.00416985061019659,
-0.10813058167695999,
0.039457663893699646,
-0.12842512130737305,
-0.06515662372112274,
-0.014850588515400887,
-0.12844271957874298,
0.044094353914260864,
-0.03978807479143143,
0.06515279412269592,
0.2640685737133026,
0.030893733724951744,
0.013010875321924686,
0.09646560996770859,
0.04502218961715698,
-0.13193576037883759,
0.008611958473920822,
-0.007392546162009239,
0.024369683116674423,
-0.08777441084384918,
-0.0607866495847702,
-0.008016563020646572,
-0.19270065426826477,
-0.06686611473560333,
0.014373651705682278,
-0.05677815154194832,
-0.09580283612012863,
-0.06235186755657196,
-0.07000952959060669,
-0.06098867952823639,
0.15159611403942108,
-0.08116251975297928,
0.1997067928314209,
-0.025787800550460815,
0.03324035927653313,
0.02715192176401615,
0.2045348435640335,
-0.03041280247271061,
0.14164412021636963,
-0.0015107824001461267,
0.05961599573493004,
-0.03278138488531113,
0.14858607947826385,
-0.11604026705026627,
-0.004160433541983366,
0.02207956463098526,
0.20108111202716827,
0.1400114893913269,
-0.01945463940501213,
-0.005463222041726112,
-0.009398506954312325,
0.055029239505529404,
0.05319187417626381,
0.08575439453125,
0.018804091960191727,
0.27714693546295166,
-0.05574635788798332,
0.03246273845434189,
-0.026943562552332878,
0.08977492898702621,
0.04825457185506821,
0.03691357001662254,
0.0933442935347557,
-0.03882439061999321,
-0.10722090303897858,
0.11807046830654144,
-0.08578154444694519,
0.1673986166715622,
0.06545998156070709,
-0.21259446442127228,
-0.04379632696509361,
-0.023681608960032463,
0.15196853876113892,
-0.03871291130781174,
0.10564253479242325,
-0.09722219407558441,
-0.18950939178466797,
-0.13068003952503204,
0.05730998516082764,
-0.2777128517627716,
-0.3271428942680359,
0.09633979201316833,
0.10375460982322693,
0.08962118625640869,
-0.004609894473105669,
0.04436779022216797,
-0.02503259666264057,
-0.003328748047351837,
-0.08848193287849426,
0.036080047488212585,
0.057304494082927704,
-0.06454472988843918,
-0.18047448992729187,
-0.04351026564836502,
0.056011863052845,
0.043494630604982376,
0.07330509275197983,
0.0031766900792717934,
-0.01168275997042656,
0.10149583220481873,
-0.05344218388199806,
0.06091596186161041,
0.02811937779188156,
-0.10765156894922256,
0.05044103413820267,
0.038226742297410965,
0.014911227859556675,
-0.04861406981945038,
-0.024563396349549294,
0.004071779083460569,
0.08722289651632309,
0.003788891015574336,
-0.11012385785579681,
0.09921956062316895,
-0.005300930235534906,
0.18845798075199127,
-0.010281804949045181,
-0.06891580671072006,
0.01509682647883892,
-0.09896513819694519,
0.12797550857067108,
-0.06052418053150177,
0.010853631421923637,
0.12654033303260803,
-0.0019496530294418335,
0.026569098234176636,
-0.2666066586971283,
0.10986480116844177,
0.037016287446022034,
0.0010360690066590905,
-0.07465021312236786
] |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# feedback-finetuned-sentiment-model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "feedback-finetuned-sentiment-model", "results": []}]} | text-classification | divy1810/feedback-finetuned-sentiment-model | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-09T10:36:23+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# feedback-finetuned-sentiment-model
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| [
"# feedback-finetuned-sentiment-model\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 50",
"### Training results",
"### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# feedback-finetuned-sentiment-model\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 50",
"### Training results",
"### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
72,
38,
6,
12,
8,
3,
90,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# feedback-finetuned-sentiment-model\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 50### Training results### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
-0.08973313868045807,
0.15542484819889069,
-0.0013341850135475397,
0.10455785691738129,
0.13948878645896912,
0.009223844856023788,
0.1196698397397995,
0.12337841838598251,
-0.07702259719371796,
0.05815371870994568,
0.0737626925110817,
0.06695200502872467,
0.030992522835731506,
0.11120699346065521,
-0.041857924312353134,
-0.21956707537174225,
0.004514477215707302,
-0.024534126743674278,
-0.043160855770111084,
0.09857277572154999,
0.10790718346834183,
-0.09147648513317108,
0.07986380159854889,
-0.0072495476342737675,
-0.13691192865371704,
0.030738014727830887,
-0.022583987563848495,
-0.043803248554468155,
0.09718319028615952,
0.02186250500380993,
0.09133800864219666,
0.00842040590941906,
0.13755275309085846,
-0.22686126828193665,
-0.0011150151258334517,
0.07556083053350449,
0.03102579526603222,
0.07278016209602356,
0.03296022117137909,
-0.018467314541339874,
0.07775568217039108,
-0.15369988977909088,
0.10948694497346878,
0.031408362090587616,
-0.0642043799161911,
-0.11068884283304214,
-0.07726290076971054,
0.08401510119438171,
0.10076895356178284,
0.09997884184122086,
0.007355967070907354,
0.12876521050930023,
-0.08025003969669342,
0.08645965158939362,
0.14657817780971527,
-0.2326265126466751,
-0.05049232393503189,
0.0804535523056984,
0.05335455387830734,
0.08547081798315048,
-0.12261179089546204,
-0.013514230959117413,
0.061876725405454636,
0.011995678767561913,
0.08070120960474014,
-0.01734309084713459,
-0.09354963898658752,
0.0031076068989932537,
-0.13327813148498535,
-0.00827573798596859,
0.19645622372627258,
0.05968405678868294,
-0.05339953675866127,
-0.11286971718072891,
-0.04740797355771065,
-0.07752241939306259,
-0.020991219207644463,
-0.03297997638583183,
0.033803705126047134,
-0.03580348193645477,
-0.039824459701776505,
-0.05545319989323616,
-0.07617872953414917,
-0.07141730189323425,
0.025247255340218544,
0.07320339977741241,
0.03792111575603485,
0.0008178575662896037,
-0.024674715474247932,
0.09802936017513275,
-0.027226775884628296,
-0.11086726188659668,
-0.013398334383964539,
-0.0037917185109108686,
-0.0671553909778595,
-0.06170584633946419,
-0.028335751965641975,
-0.04017948359251022,
0.014736481942236423,
0.14386099576950073,
-0.04983605071902275,
0.061878301203250885,
0.010776563547551632,
0.009421588853001595,
-0.020817220211029053,
0.1322280913591385,
-0.036624450236558914,
-0.04465561360120773,
0.021186012774705887,
0.11279627680778503,
0.02536323480308056,
-0.013161751441657543,
-0.10334174335002899,
-0.003475278615951538,
0.10177087038755417,
0.07743832468986511,
-0.03458353504538536,
0.04656711593270302,
-0.03312327712774277,
-0.02232503704726696,
0.03755747154355049,
-0.1367662101984024,
0.045207615941762924,
-0.009382098913192749,
-0.08169223368167877,
-0.06593076884746552,
0.045280564576387405,
0.000771111692301929,
-0.016243528574705124,
0.05786483734846115,
-0.07885852456092834,
-0.011009350419044495,
-0.09122508764266968,
-0.08344750106334686,
0.013079196214675903,
-0.055038295686244965,
0.003411851590499282,
-0.07385381311178207,
-0.24066989123821259,
-0.04831733927130699,
0.034581106156110764,
-0.03896661475300789,
-0.042471177875995636,
-0.07287361472845078,
-0.0636024922132492,
0.009359768591821194,
0.007911775261163712,
0.07178045064210892,
-0.04920639097690582,
0.07040692120790482,
-0.0073658786714077,
0.02340671606361866,
0.01765931211411953,
0.03972914069890976,
-0.11372020840644836,
0.03423195704817772,
-0.11008574813604355,
0.08133938908576965,
-0.10460048168897629,
0.05641496926546097,
-0.10096476972103119,
-0.08586382865905762,
0.015214514918625355,
-0.017965516075491905,
0.03989292308688164,
0.13913072645664215,
-0.1879204362630844,
-0.016369787976145744,
0.12836425006389618,
-0.07480979710817337,
-0.07866759598255157,
0.10721766948699951,
-0.0567113496363163,
0.06094145029783249,
0.07680458575487137,
0.17126086354255676,
0.12981747090816498,
-0.11677844822406769,
-0.011179494671523571,
0.02522829733788967,
0.05668898671865463,
0.027987897396087646,
0.04175581783056259,
-0.0017029071459546685,
-0.009273361414670944,
0.019871748983860016,
-0.0719824731349945,
-0.006642058026045561,
-0.07379639893770218,
-0.09556454420089722,
-0.05846208706498146,
-0.10669627785682678,
0.06287522614002228,
0.0380156934261322,
0.028584614396095276,
-0.07767415791749954,
-0.11101418733596802,
0.11604921519756317,
0.14012488722801208,
-0.06234702467918396,
-0.002153234090656042,
-0.06844330579042435,
0.04039163514971733,
-0.013966837897896767,
-0.021585585549473763,
-0.18008968234062195,
-0.11085062474012375,
0.036786388605833054,
-0.06244778260588646,
0.0477481409907341,
0.020866122096776962,
0.060392964631319046,
0.08112364262342453,
-0.04450613260269165,
-0.0402027927339077,
-0.059552308171987534,
0.01581374742090702,
-0.09544168412685394,
-0.20006567239761353,
-0.027103688567876816,
-0.03547808155417442,
0.1771431714296341,
-0.28260523080825806,
0.03947979211807251,
-0.04096852242946625,
0.09698484092950821,
0.023966073989868164,
-0.048437345772981644,
0.011802269145846367,
0.036921679973602295,
-0.026217278093099594,
-0.09583210945129395,
0.031188881024718285,
-0.0035936017520725727,
-0.10019078850746155,
-0.07156451791524887,
-0.15698210895061493,
0.060450684279203415,
0.06918413937091827,
0.06378211826086044,
-0.11011937260627747,
-0.002668064786121249,
-0.04658091440796852,
-0.04143306240439415,
-0.08135878294706345,
0.004080058541148901,
0.16233277320861816,
-0.0009053100948221982,
0.1248815581202507,
-0.04953595623373985,
-0.06239916384220123,
-0.011586101725697517,
-0.02117510512471199,
-0.013513998128473759,
0.0970737636089325,
0.042264461517333984,
-0.1239086166024208,
0.10282398015260696,
0.09211520850658417,
-0.06777803599834442,
0.14514416456222534,
-0.043211184442043304,
-0.06477069854736328,
-0.015604138374328613,
0.028061144053936005,
-0.005995969288051128,
0.10774429887533188,
-0.09851406514644623,
-0.008306165225803852,
0.014630205929279327,
-0.0016570633742958307,
0.01459642592817545,
-0.1656225174665451,
-0.014592732302844524,
0.03568648919463158,
-0.04596973955631256,
-0.016214484348893166,
-0.034651391208171844,
0.009859257377684116,
0.079168401658535,
0.003987625241279602,
-0.034682746976614,
0.029119977727532387,
-0.008623344823718071,
-0.09095242619514465,
0.1836271733045578,
-0.11536175012588501,
-0.16565310955047607,
-0.14212989807128906,
0.04296594485640526,
-0.07155033946037292,
-0.011139262467622757,
0.01853266917169094,
-0.08495733886957169,
-0.058882247656583786,
-0.1099487841129303,
-0.030731244012713432,
-0.030688630416989326,
-0.028171785175800323,
0.020053952932357788,
0.020427435636520386,
0.09422100335359573,
-0.139425128698349,
0.01571701467037201,
0.003953261766582727,
-0.10769326984882355,
-0.01431318186223507,
0.042348407208919525,
0.12663398683071136,
0.13561472296714783,
-0.020588699728250504,
0.004114039242267609,
-0.025266513228416443,
0.21952927112579346,
-0.06251373887062073,
0.010703375563025475,
0.11292674392461777,
0.0037624866236001253,
0.050877466797828674,
0.13730236887931824,
0.03467725217342377,
-0.10011827200651169,
0.0462375245988369,
0.07347294688224792,
-0.013540455140173435,
-0.23117519915103912,
-0.048130493611097336,
-0.01440360862761736,
-0.04808516055345535,
0.08902380615472794,
0.0684552937746048,
0.06595846265554428,
0.04672245308756828,
-0.006321853958070278,
0.05225761979818344,
0.004285647999495268,
0.08568921685218811,
0.08560077100992203,
0.02175290137529373,
0.09482191503047943,
-0.03841179236769676,
-0.02694896049797535,
0.059011202305555344,
-0.0017021562671288848,
0.24644812941551208,
-0.009850854054093361,
0.12371857464313507,
0.03414740040898323,
0.15175336599349976,
-0.021572187542915344,
0.02353479154407978,
0.018796104937791824,
-0.0003387861361261457,
0.014257477596402168,
-0.06347541511058807,
-0.03155188634991646,
0.045916810631752014,
-0.0444866381585598,
0.06432855129241943,
-0.09436386078596115,
0.04880375787615776,
0.03078136220574379,
0.2250286191701889,
0.033572398126125336,
-0.29773905873298645,
-0.09907779097557068,
0.028767982497811317,
-0.02177821844816208,
-0.06053009629249573,
0.01754821464419365,
0.11817135661840439,
-0.11556079983711243,
0.041795454919338226,
-0.04381655156612396,
0.0953291729092598,
-0.06022898107767105,
0.008321248926222324,
0.016917668282985687,
0.10755547136068344,
-0.015430573374032974,
0.1066192016005516,
-0.21219581365585327,
0.19822987914085388,
0.028453445062041283,
0.11632736027240753,
-0.058299049735069275,
0.023955361917614937,
0.024523435160517693,
0.14118003845214844,
0.1266154646873474,
-0.006451059132814407,
-0.030270379036664963,
-0.1951042115688324,
-0.0716598778963089,
0.024824516847729683,
0.09759052842855453,
-0.03100115805864334,
0.0732162669301033,
-0.05952927842736244,
0.0020453010220080614,
0.06307768821716309,
-0.10314451158046722,
-0.19430598616600037,
-0.13320392370224,
0.0003928540972992778,
0.026690827682614326,
-0.017860567197203636,
-0.08742191642522812,
-0.08946903795003891,
-0.022574955597519875,
0.19522053003311157,
-0.00971469096839428,
-0.04433672875165939,
-0.13377009332180023,
0.0618668869137764,
0.10012687742710114,
-0.0657598152756691,
0.008609711192548275,
0.013831868767738342,
0.12541215121746063,
0.036122627556324005,
-0.07848043739795685,
0.05183124914765358,
-0.0782608911395073,
-0.1511860340833664,
-0.05503178760409355,
0.10637819021940231,
0.07295192033052444,
0.04731706902384758,
0.007292457390576601,
0.006475961767137051,
0.006254927255213261,
-0.0882052555680275,
-0.004044611006975174,
0.10826649516820908,
0.052945226430892944,
0.06128730624914169,
-0.09445922821760178,
0.001449988572858274,
-0.053321003913879395,
-0.022207625210285187,
0.12117748707532883,
0.19696475565433502,
-0.08347917348146439,
0.08477721363306046,
0.10358622670173645,
-0.10085712373256683,
-0.2005254179239273,
0.07037100940942764,
0.06929593533277512,
0.013645954430103302,
0.049196794629096985,
-0.17570963501930237,
0.1163000613451004,
0.10587228834629059,
-0.025754496455192566,
0.07171488553285599,
-0.33296987414360046,
-0.1321183294057846,
0.10165014863014221,
0.11275134235620499,
-0.004057526122778654,
-0.14250639081001282,
-0.04741000384092331,
-0.04249317944049835,
-0.0996333435177803,
0.08727283775806427,
-0.089288130402565,
0.09455971419811249,
0.008737136609852314,
0.10279695689678192,
0.026365861296653748,
-0.0319291315972805,
0.14239098131656647,
0.027130918577313423,
0.10627128183841705,
-0.07215005159378052,
0.014375285245478153,
0.09630884230136871,
-0.08290861546993256,
0.0860755443572998,
-0.06731195747852325,
0.08214936405420303,
-0.1217324510216713,
-0.028183097019791603,
-0.05383945629000664,
0.09022416919469833,
-0.058038048446178436,
-0.07301781326532364,
-0.05719972401857376,
0.05405362695455551,
0.05670742690563202,
-0.02726137451827526,
0.06541828066110611,
0.016260309144854546,
0.09933767467737198,
0.09187290072441101,
0.10747518390417099,
-0.0004804246418643743,
-0.08517710119485855,
0.004507274366915226,
-0.02353617735207081,
0.06205635517835617,
-0.1322425752878189,
0.023855309933423996,
0.11357390135526657,
0.04843633621931076,
0.160578653216362,
0.027030596509575844,
-0.04608086869120598,
-0.006741337012499571,
0.029928361997008324,
-0.10928057134151459,
-0.16394586861133575,
-0.020539069548249245,
-0.02668284811079502,
-0.1397855430841446,
0.019246919080615044,
0.09576956182718277,
-0.08064283430576324,
-0.0010626973817124963,
-0.026024119928479195,
0.03915588557720184,
-0.014494415372610092,
0.16881181299686432,
0.05681859329342842,
0.06057069078087807,
-0.07215911895036697,
0.12142135202884674,
0.08849295973777771,
-0.0714607909321785,
0.05694938451051712,
0.0523250438272953,
-0.08983400464057922,
-0.03841442987322807,
0.07399926334619522,
0.19828438758850098,
-0.04242149740457535,
-0.05390026047825813,
-0.08247954398393631,
-0.07364606857299805,
0.03015812113881111,
0.11028891801834106,
0.04656502977013588,
-0.012080330401659012,
-0.03251659497618675,
0.023466628044843674,
-0.15398594737052917,
0.11670007556676865,
0.03949479013681412,
0.06724951416254044,
-0.1695629209280014,
0.08763259649276733,
0.011370190419256687,
0.025340065360069275,
-0.014943921938538551,
0.02260451763868332,
-0.09262930601835251,
-0.020728668197989464,
-0.13425543904304504,
-0.0033189861569553614,
-0.027191998437047005,
0.02612331323325634,
-0.009855055250227451,
-0.03745399788022041,
-0.026775697246193886,
0.051943693310022354,
-0.055892761796712875,
-0.06533236056566238,
0.022815532982349396,
0.07295160740613937,
-0.1617172658443451,
-0.017417792230844498,
0.026173071935772896,
-0.08154582977294922,
0.059633903205394745,
0.040196653455495834,
0.01684006303548813,
0.031338855624198914,
-0.1510920524597168,
0.02159510925412178,
0.033160269260406494,
0.03157868608832359,
0.05207535997033119,
-0.09327424317598343,
-0.02007131837308407,
-0.023787591606378555,
0.03501099720597267,
0.01836853288114071,
0.05087733268737793,
-0.1174546554684639,
-0.013966156169772148,
-0.057291965931653976,
-0.046236760914325714,
-0.06674636155366898,
0.05786685273051262,
0.10719892382621765,
0.023392461240291595,
0.17531442642211914,
-0.09060528129339218,
0.04024650901556015,
-0.18528030812740326,
-0.03526689484715462,
0.00680858688428998,
-0.028352096676826477,
-0.07834894955158234,
-0.03851974382996559,
0.06692250072956085,
-0.06381458789110184,
0.11388895660638809,
0.008402221836149693,
0.0905839130282402,
0.0327010415494442,
-0.024028485640883446,
-0.0014948552707210183,
0.0008795800385996699,
0.1933167576789856,
0.04865167662501335,
-0.016739340499043465,
0.0751684159040451,
-0.014016610570251942,
0.07655183970928192,
0.018177760764956474,
0.14777638018131256,
0.13644656538963318,
-0.06666754186153412,
0.04880857840180397,
0.06502556055784225,
-0.10447396337985992,
-0.1369708627462387,
0.10058426856994629,
-0.023739857599139214,
0.1120171770453453,
-0.05397992208600044,
0.1714705526828766,
0.11131154000759125,
-0.15135696530342102,
0.05123535916209221,
-0.05816378816962242,
-0.12629354000091553,
-0.10960020869970322,
-0.09040667861700058,
-0.08848792314529419,
-0.10709204524755478,
0.014065965078771114,
-0.1209992840886116,
0.03355233743786812,
0.04640006646513939,
0.003868283936753869,
-0.002657358068972826,
0.16711433231830597,
-0.044406913220882416,
0.03236133232712746,
0.049308836460113525,
-0.003057799767702818,
-0.03837191313505173,
-0.07585670053958893,
-0.05448675528168678,
0.05429679527878761,
0.007209875155240297,
0.0595782995223999,
-0.04303646832704544,
0.009143432602286339,
0.041658639907836914,
-0.01762210763990879,
-0.06755410879850388,
0.023354962468147278,
0.0258373711258173,
0.02609468623995781,
0.05377413332462311,
0.06098438799381256,
-0.01992884837090969,
-0.029295893386006355,
0.2946661710739136,
-0.0795273706316948,
-0.09137838333845139,
-0.1189141795039177,
0.1999250054359436,
0.022286489605903625,
-0.0030893038492649794,
0.05563674122095108,
-0.131516233086586,
0.02438828907907009,
0.1711474508047104,
0.16329368948936462,
-0.04823462292551994,
-0.011346795596182346,
-0.047129761427640915,
-0.016423577442765236,
-0.06688053905963898,
0.10295175015926361,
0.09995348006486893,
0.011453556828200817,
-0.04695742949843407,
0.00003942528201150708,
-0.010503580793738365,
-0.037845101207494736,
-0.09753450006246567,
0.0796981006860733,
0.007874220609664917,
0.01008684653788805,
-0.03244367986917496,
0.08946306258440018,
-0.016667474061250687,
-0.18239884078502655,
0.03148855268955231,
-0.12900322675704956,
-0.15143230557441711,
-0.03406655043363571,
0.09836354106664658,
-0.025166325271129608,
0.02987794019281864,
-0.013872234150767326,
-0.0000035459265745885205,
0.12654100358486176,
-0.02328411117196083,
-0.07288145273923874,
-0.0990157499909401,
0.05861346423625946,
-0.03897203132510185,
0.24468442797660828,
0.007050725165754557,
0.050724733620882034,
0.10688922554254532,
-0.0007487093680538237,
-0.13625909388065338,
0.07502476871013641,
0.07003047317266464,
-0.042260076850652695,
0.04262419417500496,
0.15864194929599762,
-0.04564924165606499,
0.127131387591362,
0.035351190716028214,
-0.12827731668949127,
-0.008032948710024357,
-0.0429244264960289,
-0.03885719180107117,
-0.0968974307179451,
0.005885008722543716,
-0.05918887257575989,
0.15696978569030762,
0.18518833816051483,
-0.036199167370796204,
0.014516917988657951,
-0.0771799236536026,
0.024825764819979668,
0.05665263161063194,
0.06335534155368805,
-0.012033677659928799,
-0.19112318754196167,
0.0222153402864933,
0.007299893535673618,
0.03655422106385231,
-0.2707149088382721,
-0.09076888114213943,
0.03446103632450104,
-0.04254179075360298,
-0.048184484243392944,
0.11274765431880951,
0.0986148938536644,
0.03058965690433979,
-0.042685966938734055,
-0.12068487703800201,
-0.04433407634496689,
0.149860680103302,
-0.14018619060516357,
-0.05459192395210266
] |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-small-yue-full-1
This model is a fine-tuned version of [safecantonese/whisper-small-yue-full](https://huggingface.co/safecantonese/whisper-small-yue-full) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"tags": ["generated_from_trainer"], "base_model": "safecantonese/whisper-small-yue-full", "model-index": [{"name": "whisper-small-yue-full-1", "results": []}]} | automatic-speech-recognition | safecantonese/whisper-small-yue-full-1 | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:safecantonese/whisper-small-yue-full",
"endpoints_compatible",
"region:us"
] | 2024-02-09T10:38:47+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #base_model-safecantonese/whisper-small-yue-full #endpoints_compatible #region-us
|
# whisper-small-yue-full-1
This model is a fine-tuned version of safecantonese/whisper-small-yue-full on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| [
"# whisper-small-yue-full-1\n\nThis model is a fine-tuned version of safecantonese/whisper-small-yue-full on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 64\n- eval_batch_size: 64\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 128\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- training_steps: 2000\n- mixed_precision_training: Native AMP",
"### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #base_model-safecantonese/whisper-small-yue-full #endpoints_compatible #region-us \n",
"# whisper-small-yue-full-1\n\nThis model is a fine-tuned version of safecantonese/whisper-small-yue-full on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 64\n- eval_batch_size: 64\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 128\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- training_steps: 2000\n- mixed_precision_training: Native AMP",
"### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
68,
46,
6,
12,
8,
3,
140,
38
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #base_model-safecantonese/whisper-small-yue-full #endpoints_compatible #region-us \n# whisper-small-yue-full-1\n\nThis model is a fine-tuned version of safecantonese/whisper-small-yue-full on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 64\n- eval_batch_size: 64\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 128\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- training_steps: 2000\n- mixed_precision_training: Native AMP### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
-0.11805036664009094,
0.1084589809179306,
-0.001704246038571,
0.06668504327535629,
0.12043286114931107,
0.004788896068930626,
0.09845339506864548,
0.12714813649654388,
-0.08162043988704681,
0.08551392704248428,
0.06129586324095726,
-0.01839037984609604,
0.06646491587162018,
0.15010614693164825,
-0.010173657909035683,
-0.2708909213542938,
0.004012777004390955,
-0.009720711037516594,
-0.07651504874229431,
0.09261668473482132,
0.1228189766407013,
-0.0980011597275734,
0.0357639454305172,
0.02914927341043949,
-0.12147540599107742,
0.008085876703262329,
-0.03746066987514496,
-0.05627289041876793,
0.09454332292079926,
0.018037281930446625,
0.09136398881673813,
0.016689546406269073,
0.10072396695613861,
-0.20680025219917297,
0.004306758753955364,
0.06706701219081879,
0.07667883485555649,
0.07800178974866867,
0.030641214922070503,
0.022661583498120308,
0.0629020407795906,
-0.16120848059654236,
0.09850374609231949,
0.029604827985167503,
-0.05749079957604408,
-0.21496731042861938,
-0.0821538046002388,
0.06846149265766144,
0.12888091802597046,
0.09657175093889236,
-0.0053954352624714375,
0.14975999295711517,
-0.05349299684166908,
0.060946717858314514,
0.130696639418602,
-0.244452565908432,
-0.0687209814786911,
0.017318136990070343,
0.06463673710823059,
0.06463165581226349,
-0.11196017265319824,
-0.017582667991518974,
0.053500592708587646,
0.02717813104391098,
0.08265664428472519,
0.0026893203612416983,
-0.009823204949498177,
-0.02500017173588276,
-0.09554465860128403,
-0.020685376599431038,
0.13596536219120026,
0.06588676571846008,
-0.05387495085597038,
-0.15869103372097015,
-0.022432321682572365,
-0.1068853884935379,
-0.036591801792383194,
-0.04597233608365059,
0.023620616644620895,
-0.03793017938733101,
-0.09681488573551178,
-0.05548844113945961,
-0.0860036090016365,
-0.0572773814201355,
0.015524842776358128,
0.15927477180957794,
0.012985657900571823,
-0.01535771880298853,
-0.013124081306159496,
0.07440434396266937,
-0.004631758667528629,
-0.14278975129127502,
-0.04046250879764557,
-0.0007335995906032622,
-0.11895465105772018,
-0.07246141135692596,
-0.04977690055966377,
-0.02987058460712433,
0.0008695101132616401,
0.14363406598567963,
-0.06059391796588898,
0.07425745576620102,
0.014375771395862103,
-0.008425362408161163,
-0.013528173789381981,
0.13954773545265198,
-0.020006848499178886,
-0.07137839496135712,
-0.011451894417405128,
0.10833581537008286,
0.038411132991313934,
-0.021252473816275597,
-0.07059964537620544,
-0.032985419034957886,
0.06419765204191208,
0.053250040858983994,
-0.03417493775486946,
0.006437534000724554,
-0.0454876646399498,
-0.03784914314746857,
0.06043845787644386,
-0.12763360142707825,
0.046373818069696426,
-0.015077990479767323,
-0.04489419609308243,
-0.017655979841947556,
-0.026927463710308075,
0.03068888932466507,
-0.01730954833328724,
0.08194253593683243,
-0.06619421392679214,
-0.025046730414032936,
-0.06600158661603928,
-0.04692236706614494,
0.012955449521541595,
-0.01063473429530859,
0.008262598887085915,
-0.07599812746047974,
-0.16957126557826996,
-0.04192133992910385,
0.03300955146551132,
-0.05475987493991852,
-0.0679088830947876,
-0.025638090446591377,
-0.04011255130171776,
0.0280196163803339,
-0.010621091350913048,
0.14683587849140167,
-0.04598463699221611,
0.058192864060401917,
0.011816825717687607,
0.009231219999492168,
0.03959827870130539,
0.031053636223077774,
-0.08987915515899658,
0.046156447380781174,
-0.10740906745195389,
0.1072096973657608,
-0.07736610621213913,
0.049954358488321304,
-0.1382882297039032,
-0.10090623050928116,
-0.06513661891222,
-0.014745961874723434,
0.05987595394253731,
0.11565551906824112,
-0.1584063023328781,
-0.056042708456516266,
0.1428009420633316,
-0.09114175289869308,
-0.09809897840023041,
0.12600012123584747,
-0.030873658135533333,
0.033988840878009796,
0.07678882777690887,
0.1285567283630371,
0.15354527533054352,
-0.10164809226989746,
-0.016593627631664276,
0.017214076593518257,
0.08070622384548187,
0.01714116334915161,
0.09604146331548691,
-0.021112823858857155,
0.022992582991719246,
0.0006896547856740654,
-0.03471992909908295,
0.03106575645506382,
-0.05014561861753464,
-0.07327049225568771,
-0.0387357659637928,
-0.09235221892595291,
0.04458549991250038,
0.02739863656461239,
0.04276135563850403,
-0.08604200929403305,
-0.12945860624313354,
0.08303651958703995,
0.14286944270133972,
-0.057601626962423325,
0.009125029668211937,
-0.09054691344499588,
0.00009249036520486698,
-0.0210630614310503,
-0.02468995377421379,
-0.18020041286945343,
-0.05125289410352707,
0.04852237179875374,
-0.0676826536655426,
0.0047767432406544685,
-0.022089922800660133,
0.08121573179960251,
0.047264885157346725,
-0.07393261790275574,
-0.019711878150701523,
-0.07071874290704727,
0.0032356621231883764,
-0.08751078695058823,
-0.14747610688209534,
-0.06397100538015366,
-0.04614254832267761,
0.22744713723659515,
-0.23666855692863464,
0.003673412837088108,
0.020749621093273163,
0.14076320827007294,
0.03934492915868759,
-0.06233856827020645,
0.007059880997985601,
0.04432099685072899,
-0.002367290435358882,
-0.1034991443157196,
0.015232243575155735,
0.02583060786128044,
-0.1529209315776825,
-0.04314954951405525,
-0.13801299035549164,
0.009664295241236687,
0.07702420651912689,
0.14313659071922302,
-0.09187553822994232,
-0.08960399776697159,
-0.05431448668241501,
-0.04188002645969391,
-0.07053545862436295,
-0.010829866863787174,
0.18545998632907867,
0.030524469912052155,
0.1107293888926506,
-0.05441148206591606,
-0.06534536927938461,
0.0034894305281341076,
0.029471125453710556,
-0.021275946870446205,
0.06490354239940643,
-0.032334402203559875,
-0.14994405210018158,
0.06682997941970825,
0.0953000858426094,
-0.06082794442772865,
0.1375180184841156,
-0.06664585322141647,
-0.09577582776546478,
-0.0175149105489254,
0.02973160706460476,
0.023410972207784653,
0.10734618455171585,
-0.08770619332790375,
0.01104804314672947,
0.034339871257543564,
0.007631376851350069,
0.015874141827225685,
-0.1375635266304016,
0.010790802538394928,
0.05784432962536812,
-0.015447591431438923,
-0.011621380224823952,
-0.04540600627660751,
0.015067005529999733,
0.05568915605545044,
0.03819483891129494,
0.0146269416436553,
0.023230884224176407,
-0.01277150772511959,
-0.08168461173772812,
0.17146950960159302,
-0.09046082198619843,
-0.16629089415073395,
-0.1170404776930809,
0.03566310554742813,
-0.019520089030265808,
-0.01654268242418766,
0.006473919842392206,
-0.08288005739450455,
-0.045637596398591995,
-0.10284001380205154,
-0.030572524294257164,
-0.04403000697493553,
0.006181361153721809,
0.08588580042123795,
0.03373127430677414,
0.08439651131629944,
-0.10028056055307388,
0.02482803538441658,
0.008940272033214569,
-0.03984851390123367,
-0.01973048970103264,
0.023780496791005135,
0.07080899178981781,
0.13660499453544617,
-0.004365389700978994,
0.008087150752544403,
-0.04998001828789711,
0.19095417857170105,
-0.11407256871461868,
-0.002290629083290696,
0.1629951298236847,
0.008127992041409016,
0.041016291826963425,
0.12908175587654114,
0.0255479346960783,
-0.06821881979703903,
0.039575379341840744,
0.027363231405615807,
-0.03156490996479988,
-0.1970503181219101,
-0.03420253098011017,
-0.051580045372247696,
-0.07510499656200409,
0.13339382410049438,
0.050374843180179596,
-0.0071925693191587925,
0.06441351026296616,
-0.03986402228474617,
0.02900158241391182,
0.016054494306445122,
0.09336990118026733,
0.05885181948542595,
0.05380586162209511,
0.10693734884262085,
-0.01743205636739731,
-0.029191454872488976,
0.0609530545771122,
0.025411434471607208,
0.20409166812896729,
-0.023915955796837807,
0.15284596383571625,
-0.011099949479103088,
0.12109160423278809,
0.010791531763970852,
0.03633882850408554,
0.024568624794483185,
-0.02495153248310089,
0.010692011564970016,
-0.06733442842960358,
-0.041922494769096375,
0.05369751155376434,
0.03730134293437004,
0.04363182187080383,
-0.07072678208351135,
0.03153194859623909,
0.008370296098291874,
0.2904065251350403,
0.08216854184865952,
-0.2962709963321686,
-0.1351192146539688,
0.026460671797394753,
-0.04046809673309326,
-0.06788574159145355,
0.002711191074922681,
0.13300539553165436,
-0.1139538511633873,
0.07399316877126694,
-0.06281193345785141,
0.08327170461416245,
-0.04281581565737724,
0.005220566410571337,
0.04413503035902977,
0.11149110645055771,
-0.010201124474406242,
0.06705282628536224,
-0.15946266055107117,
0.19202962517738342,
0.008695056661963463,
0.12571953237056732,
-0.06948535144329071,
0.05439333617687225,
0.024859433993697166,
0.041430506855249405,
0.0980866476893425,
-0.0024771641474217176,
-0.096042700111866,
-0.15681858360767365,
-0.09092371165752411,
0.03461707755923271,
0.10620749741792679,
-0.05221116170287132,
0.07803205400705338,
-0.05093538761138916,
0.006327865179628134,
0.04538467898964882,
-0.07757599651813507,
-0.15225233137607574,
-0.1621888130903244,
0.026651252061128616,
0.03060024417936802,
0.008340614847838879,
-0.0978667140007019,
-0.1076732724905014,
0.010629940778017044,
0.2091573029756546,
0.009372069500386715,
-0.05866209790110588,
-0.16027839481830597,
0.10136659443378448,
0.14560872316360474,
-0.05598548799753189,
0.022130196914076805,
0.024335358291864395,
0.1909763664007187,
0.006738733500242233,
-0.03496146574616432,
0.05236368626356125,
-0.06751111149787903,
-0.15807312726974487,
-0.04061466455459595,
0.1531439572572708,
0.022613532841205597,
0.05596810206770897,
0.014767148531973362,
0.030138030648231506,
0.018499109894037247,
-0.07400688529014587,
0.011007972061634064,
0.04101938754320145,
0.03400338813662529,
0.04266707971692085,
-0.025930801406502724,
0.006667993031442165,
-0.046591874212026596,
-0.02496352232992649,
0.14035212993621826,
0.2609763443470001,
-0.08043985068798065,
0.05700991675257683,
0.0820470079779625,
-0.03371525555849075,
-0.15903861820697784,
0.039341170340776443,
0.12154518812894821,
0.0489795058965683,
0.06785812973976135,
-0.1744544357061386,
0.09066811203956604,
0.12841036915779114,
-0.030531996861100197,
0.0161016546189785,
-0.27959972620010376,
-0.12960629165172577,
0.0638652965426445,
0.08143939077854156,
-0.031948983669281006,
-0.11472095549106598,
-0.06571438908576965,
-0.06525725871324539,
-0.07600130885839462,
0.049458470195531845,
-0.06013942509889603,
0.09913899749517441,
0.02554236724972725,
0.04162077605724335,
0.0403536818921566,
-0.038619574159383774,
0.16849219799041748,
-0.023300109431147575,
0.05496954917907715,
-0.03238809481263161,
0.0575941726565361,
0.05240713432431221,
-0.08023092150688171,
0.02945956028997898,
-0.07055608928203583,
0.06494158506393433,
-0.11033869534730911,
-0.042471058666706085,
-0.05138096213340759,
0.062128469347953796,
-0.030431536957621574,
-0.06632890552282333,
-0.04058343544602394,
0.0759643167257309,
0.05850272253155708,
-0.04006962850689888,
0.09116187691688538,
-0.0003259946533944458,
0.09694723039865494,
0.09026014059782028,
0.12898723781108856,
-0.02443074807524681,
-0.07893253117799759,
-0.004586289636790752,
-0.0325467586517334,
0.05889713019132614,
-0.12721888720989227,
0.039868224412202835,
0.10578825324773788,
0.06422305107116699,
0.12399019300937653,
0.027026066556572914,
-0.041954368352890015,
0.015539001673460007,
0.04903796687722206,
-0.030370913445949554,
-0.19477777183055878,
-0.03135308250784874,
0.04153226688504219,
-0.1771835833787918,
-0.0102992607280612,
0.1269207000732422,
-0.057571783661842346,
-0.010545915924012661,
-0.005101737100630999,
0.008164700120687485,
-0.020974185317754745,
0.17178092896938324,
0.001946124481037259,
0.07805100083351135,
-0.07202700525522232,
0.09288930892944336,
0.09971829503774643,
-0.11126088351011276,
0.04787802696228027,
0.04847794398665428,
-0.08453885465860367,
-0.015302338637411594,
0.011318810284137726,
0.038753073662519455,
0.023928504437208176,
-0.04261181503534317,
-0.06374210119247437,
-0.1151244044303894,
0.026503613218665123,
0.011312536895275116,
0.025379057973623276,
-0.009636236354708672,
-0.03054700419306755,
0.038855742663145065,
-0.14767861366271973,
0.0915861427783966,
0.04390399530529976,
0.07499513030052185,
-0.14281676709651947,
0.10993776470422745,
0.004214824642986059,
0.0165998674929142,
-0.01017947867512703,
0.004482557997107506,
-0.06210806593298912,
-0.016330692917108536,
-0.13692960143089294,
-0.010541031137108803,
-0.05789737403392792,
-0.004891923628747463,
-0.01756753958761692,
-0.03795778378844261,
-0.04975898191332817,
0.04599606245756149,
-0.062365684658288956,
-0.07273823767900467,
-0.005379010923206806,
0.05702652037143707,
-0.10127577930688858,
0.002086600288748741,
0.04750429466366768,
-0.11779063940048218,
0.07993017137050629,
0.042883701622486115,
0.03275199607014656,
0.003217153949663043,
-0.08456540107727051,
-0.012936478480696678,
0.027181440964341164,
0.02533845044672489,
0.046986501663923264,
-0.12052427232265472,
-0.019809436053037643,
-0.005330454092472792,
0.0256680715829134,
0.012314188294112682,
0.049359943717718124,
-0.10555120557546616,
-0.04086536914110184,
-0.044842109084129333,
-0.037287190556526184,
-0.06404085457324982,
0.028439268469810486,
0.09557846188545227,
0.037710435688495636,
0.1552410125732422,
-0.06556802242994308,
0.042891375720500946,
-0.20801189541816711,
-0.03446919471025467,
-0.02054447866976261,
-0.01579098589718342,
-0.0799700915813446,
-0.02789459377527237,
0.08274611830711365,
-0.03507980704307556,
0.0894274190068245,
-0.062281183898448944,
0.10191698372364044,
0.05584101006388664,
-0.07931159436702728,
0.040838565677404404,
0.021362967789173126,
0.25446218252182007,
0.09813723713159561,
-0.021754883229732513,
0.11913466453552246,
-0.025266878306865692,
0.04908718541264534,
0.10342863202095032,
0.1193593442440033,
0.1853341907262802,
0.010679461993277073,
0.06796778738498688,
0.03912842273712158,
-0.07159174978733063,
-0.15380246937274933,
0.10069947689771652,
-0.02034098654985428,
0.09636657685041428,
-0.0328308530151844,
0.1629328429698944,
0.114594005048275,
-0.18120446801185608,
0.041519977152347565,
-0.07872811704874039,
-0.10577139258384705,
-0.07901571691036224,
-0.09177631139755249,
-0.09144403785467148,
-0.12324322015047073,
0.01083928719162941,
-0.10875421017408371,
-0.006588188000023365,
0.08608853071928024,
0.019836395978927612,
0.029673386365175247,
0.14297735691070557,
-0.04936271905899048,
0.0068195778876543045,
0.0913790911436081,
-0.00359090487472713,
0.012166160158813,
-0.023694932460784912,
-0.08940272778272629,
0.06594855338335037,
0.021534482017159462,
0.0901046171784401,
-0.054646607488393784,
-0.01205169502645731,
0.037769004702568054,
0.0166018009185791,
-0.08489656448364258,
0.026507264003157616,
0.012612520717084408,
0.035930946469306946,
0.03175821527838707,
0.05553480610251427,
0.0007803253247402608,
-0.048540614545345306,
0.2931614816188812,
-0.07976754754781723,
-0.10592474043369293,
-0.16439974308013916,
0.18072904646396637,
0.0029267598874866962,
-0.0135363033041358,
0.049993887543678284,
-0.1252322942018509,
-0.004273195751011372,
0.1206434965133667,
0.11620774120092392,
-0.052498798817396164,
-0.01292632520198822,
-0.015831319615244865,
-0.02364642545580864,
-0.08512711524963379,
0.09285404533147812,
0.09019608795642853,
-0.013430938124656677,
-0.04482697695493698,
0.020979220047593117,
0.01143940631300211,
-0.05563408136367798,
-0.08887824416160583,
0.08844006806612015,
-0.004280403256416321,
0.021199757233262062,
-0.034687306731939316,
0.110648013651371,
0.035207848995923996,
-0.18463335931301117,
0.030597124248743057,
-0.15657196938991547,
-0.19684505462646484,
-0.009083440527319908,
0.06966719031333923,
0.002011751988902688,
0.05566088482737541,
0.004572288133203983,
0.017375776544213295,
0.07132808119058609,
-0.016862180083990097,
-0.00012373887875583023,
-0.08703679591417313,
0.10756585001945496,
-0.04739666357636452,
0.20609985291957855,
-0.008085057139396667,
0.03610115125775337,
0.11209803074598312,
0.028945675119757652,
-0.1469140648841858,
0.020404288545250893,
0.07600528746843338,
-0.07373059540987015,
0.0655103251338005,
0.18552422523498535,
-0.052702292799949646,
0.11741495877504349,
0.061452265828847885,
-0.154067724943161,
-0.015295778401196003,
-0.03801894560456276,
0.006821668706834316,
-0.09464892745018005,
-0.004815227817744017,
-0.05211365967988968,
0.16266104578971863,
0.19843874871730804,
-0.05908190459012985,
-0.0130104785785079,
-0.040724121034145355,
0.03305909037590027,
0.05959267541766167,
0.1289650946855545,
-0.03646235913038254,
-0.22765515744686127,
0.0005558268167078495,
0.04643145576119423,
0.02570386976003647,
-0.24015045166015625,
-0.09847994148731232,
0.02827785536646843,
-0.05123244598507881,
-0.00476955296471715,
0.12292470782995224,
0.06554896384477615,
0.01665935106575489,
-0.036495890468358994,
-0.16155025362968445,
-0.028790174052119255,
0.12966352701187134,
-0.141771599650383,
-0.039517663419246674
] |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test_trainer
This model is a fine-tuned version of [mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis](https://huggingface.co/mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6294
- Accuracy: 0.8577
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 338 | 0.4186 | 0.8517 |
| 0.5435 | 2.0 | 676 | 0.4806 | 0.8737 |
| 0.2387 | 3.0 | 1014 | 0.6294 | 0.8577 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis", "model-index": [{"name": "test_trainer", "results": []}]} | text-classification | Pranavv/test_trainer | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-09T10:41:49+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| test\_trainer
=============
This model is a fine-tuned version of mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6294
* Accuracy: 0.8577
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
85,
98,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
-0.11333578079938889,
0.13758808374404907,
-0.002994484268128872,
0.08876804262399673,
0.11559806764125824,
-0.0028574741445481777,
0.15275076031684875,
0.1219283789396286,
-0.05973992496728897,
0.07566429674625397,
0.13142257928848267,
0.13016046583652496,
0.01452337484806776,
0.1775132268667221,
-0.07368170469999313,
-0.2057904452085495,
0.04156384617090225,
0.03943505883216858,
-0.004527438897639513,
0.1340450793504715,
0.10210127383470535,
-0.11969403922557831,
0.09753009676933289,
0.0030790730379521847,
-0.15729498863220215,
-0.012035369873046875,
0.019198168069124222,
-0.0593116469681263,
0.11877036094665527,
0.03947535529732704,
0.08977176994085312,
0.0505211167037487,
0.03373920917510986,
-0.18711520731449127,
0.02383292093873024,
0.034897349774837494,
-0.02247708849608898,
0.08538415282964706,
0.021137718111276627,
-0.026901179924607277,
0.06702679395675659,
-0.11424575001001358,
0.05411733314394951,
0.026457523927092552,
-0.13534748554229736,
-0.20516729354858398,
-0.0961640402674675,
0.05752452090382576,
0.08171319216489792,
0.098883256316185,
-0.019229454919695854,
0.12967394292354584,
-0.03382733091711998,
0.08934935927391052,
0.22074730694293976,
-0.3015180826187134,
-0.06153397634625435,
0.025181500241160393,
0.014774025417864323,
0.09142560511827469,
-0.1053076758980751,
-0.0068471357226371765,
0.06105552986264229,
0.011464265175163746,
0.1296783834695816,
-0.014473224990069866,
0.023043926805257797,
-0.015277390368282795,
-0.14148718118667603,
-0.06338023394346237,
0.20038826763629913,
0.09380997717380524,
-0.056313998997211456,
-0.09888061881065369,
-0.04232984408736229,
-0.14379112422466278,
-0.03412111848592758,
-0.00818847119808197,
0.032439474016427994,
-0.025188516825437546,
-0.06907736510038376,
-0.005690041929483414,
-0.10174666345119476,
-0.03999507799744606,
-0.03787682577967644,
0.15910328924655914,
0.015225693583488464,
0.0028303908184170723,
-0.005726594477891922,
0.09029314666986465,
-0.02685266174376011,
-0.1498420089483261,
-0.009498006664216518,
0.0208875834941864,
0.041071806102991104,
-0.026794778183102608,
-0.050436217337846756,
-0.06616496294736862,
0.01670018583536148,
0.11922232061624527,
-0.08389599621295929,
0.04377918317914009,
0.010042855516076088,
0.04482490196824074,
-0.09041035920381546,
0.16780534386634827,
-0.04277423024177551,
-0.024107111617922783,
0.014402579516172409,
0.12047537416219711,
0.06467592716217041,
-0.006680881604552269,
-0.10137905180454254,
0.0483681783080101,
0.1122555211186409,
0.017227262258529663,
-0.055832281708717346,
0.0847780629992485,
-0.08080528676509857,
-0.015513583086431026,
0.042541466653347015,
-0.10164054483175278,
-0.002736936789005995,
0.004297622013837099,
-0.051231637597084045,
-0.04087964445352554,
0.01365103479474783,
0.010759442113339901,
0.012297741137444973,
0.08412159979343414,
-0.0870293527841568,
-0.009520726278424263,
-0.05560888350009918,
-0.09731904417276382,
0.024219367653131485,
-0.09766319394111633,
0.023768892511725426,
-0.10938547551631927,
-0.17085662484169006,
-0.02111317589879036,
0.0319974385201931,
-0.023927796632051468,
-0.06936609745025635,
-0.07594522088766098,
-0.07724682986736298,
0.033846866339445114,
-0.012743411585688591,
0.0536978505551815,
-0.06864311546087265,
0.08060058951377869,
0.0406544953584671,
0.06564711034297943,
-0.03607311472296715,
0.023549702018499374,
-0.09879463911056519,
0.03680075705051422,
-0.1459910124540329,
0.057913634926080704,
-0.058512210845947266,
0.09056132286787033,
-0.09717416018247604,
-0.0751749649643898,
-0.01971069537103176,
0.0027709754649549723,
0.04621066525578499,
0.11419789493083954,
-0.14162126183509827,
-0.07351313531398773,
0.17353706061840057,
-0.0927250012755394,
-0.13188354671001434,
0.11906182020902634,
-0.06202451512217522,
0.07476717233657837,
0.08416039496660233,
0.1959647685289383,
0.09751427173614502,
-0.07230408489704132,
-0.02225782908499241,
-0.028940042480826378,
0.052810247987508774,
-0.018555285409092903,
0.08962509781122208,
-0.007361927535384893,
-0.009239419363439083,
0.02431904710829258,
-0.07990339398384094,
0.03211832419037819,
-0.07999403029680252,
-0.07881815731525421,
-0.03448653593659401,
-0.09686233103275299,
0.05563483387231827,
0.0434146448969841,
0.05896128714084625,
-0.12339365482330322,
-0.1047309935092926,
0.009357732720673084,
0.08040433377027512,
-0.06813956052064896,
0.016306931152939796,
-0.09475274384021759,
0.10669655352830887,
-0.0625806376338005,
-0.003470830852165818,
-0.154427632689476,
0.00671699782833457,
0.024459652602672577,
-0.017317404970526695,
0.010704130865633488,
0.00884181261062622,
0.06119997054338455,
0.026152025908231735,
-0.07110265642404556,
-0.05034865438938141,
-0.036083340644836426,
0.006008210591971874,
-0.07658138126134872,
-0.1873854398727417,
0.012966164387762547,
-0.04861989617347717,
0.1532762050628662,
-0.19236448407173157,
0.05068781599402428,
0.06218215450644493,
0.07260747253894806,
0.04426338151097298,
-0.027069538831710815,
0.0015605121152475476,
0.0422775074839592,
-0.03123241662979126,
-0.04905159771442413,
0.055965956300497055,
0.024184182286262512,
-0.11881593614816666,
0.006188713014125824,
-0.1563977152109146,
0.19327962398529053,
0.11529029160737991,
-0.022473253309726715,
-0.05849825218319893,
0.00334033346734941,
-0.042707979679107666,
-0.016694366931915283,
-0.03529321029782295,
-0.003902989439666271,
0.13988536596298218,
-0.0146078085526824,
0.13491873443126678,
-0.10017795115709305,
-0.03129442408680916,
0.03690659999847412,
-0.03004368767142296,
-0.019684070721268654,
0.11950387805700302,
-0.01475960947573185,
-0.12371039390563965,
0.13150203227996826,
0.1752820760011673,
-0.037993401288986206,
0.13379307091236115,
-0.05179315060377121,
-0.047097526490688324,
-0.03241032361984253,
0.02199118211865425,
0.013152164407074451,
0.09462243318557739,
-0.10008835792541504,
-0.003750098403543234,
0.02285683900117874,
0.015794504433870316,
-0.0024997410364449024,
-0.18057045340538025,
-0.03404228761792183,
0.04926860332489014,
-0.06655851006507874,
0.00542705412954092,
0.008414997719228268,
-0.018605461344122887,
0.09718042612075806,
0.016361715272068977,
-0.0582679808139801,
0.04152946174144745,
-0.012602218426764011,
-0.07847769558429718,
0.19749470055103302,
-0.05622444674372673,
-0.19413061439990997,
-0.143548846244812,
-0.0471092090010643,
-0.04651622846722603,
0.011807681061327457,
0.0732778012752533,
-0.07615203410387039,
-0.038756925612688065,
-0.13205131888389587,
-0.007619866169989109,
0.03741825371980667,
0.023046644404530525,
0.08707430213689804,
-0.025107813999056816,
0.08365652710199356,
-0.09614759683609009,
-0.018378807231783867,
-0.03657420724630356,
-0.008104785345494747,
0.03461766242980957,
0.0003994425351265818,
0.1200711652636528,
0.12171273678541183,
0.0011940272524952888,
0.023820441216230392,
-0.04728882014751434,
0.23171798884868622,
-0.07405807077884674,
-0.03151552751660347,
0.1316920667886734,
-0.013421102426946163,
0.06396222114562988,
0.15720278024673462,
0.04760747402906418,
-0.10110746324062347,
0.01578284613788128,
0.0163362268358469,
-0.029607929289340973,
-0.20110033452510834,
-0.031435154378414154,
-0.03558957204222679,
0.00750834122300148,
0.07954821735620499,
0.04037387669086456,
0.055735841393470764,
0.060007065534591675,
0.007730207871645689,
0.01706559956073761,
0.016402026638388634,
0.08458778262138367,
0.12380342930555344,
0.049265164881944656,
0.12550561130046844,
-0.05695044994354248,
-0.03555592894554138,
0.043530236929655075,
-0.036513689905405045,
0.19786939024925232,
0.025402473285794258,
0.20903633534908295,
0.05343630164861679,
0.12439043819904327,
0.025026515126228333,
0.04795026779174805,
-0.007125640753656626,
-0.028963953256607056,
-0.022511174902319908,
-0.0434703603386879,
-0.03801220282912254,
0.02584061026573181,
-0.07981956005096436,
0.09125668555498123,
-0.11906752735376358,
0.018482865765690804,
0.05639725923538208,
0.2667887508869171,
0.04482073336839676,
-0.3346778452396393,
-0.11009638011455536,
0.014579623937606812,
-0.024194782599806786,
-0.012509751133620739,
0.013076146133244038,
0.0884145051240921,
-0.05893433466553688,
0.060338858515024185,
-0.059823036193847656,
0.07911541312932968,
-0.05658264458179474,
0.04552052170038223,
0.019998272880911827,
0.034633319824934006,
-0.02175593376159668,
0.0639089047908783,
-0.26357218623161316,
0.2742122709751129,
0.01030542328953743,
0.07652007043361664,
-0.0386025533080101,
-0.02273324504494667,
0.02761206589639187,
0.07639015465974808,
0.10880912840366364,
-0.02062513679265976,
-0.0299789160490036,
-0.1585923433303833,
-0.09247402101755142,
0.01231061015278101,
0.07794225215911865,
-0.06771865487098694,
0.10212589055299759,
-0.02273976244032383,
-0.004403705708682537,
0.06125268712639809,
-0.019625147804617882,
-0.06167807802557945,
-0.10133379697799683,
0.009294627234339714,
0.03827160224318504,
0.011037420481443405,
-0.0946984589099884,
-0.10756024718284607,
-0.09874647855758667,
0.12922894954681396,
-0.044158898293972015,
-0.03595925495028496,
-0.10024362057447433,
0.05401208624243736,
0.028537284582853317,
-0.08931688219308853,
0.03234705328941345,
-0.001733518554829061,
0.11377447843551636,
0.02834698185324669,
-0.049536120146512985,
0.09161611646413803,
-0.046988293528556824,
-0.18945737183094025,
-0.037352196872234344,
0.1409035474061966,
0.01622386835515499,
0.04664473980665207,
0.0018119734013453126,
0.02027822472155094,
-0.01186717301607132,
-0.07081980258226395,
0.033424776047468185,
0.013306492008268833,
0.055676743388175964,
0.013938264921307564,
-0.013804925605654716,
-0.013131401501595974,
-0.09789611399173737,
-0.04547984153032303,
0.16504934430122375,
0.302859902381897,
-0.09618370980024338,
0.015506559051573277,
0.04642896354198456,
-0.047940485179424286,
-0.18081249296665192,
0.015201295726001263,
0.02985006757080555,
0.018101586028933525,
0.03688504546880722,
-0.1101895347237587,
0.05194663628935814,
0.07981035858392715,
-0.028989572077989578,
0.06391351670026779,
-0.2875177562236786,
-0.13005176186561584,
0.1096201092004776,
0.1305209845304489,
0.14827501773834229,
-0.1677141785621643,
-0.05463182553648949,
-0.004728596657514572,
-0.09936260432004929,
0.10859698057174683,
-0.10755733400583267,
0.10153793543577194,
-0.012175693176686764,
0.046705782413482666,
0.004208272788673639,
-0.04652112349867821,
0.13751116394996643,
-0.0026950067840516567,
0.09256431460380554,
-0.06554088741540909,
-0.01950525864958763,
0.08614667505025864,
-0.07570002973079681,
0.0401286855340004,
-0.11598999798297882,
0.039067912846803665,
-0.09781724214553833,
-0.03148103132843971,
-0.07691159099340439,
0.015183293260633945,
-0.033589594066143036,
-0.029139474034309387,
-0.06678682565689087,
0.0395573191344738,
0.06567560881376266,
-0.0008416437776759267,
0.17766596376895905,
-0.013338656164705753,
0.12633678317070007,
0.1482401192188263,
0.10601861029863358,
-0.058852240443229675,
-0.04836533963680267,
-0.020084546878933907,
-0.040959589183330536,
0.042283423244953156,
-0.17467880249023438,
0.03063947707414627,
0.10079529881477356,
0.012825519777834415,
0.16095949709415436,
0.055467914789915085,
-0.04376862943172455,
0.0288358423858881,
0.06646998971700668,
-0.17196869850158691,
-0.1511901468038559,
-0.015220618806779385,
-0.008244216442108154,
-0.14656662940979004,
0.03992070257663727,
0.11748301237821579,
-0.05474497750401497,
0.00605794508010149,
-0.004644628614187241,
0.01456319447606802,
-0.010050886310636997,
0.1421964168548584,
0.05050839111208916,
0.05289170891046524,
-0.08733587712049484,
0.08545175194740295,
0.050755929201841354,
-0.10539335012435913,
0.019542058929800987,
0.01170340832322836,
-0.1025342270731926,
-0.0444912388920784,
0.013044868595898151,
0.17017236351966858,
-0.03125283494591713,
-0.050015032291412354,
-0.163613960146904,
-0.12875844538211823,
0.0720018520951271,
0.1710045337677002,
0.10116640478372574,
0.015399068593978882,
-0.026354975998401642,
-0.00817218329757452,
-0.07992296665906906,
0.11599332094192505,
0.06847972422838211,
0.06134717911481857,
-0.15241467952728271,
0.05799543485045433,
-0.005973934195935726,
0.016870921477675438,
-0.022239437326788902,
0.008835076354444027,
-0.08890096098184586,
-0.004812977276742458,
-0.09327276796102524,
0.0360674187541008,
-0.029611293226480484,
0.010285209864377975,
-0.01788190007209778,
-0.06415540724992752,
-0.07332369685173035,
0.008298531174659729,
-0.09166558086872101,
-0.024154536426067352,
0.034509338438510895,
0.061393074691295624,
-0.09986463934183121,
-0.031820643693208694,
0.02065170928835869,
-0.07500538229942322,
0.07129677385091782,
0.020245449617505074,
0.012802252545952797,
0.042819324880838394,
-0.1105252280831337,
0.04476684704422951,
0.0737324133515358,
-0.015679001808166504,
0.019129693508148193,
-0.09452810138463974,
-0.014752857387065887,
0.005795576609671116,
0.016055434942245483,
0.024406904354691505,
0.07376240193843842,
-0.11860820651054382,
0.013465167954564095,
0.002267105970531702,
-0.02649819664657116,
-0.06879979372024536,
0.04085789993405342,
0.09244725853204727,
0.020291917026042938,
0.22669488191604614,
-0.09650322049856186,
0.0015437438851222396,
-0.20072799921035767,
0.011124883778393269,
0.012672419659793377,
-0.11459523439407349,
-0.12242916971445084,
-0.0534970723092556,
0.04660521075129509,
-0.05898887664079666,
0.13699448108673096,
-0.034464720636606216,
0.0045145610347390175,
0.04404124245047569,
-0.023848677054047585,
0.03968145325779915,
0.01906362548470497,
0.2044631987810135,
0.016139326617121696,
-0.04159203916788101,
0.054303329437971115,
0.001011157175526023,
0.09380969405174255,
0.07033151388168335,
0.15973114967346191,
0.18680919706821442,
-0.0018760565435513854,
0.10744358599185944,
0.02412467822432518,
0.003958811052143574,
-0.10330364108085632,
0.07266248017549515,
-0.06364265084266663,
0.08645805716514587,
0.007752298843115568,
0.21092109382152557,
0.13443085551261902,
-0.14517803490161896,
0.02595404163002968,
-0.02927270159125328,
-0.0671650767326355,
-0.07904037088155746,
-0.10619407147169113,
-0.10915328562259674,
-0.14818072319030762,
0.002071341034024954,
-0.12273100018501282,
0.009871838614344597,
0.08414274454116821,
0.0021037834230810404,
-0.026744097471237183,
0.16049328446388245,
0.0010891144629567862,
0.00801001861691475,
0.10390128940343857,
-0.015113324858248234,
-0.0577516183257103,
-0.07856693118810654,
-0.07178934663534164,
0.023923557251691818,
0.006605134811252356,
0.033124394714832306,
-0.021162312477827072,
-0.019618768244981766,
0.028233909979462624,
-0.02919340319931507,
-0.11679836362600327,
0.017241427674889565,
0.03961755707859993,
0.04641439765691757,
0.0275882501155138,
0.01764068193733692,
-0.0025852061808109283,
0.008634294383227825,
0.1924663633108139,
-0.05073459446430206,
-0.019730376079678535,
-0.09853547811508179,
0.13978703320026398,
0.028721090406179428,
0.012814762070775032,
0.01755356229841709,
-0.09401416033506393,
0.03908700868487358,
0.1970728486776352,
0.1730974167585373,
-0.04497775062918663,
0.01087920367717743,
-0.01941748335957527,
0.002157996641471982,
-0.0047938940115273,
0.06256673485040665,
0.0948919877409935,
-0.011402880772948265,
-0.06917697191238403,
-0.01784098893404007,
-0.04841911792755127,
-0.008446192368865013,
-0.027978630736470222,
0.04839513450860977,
0.02073029614984989,
0.008618121035397053,
-0.06652793288230896,
0.06569482386112213,
-0.033907897770404816,
-0.08138035237789154,
0.029602665454149246,
-0.2098388969898224,
-0.13545911014080048,
-0.03143484145402908,
0.06336848437786102,
-0.004572081845253706,
0.046184659004211426,
-0.013827149756252766,
0.002931281691417098,
0.05123218894004822,
-0.012289384379982948,
-0.07266342639923096,
-0.07706356793642044,
0.059862758964300156,
-0.1054987832903862,
0.18924225866794586,
-0.036029767245054245,
0.02763368934392929,
0.1259865164756775,
0.023267097771167755,
-0.08085521310567856,
0.08839578181505203,
0.043532002717256546,
-0.02030070126056671,
0.03188510239124298,
0.09609914571046829,
-0.016422195360064507,
0.1100718230009079,
0.06545784324407578,
-0.1250723898410797,
0.005353061947971582,
-0.07528534531593323,
-0.07209677249193192,
-0.0566200315952301,
-0.026333244517445564,
-0.05093283951282501,
0.13890713453292847,
0.1693841964006424,
-0.05344060808420181,
-0.000396195420762524,
-0.02704005129635334,
0.029074078425765038,
0.07424737513065338,
0.025559883564710617,
-0.020122084766626358,
-0.22389721870422363,
0.011877707205712795,
0.06523996591567993,
0.008447353728115559,
-0.3106476366519928,
-0.1007918119430542,
-0.029682477936148643,
-0.03409705311059952,
-0.06172458082437515,
0.0857636034488678,
0.09595619142055511,
0.053810447454452515,
-0.058911681175231934,
-0.082517609000206,
-0.05015530437231064,
0.18015292286872864,
-0.13656842708587646,
-0.08542796224355698
] |
null | null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-bn-adapter-1.79M-squad-model2
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 4
- seed: 27
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["varun-v-rao/squad"], "base_model": "t5-base", "model-index": [{"name": "t5-base-bn-adapter-1.79M-squad-model2", "results": []}]} | null | varun-v-rao/t5-base-bn-adapter-1.79M-squad-model2 | [
"tensorboard",
"generated_from_trainer",
"dataset:varun-v-rao/squad",
"base_model:t5-base",
"license:apache-2.0",
"region:us"
] | 2024-02-09T10:44:50+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #dataset-varun-v-rao/squad #base_model-t5-base #license-apache-2.0 #region-us
|
# t5-base-bn-adapter-1.79M-squad-model2
This model is a fine-tuned version of t5-base on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 4
- seed: 27
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| [
"# t5-base-bn-adapter-1.79M-squad-model2\n\nThis model is a fine-tuned version of t5-base on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 4\n- seed: 27\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
"TAGS\n#tensorboard #generated_from_trainer #dataset-varun-v-rao/squad #base_model-t5-base #license-apache-2.0 #region-us \n",
"# t5-base-bn-adapter-1.79M-squad-model2\n\nThis model is a fine-tuned version of t5-base on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 4\n- seed: 27\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
46,
38,
6,
12,
8,
3,
90,
4,
33
] | [
"passage: TAGS\n#tensorboard #generated_from_trainer #dataset-varun-v-rao/squad #base_model-t5-base #license-apache-2.0 #region-us \n# t5-base-bn-adapter-1.79M-squad-model2\n\nThis model is a fine-tuned version of t5-base on the squad dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 4\n- seed: 27\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
-0.11923655867576599,
0.07037389278411865,
-0.0017141913995146751,
0.09047745168209076,
0.15378369390964508,
0.04316524788737297,
0.1195625513792038,
0.120480477809906,
-0.13319455087184906,
0.02343946509063244,
0.0644669234752655,
0.0509822778403759,
0.05239861086010933,
0.11787113547325134,
0.006142244674265385,
-0.2543553411960602,
0.0022229922469705343,
0.029227199032902718,
-0.10070517659187317,
0.12192083150148392,
0.12388443201780319,
-0.074828140437603,
0.06803792715072632,
0.03123040683567524,
-0.21329984068870544,
0.03758256882429123,
0.016759803518652916,
-0.07831498980522156,
0.12193109840154648,
0.00721091590821743,
0.11093959212303162,
0.012753778137266636,
0.13940741121768951,
-0.1276175081729889,
0.008060232736170292,
0.09090320765972137,
0.005937244277447462,
0.10528098791837692,
0.03285856172442436,
0.03664213791489601,
0.15561987459659576,
-0.09810742735862732,
0.0856597051024437,
0.05006873980164528,
-0.08377162367105484,
-0.18071100115776062,
-0.06740702688694,
0.11963224411010742,
0.07556591182947159,
0.0860830694437027,
0.007721596397459507,
0.15805037319660187,
-0.08309733867645264,
0.06735852360725403,
0.19754621386528015,
-0.31296512484550476,
-0.06938827037811279,
0.05035411939024925,
0.04082702100276947,
0.06966619938611984,
-0.07347553223371506,
-0.03949184715747833,
0.04691744223237038,
0.05273062735795975,
0.11190979182720184,
0.011007621884346008,
-0.08302196115255356,
0.008283651433885098,
-0.14383569359779358,
-0.043522920459508896,
0.2025749832391739,
0.032437849789857864,
-0.0522925965487957,
-0.02422613464295864,
-0.08929293602705002,
-0.11615972965955734,
0.004151471890509129,
-0.04755028337240219,
0.017707260325551033,
-0.036039385944604874,
-0.08544391393661499,
-0.06773935258388519,
-0.09233589470386505,
-0.08977474272251129,
-0.021946746855974197,
0.12265049666166306,
0.07111997902393341,
0.04547026753425598,
-0.05774734541773796,
0.1517588496208191,
0.01473158411681652,
-0.11604767292737961,
0.010501640848815441,
-0.0034025884233415127,
-0.08013498038053513,
-0.0427619032561779,
-0.04880984500050545,
-0.03441011533141136,
-0.013446943834424019,
0.13619546592235565,
-0.05721643567085266,
0.009666817262768745,
0.028785869479179382,
0.022403890267014503,
-0.0427999384701252,
0.12832888960838318,
-0.11170443892478943,
0.012579756788909435,
0.0421881265938282,
0.09077731519937515,
0.011933400295674801,
-0.000405966944526881,
-0.09964263439178467,
-0.052512310445308685,
0.11532743275165558,
0.04366776719689369,
-0.04458179324865341,
0.03431667387485504,
-0.01569267176091671,
-0.05224333330988884,
0.01342510711401701,
-0.11147647351026535,
-0.014299307018518448,
-0.016756821423768997,
-0.11840458959341049,
-0.03160970285534859,
0.039091937243938446,
-0.015409334562718868,
-0.057156339287757874,
0.015398439951241016,
-0.1029740646481514,
-0.020317604765295982,
-0.08827639371156693,
-0.07592590898275375,
0.013059137389063835,
-0.08339381963014603,
-0.00481491070240736,
-0.10127414017915726,
-0.16797557473182678,
0.012658418156206608,
0.0319235734641552,
-0.057926762849092484,
-0.08527637273073196,
-0.010617446154356003,
-0.07748134434223175,
0.0017327223904430866,
-0.01266494020819664,
0.13576187193393707,
-0.03385384753346443,
0.09965313225984573,
0.0312933549284935,
0.025322385132312775,
-0.010974083095788956,
0.03799775242805481,
-0.08060067147016525,
0.022012803703546524,
-0.13188810646533966,
0.04652102664113045,
-0.04934241622686386,
0.005709128919988871,
-0.11693724989891052,
-0.11267226934432983,
0.019100390374660492,
-0.03742701932787895,
0.10189518332481384,
0.10398773849010468,
-0.15203848481178284,
-0.03625324368476868,
0.11433318257331848,
-0.08900881558656693,
-0.10342147201299667,
0.10421172529459,
-0.03379899263381958,
0.048938337713479996,
0.03894703462719917,
0.12301952391862869,
0.08889550715684891,
-0.10643267631530762,
-0.00491517037153244,
0.028721503913402557,
0.024405010044574738,
-0.06285399943590164,
0.09156928956508636,
0.028963126242160797,
-0.03380010277032852,
0.021059632301330566,
-0.0789022222161293,
0.04862239211797714,
-0.10149499773979187,
-0.08474422991275787,
-0.0748445987701416,
-0.08907970041036606,
0.06929466873407364,
0.04098346084356308,
0.040478017181158066,
-0.05461909621953964,
-0.12201977521181107,
0.12422182410955429,
0.15587757527828217,
-0.03663719817996025,
0.018860949203372,
-0.1091603934764862,
0.09428568929433823,
-0.08682526648044586,
-0.018433578312397003,
-0.20440056920051575,
-0.09924387186765671,
0.02069748193025589,
-0.02319774217903614,
0.047807011753320694,
0.02431744709610939,
0.054175231605768204,
0.08118275552988052,
-0.05273466557264328,
-0.03551169112324715,
-0.14380478858947754,
-0.01708007976412773,
-0.08549042791128159,
-0.19369539618492126,
-0.09930851310491562,
-0.010152079164981842,
0.15701240301132202,
-0.18277622759342194,
0.03345722705125809,
-0.009898394346237183,
0.13761650025844574,
0.016144156455993652,
-0.026797588914632797,
-0.01321687363088131,
0.03722107410430908,
-0.021897263824939728,
-0.09360874444246292,
0.04155357927083969,
0.021328896284103394,
-0.08976779878139496,
-0.051088131964206696,
-0.08698717504739761,
0.09343857318162918,
0.09967152774333954,
0.020896119996905327,
-0.08346565812826157,
0.03635544702410698,
-0.094223253428936,
-0.04731816053390503,
-0.04238990321755409,
-0.0071127647534012794,
0.1570003777742386,
-0.004413822200149298,
0.13520687818527222,
-0.09204472601413727,
-0.060776352882385254,
0.022411145269870758,
0.011000880040228367,
0.02016621269285679,
0.05983777716755867,
0.08696072548627853,
-0.11214548349380493,
0.0747256800532341,
0.09690014272928238,
-0.08005505800247192,
0.13587233424186707,
-0.06679088622331619,
-0.10598719120025635,
-0.03713352978229523,
0.0012416637036949396,
-0.0032935342751443386,
0.17612823843955994,
-0.1206803172826767,
0.009646558202803135,
0.035621073096990585,
-0.004993553273379803,
0.042780183255672455,
-0.19719454646110535,
-0.01863146387040615,
0.01646798849105835,
-0.03911815583705902,
-0.0513974092900753,
-0.007445260416716337,
0.02260170504450798,
0.11800969392061234,
0.019436422735452652,
-0.0509256049990654,
0.03970855847001076,
0.014062473550438881,
-0.08261968940496445,
0.18756228685379028,
-0.09256923198699951,
-0.11636634916067123,
-0.1190890222787857,
0.09472943842411041,
-0.06822548806667328,
-0.03173767775297165,
0.0013077097246423364,
-0.08572064340114594,
-0.0173520278185606,
-0.0689576268196106,
0.03167073801159859,
-0.035770438611507416,
0.019067123532295227,
-0.0019999609794467688,
0.03612681105732918,
0.11828410625457764,
-0.14790864288806915,
0.033907096832990646,
-0.03240280598402023,
-0.11805873364210129,
-0.017756685614585876,
0.047464579343795776,
0.09628487378358841,
0.1494259089231491,
-0.045012202113866806,
0.02496466040611267,
-0.03772812336683273,
0.2194765955209732,
-0.053059548139572144,
-0.005196951795369387,
0.15700000524520874,
0.017925236374139786,
0.03037799708545208,
0.09194767475128174,
0.034830931574106216,
-0.056551650166511536,
0.01775960810482502,
0.041975658386945724,
-0.02883036620914936,
-0.27050063014030457,
-0.02535920962691307,
-0.03426773101091385,
-0.05277596786618233,
0.08795769512653351,
0.04865896329283714,
0.021206451579928398,
0.0704180896282196,
-0.02091127261519432,
0.08619827777147293,
-0.05207591503858566,
0.07593843340873718,
0.0782434269785881,
0.03462255001068115,
0.08854920417070389,
-0.03989323973655701,
-0.02698405459523201,
0.06257878243923187,
0.03818794712424278,
0.24936342239379883,
-0.0744195207953453,
0.112685427069664,
0.04680802300572395,
0.22019346058368683,
-0.02715313248336315,
0.07549073547124863,
0.0016252758214250207,
0.00692676892504096,
0.010851808823645115,
-0.049576424062252045,
-0.018364543095231056,
0.005316099151968956,
-0.04327341169118881,
0.09077005088329315,
-0.11026827245950699,
0.051788635551929474,
0.014458734542131424,
0.28640246391296387,
0.0437641516327858,
-0.31526443362236023,
-0.11972998082637787,
-0.026918943971395493,
-0.018216822296380997,
-0.05835244059562683,
0.021977433934807777,
0.12854346632957458,
-0.09303012490272522,
0.0015991661930456758,
-0.06612729281187057,
0.07550333440303802,
-0.008081763051450253,
-0.017678434029221535,
0.08665912598371506,
0.16565506160259247,
0.006499661132693291,
0.0962945744395256,
-0.20951703190803528,
0.2246829718351364,
0.01656435616314411,
0.09846119582653046,
-0.05541800335049629,
0.021488415077328682,
0.013895070180296898,
0.0991649255156517,
0.07261712104082108,
0.003715665079653263,
-0.013019319623708725,
-0.1547570675611496,
-0.07512808591127396,
0.036785826086997986,
0.07472845166921616,
-0.042628880590200424,
0.08819757401943207,
-0.04996514692902565,
0.022159378975629807,
0.05800449103116989,
-0.03692286089062691,
-0.18287381529808044,
-0.07646588981151581,
0.005931023973971605,
-0.014377607963979244,
-0.044854894280433655,
-0.10207416862249374,
-0.11786216497421265,
0.03545885905623436,
0.14437749981880188,
-0.024833092465996742,
-0.0613066665828228,
-0.1373024731874466,
0.11096975207328796,
0.13273485004901886,
-0.06270349025726318,
0.021182864904403687,
0.022487077862024307,
0.12311180680990219,
0.03917758911848068,
-0.11177805066108704,
0.06155982241034508,
-0.06815808266401291,
-0.17345860600471497,
-0.038662973791360855,
0.13166579604148865,
0.03529104217886925,
0.05868231877684593,
-0.008223376236855984,
-0.025294117629528046,
0.016315849497914314,
-0.07791886478662491,
0.009828048758208752,
0.01011329609900713,
0.035135917365550995,
0.018868133425712585,
-0.0653458908200264,
0.0190607737749815,
-0.041497278958559036,
0.004515914246439934,
0.11634915322065353,
0.18072013556957245,
-0.08340472728013992,
0.04205979406833649,
0.07510514557361603,
-0.06229918450117111,
-0.14355385303497314,
0.04488735273480415,
0.10567331314086914,
0.03253434598445892,
0.02399102970957756,
-0.19909994304180145,
0.09089658409357071,
0.09199042618274689,
-0.01924382708966732,
0.041007183492183685,
-0.3506360352039337,
-0.13143779337406158,
0.08905074745416641,
0.1464495062828064,
0.0600091814994812,
-0.1129390075802803,
-0.01459254790097475,
-0.0038883169181644917,
-0.12225954979658127,
0.13220812380313873,
-0.16862541437149048,
0.09364031255245209,
-0.023202916607260704,
0.09650973975658417,
0.022527780383825302,
-0.032652292400598526,
0.11014401167631149,
0.019002674147486687,
0.07355469465255737,
-0.04616475850343704,
-0.003136560320854187,
0.17994844913482666,
-0.05418515205383301,
0.06379856169223785,
-0.00456093717366457,
0.08702307194471359,
-0.13935022056102753,
-0.009139219298958778,
-0.08960369974374771,
0.0567341074347496,
-0.052353277802467346,
-0.08297112584114075,
-0.0390050932765007,
0.0633329376578331,
0.016089120879769325,
-0.05011546611785889,
0.05983933433890343,
0.06739425659179688,
0.10626742243766785,
0.10125289857387543,
0.08570931851863861,
0.0007437504827976227,
-0.08662008494138718,
0.004182097967714071,
-0.009153792634606361,
0.09278890490531921,
-0.16080491244792938,
0.0045425803400576115,
0.10964903980493546,
0.04253828525543213,
0.09712105244398117,
0.06265933811664581,
-0.08472146838903427,
0.01687362976372242,
0.04909088462591171,
-0.12227539718151093,
-0.14328539371490479,
-0.05194908007979393,
-0.03274642303586006,
-0.14463171362876892,
0.06919638067483902,
0.10025250166654587,
-0.10746762901544571,
-0.025979481637477875,
-0.0022433584090322256,
-0.017195509746670723,
-0.06730184704065323,
0.18279777467250824,
0.08871826529502869,
0.07112932205200195,
-0.08279426395893097,
0.12002179026603699,
0.07348326593637466,
-0.056266266852617264,
0.03275519236922264,
0.06127677485346794,
-0.10083933174610138,
-0.014885291457176208,
0.05239403247833252,
0.11032196134328842,
-0.08455456793308258,
-0.0561443530023098,
-0.12369807064533234,
-0.08654423803091049,
0.05004546046257019,
0.11761116236448288,
0.05668071284890175,
0.017120786011219025,
-0.05217822268605232,
0.0322195403277874,
-0.14924971759319305,
0.09286424517631531,
0.03709237277507782,
0.06935157626867294,
-0.16518457233905792,
0.1461455374956131,
0.018392452970147133,
0.08292218297719955,
-0.031120650470256805,
0.013933646492660046,
-0.08797937631607056,
0.0002243066264782101,
-0.15775597095489502,
-0.03332855552434921,
-0.004926877561956644,
0.011849346570670605,
-0.007916616275906563,
-0.056634485721588135,
-0.05703550949692726,
0.07546895742416382,
-0.07578060030937195,
-0.04156126827001572,
0.03627103567123413,
0.031788840889930725,
-0.11925419420003891,
0.013345534913241863,
0.0033659220207482576,
-0.09377840161323547,
0.10326186567544937,
0.058201517909765244,
0.028052672743797302,
0.05054529383778572,
-0.062479663640260696,
-0.01463208720088005,
0.004654936026781797,
0.028186753392219543,
0.07371091842651367,
-0.07692064344882965,
0.02233738824725151,
-0.038832660764455795,
0.021779902279376984,
-0.005413771606981754,
0.06474325060844421,
-0.11311455070972443,
-0.04317844286561012,
-0.04340686276555061,
-0.02245977707207203,
-0.08029446750879288,
0.04283735156059265,
0.0882292240858078,
0.04757234826683998,
0.14598986506462097,
-0.05979714170098305,
0.013654191046953201,
-0.22743074595928192,
-0.0262457262724638,
-0.007791107054799795,
-0.017660830169916153,
-0.08862880617380142,
-0.03351034224033356,
0.06408199667930603,
-0.06636349856853485,
0.07032618671655655,
-0.009708551689982414,
0.10620419681072235,
0.03446224331855774,
-0.0060810125432908535,
-0.003060725284740329,
0.012840252369642258,
0.1953609138727188,
0.03715893626213074,
0.0013146213022992015,
0.05766814202070236,
-0.00945925060659647,
0.05804261937737465,
0.06110137701034546,
0.16083604097366333,
0.09067165851593018,
-0.016348646953701973,
0.08054105937480927,
0.10531099140644073,
-0.08268515020608902,
-0.15289999544620514,
0.07713992148637772,
-0.018803857266902924,
0.0945407822728157,
-0.030916448682546616,
0.15130433440208435,
0.15233740210533142,
-0.14133630692958832,
0.029024669900536537,
-0.032275039702653885,
-0.09390214830636978,
-0.0875060111284256,
-0.061756737530231476,
-0.0494266040623188,
-0.15525837242603302,
0.024204609915614128,
-0.11745855957269669,
-0.02620812878012657,
0.10469798743724823,
0.010574649088084698,
-0.01644466258585453,
0.14838182926177979,
0.04942085221409798,
0.007062786724418402,
0.0474793016910553,
0.02284240908920765,
-0.009556801989674568,
-0.05939667671918869,
-0.05992724746465683,
0.05423702672123909,
-0.011314185336232185,
0.061234138906002045,
-0.07371660321950912,
-0.010269642807543278,
0.05382115766406059,
0.00645287474617362,
-0.0633806586265564,
0.02721702679991722,
-0.002862834371626377,
0.04315033555030823,
0.04135170206427574,
0.03402705863118172,
0.006471200846135616,
-0.042292170226573944,
0.2566281855106354,
-0.06364904344081879,
-0.09043464064598083,
-0.1491795778274536,
0.21285435557365417,
0.020971978083252907,
-0.05569243058562279,
0.08759602159261703,
-0.11157972365617752,
-0.03169385716319084,
0.14836600422859192,
0.16206155717372894,
-0.05282824859023094,
-0.03834466263651848,
0.0021824254654347897,
-0.031440287828445435,
-0.09156866371631622,
0.16777591407299042,
0.10353023558855057,
0.048415716737508774,
-0.0688173845410347,
-0.01621503382921219,
-0.04012133553624153,
-0.006306152790784836,
-0.025727054104208946,
0.0893237516283989,
0.03234946355223656,
-0.003471019910648465,
-0.035714320838451385,
0.056568171828985214,
-0.016610214486718178,
-0.15755492448806763,
0.05094114691019058,
-0.11525297909975052,
-0.18095837533473969,
-0.051770493388175964,
0.07994873821735382,
-0.01790347695350647,
0.06998378783464432,
-0.03461107984185219,
0.019297262653708458,
0.12798991799354553,
-0.006447276100516319,
-0.08820413053035736,
-0.1031770333647728,
0.12409187853336334,
-0.06602994352579117,
0.21508000791072845,
-0.03202388063073158,
0.08755369484424591,
0.11200989782810211,
0.027521319687366486,
-0.16367362439632416,
0.017176559194922447,
0.05309266969561577,
-0.041889455169439316,
0.006589781958609819,
0.15739339590072632,
-0.020720532163977623,
0.0675862729549408,
0.016144493594765663,
-0.11523960530757904,
-0.030663585290312767,
-0.0543198436498642,
0.010764341801404953,
-0.08188784867525101,
0.00020069249148946255,
-0.05854196846485138,
0.1427939087152481,
0.20063233375549316,
-0.05956639349460602,
-0.00006882408342789859,
-0.12288918346166611,
0.02619742974638939,
0.07564406841993332,
0.0645044595003128,
0.008093384094536304,
-0.17596572637557983,
0.012506779283285141,
0.013381455093622208,
-0.014857479371130466,
-0.2252572625875473,
-0.08188726007938385,
0.056663770228624344,
-0.08638346940279007,
-0.08509089052677155,
0.10768799483776093,
0.09050354361534119,
0.07308994978666306,
-0.028900884091854095,
-0.03279263898730278,
-0.08345986157655716,
0.1241326779127121,
-0.17846108973026276,
-0.07375025749206543
] |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 1200_STEPS_1e7_03beta_
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6860
- Rewards/chosen: 0.0038
- Rewards/rejected: -0.0117
- Rewards/accuracies: 0.5099
- Rewards/margins: 0.0155
- Logps/rejected: -15.1783
- Logps/chosen: -14.1044
- Logits/rejected: -0.0244
- Logits/chosen: -0.0243
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-07
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6928 | 0.1 | 50 | 0.6933 | 0.0002 | 0.0002 | 0.4220 | -0.0000 | -15.1385 | -14.1161 | -0.0215 | -0.0215 |
| 0.6921 | 0.2 | 100 | 0.6942 | -0.0040 | -0.0023 | 0.4088 | -0.0018 | -15.1468 | -14.1303 | -0.0212 | -0.0212 |
| 0.6931 | 0.29 | 150 | 0.6925 | -0.0023 | -0.0039 | 0.4132 | 0.0016 | -15.1525 | -14.1246 | -0.0212 | -0.0212 |
| 0.6859 | 0.39 | 200 | 0.6914 | -0.0009 | -0.0049 | 0.4352 | 0.0040 | -15.1557 | -14.1200 | -0.0223 | -0.0223 |
| 0.6864 | 0.49 | 250 | 0.6900 | -0.0027 | -0.0094 | 0.4703 | 0.0067 | -15.1708 | -14.1259 | -0.0227 | -0.0227 |
| 0.6874 | 0.59 | 300 | 0.6894 | -0.0015 | -0.0096 | 0.4989 | 0.0081 | -15.1714 | -14.1218 | -0.0236 | -0.0236 |
| 0.6907 | 0.68 | 350 | 0.6890 | 0.0012 | -0.0077 | 0.4989 | 0.0089 | -15.1651 | -14.1129 | -0.0229 | -0.0228 |
| 0.6906 | 0.78 | 400 | 0.6879 | 0.0009 | -0.0103 | 0.4989 | 0.0112 | -15.1738 | -14.1139 | -0.0228 | -0.0227 |
| 0.6864 | 0.88 | 450 | 0.6877 | 0.0053 | -0.0064 | 0.4901 | 0.0118 | -15.1608 | -14.0991 | -0.0241 | -0.0240 |
| 0.6857 | 0.98 | 500 | 0.6882 | 0.0038 | -0.0071 | 0.4747 | 0.0108 | -15.1628 | -14.1043 | -0.0239 | -0.0239 |
| 0.688 | 1.07 | 550 | 0.6870 | 0.0051 | -0.0083 | 0.4923 | 0.0133 | -15.1668 | -14.1000 | -0.0244 | -0.0244 |
| 0.6826 | 1.17 | 600 | 0.6859 | 0.0033 | -0.0122 | 0.5011 | 0.0154 | -15.1799 | -14.1060 | -0.0239 | -0.0239 |
| 0.6819 | 1.27 | 650 | 0.6865 | 0.0038 | -0.0105 | 0.5011 | 0.0143 | -15.1744 | -14.1043 | -0.0241 | -0.0240 |
| 0.6724 | 1.37 | 700 | 0.6862 | 0.0037 | -0.0113 | 0.5077 | 0.0150 | -15.1771 | -14.1045 | -0.0243 | -0.0242 |
| 0.6791 | 1.46 | 750 | 0.6865 | 0.0042 | -0.0103 | 0.4945 | 0.0144 | -15.1735 | -14.1030 | -0.0248 | -0.0248 |
| 0.6805 | 1.56 | 800 | 0.6862 | 0.0021 | -0.0128 | 0.5011 | 0.0150 | -15.1821 | -14.1097 | -0.0246 | -0.0246 |
| 0.6857 | 1.66 | 850 | 0.6864 | 0.0032 | -0.0114 | 0.4945 | 0.0147 | -15.1774 | -14.1061 | -0.0243 | -0.0242 |
| 0.6843 | 1.76 | 900 | 0.6850 | 0.0038 | -0.0136 | 0.5033 | 0.0174 | -15.1847 | -14.1042 | -0.0239 | -0.0239 |
| 0.6772 | 1.86 | 950 | 0.6860 | 0.0039 | -0.0116 | 0.4923 | 0.0155 | -15.1780 | -14.1040 | -0.0253 | -0.0252 |
| 0.6786 | 1.95 | 1000 | 0.6865 | 0.0041 | -0.0105 | 0.4835 | 0.0146 | -15.1743 | -14.1031 | -0.0241 | -0.0241 |
| 0.6839 | 2.05 | 1050 | 0.6858 | 0.0034 | -0.0124 | 0.5143 | 0.0159 | -15.1807 | -14.1054 | -0.0239 | -0.0239 |
| 0.6829 | 2.15 | 1100 | 0.6868 | 0.0019 | -0.0121 | 0.4989 | 0.0140 | -15.1796 | -14.1107 | -0.0243 | -0.0243 |
| 0.6816 | 2.25 | 1150 | 0.6860 | 0.0038 | -0.0118 | 0.5143 | 0.0156 | -15.1786 | -14.1042 | -0.0244 | -0.0243 |
| 0.6777 | 2.34 | 1200 | 0.6860 | 0.0038 | -0.0117 | 0.5099 | 0.0155 | -15.1783 | -14.1044 | -0.0244 | -0.0243 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.0.0+cu117
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "meta-llama/Llama-2-7b-hf", "model-index": [{"name": "1200_STEPS_1e7_03beta_", "results": []}]} | text-generation | tsavage68/1200_STEPS_1e7_03beta_DPO_zeroshot | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"dpo",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T10:45:53+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #trl #dpo #generated_from_trainer #base_model-meta-llama/Llama-2-7b-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| 1200\_STEPS\_1e7\_03beta\_
==========================
This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6860
* Rewards/chosen: 0.0038
* Rewards/rejected: -0.0117
* Rewards/accuracies: 0.5099
* Rewards/margins: 0.0155
* Logps/rejected: -15.1783
* Logps/chosen: -14.1044
* Logits/rejected: -0.0244
* Logits/chosen: -0.0243
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-07
* train\_batch\_size: 4
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 100
* training\_steps: 1200
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.0.0+cu117
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-07\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 1200",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #trl #dpo #generated_from_trainer #base_model-meta-llama/Llama-2-7b-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-07\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 1200",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
78,
145,
4,
33
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #trl #dpo #generated_from_trainer #base_model-meta-llama/Llama-2-7b-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-07\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 1200### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
-0.1393183022737503,
0.09179126471281052,
-0.002032330958172679,
0.0762460008263588,
0.1418241262435913,
0.0116720674559474,
0.10311859846115112,
0.13484546542167664,
-0.1070646196603775,
0.08826933056116104,
0.13600213825702667,
0.11563953757286072,
0.05311112478375435,
0.1796148717403412,
-0.03506099060177803,
-0.31452369689941406,
0.0060284812934696674,
-0.015861311927437782,
-0.16378046572208405,
0.12821359932422638,
0.09272246062755585,
-0.1195053905248642,
0.05046975612640381,
-0.034440938383340836,
-0.11934401839971542,
-0.035708505660295486,
-0.023015394806861877,
-0.04137890785932541,
0.1370260864496231,
0.004780526272952557,
0.11193756759166718,
0.055526454001665115,
0.09844009578227997,
-0.22439049184322357,
0.011697368696331978,
0.06085282191634178,
0.04348500072956085,
0.08401064574718475,
0.06589754670858383,
-0.03058309108018875,
0.08478409051895142,
-0.0991053581237793,
0.0705970823764801,
0.036082156002521515,
-0.12218095362186432,
-0.23023799061775208,
-0.10281093418598175,
0.05519964545965195,
0.1524069458246231,
0.0822262093424797,
-0.021946854889392853,
0.07092402875423431,
-0.07975225895643234,
0.08292078226804733,
0.24291260540485382,
-0.2663031816482544,
-0.08730300515890121,
0.06295747309923172,
0.06293477863073349,
0.06965404003858566,
-0.12486580014228821,
-0.008098986931145191,
0.04134569317102432,
0.01283229049295187,
0.12201528996229172,
0.0056802621111273766,
0.07092056423425674,
0.005824311636388302,
-0.14911453425884247,
-0.03651455044746399,
0.10778065770864487,
0.07743056863546371,
-0.03922032564878464,
-0.08161081373691559,
-0.03909715265035629,
-0.22995662689208984,
-0.04024278745055199,
-0.01799965836107731,
0.030744094401597977,
-0.052468907088041306,
-0.09749308228492737,
0.007727006915956736,
-0.0724382996559143,
-0.10717623680830002,
0.048968859016895294,
0.1479131132364273,
0.03839394077658653,
-0.045437783002853394,
0.027188953012228012,
0.16396819055080414,
0.056954525411129,
-0.15446795523166656,
-0.004889973904937506,
0.016253245994448662,
-0.07700616121292114,
-0.04255169630050659,
-0.02968650497496128,
0.008581995964050293,
0.007954418659210205,
0.14890222251415253,
-0.05058920755982399,
0.043066564947366714,
0.050554484128952026,
0.02835455909371376,
-0.11384536325931549,
0.14759577810764313,
-0.08124031126499176,
-0.08990049362182617,
-0.03184458985924721,
0.14568723738193512,
-0.0036383953411132097,
-0.007862679660320282,
-0.08336759358644485,
0.010956459678709507,
0.11563067138195038,
0.07015403360128403,
-0.027914542704820633,
0.040522437542676926,
-0.0708053857088089,
-0.015183557756245136,
0.031071696430444717,
-0.09792450815439224,
0.015094679780304432,
-0.0005790675058960915,
-0.08447718620300293,
-0.054125577211380005,
0.0049181594513356686,
0.017116611823439598,
0.019399410113692284,
0.13414429128170013,
-0.08081430941820145,
-0.024369439110159874,
-0.09946639090776443,
-0.09668730944395065,
0.007134072948247194,
-0.07855430990457535,
-0.008768975734710693,
-0.0786585882306099,
-0.1595200002193451,
-0.06243809685111046,
0.05145309865474701,
-0.05996435508131981,
-0.06802089512348175,
-0.08518867939710617,
-0.10309150815010071,
0.02899133786559105,
-0.00843639113008976,
0.1469476968050003,
-0.04883531481027603,
0.13486284017562866,
0.011673488654196262,
0.07657372951507568,
0.05941919609904289,
0.04436846449971199,
-0.05200624465942383,
0.07051334530115128,
-0.22280766069889069,
0.06969712674617767,
-0.06909754872322083,
0.09508077800273895,
-0.12431425601243973,
-0.09488629549741745,
-0.03108564391732216,
-0.009627992287278175,
0.0950126126408577,
0.16026847064495087,
-0.17663255333900452,
-0.07039610296487808,
0.2026297003030777,
-0.06594929844141006,
-0.13316696882247925,
0.10976304858922958,
-0.03188738971948624,
0.04489312693476677,
0.03929942846298218,
0.14050689339637756,
0.10027316957712173,
-0.07956159859895706,
0.010659360326826572,
-0.043041784316301346,
0.09604057669639587,
0.02270575426518917,
0.09991461038589478,
-0.03302086517214775,
0.016217540949583054,
-0.004943992011249065,
-0.06641895323991776,
0.049444086849689484,
-0.103651262819767,
-0.08359289169311523,
-0.0020824666135013103,
-0.10206769406795502,
0.061892423778772354,
0.04302193969488144,
0.04659995064139366,
-0.0885995477437973,
-0.11415955424308777,
0.011153630912303925,
0.11118456721305847,
-0.07502980530261993,
0.016754450276494026,
-0.04000931605696678,
0.0689697340130806,
-0.0318099670112133,
-0.003063777694478631,
-0.14920395612716675,
-0.06060659512877464,
0.020930802449584007,
0.026469960808753967,
-0.018924402073025703,
-0.02836024947464466,
0.0813441276550293,
0.06844330579042435,
-0.08370009809732437,
-0.08099693059921265,
-0.05531228333711624,
-0.004828406497836113,
-0.10849344730377197,
-0.23921307921409607,
-0.05747910216450691,
-0.033642325550317764,
0.21543964743614197,
-0.260956734418869,
0.049561288207769394,
0.00666876370087266,
0.12082282453775406,
0.036356355994939804,
-0.0344715379178524,
-0.0053896112367510796,
0.05385725200176239,
-0.03321037068963051,
-0.09004858136177063,
0.04293808341026306,
-0.009181367233395576,
-0.13211531937122345,
-0.010546525940299034,
-0.12840884923934937,
0.11915433406829834,
0.09669579565525055,
0.008075096644461155,
-0.13998329639434814,
-0.08854275941848755,
-0.07075738161802292,
-0.04742705449461937,
-0.028017273172736168,
-0.009179426357150078,
0.094540536403656,
0.03804491087794304,
0.12404736131429672,
-0.07892201095819473,
-0.05591217800974846,
0.02895471639931202,
-0.006224303040653467,
0.01947491616010666,
0.14742791652679443,
0.046990204602479935,
-0.06098659336566925,
0.12412893772125244,
0.12583427131175995,
-0.043338872492313385,
0.14378434419631958,
-0.04507310315966606,
-0.09361335635185242,
-0.026346689090132713,
0.06037631258368492,
0.046002864837646484,
0.1334335058927536,
-0.07938457280397415,
-0.007702175993472338,
0.0020932918414473534,
0.028981495648622513,
0.003499047365039587,
-0.20366981625556946,
-0.04816203564405441,
0.048789795488119125,
-0.05547488480806351,
0.0018823115387931466,
-0.026471158489584923,
-0.017575498670339584,
0.10511373728513718,
0.043351028114557266,
-0.05696357041597366,
0.012660191394388676,
-0.006592388730496168,
-0.07916243374347687,
0.23044873774051666,
-0.0849807858467102,
-0.1188984364271164,
-0.11056414991617203,
0.009412284940481186,
-0.010377942584455013,
0.016413874924182892,
0.02904708869755268,
-0.10179871320724487,
0.012077759020030499,
-0.07026922702789307,
0.018600579351186752,
-0.021397661417722702,
0.0389113686978817,
-0.026384664699435234,
0.022927718237042427,
0.0534534826874733,
-0.08278932422399521,
0.02098771370947361,
-0.015993870794773102,
-0.053238242864608765,
0.045261237770318985,
0.0023549457546323538,
0.11163365840911865,
0.18577635288238525,
0.017537854611873627,
0.017474355176091194,
-0.047963451594114304,
0.14219650626182556,
-0.13213156163692474,
0.004320820793509483,
0.09893056750297546,
0.021701080724596977,
0.05779420584440231,
0.16286396980285645,
0.03780258446931839,
-0.09858900308609009,
0.056568700820207596,
0.04126549884676933,
-0.025485122576355934,
-0.21669462323188782,
-0.006186614278703928,
-0.038003843277692795,
0.031186634674668312,
0.10308392345905304,
0.030559172853827477,
0.02747196890413761,
0.061825986951589584,
-0.0225048940628767,
-0.0014842312084510922,
0.009657692164182663,
0.07641638070344925,
-0.006950599141418934,
0.030416401103138924,
0.12031985074281693,
-0.014807908795773983,
-0.04077397286891937,
0.009420827031135559,
0.02021464891731739,
0.2338501513004303,
-0.03061983548104763,
0.11898288130760193,
0.046719349920749664,
0.14934834837913513,
-0.010445707477629185,
0.08233808726072311,
0.027131231501698494,
-0.04679834842681885,
0.00442399550229311,
-0.05845470353960991,
-0.02737496607005596,
0.05611835792660713,
0.009524277411401272,
0.06174055486917496,
-0.14609788358211517,
0.04021992161870003,
0.04074149951338768,
0.3216713070869446,
0.09521473944187164,
-0.3200629651546478,
-0.09652818739414215,
0.011347365565598011,
-0.04334448650479317,
-0.03889163210988045,
0.01554038655012846,
0.123055599629879,
-0.11189860105514526,
0.040168121457099915,
-0.08378422260284424,
0.07340095192193985,
-0.0563025176525116,
0.0006697721546515822,
0.061335526406764984,
0.07004035264253616,
-0.03237374126911163,
0.06144168972969055,
-0.2671686112880707,
0.3142991065979004,
-0.005520142149180174,
0.0663713589310646,
-0.03382843732833862,
0.010994086973369122,
0.03681758791208267,
0.045163482427597046,
0.11321792751550674,
-0.006273183971643448,
-0.016403881832957268,
-0.19710227847099304,
-0.09711791574954987,
0.0010448074899613857,
0.14233660697937012,
-0.13545261323451996,
0.13485506176948547,
-0.020371386781334877,
-0.02212722972035408,
0.04951077327132225,
-0.05359740927815437,
-0.07474493980407715,
-0.07667071372270584,
0.015837302431464195,
-0.04838578775525093,
0.09257218986749649,
-0.11213507503271103,
-0.10294575244188309,
-0.05642145872116089,
0.16175243258476257,
-0.08313973993062973,
-0.023696739226579666,
-0.14469006657600403,
0.08165226131677628,
0.11691353470087051,
-0.07467783987522125,
0.04799332097172737,
0.011882729828357697,
0.09756378829479218,
0.006507467944175005,
0.020168369635939598,
0.12725766003131866,
-0.07493766397237778,
-0.24345074594020844,
-0.07379928976297379,
0.16425682604312897,
0.04737541452050209,
0.06218978762626648,
-0.020636145025491714,
0.013225488364696503,
0.008750849403440952,
-0.0818474218249321,
0.05467457324266434,
0.003828031476587057,
0.0675240308046341,
0.043265461921691895,
-0.04793025553226471,
0.07724828273057938,
-0.07193075865507126,
-0.06222173199057579,
0.13095422089099884,
0.33483606576919556,
-0.09635496139526367,
0.011472707614302635,
0.04931297525763512,
-0.03371739387512207,
-0.1837364137172699,
0.049637794494628906,
0.1108059287071228,
0.035809386521577835,
0.007961256429553032,
-0.18241021037101746,
0.04599817842245102,
0.10601646453142166,
-0.03383270278573036,
0.10892705619335175,
-0.30864861607551575,
-0.13372834026813507,
0.07255978882312775,
0.1209786981344223,
-0.005869273561984301,
-0.1761765480041504,
-0.05795113742351532,
-0.014031792990863323,
-0.0807437151670456,
0.04537206143140793,
-0.04926569014787674,
0.11976839601993561,
-0.00927270669490099,
0.017917636781930923,
0.027491768822073936,
-0.06395022571086884,
0.14115381240844727,
0.002152033383026719,
0.07888731360435486,
-0.025404060259461403,
-0.007098656613379717,
0.02518838830292225,
-0.0958738923072815,
0.001809804467484355,
-0.06530822068452835,
0.03624776005744934,
-0.10497786104679108,
-0.025941554456949234,
-0.09355448931455612,
0.035661518573760986,
-0.06296688318252563,
-0.07612354308366776,
-0.02113805152475834,
0.0692538470029831,
0.059495046734809875,
-0.000994945876300335,
0.11530106514692307,
-0.04499976709485054,
0.18053390085697174,
0.07707355171442032,
0.09896054863929749,
-0.0052468422800302505,
-0.026921294629573822,
0.006530153565108776,
-0.018569860607385635,
0.04550619050860405,
-0.1630646139383316,
0.010358734987676144,
0.14169880747795105,
0.048300713300704956,
0.1383179873228073,
0.07455778867006302,
-0.054039690643548965,
-0.002657588105648756,
0.08455534279346466,
-0.1060745120048523,
-0.09982594847679138,
-0.01662692427635193,
-0.004295541904866695,
-0.15453535318374634,
0.05856222286820412,
0.10226317495107651,
-0.05915098264813423,
-0.004640514962375164,
0.0031607626006007195,
0.01518670842051506,
-0.03803389519453049,
0.21645911037921906,
0.06499888002872467,
0.10749968886375427,
-0.07032289355993271,
0.07633794099092484,
0.0394078865647316,
-0.1295686662197113,
0.002498235087841749,
0.09313361346721649,
-0.09165076911449432,
-0.017583414912223816,
0.0352177768945694,
0.06955299526453018,
-0.011164396069943905,
-0.013891064561903477,
-0.1367364078760147,
-0.12457802146673203,
0.06271057575941086,
0.13299183547496796,
0.047167856246232986,
0.03990764543414116,
-0.007568755187094212,
0.045927222818136215,
-0.13737502694129944,
0.11776640266180038,
0.0718764066696167,
0.09781058877706528,
-0.15457916259765625,
0.17085106670856476,
-0.015758251771330833,
0.019763179123401642,
-0.00698248203843832,
0.02648162469267845,
-0.12083495408296585,
0.01095782220363617,
-0.08920542150735855,
-0.07374616712331772,
-0.050569433718919754,
-0.02447526343166828,
-0.012345978058874607,
-0.037433210760354996,
-0.015616913326084614,
-0.007456702645868063,
-0.11028221994638443,
-0.05313751846551895,
-0.006245793309062719,
0.041434358805418015,
-0.09266477823257446,
-0.03440343961119652,
0.028545433655381203,
-0.1157660037279129,
0.09792108833789825,
0.02282113954424858,
0.055610161274671555,
0.006489242427051067,
-0.08671729266643524,
0.048534881323575974,
0.025602340698242188,
-0.03488725796341896,
0.03602144867181778,
-0.13137286901474,
-0.01998015306890011,
-0.07015024125576019,
0.023960541933774948,
0.02036895789206028,
0.013447019271552563,
-0.1393921971321106,
0.016804875805974007,
-0.03725937008857727,
-0.0447179451584816,
-0.06803189218044281,
0.04865936189889908,
0.04484105855226517,
0.009654705412685871,
0.13732945919036865,
-0.07363197952508926,
0.05417099967598915,
-0.21995916962623596,
-0.02023734338581562,
-0.020662950351834297,
-0.07390841841697693,
-0.059856731444597244,
-0.031100768595933914,
0.09003210067749023,
-0.06077376380562782,
0.04740811139345169,
-0.05757075548171997,
0.06628945469856262,
0.0287914015352726,
-0.11407186090946198,
0.08199067413806915,
0.055179499089717865,
0.17899319529533386,
0.05870143324136734,
-0.03755759820342064,
0.056252241134643555,
0.05300760269165039,
0.07399261742830276,
0.08573877811431885,
0.19327671825885773,
0.14073652029037476,
0.008584070950746536,
0.09491685032844543,
0.030790336430072784,
-0.11099264770746231,
-0.15404178202152252,
0.07282307744026184,
-0.03541440889239311,
0.08171095699071884,
-0.03012070059776306,
0.18625539541244507,
0.13410122692584991,
-0.19823101162910461,
0.017717592418193817,
-0.04120210185647011,
-0.09466315805912018,
-0.09100904315710068,
-0.040731411427259445,
-0.06620029360055923,
-0.17350643873214722,
0.002820468507707119,
-0.1043599545955658,
0.00833263248205185,
0.10016939789056778,
0.027184393256902695,
0.03166208416223526,
0.1842142641544342,
0.05902206152677536,
0.03173193708062172,
0.10302111506462097,
0.029407870024442673,
0.01449019368737936,
-0.040177855640649796,
-0.11342030763626099,
0.008726200088858604,
-0.05817209556698799,
0.02974017523229122,
-0.07982254773378372,
-0.09796697646379471,
0.05506235361099243,
0.0401853583753109,
-0.10774514079093933,
0.022755656391382217,
0.015682166442275047,
0.06546729058027267,
0.07918300479650497,
0.013511260971426964,
-0.011530299670994282,
-0.026455726474523544,
0.2736748456954956,
-0.11096831411123276,
-0.057911474257707596,
-0.11604049056768417,
0.276109904050827,
0.023089919239282608,
0.004437898751348257,
0.006575553212314844,
-0.10406199097633362,
0.021270373836159706,
0.16987177729606628,
0.16983802616596222,
-0.05816798657178879,
-0.011164343915879726,
0.023655472323298454,
-0.01694709248840809,
-0.04286215454339981,
0.08012169599533081,
0.11874320358037949,
0.038619514554739,
-0.07839573919773102,
-0.029237525537610054,
-0.021336648613214493,
-0.05925978720188141,
-0.027936778962612152,
0.07435966283082962,
0.04185878857970238,
0.021818222478032112,
-0.03681493550539017,
0.1153947114944458,
-0.0422409325838089,
-0.14037849009037018,
0.06461099535226822,
-0.19820751249790192,
-0.17797724902629852,
-0.05992492288351059,
0.019284240901470184,
-0.005057427100837231,
0.0735197588801384,
-0.003134271129965782,
-0.02933604270219803,
0.08874431997537613,
-0.0028697072993963957,
-0.021355431526899338,
-0.11166331171989441,
0.06775889545679092,
-0.060605891048908234,
0.19891080260276794,
-0.06368661671876907,
-0.018367283046245575,
0.1399906873703003,
0.02789357118308544,
-0.09125188738107681,
0.041055336594581604,
0.09433384984731674,
-0.08889453113079071,
0.043798550963401794,
0.1633727103471756,
-0.037438392639160156,
0.11478983610868454,
0.04705578833818436,
-0.15230421721935272,
0.027293048799037933,
-0.0799601599574089,
-0.05998973548412323,
-0.09156941622495651,
0.0032499164808541536,
-0.02276538498699665,
0.14698822796344757,
0.23762516677379608,
-0.06781813502311707,
0.02527342364192009,
-0.05763641744852066,
0.004105256870388985,
0.06053725257515907,
0.09897112846374512,
-0.030756356194615364,
-0.26314452290534973,
0.01300955843180418,
0.047255389392375946,
-0.0009899468859657645,
-0.2761410176753998,
-0.09146128594875336,
0.033815257251262665,
-0.05499590188264847,
-0.0767478421330452,
0.10458691418170929,
0.05527058616280556,
0.05792570114135742,
-0.047644905745983124,
-0.09549151360988617,
-0.0706389769911766,
0.19909842312335968,
-0.17817947268486023,
-0.07694614678621292
] |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilhubert-finetuned-ks-open-close
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0835
- Accuracy: 0.9725
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1629 | 1.0 | 285 | 0.1889 | 0.9329 |
| 0.1397 | 2.0 | 570 | 0.1106 | 0.9638 |
| 0.1085 | 3.0 | 855 | 0.0959 | 0.9678 |
| 0.0882 | 4.0 | 1141 | 0.0874 | 0.9704 |
| 0.0892 | 5.0 | 1425 | 0.0835 | 0.9725 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["audiofolder"], "metrics": ["accuracy"], "base_model": "ntu-spml/distilhubert", "model-index": [{"name": "distilhubert-finetuned-ks-open-close", "results": [{"task": {"type": "audio-classification", "name": "Audio Classification"}, "dataset": {"name": "audiofolder", "type": "audiofolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9724984249595968, "name": "Accuracy"}]}]}]} | audio-classification | iamhack/distilhubert-finetuned-ks-open-close | [
"transformers",
"tensorboard",
"safetensors",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:audiofolder",
"base_model:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2024-02-09T10:47:15+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #hubert #audio-classification #generated_from_trainer #dataset-audiofolder #base_model-ntu-spml/distilhubert #license-apache-2.0 #model-index #endpoints_compatible #region-us
| distilhubert-finetuned-ks-open-close
====================================
This model is a fine-tuned version of ntu-spml/distilhubert on the audiofolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0835
* Accuracy: 0.9725
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 5
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.0+cu121
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #hubert #audio-classification #generated_from_trainer #dataset-audiofolder #base_model-ntu-spml/distilhubert #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
76,
144,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #hubert #audio-classification #generated_from_trainer #dataset-audiofolder #base_model-ntu-spml/distilhubert #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
-0.1615830957889557,
0.1398753970861435,
-0.0016618343070149422,
0.07921982556581497,
0.1360265463590622,
-0.0053278920240700245,
0.09985001385211945,
0.11839263141155243,
-0.09791534394025803,
0.10151681303977966,
0.09307698905467987,
0.1065090000629425,
0.03669612109661102,
0.13653548061847687,
-0.04326090216636658,
-0.2890721559524536,
0.003490997012704611,
0.006989047862589359,
-0.1707390546798706,
0.11099109798669815,
0.09610409289598465,
-0.1079963743686676,
0.05274572968482971,
0.019685665145516396,
-0.13816557824611664,
0.010674882680177689,
-0.023034250363707542,
-0.058583684265613556,
0.08349626511335373,
0.03613116592168808,
0.08113352954387665,
0.04653578996658325,
0.09021066874265671,
-0.20703621208667755,
0.012995574623346329,
0.0701482892036438,
0.020879345014691353,
0.08536889404058456,
0.09668292105197906,
-0.032311610877513885,
0.09209035336971283,
-0.05959036946296692,
0.05615387484431267,
0.05262070894241333,
-0.09337858110666275,
-0.29779747128486633,
-0.1017712727189064,
0.07880333065986633,
0.12774908542633057,
0.07932351529598236,
-0.03781583532691002,
0.06863173842430115,
-0.06933744996786118,
0.09397771209478378,
0.26424962282180786,
-0.2544861137866974,
-0.07301576435565948,
0.031041545793414116,
0.06922893226146698,
0.04142170771956444,
-0.11931337416172028,
-0.01718633621931076,
0.0755414068698883,
0.019472502171993256,
0.10493593662977219,
0.00024448594194836915,
0.03214338421821594,
-0.005540455691516399,
-0.1529088318347931,
-0.03116725943982601,
0.16555747389793396,
0.09888205677270889,
-0.05718785524368286,
-0.07380355149507523,
-0.014528225176036358,
-0.23592248558998108,
-0.025550078600645065,
0.005343062337487936,
0.04047646373510361,
-0.06562187522649765,
-0.11988968402147293,
0.03158304840326309,
-0.07578698545694351,
-0.09569528698921204,
0.04792114719748497,
0.14533166587352753,
0.04971630498766899,
-0.0181408878415823,
0.013523666188120842,
0.1260416954755783,
0.03292226418852806,
-0.15254613757133484,
-0.012910427525639534,
0.025381265208125114,
-0.08419791609048843,
-0.033455610275268555,
-0.026780514046549797,
-0.010472067631781101,
-0.0021296858321875334,
0.18986168503761292,
-0.06334365159273148,
0.05743878707289696,
0.05728722736239433,
0.035372842103242874,
-0.06849188357591629,
0.13108161091804504,
-0.07409008592367172,
-0.07386514544487,
-0.03946052864193916,
0.11139407008886337,
0.00019891657575499266,
-0.011229426600039005,
-0.0723746046423912,
0.051034584641456604,
0.09785152226686478,
0.021929936483502388,
-0.03598230332136154,
0.00863677728921175,
-0.07414215058088303,
-0.033663585782051086,
0.048510316759347916,
-0.07985077798366547,
0.036312494426965714,
0.026264173910021782,
-0.08095420897006989,
-0.026886621490120888,
0.011371384374797344,
0.03270351141691208,
0.024499015882611275,
0.18140873312950134,
-0.0925324335694313,
-0.021886520087718964,
-0.07961545884609222,
-0.08519682288169861,
0.037651173770427704,
-0.08430972695350647,
0.02556130662560463,
-0.05773361027240753,
-0.12978968024253845,
-0.054786741733551025,
0.07003810256719589,
-0.03332441300153732,
-0.09028307348489761,
-0.05645093694329262,
-0.10466911643743515,
0.040729451924562454,
-0.028818348422646523,
0.14506584405899048,
-0.0627548024058342,
0.1122865155339241,
0.04114105552434921,
0.05451451987028122,
0.027745965868234634,
0.06978438049554825,
-0.053138941526412964,
0.06251586973667145,
-0.19547396898269653,
0.052451588213443756,
-0.11035223305225372,
0.052965596318244934,
-0.11367115378379822,
-0.11809353530406952,
-0.02404630370438099,
0.020126523450016975,
0.0636119544506073,
0.09612244367599487,
-0.16048172116279602,
-0.12137752026319504,
0.15465398132801056,
-0.08404231816530228,
-0.12094644457101822,
0.12376844137907028,
-0.01455499418079853,
-0.011045738123357296,
0.04687883332371712,
0.15274682641029358,
0.10809974372386932,
-0.11787740141153336,
-0.059612784534692764,
-0.04288908466696739,
0.11633041501045227,
-0.0000016305552890116815,
0.11459623277187347,
-0.03254282474517822,
0.039722759276628494,
-0.0076932525262236595,
-0.04908587783575058,
0.03217627853155136,
-0.09648687392473221,
-0.0744989812374115,
-0.011956044472754002,
-0.10054440051317215,
0.0333636999130249,
0.06407377868890762,
0.043252505362033844,
-0.09624360501766205,
-0.13198722898960114,
0.06212613731622696,
0.1169440820813179,
-0.07617699354887009,
0.012865601107478142,
-0.06036556139588356,
0.09431225061416626,
-0.058213647454977036,
-0.04281260445713997,
-0.16700753569602966,
-0.013773750513792038,
0.015618548728525639,
-0.04206671565771103,
0.016201365739107132,
-0.01393871009349823,
0.06506658345460892,
0.0772111713886261,
-0.06850215792655945,
-0.08856505155563354,
-0.06746266037225723,
-0.006701757200062275,
-0.0751916840672493,
-0.2420278787612915,
-0.08296746760606766,
-0.024364318698644638,
0.17238903045654297,
-0.24973376095294952,
0.018794434145092964,
0.01284649409353733,
0.13834130764007568,
0.06888434290885925,
-0.061149533838033676,
-0.013008181937038898,
0.06625824421644211,
-0.025907199829816818,
-0.06462574005126953,
0.018104322254657745,
0.00874112918972969,
-0.0951022207736969,
-0.040506329387426376,
-0.10723956674337387,
0.15150763094425201,
0.09748037159442902,
0.006058230996131897,
-0.09895161539316177,
-0.04357434809207916,
-0.08786623179912567,
-0.05555126816034317,
-0.040049053728580475,
-0.014123610220849514,
0.11184253543615341,
0.014297502115368843,
0.12337779998779297,
-0.09036245942115784,
-0.05653320997953415,
0.045523885637521744,
-0.013476773165166378,
-0.006373829673975706,
0.13550591468811035,
0.09202540665864944,
-0.055179160088300705,
0.14671559631824493,
0.10463114827871323,
-0.07810629904270172,
0.1805448681116104,
-0.06979534775018692,
-0.11793211102485657,
-0.018411163240671158,
0.02533748373389244,
0.02730870060622692,
0.14627106487751007,
-0.1043776348233223,
0.004537604283541441,
0.015372869558632374,
0.032716311514377594,
0.014712780714035034,
-0.1984296441078186,
-0.015480582602322102,
0.041869472712278366,
-0.04780595004558563,
-0.04956464841961861,
-0.002364593558013439,
-0.02285822667181492,
0.07668152451515198,
-0.009307121858000755,
-0.052435748279094696,
0.0063423714600503445,
0.005838167853653431,
-0.07532712817192078,
0.18796920776367188,
-0.08101558685302734,
-0.11429441720247269,
-0.1603935956954956,
-0.021230001002550125,
-0.03308245539665222,
-0.0010108640417456627,
0.06467127054929733,
-0.09690436720848083,
-0.03179309889674187,
-0.054357558488845825,
0.05423802137374878,
-0.02157086692750454,
0.027921054512262344,
0.015913821756839752,
0.029466189444065094,
0.08773211389780045,
-0.10633688420057297,
0.033219870179891586,
-0.020103007555007935,
-0.02643861435353756,
0.008202638477087021,
0.03524700179696083,
0.08644755929708481,
0.1582559496164322,
0.05373752862215042,
0.021865198388695717,
-0.03246317431330681,
0.1959322988986969,
-0.12520797550678253,
-0.01541701890528202,
0.10745684802532196,
-0.01603221893310547,
0.039457160979509354,
0.13515418767929077,
0.055472079664468765,
-0.08338156342506409,
0.02946864441037178,
0.08925523608922958,
-0.031236007809638977,
-0.24207019805908203,
-0.012201416306197643,
-0.051039665937423706,
0.021418172866106033,
0.09072645753622055,
0.03664914891123772,
0.04466475546360016,
0.05739269778132439,
-0.017411716282367706,
0.026097888126969337,
-0.009478983469307423,
0.05446137860417366,
0.005816934164613485,
0.039132434874773026,
0.12321339547634125,
-0.02597358450293541,
-0.02352912165224552,
0.03301868215203285,
0.011719775386154652,
0.25397226214408875,
-0.0065715136006474495,
0.13385188579559326,
0.07824975252151489,
0.13688543438911438,
0.001267425250262022,
0.07503630220890045,
0.0027745328843593597,
-0.03914429992437363,
0.019713617861270905,
-0.05822545662522316,
0.010059019550681114,
0.046329203993082047,
0.04559594765305519,
0.06840803474187851,
-0.14436104893684387,
0.030610011890530586,
0.017481008544564247,
0.3194413185119629,
0.09576907753944397,
-0.31776389479637146,
-0.10934492945671082,
0.009937824681401253,
-0.054674047976732254,
-0.03239862993359566,
0.03359726071357727,
0.12915004789829254,
-0.08114386349916458,
0.0721835047006607,
-0.06963451951742172,
0.0951848030090332,
-0.040171314030885696,
-0.013208222575485706,
0.1053018569946289,
0.09138662368059158,
-0.021852336823940277,
0.049545977264642715,
-0.20413392782211304,
0.28225797414779663,
0.004402931313961744,
0.08387762308120728,
-0.01692875102162361,
0.035019632428884506,
0.040324438363313675,
0.012364924885332584,
0.1098770871758461,
-0.008917339146137238,
-0.09167344123125076,
-0.18823154270648956,
-0.1017673909664154,
-0.0033226204104721546,
0.12718844413757324,
-0.0991954654455185,
0.1097814217209816,
-0.017865780740976334,
-0.03258572146296501,
0.052997224032878876,
-0.05932391807436943,
-0.10690639168024063,
-0.0882113128900528,
0.009526257403194904,
0.0044626472517848015,
0.03739337623119354,
-0.10457351058721542,
-0.12629812955856323,
-0.10157842934131622,
0.127873495221138,
-0.09619417041540146,
-0.02368933893740177,
-0.12991942465305328,
0.09060975164175034,
0.14837002754211426,
-0.06344704329967499,
0.06681884080171585,
0.023802997544407845,
0.11709879338741302,
0.028021741658449173,
-0.01514383964240551,
0.11126985400915146,
-0.09161274880170822,
-0.22820788621902466,
-0.06471524387598038,
0.18279290199279785,
0.057075150310993195,
0.05512439087033272,
-0.043479446321725845,
0.029800890013575554,
0.013071965426206589,
-0.07118464261293411,
0.0650290697813034,
-0.03898525983095169,
0.053522657603025436,
0.05460018292069435,
-0.029238509014248848,
0.0006464087055064738,
-0.034487657248973846,
-0.05495917797088623,
0.09362928569316864,
0.3139086663722992,
-0.07653658837080002,
0.017496732994914055,
0.03218403458595276,
-0.03684613108634949,
-0.14984843134880066,
0.07025366276502609,
0.153612419962883,
0.03624630346894264,
0.04010020196437836,
-0.20010210573673248,
0.11770068854093552,
0.09659452736377716,
-0.033434558659791946,
0.1511242687702179,
-0.28084245324134827,
-0.12914790213108063,
0.10229967534542084,
0.08778244256973267,
-0.034271080046892166,
-0.15570169687271118,
-0.06923305988311768,
-0.005847549997270107,
-0.15047922730445862,
0.08453214168548584,
-0.07019158452749252,
0.1231294646859169,
0.010906161740422249,
0.014457128942012787,
0.013185696676373482,
-0.04926302656531334,
0.12571315467357635,
0.035880472511053085,
0.07887336611747742,
0.0039139073342084885,
0.012142409570515156,
0.016402188688516617,
-0.06250793486833572,
0.004529292229562998,
-0.09236612170934677,
0.021250275894999504,
-0.10574965178966522,
-0.031565502285957336,
-0.08186209201812744,
0.03936136141419411,
-0.06369613111019135,
-0.042387954890728,
-0.03064776211977005,
0.05584859848022461,
0.03500491380691528,
-0.007462845183908939,
0.1624355912208557,
-0.020632505416870117,
0.1577833890914917,
0.1067064106464386,
0.06610556691884995,
-0.012144658714532852,
-0.12970267236232758,
-0.012904160656034946,
-0.0239956546574831,
0.06219262257218361,
-0.13793402910232544,
0.03492233157157898,
0.1365111768245697,
0.060541778802871704,
0.14899522066116333,
0.06383281201124191,
-0.07326611131429672,
0.013432943262159824,
0.0901794359087944,
-0.08864285051822662,
-0.08868049085140228,
-0.0330498069524765,
-0.030028540641069412,
-0.13862928748130798,
0.05854536220431328,
0.09813343733549118,
-0.05015185847878456,
-0.007852155715227127,
0.008528947830200195,
0.012409243732690811,
-0.06165982037782669,
0.23744916915893555,
0.05325707048177719,
0.08474788814783096,
-0.10071995854377747,
0.09313802421092987,
0.0271504744887352,
-0.13803412020206451,
0.0051778219640254974,
0.05094000697135925,
-0.04764936491847038,
-0.004261087626218796,
0.018405834212899208,
0.0935409739613533,
-0.009098212234675884,
-0.05636071786284447,
-0.1345154047012329,
-0.11822275072336197,
0.0692894235253334,
0.1091914102435112,
0.058950506150722504,
0.024468692019581795,
-0.02192261442542076,
0.06821580231189728,
-0.11345621943473816,
0.12918663024902344,
0.11118166893720627,
0.10206268727779388,
-0.20269925892353058,
0.11717306077480316,
0.005682316608726978,
-0.004538502544164658,
-0.0033927620388567448,
0.023254262283444405,
-0.11487162858247757,
0.0024078066926449537,
-0.09246420860290527,
-0.0447220616042614,
-0.05307044833898544,
-0.007773590739816427,
0.00417781388387084,
-0.04118535295128822,
-0.07004348188638687,
0.035901352763175964,
-0.12190039455890656,
-0.04338037222623825,
0.017325159162282944,
0.07033104449510574,
-0.1130552738904953,
-0.009095178917050362,
0.04539833590388298,
-0.11108271032571793,
0.08174337446689606,
0.046734459698200226,
0.03624020889401436,
0.026234593242406845,
-0.10207148641347885,
0.011000514961779118,
0.05875981226563454,
-0.02254941500723362,
0.047378018498420715,
-0.15924672782421112,
-0.013138975016772747,
-0.044521793723106384,
0.04735938832163811,
-0.01563318818807602,
0.006909500807523727,
-0.12812474370002747,
-0.039846841245889664,
-0.047129157930612564,
-0.04797063767910004,
-0.05137544870376587,
0.03715651109814644,
0.08784133940935135,
0.02556367591023445,
0.19655585289001465,
-0.06530003249645233,
0.019138583913445473,
-0.22404402494430542,
0.010696705430746078,
-0.008432985283434391,
-0.08683717995882034,
-0.08327607810497284,
-0.008342544548213482,
0.06850191950798035,
-0.06424274295568466,
0.0770004540681839,
-0.05407194793224335,
0.0349947065114975,
0.02880663424730301,
-0.08382954448461533,
0.056088872253894806,
0.052404534071683884,
0.1887541562318802,
0.020061522722244263,
-0.018372178077697754,
0.03812684491276741,
0.02209644392132759,
0.08225084096193314,
0.08601043373346329,
0.17916753888130188,
0.14125971496105194,
-0.04870950058102608,
0.08886124938726425,
0.053639210760593414,
-0.11869493871927261,
-0.18477925658226013,
0.10080821067094803,
-0.04035397991538048,
0.12946510314941406,
0.009395222179591656,
0.18438123166561127,
0.1005653366446495,
-0.2002844661474228,
0.048542384058237076,
-0.038477737456560135,
-0.08388601243495941,
-0.1116815134882927,
-0.04945804551243782,
-0.07446976751089096,
-0.19809655845165253,
0.03078337386250496,
-0.1238107979297638,
0.03732221946120262,
0.06259690225124359,
0.025355136021971703,
0.02858193777501583,
0.17738042771816254,
-0.002048963215202093,
0.025534968823194504,
0.08654645830392838,
0.01565253734588623,
-0.044443365186452866,
-0.043721284717321396,
-0.08327453583478928,
0.04685738682746887,
-0.04346371814608574,
0.04180331900715828,
-0.09621831029653549,
-0.11319496482610703,
0.08144129812717438,
0.04682314023375511,
-0.09568905085325241,
0.022268302738666534,
0.002209795406088233,
0.0671575516462326,
0.07021645456552505,
0.005089521408081055,
0.016762901097536087,
-0.017790667712688446,
0.24007797241210938,
-0.10736396908760071,
-0.007207859307527542,
-0.15749934315681458,
0.20496529340744019,
0.009045005775988102,
-0.0030873839277774096,
0.0355646014213562,
-0.10298521816730499,
-0.00017672906687948853,
0.13937486708164215,
0.1528109759092331,
-0.010022545233368874,
-0.030811866745352745,
0.013736243359744549,
-0.015032834373414516,
-0.06363540887832642,
0.053160738199949265,
0.11153675615787506,
0.106455497443676,
-0.059110112488269806,
-0.0574890673160553,
-0.03890950605273247,
-0.05620575696229935,
0.012892934493720531,
0.0840049684047699,
0.03607824444770813,
-0.019491707906126976,
-0.023355262354016304,
0.11305028945207596,
-0.07617713510990143,
-0.1558087170124054,
0.05202692002058029,
-0.16833192110061646,
-0.19013608992099762,
-0.05783453583717346,
0.06802166998386383,
0.024168124422430992,
0.053393375128507614,
-0.0052850618958473206,
-0.05167797952890396,
0.09610412269830704,
-0.0014483581762760878,
-0.04989607632160187,
-0.12503878772258759,
0.06763029098510742,
-0.0762687399983406,
0.2145402431488037,
-0.05315336957573891,
0.03237186744809151,
0.109428271651268,
0.053846850991249084,
-0.06082339584827423,
0.015493004582822323,
0.06487103551626205,
-0.1441418081521988,
0.02169685997068882,
0.1979130059480667,
-0.04716942086815834,
0.15407386422157288,
0.025191795080900192,
-0.14829155802726746,
0.012446981854736805,
-0.06570666283369064,
-0.07081235200166702,
-0.08988986909389496,
-0.0033850260078907013,
-0.06512069702148438,
0.12288787215948105,
0.20581106841564178,
-0.060384515672922134,
-0.013901510275900364,
-0.05558433011174202,
0.045221492648124695,
0.08141692727804184,
0.10589871555566788,
-0.0035898808855563402,
-0.28340277075767517,
0.04662309214472771,
0.0034345549065619707,
-0.01670582965016365,
-0.2689126133918762,
-0.07519789785146713,
0.023427078500390053,
-0.06678373366594315,
-0.06760057806968689,
0.0769486278295517,
0.07605765014886856,
0.05867033824324608,
-0.06328419595956802,
-0.05321073532104492,
-0.07080455124378204,
0.17301474511623383,
-0.17470189929008484,
-0.0768028199672699
] |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# donut-base-sroie
This model is a fine-tuned version of [Saksham278/donut-base-sroie](https://huggingface.co/Saksham278/donut-base-sroie) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "base_model": "Saksham278/donut-base-sroie", "model-index": [{"name": "donut-base-sroie", "results": []}]} | null | Saksham278/donut-base-sroie | [
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:Saksham278/donut-base-sroie",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-09T10:49:16+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #dataset-imagefolder #base_model-Saksham278/donut-base-sroie #license-mit #endpoints_compatible #region-us
|
# donut-base-sroie
This model is a fine-tuned version of Saksham278/donut-base-sroie on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| [
"# donut-base-sroie\n\nThis model is a fine-tuned version of Saksham278/donut-base-sroie on the imagefolder dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #dataset-imagefolder #base_model-Saksham278/donut-base-sroie #license-mit #endpoints_compatible #region-us \n",
"# donut-base-sroie\n\nThis model is a fine-tuned version of Saksham278/donut-base-sroie on the imagefolder dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
71,
39,
6,
12,
8,
3,
103,
4,
38
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #dataset-imagefolder #base_model-Saksham278/donut-base-sroie #license-mit #endpoints_compatible #region-us \n# donut-base-sroie\n\nThis model is a fine-tuned version of Saksham278/donut-base-sroie on the imagefolder dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
-0.09785477072000504,
0.1310334950685501,
-0.000744475401006639,
0.09542689472436905,
0.13243085145950317,
0.016905685886740685,
0.07995743304491043,
0.10238049179315567,
-0.06652273237705231,
0.0855102464556694,
0.06007327139377594,
-0.004968210123479366,
0.062053289264440536,
0.1656154990196228,
-0.011462061665952206,
-0.2410934865474701,
0.020502081140875816,
-0.027754737064242363,
-0.044157713651657104,
0.09056826680898666,
0.07301199436187744,
-0.08387073874473572,
0.07346751540899277,
-0.03949733451008797,
-0.15210619568824768,
0.01237193401902914,
-0.06070047989487648,
-0.04081623628735542,
0.07311667501926422,
0.0015830438351258636,
0.09963268786668777,
0.0310763418674469,
0.16571524739265442,
-0.21244904398918152,
0.005847457330673933,
0.07274607568979263,
0.037774354219436646,
0.07433587312698364,
0.03672851249575615,
0.007034737151116133,
0.05028011277318001,
-0.1908625215291977,
0.1039557084441185,
0.022897688671946526,
-0.06828352063894272,
-0.06799457967281342,
-0.09324754774570465,
0.1047273650765419,
0.10498043894767761,
0.09463448077440262,
0.010537692345678806,
0.08745253086090088,
-0.0449199378490448,
0.07098633795976639,
0.1462734490633011,
-0.17977502942085266,
-0.05367949977517128,
0.09530362486839294,
0.0390046201646328,
0.08339634537696838,
-0.11890055984258652,
0.0066217281855642796,
0.06459324061870575,
0.017155537381768227,
0.0605587363243103,
-0.007157668936997652,
-0.0840749591588974,
-0.019458923488855362,
-0.13303634524345398,
-0.04704974591732025,
0.1264704167842865,
0.08032221347093582,
-0.05531402677297592,
-0.08415296673774719,
-0.03569108620285988,
-0.12539371848106384,
-0.03318857401609421,
-0.03789207339286804,
0.03733692690730095,
-0.039761338382959366,
-0.07284975051879883,
-0.08365622162818909,
-0.08045849949121475,
-0.07746721804141998,
0.01078818179666996,
0.04273146390914917,
0.029680004343390465,
-0.0004050885618198663,
-0.003982864785939455,
0.09734639525413513,
-0.020214634016156197,
-0.12079057842493057,
-0.025700895115733147,
-0.009053248912096024,
-0.1160695031285286,
-0.054821230471134186,
0.005196539685130119,
-0.015747172757983208,
-0.0034434618428349495,
0.11789549142122269,
-0.07858559489250183,
0.07453414052724838,
-0.05003054067492485,
0.003738476661965251,
-0.024141749367117882,
0.12214845418930054,
-0.052851784974336624,
-0.013281500898301601,
-0.006734228692948818,
0.09984537214040756,
0.006756829563528299,
-0.023665597662329674,
-0.09082982689142227,
-0.033272724598646164,
0.0773978978395462,
0.05586867034435272,
-0.011146281845867634,
0.029663804918527603,
-0.016645217314362526,
-0.008812153711915016,
0.08707637339830399,
-0.12437033653259277,
0.0585591159760952,
-0.00857565738260746,
-0.07056708633899689,
-0.02711174264550209,
0.056009240448474884,
0.010048947297036648,
-0.02092117629945278,
0.07665108889341354,
-0.05736473947763443,
-0.0032086148858070374,
-0.07208921015262604,
-0.04153325408697128,
0.05162031203508377,
-0.06670909374952316,
-0.019771862775087357,
-0.0765213742852211,
-0.22424627840518951,
-0.06596019119024277,
0.013442750088870525,
-0.06027974560856819,
-0.033771075308322906,
-0.06068069115281105,
-0.0773775577545166,
0.009552213363349438,
0.01175407599657774,
0.11356489360332489,
-0.0392267219722271,
0.0632408857345581,
-0.034686196595430374,
0.014156908728182316,
0.03124465048313141,
0.035017430782318115,
-0.08529353886842728,
0.0394296832382679,
-0.15003402531147003,
0.10137469321489334,
-0.1095869317650795,
0.0517214760184288,
-0.12021681666374207,
-0.09175658971071243,
-0.06657025218009949,
-0.036321971565485,
0.03745114058256149,
0.15066146850585938,
-0.18946930766105652,
0.008098295889794827,
0.1309882253408432,
-0.0938921570777893,
-0.06074577942490578,
0.10917633771896362,
-0.04272708669304848,
-0.012951924465596676,
0.05799146369099617,
0.16623759269714355,
0.10534559190273285,
-0.15819457173347473,
-0.017739243805408478,
-0.006180930882692337,
0.04534006118774414,
0.016125215217471123,
0.06877276301383972,
-0.008495897054672241,
0.013441380113363266,
-0.001049492508172989,
-0.07991068810224533,
0.012495282106101513,
-0.07053054869174957,
-0.08042199909687042,
-0.05121690407395363,
-0.08265680074691772,
0.05160144716501236,
0.04159462824463844,
0.024455204606056213,
-0.04892609640955925,
-0.10780686140060425,
0.09010595083236694,
0.12005706131458282,
-0.05489233881235123,
0.006552271544933319,
-0.048345599323511124,
0.0022518853656947613,
-0.036779407411813736,
-0.033685531467199326,
-0.17532557249069214,
-0.1460953801870346,
0.026079297065734863,
-0.08411746472120285,
0.060049869120121,
-0.027767304331064224,
0.06074891611933708,
0.08038268983364105,
-0.03544964641332626,
-0.033090461045503616,
-0.08000971376895905,
0.022323397919535637,
-0.07371438294649124,
-0.16748134791851044,
-0.05197881907224655,
-0.0310926903039217,
0.19666948914527893,
-0.2408929318189621,
0.023146506398916245,
-0.0073382011614739895,
0.14036568999290466,
0.023074980825185776,
-0.06588614732027054,
0.03313535451889038,
0.04334382712841034,
0.0161174014210701,
-0.10868728160858154,
0.016050400212407112,
0.008972302079200745,
-0.05386914685368538,
-0.04297938197851181,
-0.14225426316261292,
-0.033943310379981995,
0.06841061264276505,
0.11063677817583084,
-0.11872053146362305,
-0.028869586065411568,
-0.04747840762138367,
-0.04895491153001785,
-0.10048656165599823,
-0.014706629328429699,
0.1478378176689148,
0.017136722803115845,
0.10415221750736237,
-0.02683459408581257,
-0.05173768475651741,
0.013998666778206825,
0.020492685958743095,
-0.0510961152613163,
0.06795184314250946,
0.0644146129488945,
-0.11778533458709717,
0.09918176382780075,
0.06201229989528656,
-0.0034963747020810843,
0.12802913784980774,
-0.0531294085085392,
-0.08803592622280121,
-0.01926051825284958,
0.024691622704267502,
-0.013463050127029419,
0.13021254539489746,
-0.05369071662425995,
0.007207307033240795,
0.013329987414181232,
0.00935645867139101,
0.028203032910823822,
-0.15622785687446594,
-0.0006921326275914907,
0.034511715173721313,
-0.04540709778666496,
0.008536696434020996,
-0.04695506393909454,
0.023232759907841682,
0.06727969646453857,
0.022164249792695045,
-0.012896465137600899,
0.01284161489456892,
-0.005427290219813585,
-0.09210050106048584,
0.14710092544555664,
-0.10512034595012665,
-0.12457328289747238,
-0.13228905200958252,
0.07751218974590302,
-0.027026282623410225,
-0.0211814995855093,
0.012426041066646576,
-0.06497088074684143,
-0.04868846386671066,
-0.10857230424880981,
-0.043467938899993896,
-0.0344071239233017,
-0.03116007149219513,
0.07034528255462646,
0.017310267314314842,
0.048267021775245667,
-0.09502138197422028,
0.01482781395316124,
0.0033821433316916227,
-0.08204743266105652,
-0.018233003094792366,
0.027660995721817017,
0.13083341717720032,
0.13186129927635193,
-0.022000083699822426,
0.009670277126133442,
-0.044351790100336075,
0.15988090634346008,
-0.0754963755607605,
0.034344013780355453,
0.09558695554733276,
-0.0019234571373090148,
0.0561276413500309,
0.1459340751171112,
0.011438577435910702,
-0.07002495974302292,
0.03581563010811806,
0.0548633337020874,
-0.04386794939637184,
-0.21313796937465668,
-0.05304504930973053,
-0.03178535774350166,
-0.0372731015086174,
0.10255170613527298,
0.06163404881954193,
0.03084891475737095,
0.050968293100595474,
-0.01923874206840992,
0.050088491290807724,
0.011489423923194408,
0.07558779418468475,
0.08065319061279297,
0.027878424152731895,
0.056315094232559204,
-0.04914715886116028,
-0.026455186307430267,
0.057088375091552734,
0.012709004804491997,
0.2412322610616684,
-0.025338569656014442,
0.09666147083044052,
0.011733124032616615,
0.12229065597057343,
-0.011187305673956871,
0.017375435680150986,
0.041178323328495026,
0.017292164266109467,
0.01464375015348196,
-0.07214903086423874,
-0.019294196739792824,
0.040756143629550934,
-0.034755073487758636,
0.0115865683183074,
-0.07883447408676147,
0.05933741107583046,
0.01173694059252739,
0.22043751180171967,
0.04422983527183533,
-0.3079436719417572,
-0.08173877000808716,
0.021555164828896523,
0.016142435371875763,
-0.06134587526321411,
-0.002069325651973486,
0.13600876927375793,
-0.12931884825229645,
0.06718412786722183,
-0.03925836458802223,
0.08714161813259125,
-0.04487805813550949,
-0.010167506523430347,
0.028530245646834373,
0.07684692740440369,
-0.007669985294342041,
0.07818805426359177,
-0.15265168249607086,
0.20861437916755676,
0.007762235123664141,
0.10565885156393051,
-0.05406194180250168,
0.03432082757353783,
0.031119415536522865,
0.10152345895767212,
0.1522565633058548,
-0.001773729221895337,
-0.1341862827539444,
-0.1857667863368988,
-0.10841037333011627,
0.02259671688079834,
0.10411882400512695,
-0.029064247384667397,
0.05261951684951782,
-0.02921302430331707,
-0.025188518688082695,
0.035393014550209045,
-0.057823166251182556,
-0.2019011527299881,
-0.12403371930122375,
0.006492694839835167,
-0.0019174175104126334,
0.0034713703207671642,
-0.07700245827436447,
-0.0975775495171547,
-0.024373726919293404,
0.13053789734840393,
0.008065060712397099,
-0.02743281051516533,
-0.1500139832496643,
0.1098918542265892,
0.12284722924232483,
-0.06411707401275635,
0.015492552891373634,
0.028132108971476555,
0.15773889422416687,
0.03219788148999214,
-0.09165460616350174,
0.03992995247244835,
-0.060296934098005295,
-0.1496976912021637,
-0.0420612096786499,
0.11115283519029617,
0.06333240866661072,
0.04885198175907135,
0.009131756611168385,
0.042195431888103485,
0.057036180049180984,
-0.0838833674788475,
0.018099404871463776,
0.08622615039348602,
0.07777240872383118,
0.08632981032133102,
-0.06797371804714203,
-0.04538464918732643,
-0.03221271559596062,
0.0014419767539948225,
0.11691296100616455,
0.16453470289707184,
-0.0891895592212677,
0.07513478398323059,
0.0731712281703949,
-0.08678431063890457,
-0.20294424891471863,
0.08650019764900208,
0.07686159759759903,
0.018295835703611374,
0.04182424768805504,
-0.15651138126850128,
0.09379886835813522,
0.12486959993839264,
-0.036378879100084305,
0.008579127490520477,
-0.31225845217704773,
-0.12663640081882477,
0.06404868513345718,
0.09634964913129807,
-0.0672421008348465,
-0.13853465020656586,
-0.05049614608287811,
-0.01553706731647253,
-0.17747294902801514,
0.0905621200799942,
-0.04776304215192795,
0.08117948472499847,
0.018123069778084755,
0.07625426352024078,
0.029838139191269875,
-0.037341564893722534,
0.13911846280097961,
0.04813455045223236,
0.07351602613925934,
-0.07671180367469788,
0.023942645639181137,
0.08168671280145645,
-0.09384344518184662,
0.08560004830360413,
0.00963511411100626,
0.07292374223470688,
-0.12535180151462555,
-0.008109592832624912,
-0.04820041358470917,
0.05760671943426132,
-0.05239531025290489,
-0.04417039453983307,
-0.052292436361312866,
0.07843214273452759,
0.05810832604765892,
-0.03525787591934204,
0.05815792456269264,
0.026233868673443794,
0.0860530361533165,
0.0843677967786789,
0.06052633747458458,
0.024550218135118484,
-0.0726051926612854,
-0.0063327280804514885,
-0.04013218730688095,
0.030844619497656822,
-0.14070121943950653,
0.017684178426861763,
0.1290530562400818,
0.06171577051281929,
0.14260002970695496,
0.021262170746922493,
-0.049377989023923874,
0.0038073656614869833,
0.03128385543823242,
-0.06401198357343674,
-0.17594727873802185,
-0.005743904504925013,
-0.043850045651197433,
-0.12156379967927933,
-0.02552611380815506,
0.09332027286291122,
-0.08513662964105606,
0.0017354527954012156,
-0.03459973633289337,
0.04598699510097504,
-0.012349630706012249,
0.16016501188278198,
0.0486462228000164,
0.06325139105319977,
-0.06801358610391617,
0.1188005656003952,
0.10241345316171646,
-0.10846522450447083,
0.06000707671046257,
0.08946342021226883,
-0.07904807478189468,
-0.032970596104860306,
0.08943503350019455,
0.16157189011573792,
-0.030056923627853394,
-0.0339803546667099,
-0.03817946836352348,
-0.08104229718446732,
0.035841427743434906,
0.09831138700246811,
0.017371943220496178,
-0.008289983496069908,
0.004903878550976515,
0.02250070311129093,
-0.16255298256874084,
0.09685461223125458,
0.03547949716448784,
0.08585959672927856,
-0.15278342366218567,
0.08099188655614853,
0.0063064987771213055,
0.05725914239883423,
-0.02052910439670086,
0.020737003535032272,
-0.07798026502132416,
-0.018442774191498756,
-0.09323545545339584,
0.03069702722132206,
-0.04274141788482666,
0.0032301745377480984,
-0.017484530806541443,
-0.05849984660744667,
-0.028941957280039787,
0.05660359933972359,
-0.05253065004944801,
-0.07832234352827072,
-0.0010440123733133078,
0.07071104645729065,
-0.15586154162883759,
-0.007199001964181662,
0.040274154394865036,
-0.08259215205907822,
0.04553140327334404,
0.045971669256687164,
0.027885090559720993,
0.027272667735815048,
-0.13524028658866882,
-0.009615182876586914,
0.03879799321293831,
0.022749388590455055,
0.04500999674201012,
-0.08017773926258087,
-0.009510498493909836,
-0.026975350454449654,
0.023119891062378883,
0.02047995664179325,
0.07259631901979446,
-0.11192581802606583,
-0.028008226305246353,
-0.07897308468818665,
-0.02413918823003769,
-0.05232241377234459,
0.06140594184398651,
0.10047192126512527,
0.03831231966614723,
0.11241418123245239,
-0.08491148799657822,
0.057023756206035614,
-0.1899888515472412,
-0.049287375062704086,
-0.007816092111170292,
-0.015768611803650856,
-0.02431507036089897,
-0.040976375341415405,
0.07895198464393616,
-0.03503664210438728,
0.07153747230768204,
-0.00013131831656210124,
0.11146025359630585,
0.04907730966806412,
-0.02076168730854988,
-0.007583532482385635,
0.029914487153291702,
0.14972446858882904,
0.03884845972061157,
-0.03468874841928482,
0.10958316177129745,
-0.01475646160542965,
0.06166671961545944,
0.04149525240063667,
0.16953440010547638,
0.1574229747056961,
-0.051944300532341,
0.042526885867118835,
0.10707089304924011,
-0.08406919986009598,
-0.13480351865291595,
0.10477324575185776,
-0.04747768118977547,
0.09951654076576233,
-0.0775282010436058,
0.09824907034635544,
0.09378837049007416,
-0.18099582195281982,
0.03935258463025093,
-0.0505688451230526,
-0.11284150183200836,
-0.08149535953998566,
-0.07006718963384628,
-0.08427617698907852,
-0.09778004884719849,
0.033952873200178146,
-0.10087140649557114,
0.014478824101388454,
0.12219252437353134,
0.006403968203812838,
0.045174382627010345,
0.17673763632774353,
-0.05178143084049225,
0.041383642703294754,
0.05958414077758789,
0.010703164152801037,
-0.017383387312293053,
-0.055465348064899445,
-0.04827936366200447,
0.09756298363208771,
0.029395677149295807,
0.08031489700078964,
-0.04216119647026062,
0.030908294022083282,
0.05271930992603302,
-0.013487781397998333,
-0.07344117015600204,
0.024258598685264587,
0.027034692466259003,
0.0359865166246891,
0.03508664295077324,
0.07124049216508865,
-0.010503517463803291,
-0.03502999246120453,
0.2856064736843109,
-0.05817286670207977,
-0.10658896714448929,
-0.1388230323791504,
0.0912187471985817,
0.03388049453496933,
-0.029445165768265724,
0.0645315945148468,
-0.14021244645118713,
0.02141927182674408,
0.10239164531230927,
0.13503976166248322,
-0.024934515357017517,
-0.005822312086820602,
-0.028586113825440407,
-0.005606064107269049,
-0.03051142767071724,
0.10783926397562027,
0.09720290452241898,
0.02084212563931942,
-0.06354027986526489,
-0.013606028631329536,
0.010046660900115967,
-0.07230091840028763,
-0.08743945509195328,
0.04135536029934883,
-0.005913340952247381,
0.0025183046236634254,
-0.033828575164079666,
0.12198237329721451,
-0.009134729392826557,
-0.18570435047149658,
0.06227560713887215,
-0.15550799667835236,
-0.15143941342830658,
-0.013222847133874893,
0.11706250160932541,
-0.011520611122250557,
0.01440543495118618,
-0.018818175420165062,
0.008370522409677505,
0.10703588277101517,
-0.012460409663617611,
-0.062025394290685654,
-0.0697098970413208,
0.0159110426902771,
-0.029718808829784393,
0.25200405716896057,
0.014637156389653683,
0.052409615367650986,
0.08702440559864044,
0.018501564860343933,
-0.17444917559623718,
0.0353504978120327,
0.05582588538527489,
-0.024971023201942444,
0.035578709095716476,
0.18740549683570862,
-0.06599855422973633,
0.07343581318855286,
0.045431800186634064,
-0.1426234245300293,
-0.009966788813471794,
-0.030170008540153503,
0.004097108729183674,
-0.07261773943901062,
0.006423760671168566,
-0.06948450952768326,
0.1659124344587326,
0.1543903946876526,
-0.022603297606110573,
0.01897783949971199,
-0.06891662627458572,
0.014863591641187668,
0.03902396559715271,
0.09700290858745575,
-0.007648379076272249,
-0.15881457924842834,
0.002363735344260931,
-0.04225080460309982,
0.04242902249097824,
-0.23387663066387177,
-0.10458020865917206,
0.006497495342046022,
-0.04260179400444031,
-0.0419495664536953,
0.13263627886772156,
0.1009858101606369,
0.012076595798134804,
-0.030080512166023254,
-0.13688404858112335,
-0.024448534473776817,
0.1466190665960312,
-0.14364153146743774,
-0.03885374590754509
] |
null | null | diffusers |
# DreamBooth trained by AutoTrain
Text encoder was not trained.
| {"tags": ["text-to-image", "diffusers", "autotrain"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "photo of virat", "inference": true} | text-to-image | anushvst/virat | [
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"has_space",
"region:us"
] | 2024-02-09T10:52:48+00:00 | [] | [] | TAGS
#diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us
|
# DreamBooth trained by AutoTrain
Text encoder was not trained.
| [
"# DreamBooth trained by AutoTrain\n\nText encoder was not trained."
] | [
"TAGS\n#diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us \n",
"# DreamBooth trained by AutoTrain\n\nText encoder was not trained."
] | [
45,
19
] | [
"passage: TAGS\n#diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us \n# DreamBooth trained by AutoTrain\n\nText encoder was not trained."
] | [
-0.02063869684934616,
0.12998254597187042,
-0.00014558587281499058,
0.05282456427812576,
0.16523675620555878,
0.04722703993320465,
0.16625140607357025,
0.08092519640922546,
-0.021600954234600067,
0.06268861889839172,
0.19911405444145203,
-0.005327701102942228,
0.005592701490968466,
0.22998546063899994,
-0.094501793384552,
-0.15147385001182556,
0.05843960493803024,
-0.017813973128795624,
0.08953600376844406,
0.04556926712393761,
0.01589704304933548,
-0.08332102000713348,
0.06851272284984589,
-0.1127990260720253,
-0.21184474229812622,
0.06736689060926437,
0.028242893517017365,
-0.08190154284238815,
0.023159906268119812,
0.057201284915208817,
0.11752432584762573,
0.05736266449093819,
0.06915528327226639,
-0.09377864748239517,
0.030588991940021515,
0.09211067855358124,
-0.037628743797540665,
0.060378964990377426,
0.002463718643411994,
0.007739691063761711,
-0.03909904137253761,
0.01951049454510212,
0.05348891019821167,
0.033195290714502335,
-0.09112479537725449,
0.09422965347766876,
0.008997537195682526,
0.05966416001319885,
0.005606517195701599,
0.1256808042526245,
-0.02887202799320221,
0.0914452075958252,
0.0028242841362953186,
0.10286186635494232,
0.050214264541864395,
-0.15577325224876404,
-0.05811230465769768,
0.22586119174957275,
0.06323451548814774,
0.18434374034404755,
-0.1056840792298317,
0.08215278387069702,
0.1282002329826355,
0.0043175057508051395,
-0.024307064712047577,
-0.0056144483387470245,
-0.053464896976947784,
-0.0875391811132431,
-0.04101261869072914,
-0.04863812029361725,
0.19171690940856934,
0.013884141109883785,
-0.014532854780554771,
-0.08809809386730194,
-0.1092078685760498,
-0.03936294838786125,
0.015471521764993668,
0.009576751850545406,
-0.05643317475914955,
0.06334297358989716,
-0.04036302492022514,
-0.0881064385175705,
-0.048688579350709915,
-0.03869857266545296,
-0.07886603474617004,
0.09238439798355103,
-0.0456368625164032,
0.0745692178606987,
-0.0938243567943573,
0.13909384608268738,
-0.026598775759339333,
-0.12820684909820557,
0.06501864641904831,
-0.0971466526389122,
0.015486733056604862,
0.06505174934864044,
-0.019916843622922897,
-0.1562809944152832,
0.019901327788829803,
0.030637366697192192,
0.07526841759681702,
0.05189061909914017,
-0.08258821815252304,
0.09015702456235886,
0.007376048713922501,
0.09042561054229736,
-0.016077103093266487,
-0.024903813377022743,
0.06223255768418312,
0.080438993871212,
0.023856146261096,
-0.14336538314819336,
-0.16565988957881927,
0.06790684908628464,
-0.017159676179289818,
0.04283891245722771,
0.03642508387565613,
-0.010275715962052345,
-0.031149128451943398,
-0.004403593484312296,
0.047221966087818146,
-0.04838476702570915,
0.023466823622584343,
-0.07434477657079697,
-0.008917812258005142,
0.014335056766867638,
0.1431507170200348,
0.007567800115793943,
-0.006044706329703331,
-0.008012169972062111,
-0.10112743824720383,
-0.01249670796096325,
-0.06397054344415665,
-0.082596056163311,
-0.05697616934776306,
-0.11640746891498566,
0.03807840123772621,
-0.16242456436157227,
-0.1366284042596817,
-0.010717466473579407,
0.012121928855776787,
-0.08239061385393143,
-0.0024879504926502705,
-0.08431833982467651,
-0.12462550401687622,
0.1450532078742981,
-0.013907280750572681,
-0.03597475588321686,
0.0006233238964341581,
0.06648663431406021,
-0.010329908691346645,
0.10745283216238022,
-0.17473040521144867,
0.01794232614338398,
-0.07896706461906433,
-0.0015359485987573862,
-0.08321953564882278,
0.16549469530582428,
-0.03203589841723442,
0.033024370670318604,
-0.03292569890618324,
0.04207007214426994,
0.0021412093192338943,
0.008031118661165237,
0.05329214408993721,
0.15599198639392853,
-0.19367799162864685,
-0.04072578251361847,
0.0876203402876854,
-0.08026987314224243,
-0.011655561625957489,
0.041991058737039566,
-0.022804416716098785,
0.047191135585308075,
0.005142057780176401,
0.15102070569992065,
-0.07513030618429184,
-0.1523657888174057,
-0.00003674626350402832,
0.019653983414173126,
-0.03947019204497337,
0.06174682825803757,
-0.03899246081709862,
0.060578037053346634,
-0.07573825865983963,
0.03253980353474617,
-0.005597305484116077,
0.08249075710773468,
-0.06469673663377762,
-0.07055705785751343,
-0.06726926565170288,
-0.021799663081765175,
0.06577687710523605,
0.01678086258471012,
0.07544080168008804,
-0.030378416180610657,
-0.07784181833267212,
0.03869107738137245,
0.04462023451924324,
-0.009920100681483746,
-0.007784112356603146,
-0.013205957598984241,
-0.04446694254875183,
-0.12920789420604706,
0.003658822737634182,
-0.09591405093669891,
-0.0857297033071518,
0.00785818975418806,
0.23912277817726135,
0.09514347463846207,
0.14679308235645294,
0.059998251497745514,
0.04194987192749977,
-0.031193705275654793,
-0.12705348432064056,
-0.0008300838526338339,
0.029192514717578888,
-0.08331938832998276,
-0.09998124092817307,
0.0904180034995079,
-0.09146905690431595,
-0.004678551107645035,
-0.1545001119375229,
0.007734695915132761,
-0.07803455740213394,
0.15830396115779877,
0.028678199276328087,
-0.031181402504444122,
-0.03010755404829979,
0.0402386300265789,
-0.09691616147756577,
-0.1099129319190979,
-0.0022663131821900606,
0.0153842493891716,
-0.0945914015173912,
0.06970567256212234,
-0.2405780851840973,
0.0574164092540741,
0.14391222596168518,
-0.005025625228881836,
-0.07321476936340332,
0.11765623092651367,
0.0489165261387825,
-0.013706451281905174,
-0.023128986358642578,
-0.02168380096554756,
0.1244552806019783,
-0.07626726478338242,
0.19949495792388916,
-0.01798384077847004,
0.08187845349311829,
0.05062877759337425,
-0.06974431127309799,
-0.135806143283844,
-0.000004087520210305229,
-0.03837069496512413,
-0.0334748737514019,
0.11700894683599472,
0.09331324696540833,
-0.060808680951595306,
0.27977684140205383,
0.002255344530567527,
-0.0019275352824479342,
-0.03330899775028229,
-0.014577753841876984,
-0.0332055389881134,
0.12854062020778656,
-0.012121065519750118,
0.00992091279476881,
0.015768490731716156,
-0.014307437464594841,
0.01476898044347763,
-0.09258662909269333,
-0.015657516196370125,
-0.029646404087543488,
-0.0163404643535614,
0.1258670836687088,
0.016155531629920006,
-0.035148244351148605,
0.07309972494840622,
-0.04378744959831238,
-0.0816405862569809,
0.11111503094434738,
-0.022147411480545998,
-0.0004421356425154954,
0.05905456468462944,
-0.15857146680355072,
-0.2807832360267639,
-0.1459890753030777,
0.005951586179435253,
-0.11860986053943634,
0.04109755903482437,
0.052975885570049286,
-0.10799627006053925,
-0.07004248350858688,
-0.08202385157346725,
-0.08629177510738373,
-0.05557532608509064,
0.0011311533162370324,
0.11728531867265701,
-0.06409677118062973,
0.05387398600578308,
-0.06229059770703316,
-0.00887343194335699,
-0.013896237127482891,
0.0027349803131073713,
0.09634215384721756,
0.02155768871307373,
0.04409273341298103,
0.20931857824325562,
-0.01992671564221382,
0.03497228026390076,
-0.007471531629562378,
0.25480857491493225,
-0.07225025445222855,
0.051100753247737885,
0.11487668752670288,
0.031045233830809593,
0.052618835121393204,
0.1828797161579132,
-0.01034550741314888,
-0.0642908588051796,
0.06494352221488953,
-0.012484862469136715,
-0.10492375493049622,
-0.11105634272098541,
-0.0924028679728508,
-0.04872503876686096,
-0.06293869018554688,
0.029581304639577866,
0.06633029878139496,
0.18465307354927063,
0.03403869643807411,
-0.0085936663672328,
0.038062650710344315,
-0.038405340164899826,
0.05253121256828308,
0.05000557377934456,
-0.054350171238183975,
0.10506314784288406,
-0.05272989347577095,
-0.07878284156322479,
0.09704536944627762,
0.029444830492138863,
0.08175686746835709,
-0.005787411238998175,
-0.051862932741642,
-0.054340463131666183,
0.05357728153467178,
0.12942302227020264,
0.016036581248044968,
0.0732298195362091,
-0.037278078496456146,
-0.04033561050891876,
-0.043483830988407135,
-0.012224663980305195,
0.08897408843040466,
0.023024603724479675,
0.013343557715415955,
-0.06517297029495239,
0.09141328185796738,
-0.0036450172774493694,
0.03365681692957878,
0.10284296423196793,
-0.24468940496444702,
0.03720756992697716,
0.05340345576405525,
0.009430313482880592,
-0.15917426347732544,
-0.001802100450731814,
0.2596781551837921,
-0.0778416246175766,
-0.016604389995336533,
-0.005158600863069296,
0.07767105102539062,
0.07948087900876999,
-0.01405559852719307,
-0.12727415561676025,
0.08470404893159866,
-0.03762264549732208,
-0.009994231164455414,
-0.21587730944156647,
0.04233643785119057,
0.006741201039403677,
0.09690377861261368,
-0.02572929486632347,
0.016345487907528877,
0.0344662107527256,
0.14141175150871277,
0.0716816708445549,
0.00973005685955286,
-0.08598282933235168,
-0.14106571674346924,
-0.08402053266763687,
-0.05161529779434204,
0.10742203146219254,
0.09498894214630127,
-0.004010304808616638,
-0.011004406958818436,
0.029761290177702904,
0.04038768634200096,
-0.048020366579294205,
-0.20780979096889496,
-0.12313251197338104,
0.03342318534851074,
0.18468953669071198,
0.07250070571899414,
-0.042261723428964615,
-0.07773694396018982,
0.058913350105285645,
0.15853528678417206,
-0.06002082675695419,
-0.03646547347307205,
-0.12438587844371796,
-0.01314868126064539,
0.04682208597660065,
-0.004984802100807428,
0.07632478326559067,
-0.11283677071332932,
0.055372435599565506,
-0.05680480971932411,
-0.15995463728904724,
0.08369133621454239,
-0.09573204070329666,
-0.09156695753335953,
-0.09880076348781586,
-0.02600095607340336,
-0.07628563791513443,
-0.01809440366923809,
0.02631893940269947,
0.03644336014986038,
-0.09317634254693985,
-0.08042453974485397,
0.07387512177228928,
0.052659958600997925,
-0.0790650025010109,
0.11336636543273926,
0.039935242384672165,
-0.05932047963142395,
0.009086593985557556,
-0.020160207524895668,
0.16297784447669983,
0.2692966163158417,
-0.09637150168418884,
0.1332009732723236,
0.10272762179374695,
-0.07975436747074127,
-0.2972416281700134,
-0.06331747770309448,
-0.001001058961264789,
0.033033158630132675,
-0.037056490778923035,
-0.08421573042869568,
0.01754319854080677,
-0.037301890552043915,
-0.026686429977416992,
0.09380273520946503,
-0.25594666600227356,
-0.07236529886722565,
0.12090659141540527,
0.011188359931111336,
0.3046357333660126,
-0.12652114033699036,
-0.03758466988801956,
-0.07161959260702133,
0.030579380691051483,
0.09310808032751083,
0.05593981221318245,
0.1552010029554367,
-0.01064409501850605,
0.029015347361564636,
0.016381043940782547,
-0.03504854813218117,
0.15569667518138885,
-0.09976516664028168,
0.07290340214967728,
-0.09811180084943771,
0.02065517008304596,
0.1682867556810379,
-0.07824182510375977,
0.06025531142950058,
-0.08820004016160965,
0.08328087627887726,
-0.14803707599639893,
0.024164263159036636,
-0.030000343918800354,
0.019950132817029953,
0.023836227133870125,
-0.09545804560184479,
-0.05183679237961769,
-0.024305418133735657,
0.031683988869190216,
0.0011127261677756906,
0.008928169496357441,
-0.03344632312655449,
0.021105246618390083,
0.31053033471107483,
-0.045023828744888306,
-0.08844760805368423,
-0.032576143741607666,
0.0008607114432379603,
-0.07616515457630157,
0.15518175065517426,
-0.140009805560112,
0.016880689188838005,
0.08636961877346039,
-0.028658051043748856,
0.19429416954517365,
0.04890631139278412,
-0.034792251884937286,
0.06410761177539825,
0.08606549352407455,
-0.17321881651878357,
0.023975208401679993,
-0.08413522690534592,
0.03825248405337334,
0.07573363929986954,
-0.08445089310407639,
0.1707473248243332,
-0.07278440147638321,
0.0452447347342968,
-0.039885539561510086,
0.022516414523124695,
-0.02864324487745762,
0.07788124680519104,
0.05243882164359093,
0.03179828077554703,
-0.08249194175004959,
0.1251235008239746,
0.038169246166944504,
-0.00042698116158135235,
0.13369235396385193,
0.09562437236309052,
-0.02339347079396248,
-0.029987553134560585,
-0.006221109069883823,
0.24116981029510498,
-0.1580258458852768,
-0.008135645650327206,
-0.04209064692258835,
-0.0893833190202713,
-0.022283220663666725,
0.033660776913166046,
0.004361500032246113,
0.008071556687355042,
-0.06307882070541382,
-0.04562815651297569,
-0.10188619047403336,
0.03915635868906975,
0.04616845026612282,
0.06768101453781128,
-0.2191275805234909,
0.009082616306841373,
0.027556031942367554,
0.05952044948935509,
-0.13306017220020294,
-0.09101494401693344,
-0.15259279310703278,
0.00039742272929288447,
-0.13059686124324799,
0.06406794488430023,
0.061592768877744675,
-0.04854949936270714,
0.035067036747932434,
-0.043882932513952255,
0.0004143699479755014,
0.028861405327916145,
-0.04535970091819763,
-0.011117871850728989,
0.015505447052419186,
0.006177510134875774,
-0.030567757785320282,
-0.053487807512283325,
-0.043063435703516006,
-0.029680561274290085,
0.054787784814834595,
0.02104547619819641,
-0.0758507251739502,
-0.023473115637898445,
-0.18298010528087616,
-0.01812969706952572,
0.13245733082294464,
0.002356436103582382,
-0.008200457319617271,
0.14597384631633759,
-0.03255922719836235,
0.02350054867565632,
0.045941680669784546,
0.00834833923727274,
0.04824364185333252,
-0.10032500326633453,
-0.11206801235675812,
-0.07519271969795227,
-0.05291133001446724,
-0.07905688136816025,
0.08345643430948257,
0.10799416899681091,
0.07442577183246613,
0.11548545211553574,
-0.13865616917610168,
0.0669560506939888,
-0.07766007632017136,
-0.0069593568332493305,
-0.02534155361354351,
-0.07888507097959518,
0.010779611766338348,
-0.010162390768527985,
0.045325834304094315,
-0.0136204082518816,
0.14034360647201538,
0.09084946662187576,
-0.13309991359710693,
-0.0024138707667589188,
-0.00929619837552309,
-0.02631843276321888,
-0.016799401491880417,
0.2551889717578888,
0.10145963728427887,
-0.006956462282687426,
-0.08942729234695435,
0.021860230714082718,
0.13579104840755463,
0.12235307693481445,
0.0031318129040300846,
0.015644969418644905,
0.024258719757199287,
0.16481736302375793,
0.003965459298342466,
-0.016579868271946907,
-0.059937287122011185,
0.03426060453057289,
-0.10462383925914764,
0.12247732281684875,
-0.11873367428779602,
-0.14781181514263153,
0.10172251611948013,
-0.02050800621509552,
-0.03943636640906334,
0.0037147165276110172,
-0.0770074650645256,
-0.09716072678565979,
-0.027703063562512398,
-0.06800327450037003,
-0.17266669869422913,
0.027315571904182434,
-0.06229201331734657,
0.12446222454309464,
0.06566920876502991,
0.0076821851544082165,
-0.07402129471302032,
0.09486519545316696,
0.02963947132229805,
-0.0744946077466011,
0.11698131263256073,
0.006778121460229158,
-0.004627263639122248,
-0.0994558110833168,
-0.04522183537483215,
0.07144024223089218,
0.1097252368927002,
-0.0014914624625816941,
0.062032222747802734,
0.03687533363699913,
0.07175064831972122,
-0.021852314472198486,
-0.1358582228422165,
0.009430473670363426,
0.06657078862190247,
-0.014820176176726818,
0.17750272154808044,
0.0526888370513916,
0.01400719489902258,
-0.033916763961315155,
0.20015233755111694,
-0.1060686707496643,
-0.08181434869766235,
-0.08442502468824387,
0.1438642144203186,
-0.10308082401752472,
0.12064629793167114,
-0.08840858936309814,
-0.10166779905557632,
-0.1078825369477272,
0.13028131425380707,
0.14787504076957703,
-0.1705704927444458,
-0.00973743386566639,
-0.059736791998147964,
-0.007106183096766472,
-0.04775403439998627,
0.1790471076965332,
0.027036966755986214,
0.0724797174334526,
-0.06618554890155792,
0.02645842544734478,
-0.05293412134051323,
-0.10053800046443939,
-0.07210712134838104,
-0.07806676626205444,
0.0033898563124239445,
-0.046734727919101715,
-0.1227606013417244,
-0.054156865924596786,
-0.1298540085554123,
0.07478922605514526,
0.13676925003528595,
-0.09898433089256287,
-0.036553580313920975,
0.0014106096932664514,
0.16058985888957977,
-0.02301349863409996,
-0.022042766213417053,
-0.07060083746910095,
0.055989354848861694,
0.09736833721399307,
-0.06496482342481613,
-0.017015384510159492,
-0.018570678308606148,
-0.058492738753557205,
-0.2682178318500519,
0.16817118227481842,
-0.004298606421798468,
0.03949829190969467,
0.031070971861481667,
0.03424987196922302,
-0.05919113755226135,
0.13132870197296143,
-0.048278603702783585,
-0.026828566566109657,
-0.026347270235419273,
0.1921045184135437,
-0.024049637839198112,
0.05515880510210991,
0.037085678428411484,
-0.14625179767608643,
-0.030122820287942886,
0.011054210364818573,
-0.07361718267202377,
0.004916774109005928,
-0.043997615575790405,
-0.020118191838264465,
0.11343158781528473,
0.033629145473241806,
-0.016268683597445488,
0.012032032944262028,
-0.01385825127363205,
0.004258410073816776,
-0.01866539753973484,
-0.0066040013916790485,
0.027589673176407814,
-0.1268121898174286,
-0.026856685057282448,
0.0937948226928711,
0.038554634898900986,
-0.2399676889181137,
-0.056827764958143234,
-0.20476673543453217,
0.0481138676404953,
-0.07275717705488205,
0.13741889595985413,
0.15254093706607819,
-0.023496365174651146,
-0.007380692288279533,
-0.12191148102283478,
0.015328208915889263,
0.04335465282201767,
0.00498174037784338,
-0.033090222626924515
] |
null | null | transformers |
# OpenBuddy - Open Multilingual Chatbot
GitHub and Usage Guide: [https://github.com/OpenBuddy/OpenBuddy](https://github.com/OpenBuddy/OpenBuddy)
Website and Demo: [https://openbuddy.ai](https://openbuddy.ai)
Evaluation result of this model: [Evaluation.txt](Evaluation.txt)
# Copyright Notice
Base model: https://huggingface.co/codellama/CodeLlama-70b-hf
License: llama2
This model is built upon Meta's LLaMA series of models and is subject to Meta's licensing agreement.
This model is intended for use only by individuals who have obtained approval from Meta and are eligible to download LLaMA.
If you have not obtained approval from Meta, you must visit the https://ai.meta.com/llama/ page, read and agree to the model's licensing agreement, submit an application, and wait for approval from Meta before downloading the model from this page.
## Disclaimer
All OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions.
OpenBuddy is provided "as-is" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software.
By using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy.
## 免责声明
所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。
OpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。
使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。 | {"language": ["zh", "en", "fr", "de", "ja", "ko", "it", "ru"], "license": "llama2", "library_name": "transformers", "pipeline_tag": "text-generation", "inference": false} | text-generation | OpenBuddy/openbuddy-codellama-70b-v17.1-4k | [
"transformers",
"safetensors",
"llama",
"text-generation",
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T11:02:01+00:00 | [] | [
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru"
] | TAGS
#transformers #safetensors #llama #text-generation #zh #en #fr #de #ja #ko #it #ru #license-llama2 #autotrain_compatible #text-generation-inference #region-us
|
# OpenBuddy - Open Multilingual Chatbot
GitHub and Usage Guide: URL
Website and Demo: URL
Evaluation result of this model: URL
!Demo
# Copyright Notice
Base model: URL
License: llama2
This model is built upon Meta's LLaMA series of models and is subject to Meta's licensing agreement.
This model is intended for use only by individuals who have obtained approval from Meta and are eligible to download LLaMA.
If you have not obtained approval from Meta, you must visit the URL page, read and agree to the model's licensing agreement, submit an application, and wait for approval from Meta before downloading the model from this page.
## Disclaimer
All OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions.
OpenBuddy is provided "as-is" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software.
By using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy.
## 免责声明
所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。
OpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。
使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。 | [
"# OpenBuddy - Open Multilingual Chatbot\n\nGitHub and Usage Guide: URL\n\nWebsite and Demo: URL\n\nEvaluation result of this model: URL\n\n!Demo",
"# Copyright Notice\n\nBase model: URL\n\nLicense: llama2\n\nThis model is built upon Meta's LLaMA series of models and is subject to Meta's licensing agreement.\n\nThis model is intended for use only by individuals who have obtained approval from Meta and are eligible to download LLaMA.\n\nIf you have not obtained approval from Meta, you must visit the URL page, read and agree to the model's licensing agreement, submit an application, and wait for approval from Meta before downloading the model from this page.",
"## Disclaimer\n\nAll OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions.\n\nOpenBuddy is provided \"as-is\" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software.\n\nBy using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy.",
"## 免责声明\n\n所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。\n\nOpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。\n\n使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #zh #en #fr #de #ja #ko #it #ru #license-llama2 #autotrain_compatible #text-generation-inference #region-us \n",
"# OpenBuddy - Open Multilingual Chatbot\n\nGitHub and Usage Guide: URL\n\nWebsite and Demo: URL\n\nEvaluation result of this model: URL\n\n!Demo",
"# Copyright Notice\n\nBase model: URL\n\nLicense: llama2\n\nThis model is built upon Meta's LLaMA series of models and is subject to Meta's licensing agreement.\n\nThis model is intended for use only by individuals who have obtained approval from Meta and are eligible to download LLaMA.\n\nIf you have not obtained approval from Meta, you must visit the URL page, read and agree to the model's licensing agreement, submit an application, and wait for approval from Meta before downloading the model from this page.",
"## Disclaimer\n\nAll OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions.\n\nOpenBuddy is provided \"as-is\" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software.\n\nBy using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy.",
"## 免责声明\n\n所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。\n\nOpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。\n\n使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。"
] | [
62,
35,
115,
298,
234
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #zh #en #fr #de #ja #ko #it #ru #license-llama2 #autotrain_compatible #text-generation-inference #region-us \n# OpenBuddy - Open Multilingual Chatbot\n\nGitHub and Usage Guide: URL\n\nWebsite and Demo: URL\n\nEvaluation result of this model: URL\n\n!Demo# Copyright Notice\n\nBase model: URL\n\nLicense: llama2\n\nThis model is built upon Meta's LLaMA series of models and is subject to Meta's licensing agreement.\n\nThis model is intended for use only by individuals who have obtained approval from Meta and are eligible to download LLaMA.\n\nIf you have not obtained approval from Meta, you must visit the URL page, read and agree to the model's licensing agreement, submit an application, and wait for approval from Meta before downloading the model from this page."
] | [
0.0355352908372879,
0.041691359132528305,
-0.003628121456131339,
0.00942362379282713,
0.13810567557811737,
-0.05915393307805061,
0.2297053039073944,
-0.001358836074359715,
0.06315168738365173,
-0.026569273322820663,
0.04208668693900108,
0.1696920543909073,
0.0010714700911194086,
0.10132472962141037,
-0.05285874009132385,
-0.15432116389274597,
0.03328438848257065,
-0.08503014594316483,
-0.04840660095214844,
0.0892367884516716,
0.12849061191082,
0.005756818689405918,
0.11409611999988556,
0.02110929973423481,
0.03131222352385521,
0.04589910805225372,
0.05633747577667236,
-0.07347588986158371,
0.01779700629413128,
0.079951710999012,
0.01819486729800701,
0.07555793970823288,
-0.027722155675292015,
-0.115361787378788,
0.06732244044542313,
0.04104910045862198,
-0.023419223725795746,
-0.021987926214933395,
-0.01626482419669628,
-0.0983765497803688,
0.2639579176902771,
0.01753922365605831,
-0.035623349249362946,
0.055461104959249496,
-0.07400914281606674,
0.012166613712906837,
-0.07545432448387146,
-0.04015354812145233,
0.09087206423282623,
0.06159116327762604,
0.01653537154197693,
0.09043865650892258,
-0.09950065612792969,
0.05546261742711067,
0.24268843233585358,
-0.19938814640045166,
-0.05751964449882507,
0.14150811731815338,
0.03015875443816185,
0.11486820876598358,
0.02237672358751297,
0.13610047101974487,
0.05518229305744171,
-0.03710916265845299,
-0.00265162019059062,
-0.08678878843784332,
0.1126374751329422,
-0.02938011847436428,
-0.11774126440286636,
0.036563169211149216,
0.35091862082481384,
0.00007852277485653758,
-0.10550345480442047,
-0.00828242301940918,
-0.07645267248153687,
0.26457393169403076,
0.0003024837642442435,
0.049650222063064575,
0.07259705662727356,
0.03205498307943344,
0.0029213184025138617,
-0.14572378993034363,
-0.04413701966404915,
-0.103060781955719,
-0.07359424233436584,
0.29493942856788635,
0.025922128930687904,
0.04725050926208496,
-0.07602977752685547,
0.03289462998509407,
-0.12896019220352173,
-0.06259053945541382,
-0.07728873193264008,
-0.08136113733053207,
0.08321985602378845,
0.050307609140872955,
-0.04610342159867287,
-0.22112663090229034,
0.13037286698818207,
0.11932249367237091,
0.04973346367478371,
-0.005243712570518255,
-0.03386611118912697,
0.07306262105703354,
0.04168011248111725,
-0.056113000959157944,
-0.023237871006131172,
-0.01563083939254284,
0.13069520890712738,
-0.026642508804798126,
0.1520010381937027,
-0.02647864632308483,
-0.1260741800069809,
0.05005989968776703,
-0.14771528542041779,
0.15001268684864044,
0.03173830360174179,
0.12268346548080444,
0.027814462780952454,
0.0016857098089531064,
0.10484202951192856,
-0.07414693385362625,
-0.05093809589743614,
-0.015130172483623028,
-0.10197743773460388,
-0.1252470463514328,
0.11107359081506729,
0.05888351798057556,
-0.007448304444551468,
0.017777230590581894,
-0.06457536667585373,
-0.01899677701294422,
-0.08483905345201492,
-0.055532097816467285,
0.06953886896371841,
0.03579200059175491,
0.016602832823991776,
-0.19619198143482208,
-0.20523330569267273,
-0.05489172786474228,
0.04304494708776474,
0.03327925130724907,
-0.024174518883228302,
0.04907454550266266,
0.03658699616789818,
-0.04634452611207962,
-0.04502440243959427,
-0.03625611588358879,
-0.06978362053632736,
0.07382622361183167,
-0.03241405263543129,
0.011686085723340511,
-0.19123803079128265,
0.03334878012537956,
-0.12234488874673843,
0.0673249289393425,
-0.23074978590011597,
-0.01419505849480629,
-0.059246521443128586,
0.10774187743663788,
-0.060404520481824875,
0.054766371846199036,
-0.029940476641058922,
-0.006001305766403675,
0.023760147392749786,
0.12049352377653122,
-0.00988412369042635,
0.004048078786581755,
0.11658662557601929,
-0.19435632228851318,
-0.1240244209766388,
0.0834152102470398,
-0.0132391806691885,
0.11695926636457443,
0.11633655428886414,
0.22319704294204712,
0.08772959560155869,
-0.14549972116947174,
0.0514712780714035,
-0.06782209873199463,
-0.03256070986390114,
-0.10079184919595718,
0.06672865897417068,
-0.025240514427423477,
-0.1644965261220932,
0.004379307851195335,
-0.17860612273216248,
0.106935054063797,
0.028423771262168884,
-0.032449234277009964,
-0.017962398007512093,
-0.1138533428311348,
-0.09609470516443253,
-0.0383719876408577,
-0.04383362829685211,
-0.06343413889408112,
0.00546744279563427,
-0.05970320105552673,
0.1360408067703247,
-0.0220791008323431,
0.015351314097642899,
-0.1009565070271492,
0.1631918102502823,
-0.061642084270715714,
0.03639907389879227,
-0.017050575464963913,
-0.09268607944250107,
-0.015850553289055824,
-0.00783560425043106,
0.11509181559085846,
-0.0642869621515274,
-0.021101798862218857,
-0.0012669304851442575,
-0.02367081679403782,
0.049636341631412506,
0.06590494513511658,
-0.004555982071906328,
-0.0108458511531353,
-0.19249536097049713,
0.07046199589967728,
-0.009393556043505669,
0.07190695405006409,
-0.05930890887975693,
0.04100961610674858,
-0.018276195973157883,
0.03605502098798752,
-0.021526284515857697,
0.020869264379143715,
0.09803344309329987,
-0.025620652362704277,
-0.031637415289878845,
0.007762480061501265,
0.05895344913005829,
0.005908552091568708,
-0.046385690569877625,
0.22853396832942963,
-0.15919362008571625,
0.07022445648908615,
0.11464317888021469,
-0.06510096788406372,
0.05240456387400627,
-0.034346360713243484,
0.015699997544288635,
-0.009646504186093807,
-0.04483523592352867,
0.026717504486441612,
0.07313870638608932,
-0.014591553248465061,
0.09428291022777557,
-0.1370467096567154,
0.05362075939774513,
0.0027983817271888256,
-0.1671288162469864,
-0.024362539872527122,
0.05060233920812607,
0.2705751657485962,
-0.21403811872005463,
0.040283966809511185,
0.20153643190860748,
-0.10781785845756531,
0.24490538239479065,
0.05159428343176842,
0.02986590377986431,
-0.043984055519104004,
0.05605368688702583,
-0.0040176305919885635,
0.0786859393119812,
-0.1038009449839592,
-0.05540323629975319,
-0.003657895838841796,
-0.0036751916632056236,
0.06594172865152359,
-0.08815766125917435,
-0.05723099783062935,
-0.027319859713315964,
-0.05421623960137367,
0.05643944442272186,
0.05748451501131058,
-0.10886696726083755,
0.09710019826889038,
0.01514850091189146,
-0.08272482454776764,
0.009151686914265156,
-0.035699937492609024,
-0.08096879720687866,
0.13555672764778137,
-0.09482507407665253,
-0.0927640050649643,
-0.022171949967741966,
-0.08038145303726196,
-0.17919892072677612,
0.020900370553135872,
0.06205039471387863,
-0.02489270269870758,
-0.06921058893203735,
-0.11016345024108887,
0.005377442110329866,
0.00481944577768445,
-0.04946710914373398,
-0.13016220927238464,
0.014644662849605083,
-0.05312279984354973,
-0.11897654086351395,
-0.0801001563668251,
-0.04783619940280914,
-0.10525264590978622,
-0.0017854777397587895,
-0.13040399551391602,
0.15676115453243256,
0.16084493696689606,
-0.0028366914484649897,
0.016914263367652893,
-0.0028019691817462444,
0.05130276829004288,
-0.05461132898926735,
0.0005488424794748425,
0.26803749799728394,
0.12596110999584198,
0.05863232910633087,
0.11265251040458679,
0.0010745457839220762,
-0.06912080198526382,
0.09603962302207947,
0.051926370710134506,
-0.13831184804439545,
-0.17791147530078888,
-0.0796516016125679,
-0.050520945340394974,
-0.018345747143030167,
-0.07004589587450027,
0.08817863464355469,
0.036324162036180496,
0.12232507020235062,
-0.04863838478922844,
-0.1595633178949356,
0.05044903606176376,
0.07158834487199783,
0.1401190459728241,
-0.06517087668180466,
0.09568821638822556,
-0.09085268527269363,
0.005832238122820854,
0.12098196148872375,
0.04972555860877037,
0.1290694773197174,
0.015396692790091038,
0.05291655287146568,
0.16161297261714935,
0.03716621547937393,
0.01559417974203825,
0.017648324370384216,
0.024676568806171417,
0.027878282591700554,
-0.06351722031831741,
-0.12298311293125153,
-0.12957607209682465,
0.11624221503734589,
-0.030624041333794594,
-0.08208770304918289,
-0.06647207587957382,
0.05000029131770134,
0.05042944848537445,
0.1290404051542282,
-0.015610038302838802,
-0.1631781905889511,
0.05447661504149437,
0.0694456696510315,
0.01945486292243004,
0.008094646967947483,
0.08407223969697952,
0.013115428388118744,
-0.07319138199090958,
0.1479034274816513,
0.033529724925756454,
0.0935932993888855,
-0.04085668921470642,
-0.0334911048412323,
-0.09170874953269958,
0.025219107046723366,
-0.07817725092172623,
0.07834465056657791,
-0.24454587697982788,
0.05551902949810028,
0.0013270385097712278,
0.009928618557751179,
-0.03267355263233185,
-0.04269573092460632,
0.08721013367176056,
0.26920944452285767,
0.0600624717772007,
0.02846997231245041,
0.010723715648055077,
0.038661640137434006,
-0.04249555617570877,
0.08363747596740723,
-0.01610083132982254,
-0.027733586728572845,
0.021844079717993736,
-0.047233399003744125,
0.029692087322473526,
-0.014624164439737797,
0.09942075610160828,
-0.1616377979516983,
-0.057501766830682755,
0.0026543084532022476,
0.14977404475212097,
0.009739100933074951,
-0.09172957390546799,
0.002301885513588786,
0.0906219482421875,
0.24934802949428558,
0.09084811061620712,
-0.11599136143922806,
-0.07630035281181335,
-0.19109976291656494,
-0.048318490386009216,
-0.021511219441890717,
-0.032194945961236954,
-0.09657014161348343,
-0.012370281852781773,
-0.07568209618330002,
-0.09849058836698532,
0.07978873699903488,
-0.08340058475732803,
-0.054828133434057236,
-0.04562688618898392,
0.05033643916249275,
-0.01994108408689499,
-0.03924045339226723,
0.04467133805155754,
-0.06821248680353165,
-0.03590909764170647,
-0.12345816195011139,
-0.07551655173301697,
0.1424889713525772,
-0.017568865790963173,
0.09505481272935867,
-0.03825259581208229,
0.06009093299508095,
-0.026001978665590286,
-0.05650024488568306,
0.03415805101394653,
0.13574892282485962,
0.01723382994532585,
0.08645077794790268,
0.18572284281253815,
-0.11324404180049896,
-0.21721021831035614,
-0.10261997580528259,
-0.11738599836826324,
-0.07550174742937088,
-0.045283060520887375,
-0.10584916919469833,
0.07217542082071304,
0.024470416828989983,
-0.08463229238986969,
0.04523998126387596,
-0.18132752180099487,
-0.07688064873218536,
0.08706598728895187,
0.11804408580064774,
0.42091450095176697,
-0.2134152352809906,
-0.05306714028120041,
-0.09043432027101517,
0.01339761819690466,
0.1317228078842163,
-0.2299058884382248,
0.10706475377082825,
0.016623850911855698,
0.07678951323032379,
0.021404175087809563,
-0.024812910705804825,
0.1316753476858139,
-0.045775141566991806,
0.08110111951828003,
-0.14875547587871552,
-0.013659290038049221,
0.086326964199543,
-0.08482830971479416,
0.14207132160663605,
-0.18783515691757202,
0.04585188999772072,
-0.020528407767415047,
-0.07989472150802612,
-0.04613630101084709,
0.17242981493473053,
0.004120246507227421,
-0.10950860381126404,
-0.10659945756196976,
0.031917814165353775,
-0.020247384905815125,
0.0689111053943634,
0.1630379557609558,
-0.1358441263437271,
0.0055777812376618385,
0.17552655935287476,
0.15405483543872833,
-0.09685900807380676,
0.06438475102186203,
0.021976418793201447,
-0.04114770144224167,
0.11536984890699387,
-0.2500462234020233,
-0.011160023510456085,
0.0015851655043661594,
-0.010971569456160069,
0.04073403775691986,
-0.01123192347586155,
-0.11981311440467834,
0.0498371385037899,
0.11863623559474945,
-0.14110802114009857,
-0.007811038754880428,
-0.009982950985431671,
0.08684278279542923,
0.0615742988884449,
0.11321261525154114,
0.2041962444782257,
-0.06600773334503174,
0.047760944813489914,
-0.03078487701714039,
0.05883975327014923,
-0.06100763380527496,
0.05775269865989685,
0.07876140624284744,
-0.0356946624815464,
-0.05146309733390808,
0.08073977380990982,
0.05354093760251999,
0.04625631123781204,
0.012027137912809849,
0.026549620553851128,
-0.07009076327085495,
-0.0968785285949707,
-0.17919600009918213,
0.05356942117214203,
-0.14936667680740356,
-0.1938285082578659,
-0.10565683245658875,
-0.05729064717888832,
-0.04119163379073143,
0.1271025836467743,
0.045686740428209305,
0.01967427507042885,
0.06678150594234467,
-0.04909276217222214,
-0.013055328279733658,
0.04478592053055763,
-0.09392134100198746,
0.022326553240418434,
-0.14534389972686768,
-0.061108384281396866,
0.054467447102069855,
0.1120942160487175,
-0.061247196048498154,
-0.027401814237236977,
-0.07221119850873947,
0.0300556980073452,
-0.16946925222873688,
0.014857079833745956,
-0.12964759767055511,
-0.0070477863773703575,
-0.007133638020604849,
-0.030294688418507576,
-0.09552806615829468,
0.037412140518426895,
-0.051915284246206284,
0.010910971090197563,
-0.0013209505705162883,
0.1102459728717804,
-0.10102710872888565,
-0.039319608360528946,
0.007086006924510002,
0.014369380660355091,
0.07378651201725006,
0.009848624467849731,
-0.06584741175174713,
-0.011395770125091076,
-0.16522370278835297,
0.09712173044681549,
0.050570838153362274,
0.012686590664088726,
-0.062465570867061615,
-0.08428074419498444,
0.03075925074517727,
0.05235932767391205,
0.02712634578347206,
0.012308548204600811,
0.11703243106603622,
-0.14125874638557434,
0.08427232503890991,
0.07718625664710999,
-0.07193032652139664,
0.01067995373159647,
0.0014035863569006324,
-0.036473747342824936,
0.10957559198141098,
0.08966752141714096,
-0.05298244580626488,
-0.01834404468536377,
-0.03415577858686447,
0.03168134391307831,
0.0035617519170045853,
-0.032920029014348984,
0.008166352286934853,
-0.04151614010334015,
-0.03351963683962822,
-0.020121419802308083,
0.26091697812080383,
0.1827816367149353,
-0.11357415467500687,
0.013742208480834961,
0.08444809168577194,
0.25791558623313904,
-0.023221340030431747,
0.03975066542625427,
-0.023127557709813118,
0.07066235691308975,
-0.00032849478884600103,
0.02047944813966751,
0.05820036306977272,
-0.1501164436340332,
0.063265860080719,
0.11301718652248383,
0.08233308047056198,
0.020646274089813232,
0.07393744587898254,
-0.05127234756946564,
0.029402639716863632,
-0.09305807203054428,
0.04674415662884712,
0.022939911112189293,
-0.062083907425403595,
-0.017306841909885406,
0.25867655873298645,
0.017288407310843468,
-0.0426611602306366,
0.0920020192861557,
0.027418065816164017,
-0.197491854429245,
-0.17782211303710938,
-0.07998371124267578,
-0.14758765697479248,
-0.006791474763303995,
-0.06480950117111206,
-0.03463899716734886,
0.09083283692598343,
-0.010221356526017189,
-0.06079196557402611,
0.029017625376582146,
-0.07903849333524704,
-0.13051193952560425,
0.02317354641854763,
-0.03266427665948868,
-0.01982278749346733,
-0.05892253294587135,
-0.054983336478471756,
0.002041950123384595,
-0.04895457997918129,
-0.019616179168224335,
0.10697122663259506,
0.045351359993219376,
0.08061718940734863,
-0.043564531952142715,
-0.02041737549006939,
-0.06869906932115555,
0.03912214934825897,
0.07600381970405579,
0.10646034777164459,
0.03391878679394722,
-0.0893278643488884,
0.06016599386930466,
0.17983287572860718,
0.005595255643129349,
-0.042923714965581894,
-0.04470927268266678,
0.23496423661708832,
-0.09983230382204056,
0.043066494166851044,
-0.04829447343945503,
-0.041247203946113586,
0.00697078974917531,
0.17289119958877563,
0.24285829067230225,
-0.06772153079509735,
0.039214033633470535,
-0.12007512152194977,
0.01581721194088459,
0.008545802906155586,
0.10589556396007538,
-0.01085467729717493,
0.2817431390285492,
0.008955666795372963,
0.061284516006708145,
-0.0580408051609993,
0.05290300026535988,
-0.14612343907356262,
0.012564022094011307,
-0.009660840034484863,
-0.05011192336678505,
-0.03600020706653595,
0.1325923353433609,
-0.08192440122365952,
-0.01885581947863102,
-0.012397117912769318,
0.016279038041830063,
-0.016894077882170677,
-0.024792978540062904,
-0.011408623307943344,
-0.0072175986133515835,
0.047981146723032,
-0.0754149779677391,
-0.051645368337631226,
0.02983255125582218,
0.015744175761938095,
-0.18368595838546753,
-0.0704851746559143,
0.11894447356462479,
-0.021593080833554268,
0.19414037466049194,
-0.015486643649637699,
0.037552062422037125,
0.0891302153468132,
-0.050525497645139694,
-0.061369411647319794,
0.17146185040473938,
0.03232664242386818,
-0.07274598628282547,
0.04171796143054962,
-0.11669030040502548,
-0.03736834600567818,
0.18349558115005493,
0.04866954684257507,
0.06153478845953941,
0.04833138734102249,
0.05643675476312637,
-0.0027893211226910353,
-0.12688793241977692,
0.0908442810177803,
-0.17573116719722748,
0.09874846041202545,
0.02872372604906559,
-0.040361303836107254,
-0.018017707392573357,
-0.0963396355509758,
0.02782401256263256,
0.028895970433950424,
-0.13467232882976532,
0.033057503402233124,
-0.11579442024230957,
0.0008325236267410219,
0.06374018639326096,
0.04452018439769745,
-0.31547513604164124,
0.06519363075494766,
-0.0638972595334053,
0.0023793522268533707,
0.005105165299028158,
0.015705673024058342,
0.12131024152040482,
-0.02829624153673649,
-0.042354289442300797,
-0.15874610841274261,
-0.010151614435017109,
0.07796978205442429,
-0.13454581797122955,
-0.12900641560554504
] |
null | null | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-CartPole-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "mean_reward", "value": "500.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | slc48/Reinforce-CartPole-v1 | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | 2024-02-09T11:06:18+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
39,
54
] | [
"passage: TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
0.007526164408773184,
-0.12498430907726288,
-0.0013541718944907188,
0.09601131081581116,
0.11848696321249008,
-0.04186001420021057,
0.11405468732118607,
0.05624859035015106,
0.09539441019296646,
0.04239490255713463,
0.13636724650859833,
0.06906966865062714,
-0.004102868959307671,
0.12412862479686737,
0.09840741008520126,
-0.26058563590049744,
0.07420794665813446,
-0.04403980076313019,
-0.009944677352905273,
0.10139261186122894,
0.07836852967739105,
-0.08325441926717758,
0.051592715084552765,
0.00009572553972247988,
-0.044259943068027496,
0.0321260429918766,
0.013628939166665077,
-0.053157225251197815,
0.1606452465057373,
-0.07313758134841919,
0.10494591295719147,
-0.03843724727630615,
0.14574295282363892,
-0.1126825287938118,
0.04758213832974434,
0.05111503228545189,
-0.04548581689596176,
0.03848232328891754,
-0.12538743019104004,
-0.06033875793218613,
0.026815801858901978,
-0.015865681692957878,
0.12249194830656052,
0.03647647053003311,
-0.1777559220790863,
-0.13461355865001678,
-0.0165896974503994,
0.12325166910886765,
0.1627800315618515,
0.00512364786118269,
0.014270431362092495,
0.16791965067386627,
-0.1761058121919632,
0.025937072932720184,
0.11400806158781052,
-0.37275227904319763,
-0.00034436015994288027,
0.2240462601184845,
0.06164427846670151,
0.1252165287733078,
-0.12646614015102386,
0.010440526530146599,
0.07403992861509323,
0.04368630796670914,
0.049784936010837555,
-0.015430688858032227,
-0.12260042130947113,
0.08455035835504532,
-0.1383819431066513,
-0.058066487312316895,
0.1495426446199417,
-0.019741326570510864,
-0.009476418606936932,
-0.016515808179974556,
-0.009238536469638348,
-0.050979889929294586,
-0.03430935740470886,
-0.11778499186038971,
0.10755524039268494,
0.04975730925798416,
0.0038771627005189657,
-0.04602450504899025,
-0.05612579360604286,
-0.09815777093172073,
-0.03123871050775051,
0.0372777059674263,
-0.013706400990486145,
0.01091629359871149,
0.027692900970578194,
0.09935613721609116,
-0.13446329534053802,
0.01825822703540325,
-0.028096558526158333,
-0.028040969744324684,
-0.1316804438829422,
-0.11984307318925858,
-0.026084421202540398,
0.004223645199090242,
0.03029833547770977,
0.20433813333511353,
0.020139509811997414,
0.059011414647102356,
-0.0022708347532898188,
0.09776382148265839,
0.029780851677060127,
0.13517548143863678,
-0.04466623440384865,
0.19488364458084106,
0.07711011171340942,
0.05364556983113289,
0.03204274922609329,
-0.05344729498028755,
-0.19369827210903168,
0.04861246794462204,
0.06659778952598572,
0.08274952322244644,
-0.1178959533572197,
0.0059632807970047,
-0.10316018015146255,
0.0028950648847967386,
-0.10474003106355667,
-0.0642905905842781,
-0.02892979420721531,
0.031841445714235306,
-0.10535725951194763,
0.028785312548279762,
0.025052599608898163,
0.04140377417206764,
0.0676041767001152,
-0.12253966927528381,
-0.07404746115207672,
-0.021733485162258148,
-0.12817098200321198,
-0.09923440217971802,
0.08802318572998047,
-0.026199497282505035,
-0.005110981408506632,
-0.1253623217344284,
-0.2661486268043518,
-0.05670225992798805,
0.06396034359931946,
-0.03231031447649002,
-0.08589376509189606,
-0.1633463054895401,
0.026403428986668587,
-0.07700273394584656,
0.05221332609653473,
0.04776721075177193,
-0.03665859252214432,
0.02023705095052719,
-0.07958202809095383,
0.12739010155200958,
0.049698662012815475,
0.00541001046076417,
-0.09916839748620987,
0.07882837951183319,
-0.3034103214740753,
-0.02581131085753441,
-0.15228183567523956,
0.0772043839097023,
-0.07893010973930359,
0.01308529730886221,
0.05044940114021301,
0.043790437281131744,
-0.016942394897341728,
0.16269747912883759,
-0.17043575644493103,
-0.05301272124052048,
0.026445282623171806,
-0.09261117875576019,
-0.09916394203901291,
0.07275339215993881,
-0.06339669227600098,
0.21263530850410461,
0.08751397579908371,
0.17006252706050873,
-0.011036526411771774,
-0.16256992518901825,
0.1207515075802803,
0.07522942125797272,
-0.1639646589756012,
0.004287737421691418,
0.061784300953149796,
-0.0016935690073296428,
0.02746843732893467,
-0.01872866041958332,
-0.07289361208677292,
0.06302516162395477,
-0.07825060933828354,
0.022581040859222412,
0.06258945167064667,
-0.09531243145465851,
0.23986859619617462,
-0.005434412509202957,
0.0862451046705246,
-0.025957979261875153,
-0.09802921861410141,
0.00908072479069233,
0.07164718210697174,
-0.0014321404742076993,
0.01703714393079281,
-0.14553219079971313,
0.23044352233409882,
-0.07965081930160522,
0.011176814325153828,
-0.11607582122087479,
-0.1256982982158661,
0.011873425915837288,
0.13336114585399628,
0.059921663254499435,
0.16569606959819794,
0.09518871456384659,
-0.032197169959545135,
0.017584815621376038,
-0.0023385772947221994,
-0.09040450304746628,
0.01580043137073517,
-0.0021571461111307144,
-0.12167251110076904,
-0.07353103160858154,
-0.08134473115205765,
0.12585052847862244,
-0.20988115668296814,
0.015492538921535015,
0.04099845886230469,
0.008103687316179276,
0.04467369243502617,
0.023746047168970108,
-0.013269703835248947,
-0.00007021807687124237,
0.03244573250412941,
-0.10098352283239365,
0.12937165796756744,
0.013381263241171837,
0.014676140621304512,
-0.006365173030644655,
-0.05572463944554329,
0.03720450773835182,
0.040439579635858536,
-0.11237845569849014,
-0.11330515146255493,
-0.009658765979111195,
-0.0015364213613793254,
0.02637762948870659,
-0.022321155294775963,
0.052120618522167206,
0.27587956190109253,
0.05387469753623009,
0.10401033610105515,
-0.05769326910376549,
0.015315087512135506,
-0.015322818420827389,
-0.07135670632123947,
0.06358719617128372,
0.025013601407408714,
0.08050397783517838,
-0.03531401976943016,
0.03759452700614929,
0.1675453782081604,
-0.015888912603259087,
0.11127935349941254,
-0.06545067578554153,
-0.03844274953007698,
-0.043109722435474396,
0.05627678707242012,
0.015021559782326221,
0.04564907029271126,
0.0000015355876712419558,
-0.08444724231958389,
-0.03503387048840523,
-0.03988509997725487,
-0.010637006722390652,
-0.12273643165826797,
-0.00499896751716733,
0.01265440508723259,
-0.021940499544143677,
0.04488934203982353,
0.07375624030828476,
-0.04849626496434212,
0.025821007788181305,
0.06070821359753609,
-0.10193055868148804,
0.08957115560770035,
0.015067169442772865,
-0.06946801394224167,
0.13769419491291046,
-0.07484805583953857,
-0.045293889939785004,
-0.1025395318865776,
-0.1568877100944519,
0.09384927153587341,
0.06704871356487274,
-0.05427970737218857,
-0.1503879576921463,
-0.0016851738328114152,
-0.008973666466772556,
0.09206123650074005,
-0.006399387493729591,
-0.12621140480041504,
0.01989075168967247,
0.08295059949159622,
-0.05633419007062912,
-0.09804849326610565,
-0.0075809285044670105,
-0.05280788615345955,
-0.17707788944244385,
-0.03888550028204918,
-0.06398582458496094,
-0.06734282523393631,
0.23586803674697876,
0.02017230913043022,
0.08274748176336288,
-0.044721852988004684,
0.04250151664018631,
-0.012231717817485332,
0.0006326579605229199,
0.10689259320497513,
-0.09043551236391068,
-0.017900818958878517,
-0.001320177922025323,
-0.024820495396852493,
-0.07327181100845337,
0.029733488336205482,
-0.04272191599011421,
-0.08249637484550476,
-0.1415451467037201,
-0.04993678629398346,
-0.011005163192749023,
0.10754310339689255,
0.07337497919797897,
0.0048001972027122974,
-0.11733713001012802,
0.062058478593826294,
0.13692134618759155,
0.031207585707306862,
0.004062763415277004,
0.028157465159893036,
0.14977529644966125,
-0.10706274956464767,
-0.022463621571660042,
-0.038119975477457047,
-0.054863203316926956,
0.004114252515137196,
0.016883620992302895,
0.08840765058994293,
0.1410384476184845,
0.11468084901571274,
0.047563645988702774,
0.0464191697537899,
0.06561273336410522,
0.1694946140050888,
0.059157438576221466,
-0.10448314249515533,
-0.044678982347249985,
-0.0040070898830890656,
-0.10903503000736237,
0.057307638227939606,
0.16030821204185486,
0.06326017528772354,
-0.14463356137275696,
0.021787412464618683,
-0.038982175290584564,
0.13649246096611023,
0.020638149231672287,
-0.2677258849143982,
-0.008139112964272499,
0.023630544543266296,
-0.0010347915813326836,
-0.012379839085042477,
0.10821118950843811,
-0.040134772658348083,
-0.233198344707489,
-0.12299054861068726,
0.010077533312141895,
0.031144635751843452,
-0.1509784311056137,
0.015542911365628242,
-0.14036494493484497,
0.08027976751327515,
-0.007007129956036806,
0.07418135553598404,
-0.025149788707494736,
0.15060245990753174,
-0.028731435537338257,
0.01628703810274601,
-0.07902143895626068,
-0.047717493027448654,
0.09898673743009567,
-0.0046631391160190105,
0.1931537538766861,
0.005480166990309954,
-0.023713182657957077,
-0.12098433077335358,
-0.05229806900024414,
-0.04967813938856125,
0.010598190128803253,
-0.05373382940888405,
0.0765683576464653,
-0.02441473677754402,
-0.0039579677395522594,
-0.010900177992880344,
0.08942947536706924,
-0.05291692912578583,
0.03636563941836357,
-0.11246588081121445,
-0.05034820735454559,
0.14550213515758514,
-0.09163831174373627,
-0.10174685716629028,
-0.16205860674381256,
0.14137998223304749,
0.15070600807666779,
0.058216437697410583,
-0.04001476243138313,
0.03867831453680992,
-0.019183965399861336,
-0.024241572245955467,
0.07880574464797974,
0.009653856977820396,
0.1324782371520996,
-0.08983246237039566,
0.014327390119433403,
0.14589735865592957,
-0.05275948345661163,
0.016191845759749413,
-0.02304735779762268,
0.12202176451683044,
0.04650457948446274,
0.06189403310418129,
0.018547222018241882,
0.06655703485012054,
0.06466961652040482,
-0.02262885868549347,
0.08456692099571228,
0.030712679028511047,
-0.18644161522388458,
0.058530256152153015,
-0.09805119782686234,
0.22581584751605988,
0.05066308751702309,
0.06047345697879791,
0.2993181645870209,
0.21986234188079834,
-0.05372472479939461,
0.1669820249080658,
0.044286344200372696,
-0.05891284719109535,
-0.21245966851711273,
-0.03684934973716736,
-0.030655447393655777,
0.09436552971601486,
0.15607263147830963,
-0.0981721356511116,
-0.04201313853263855,
-0.00972361396998167,
-0.032264553010463715,
0.020120708271861076,
-0.24663487076759338,
-0.01734781451523304,
0.14379777014255524,
0.10629188269376755,
0.2451348900794983,
-0.006132842972874641,
0.023609744384884834,
0.049030207097530365,
0.018605992197990417,
-0.02483358606696129,
-0.21013511717319489,
0.09079083055257797,
0.006071676965802908,
0.04935038834810257,
0.022885039448738098,
-0.006052911281585693,
0.04500092566013336,
-0.073696069419384,
0.08904470503330231,
-0.08561883866786957,
-0.08341272175312042,
0.2185351401567459,
-0.03945168852806091,
-0.00661163916811347,
0.12917985022068024,
-0.011526807211339474,
-0.1097102016210556,
-0.015364703722298145,
0.027403371408581734,
0.030678823590278625,
-0.030246863141655922,
-0.03609466925263405,
0.024012766778469086,
0.10202405601739883,
-0.04282205551862717,
0.04565315693616867,
0.10240072011947632,
-0.020902957767248154,
0.15945613384246826,
0.13205459713935852,
0.10420060157775879,
0.002927543595433235,
-0.06464727967977524,
0.014349685050547123,
-0.055471502244472504,
0.02962767891585827,
-0.17038846015930176,
-0.0070191239938139915,
0.055695805698633194,
0.04772466421127319,
0.0945243164896965,
0.11333164572715759,
-0.127106174826622,
0.0300484336912632,
0.028996523469686508,
-0.06286120414733887,
-0.06029998138546944,
-0.002275418024510145,
-0.016458535566926003,
-0.008173024281859398,
-0.09947093576192856,
0.07884971052408218,
-0.10555081814527512,
-0.03306307643651962,
0.05025126785039902,
-0.0607193186879158,
-0.12852220237255096,
-0.010904680006206036,
0.1252979338169098,
0.061709314584732056,
-0.05078592896461487,
0.14939077198505402,
0.06109785661101341,
-0.08055379986763,
0.037185851484537125,
0.027442200109362602,
-0.08008874952793121,
-0.10198270529508591,
-0.0004569833690766245,
0.31761088967323303,
0.06076094135642052,
-0.0329466350376606,
-0.11946453154087067,
-0.15002015233039856,
0.04840146750211716,
0.1035679280757904,
0.12359631806612015,
0.011757869273424149,
-0.05322748050093651,
0.02236519381403923,
-0.05275069922208786,
0.03814244270324707,
0.06910209357738495,
-0.03928454965353012,
-0.13761694729328156,
0.0077122850343585014,
0.026647454127669334,
0.10174071043729782,
-0.06771174818277359,
-0.09184598177671432,
-0.18085066974163055,
0.09208621084690094,
-0.03432070091366768,
-0.10890032351016998,
0.027215104550123215,
-0.017406610772013664,
0.014248576015233994,
0.07639352232217789,
-0.047281619161367416,
0.01244808267802,
-0.1517520695924759,
0.07082249224185944,
0.05706808716058731,
0.08926787972450256,
0.000014311663107946515,
-0.054843269288539886,
0.07618319988250732,
-0.05763502046465874,
0.06680037826299667,
-0.053477559238672256,
0.005539732985198498,
0.10781200975179672,
-0.23264040052890778,
-0.021164139732718468,
0.009476077742874622,
-0.04681631922721863,
0.08765807747840881,
-0.19047698378562927,
0.024190550670027733,
-0.08897756040096283,
-0.024605726823210716,
0.01802127994596958,
-0.1086471825838089,
-0.04306677728891373,
0.08475461602210999,
0.037119291722774506,
-0.031288959085941315,
-0.04612116143107414,
-0.019314980134367943,
-0.0914498046040535,
0.053634315729141235,
0.07442525774240494,
-0.0687926784157753,
0.08314394950866699,
-0.05507456883788109,
0.00841207429766655,
-0.052043743431568146,
0.06760627031326294,
-0.012366239912807941,
-0.12672528624534607,
-0.02123171091079712,
-0.044928714632987976,
0.11662110686302185,
-0.023402327671647072,
0.022080281749367714,
0.014599837362766266,
0.0323631577193737,
-0.012065601535141468,
0.05028461292386055,
0.1019197478890419,
0.05136820673942566,
0.014879679307341576,
0.02292765863239765,
0.055746350437402725,
0.0757644772529602,
-0.1134679913520813,
0.06457309424877167,
-0.02098844014108181,
-0.08620109409093857,
0.1013324111700058,
0.06909440457820892,
0.037490107119083405,
0.15593400597572327,
0.22674402594566345,
0.10539932548999786,
-0.03564648702740669,
-0.03126971051096916,
0.12967991828918457,
0.17799612879753113,
-0.07682197540998459,
0.015780627727508545,
-0.0020607721526175737,
-0.017265556380152702,
-0.09849067777395248,
-0.13722245395183563,
-0.060460351407527924,
-0.2453264594078064,
0.1078341007232666,
-0.03288164362311363,
-0.04169659689068794,
0.128489688038826,
0.027952738106250763,
0.03724630922079086,
0.08183616399765015,
-0.12909026443958282,
-0.013460557907819748,
0.07749562710523605,
-0.08914026618003845,
-0.033571500331163406,
-0.17521262168884277,
-0.06771576404571533,
-0.08741120994091034,
-0.15989220142364502,
-0.06844990700483322,
0.029948782175779343,
0.035394806414842606,
0.010386589914560318,
-0.039711855351924896,
-0.01962728053331375,
0.011063394136726856,
-0.0025537724141031504,
-0.04985455423593521,
-0.01753084547817707,
0.021317757666110992,
-0.11333847790956497,
-0.024336790665984154,
0.16320326924324036,
-0.03297848999500275,
-0.18396754562854767,
-0.0405106395483017,
0.2157316505908966,
0.025046708062291145,
0.0590171180665493,
-0.073721744120121,
-0.016323629766702652,
0.021523483097553253,
0.20813441276550293,
0.10171995311975479,
-0.10821312665939331,
0.015457749366760254,
-0.03655189648270607,
0.0013793212128803134,
-0.061893612146377563,
0.10775819420814514,
0.06519263982772827,
-0.07549984753131866,
-0.17567221820354462,
-0.04389495030045509,
-0.08628730475902557,
0.03370477631688118,
-0.14383791387081146,
-0.03786516562104225,
0.1168690100312233,
0.004516853019595146,
-0.053927481174468994,
0.07883694022893906,
-0.17713546752929688,
0.03441957011818886,
-0.04880853369832039,
-0.13215437531471252,
-0.09491758048534393,
-0.10123858600854874,
0.0027463934384286404,
0.08913854509592056,
0.15567956864833832,
-0.06151591241359711,
-0.07471925020217896,
-0.009579092264175415,
-0.028091613203287125,
-0.052700337022542953,
-0.07900123298168182,
0.059512585401535034,
0.0007560851518064737,
0.16147300601005554,
-0.07439453154802322,
0.09558981657028198,
0.09099138528108597,
-0.021246420219540596,
-0.00915549136698246,
0.032866667956113815,
-0.003863809397444129,
-0.07436864078044891,
-0.04970616102218628,
0.02312966249883175,
0.027639856562018394,
0.10846075415611267,
-0.030836544930934906,
-0.1934703141450882,
0.11230092495679855,
0.09140218049287796,
-0.04296138137578964,
-0.046487610787153244,
0.05351927503943443,
-0.07097935676574707,
0.1252279132604599,
0.03444884717464447,
-0.02163051813840866,
0.013762647286057472,
-0.06370721012353897,
0.08370721340179443,
0.11594565212726593,
-0.048265840858221054,
-0.08278503268957138,
-0.06164652109146118,
0.012770666740834713,
0.02961382456123829,
-0.13650155067443848,
-0.21160630881786346,
-0.10802312940359116,
-0.1383298933506012,
0.004740108735859394,
-0.04703504592180252,
0.08498300611972809,
0.12991970777511597,
0.09780163317918777,
-0.011416295543313026,
-0.004867587238550186,
0.018085451796650887,
0.13192623853683472,
-0.11232008039951324,
-0.08192373812198639
] |
null | null | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "264.25 +/- 22.26", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | Sir-Hadian/ppo-LunarLander-v2 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-09T11:06:36+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
39,
41,
17
] | [
"passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
0.03942384943366051,
0.04900386184453964,
-0.005304091144353151,
0.026427261531352997,
0.107408307492733,
-0.026511888951063156,
0.11188238859176636,
0.0814051404595375,
0.10722193866968155,
0.04762078449130058,
0.08338645845651627,
0.06030960753560066,
0.05080918222665787,
0.2571701407432556,
0.04754156619310379,
-0.22987541556358337,
0.036159250885248184,
-0.04869936779141426,
0.12395193427801132,
0.07178173214197159,
-0.0038484656251966953,
-0.06485428661108017,
0.020415637642145157,
-0.013290755450725555,
0.05367108806967735,
0.04282612353563309,
-0.01716216839849949,
-0.08207534998655319,
0.07169748842716217,
-0.06345846503973007,
0.06986866891384125,
0.07677983492612839,
0.13218913972377777,
-0.17832116782665253,
0.029566360637545586,
0.02571309357881546,
-0.07189024239778519,
0.01342033501714468,
0.008019951172173023,
0.05120139941573143,
0.17303818464279175,
0.019879888743162155,
0.07844575494527817,
-0.0025605305563658476,
-0.15412317216396332,
-0.018950799480080605,
0.0436202734708786,
0.12546207010746002,
0.08808347582817078,
0.04605821147561073,
0.01970590092241764,
0.17503218352794647,
-0.054352790117263794,
-0.028833400458097458,
0.21759237349033356,
-0.2881564497947693,
-0.031460098922252655,
0.321048766374588,
0.06997483223676682,
0.09725230932235718,
-0.07540661096572876,
-0.03619609400629997,
0.007783263456076384,
-0.013137873262166977,
-0.028666524216532707,
-0.07447073608636856,
0.17313385009765625,
0.05152064561843872,
-0.05057951435446739,
-0.09541505575180054,
0.16948209702968597,
0.006921638268977404,
0.0018855923553928733,
-0.019282981753349304,
0.009060598909854889,
0.07402525842189789,
-0.016097044572234154,
-0.07255112379789352,
0.057438433170318604,
0.05330665782094002,
0.019649166613817215,
-0.1435653269290924,
-0.10762494057416916,
-0.022740179672837257,
-0.008012006990611553,
0.17786912620067596,
-0.009255532175302505,
0.042902372777462006,
0.003065188182517886,
0.10384012013673782,
-0.12480384111404419,
-0.03354184702038765,
-0.0454259067773819,
-0.07565800100564957,
-0.0223417766392231,
-0.02058211714029312,
-0.03580251708626747,
0.07184842973947525,
0.11971849203109741,
0.027368178591132164,
0.09350208193063736,
0.047715865075588226,
-0.03206788748502731,
0.06343851238489151,
0.05555703118443489,
0.14222665131092072,
0.05807621404528618,
0.012854371219873428,
0.13179877400398254,
0.055213116109371185,
0.033023182302713394,
-0.0613492950797081,
-0.18252409994602203,
0.07489913702011108,
-0.07031869143247604,
0.007941240444779396,
0.12051256000995636,
-0.04480670019984245,
-0.1183447614312172,
-0.037500523030757904,
-0.017392054200172424,
-0.06224250793457031,
-0.025395862758159637,
0.0547584593296051,
-0.02883218228816986,
-0.03973718360066414,
0.0011496668448671699,
0.09384800493717194,
0.00953749567270279,
-0.1752052903175354,
0.03303423151373863,
-0.025042934343218803,
-0.10782608389854431,
0.009975161403417587,
0.0022444494534283876,
0.03394931182265282,
0.04408763721585274,
-0.11822668462991714,
-0.30899152159690857,
-0.07652641832828522,
0.05490870401263237,
-0.06516939401626587,
-0.18425025045871735,
-0.13193942606449127,
0.02454492449760437,
-0.09037084132432938,
-0.044885024428367615,
-0.12759265303611755,
-0.028549788519740105,
0.01743689924478531,
0.011519349180161953,
0.10758619755506516,
-0.0106219332665205,
-0.012188062071800232,
-0.1571401208639145,
0.008273907005786896,
-0.20951123535633087,
0.0890483483672142,
-0.019150104373693466,
0.037884220480918884,
-0.032381169497966766,
-0.07404014468193054,
0.030707746744155884,
0.052499737590551376,
-0.01474119070917368,
0.13510210812091827,
-0.15592676401138306,
-0.03691192343831062,
-0.007996266707777977,
-0.13611900806427002,
-0.04786273464560509,
-0.10358831286430359,
-0.04357128217816353,
0.13354332745075226,
0.018664736300706863,
0.15356586873531342,
-0.08709818124771118,
-0.0722038671374321,
0.20489206910133362,
-0.010411538183689117,
-0.12820468842983246,
-0.076752208173275,
0.10165707021951675,
0.021510310471057892,
-0.056606587022542953,
-0.02523270808160305,
-0.1839766949415207,
-0.0152357779443264,
-0.04550420492887497,
-0.047039128839969635,
0.01796751655638218,
-0.010888241231441498,
0.13837894797325134,
0.08494598418474197,
0.05018039792776108,
-0.06086122244596481,
-0.006730288732796907,
0.10779471695423126,
0.08823856711387634,
0.008680110797286034,
0.023406028747558594,
-0.05774238705635071,
0.09552932530641556,
-0.04003755748271942,
-0.0142367510125041,
-0.08283266425132751,
-0.036246106028556824,
-0.026256313547492027,
0.17507147789001465,
0.09440762549638748,
0.2257927656173706,
0.09567736834287643,
0.039160262793302536,
0.031270865350961685,
-0.13181598484516144,
-0.1425403207540512,
-0.0017254541162401438,
0.09020978957414627,
-0.14270411431789398,
-0.04119925573468208,
-0.08974775671958923,
-0.17768175899982452,
-0.12202505767345428,
0.0006432619411498308,
-0.17960017919540405,
0.06390921026468277,
0.05408334732055664,
-0.035177867859601974,
0.03272094577550888,
0.13032332062721252,
-0.011533179320394993,
-0.03967514634132385,
0.0831870287656784,
0.0379033200442791,
-0.041234664618968964,
-0.021742934361100197,
0.11885567009449005,
0.15673065185546875,
0.13124459981918335,
-0.03511447086930275,
0.004914294462651014,
0.07076404243707657,
-0.02309088408946991,
0.06539414077997208,
0.0558244064450264,
0.20973342657089233,
0.188301220536232,
0.038996949791908264,
0.008822928182780743,
-0.07048165798187256,
0.0855446457862854,
-0.0742373839020729,
-0.14302679896354675,
-0.05579735338687897,
0.08729292452335358,
0.016605578362941742,
0.023469142615795135,
0.08711627870798111,
0.024545932188630104,
0.09132762253284454,
0.15968108177185059,
0.01990218088030815,
-0.09659269452095032,
-0.050218869000673294,
0.01175848301500082,
0.027713103219866753,
0.04794301092624664,
-0.04514073207974434,
-0.00937939714640379,
0.017020760104060173,
-0.10303554683923721,
0.031789086759090424,
-0.1413339376449585,
-0.1358717679977417,
0.044326696544885635,
0.003906996920704842,
0.010907664895057678,
0.02786896750330925,
-0.0038291432429105043,
0.019039705395698547,
0.04351753741502762,
-0.06975466758012772,
0.047416772693395615,
-0.024745507165789604,
-0.020031947642564774,
0.03340689837932587,
-0.057257164269685745,
-0.205775648355484,
-0.17696654796600342,
0.00013708483311347663,
-0.09910997003316879,
0.10194740444421768,
0.018308809027075768,
-0.12373185902833939,
0.047737859189510345,
-0.05822649225592613,
0.027574289590120316,
-0.01875593699514866,
-0.049130141735076904,
0.10507171601057053,
0.1525275856256485,
-0.016146350651979446,
0.018018173053860664,
-0.04865182936191559,
-0.10157987475395203,
-0.19632206857204437,
0.0691583976149559,
0.04680244252085686,
0.014610917307436466,
0.10669491440057755,
0.018072687089443207,
0.02367905154824257,
-0.007674071006476879,
-0.016521066427230835,
-0.011659215204417706,
-0.08781040459871292,
0.31909599900245667,
0.04510033503174782,
-0.025173069909214973,
0.02041010931134224,
-0.0043001663871109486,
-0.028083480894565582,
0.03263787180185318,
-0.0985708013176918,
-0.07548979669809341,
-0.08774089068174362,
-0.04367410019040108,
-0.09784720093011856,
0.053299110382795334,
0.05916472524404526,
0.003188040340319276,
-0.07727594673633575,
0.04221395403146744,
0.11369874328374863,
-0.0923808291554451,
-0.07137343287467957,
0.07477962225675583,
0.0972946360707283,
-0.07331304252147675,
0.00012658814375754446,
0.00874367356300354,
0.023951783776283264,
0.037102166563272476,
0.06778035312891006,
-0.03966575115919113,
0.08589404821395874,
-0.19917890429496765,
0.0372927263379097,
0.106058269739151,
0.023754918947815895,
0.0638108178973198,
0.07643651217222214,
-0.1058402881026268,
-0.008500572293996811,
-0.032518330961465836,
-0.21341575682163239,
0.1668180525302887,
0.1355515867471695,
0.06788124144077301,
-0.025637222453951836,
-0.00461410591378808,
-0.0649740919470787,
0.05773647129535675,
0.02723747305572033,
-0.14758841693401337,
0.004883295856416225,
0.06064270809292793,
0.026899009943008423,
0.01614922471344471,
0.07971042394638062,
0.014697225764393806,
-0.1801026314496994,
-0.014406266622245312,
0.10730406641960144,
0.002390873385593295,
0.0053148469887673855,
-0.03175045922398567,
-0.1755964607000351,
0.0751047357916832,
0.004285442177206278,
0.07233936339616776,
-0.1676585078239441,
0.14297930896282196,
-0.10089799761772156,
0.07726949453353882,
-0.004285062663257122,
-0.021311495453119278,
0.02507244050502777,
-0.0541163794696331,
0.15163759887218475,
0.01058570109307766,
-0.021810131147503853,
-0.1200498715043068,
-0.1717042326927185,
-0.019227758049964905,
-0.11788936704397202,
-0.11679866164922714,
0.050424277782440186,
0.062185097485780716,
0.04923136904835701,
-0.061147067695856094,
0.1518532931804657,
-0.047422297298908234,
0.060713399201631546,
-0.06893875449895859,
-0.06755045056343079,
0.03764858841896057,
-0.12588608264923096,
-0.08176055550575256,
0.05573027580976486,
0.19166934490203857,
0.15833087265491486,
-0.02816431224346161,
-0.03472423925995827,
-0.047419581562280655,
-0.006212298292666674,
-0.007802055217325687,
0.0275666993111372,
0.023223137483000755,
0.07315318286418915,
-0.07681374251842499,
-0.11649256944656372,
0.033787861466407776,
-0.06713802367448807,
-0.055589709430933,
-0.015439179725944996,
0.1513158082962036,
0.04671623185276985,
0.07720734924077988,
-0.018946662545204163,
0.03887668624520302,
-0.001724981120787561,
-0.056474871933460236,
0.16197094321250916,
0.03885216265916824,
-0.05193585529923439,
0.06837689876556396,
0.053174007683992386,
0.043745119124650955,
0.03011113777756691,
-0.026783017441630363,
0.206032395362854,
0.1980147808790207,
0.014206883497536182,
0.2175983190536499,
0.03177616000175476,
-0.03772832080721855,
-0.1300560086965561,
-0.065880686044693,
-0.006372632458806038,
0.03559038043022156,
0.08070417493581772,
-0.18207235634326935,
-0.015011128038167953,
-0.05689644813537598,
-0.034518610686063766,
-0.15059494972229004,
-0.28553900122642517,
-0.05957856774330139,
0.20075850188732147,
0.14706264436244965,
0.27519428730010986,
-0.10432573407888412,
0.035197313874959946,
0.02663275972008705,
-0.04912831634283066,
-0.006501141935586929,
0.00018665487004909664,
0.10268618166446686,
-0.15421873331069946,
0.1176437959074974,
0.08486983180046082,
-0.019002694636583328,
0.01058861706405878,
-0.1619086116552353,
0.00936629343777895,
-0.12191236019134521,
0.05354422330856323,
0.1400289237499237,
-0.048128653317689896,
-0.054873593151569366,
0.14033560454845428,
-0.024562934413552284,
-0.22685599327087402,
-0.04648222774267197,
-0.043600670993328094,
-0.010640020482242107,
0.026607351377606392,
-0.1013401448726654,
0.04101909324526787,
0.1330099105834961,
0.009380043484270573,
0.1147187277674675,
0.11749245226383209,
-0.052566803991794586,
0.10792597383260727,
0.2257719188928604,
-0.018785694614052773,
0.04689010605216026,
-0.12743118405342102,
-0.0012336712097749114,
-0.028270328417420387,
0.013657891191542149,
-0.09504974633455276,
-0.09938385337591171,
0.02366873063147068,
0.02872389927506447,
0.009118586778640747,
0.0921793207526207,
-0.029922157526016235,
0.0759170651435852,
0.06817561388015747,
-0.13014446198940277,
-0.16288450360298157,
0.015828335657715797,
-0.007344507612287998,
0.08354310691356659,
0.00027861111448146403,
0.08878035843372345,
-0.11932205408811569,
-0.018093237653374672,
-0.03153328225016594,
-0.03319635987281799,
-0.130486860871315,
-0.07138993591070175,
0.06156524643301964,
0.028095467016100883,
-0.06602972000837326,
0.1398407518863678,
0.026440169662237167,
0.15942534804344177,
0.049197953194379807,
0.012499804608523846,
0.07227300107479095,
-0.05345509201288223,
0.1283530443906784,
0.13818155229091644,
-0.00868943240493536,
-0.05460423603653908,
-0.1013643890619278,
-0.10236792266368866,
0.08925779908895493,
-0.05773641914129257,
0.07476430386304855,
-0.14885357022285461,
-0.06675903499126434,
0.015772046521306038,
0.016141414642333984,
-0.09562095999717712,
0.02571965754032135,
-0.01625603251159191,
-0.18119946122169495,
0.056570518761873245,
-0.048285093158483505,
0.0440407395362854,
-0.06347788125276566,
-0.1110161691904068,
-0.17226378619670868,
0.06091433763504028,
0.08593481779098511,
-0.053876690566539764,
-0.12229149043560028,
0.011023230850696564,
-0.00012518465518951416,
-0.06341652572154999,
-0.05023367330431938,
0.09722746908664703,
-0.11020902544260025,
0.031452205032110214,
-0.012567701749503613,
0.08853451162576675,
-0.03510405123233795,
-0.011538895778357983,
0.044220831245183945,
-0.08039166033267975,
-0.009481523185968399,
0.03534642979502678,
-0.026372017338871956,
-0.04127239063382149,
-0.2689029574394226,
0.0036654395516961813,
0.0341104120016098,
0.02497158572077751,
0.07856601476669312,
0.011906822212040424,
0.021174922585487366,
0.03993808850646019,
-0.15396519005298615,
-0.013395369984209538,
0.14574195444583893,
-0.07689505815505981,
-0.022186370566487312,
0.05703273415565491,
-0.09054436534643173,
0.013882770203053951,
-0.030287226662039757,
0.1345842480659485,
0.023923413828015327,
0.06404478847980499,
-0.0851147472858429,
0.10106813907623291,
-0.1451139897108078,
-0.04998219385743141,
-0.01244612317532301,
0.09761348366737366,
0.07019034773111343,
-0.10272270441055298,
0.014697125181555748,
0.04210108891129494,
0.19416837394237518,
0.016384804621338844,
-0.0356343574821949,
-0.03396720811724663,
0.004015897400677204,
0.22076453268527985,
0.03044266067445278,
0.10457023978233337,
0.07281364500522614,
-0.026583973318338394,
0.12624378502368927,
0.09929762035608292,
0.11280370503664017,
-0.055645186454057693,
0.13904185593128204,
0.04667386785149574,
0.038641396909952164,
0.0614289753139019,
0.06836545467376709,
0.09098632633686066,
-0.0008288522367365658,
0.1138714924454689,
0.013811973854899406,
-0.02422109805047512,
-0.021335409954190254,
0.17759373784065247,
0.10501719266176224,
-0.14769648015499115,
0.029047364369034767,
-0.01258957851678133,
0.039933037012815475,
-0.014194529503583908,
-0.15634691715240479,
-0.07240267097949982,
-0.3315149247646332,
0.1226184144616127,
-0.07119352370500565,
0.019930170848965645,
0.007913772016763687,
-0.037425633519887924,
-0.03296699747443199,
-0.04477746784687042,
0.13151589035987854,
-0.013641550205647945,
-0.006079165264964104,
-0.04815853759646416,
-0.015360191464424133,
-0.11607866734266281,
-0.11200575530529022,
-0.013207737356424332,
-0.13671602308750153,
-0.010119039565324783,
0.05595948174595833,
0.003977729007601738,
0.01821410097181797,
-0.03142618387937546,
0.0024383175186812878,
0.06541839241981506,
-0.05751744285225868,
0.056182678788900375,
0.12097269296646118,
0.08766137808561325,
-0.1058853268623352,
0.031048951670527458,
0.2011747509241104,
0.04359564557671547,
-0.12483977526426315,
0.01449228823184967,
0.1819491684436798,
0.004885740112513304,
0.017068125307559967,
-0.006097703706473112,
-0.0540788508951664,
-0.07554277032613754,
0.1251034289598465,
0.08296554535627365,
-0.09985227137804031,
0.015833314508199692,
-0.0726347416639328,
-0.01594804972410202,
-0.06374675035476685,
0.10130585730075836,
0.09538925439119339,
0.04440245032310486,
-0.10621760785579681,
-0.08487539738416672,
-0.10891728103160858,
0.040588874369859695,
-0.08629853278398514,
-0.07311757653951645,
0.09629398584365845,
-0.07057105004787445,
-0.07029950618743896,
0.025521177798509598,
-0.17978744208812714,
-0.009467960335314274,
0.1711762249469757,
-0.24654000997543335,
-0.0916430801153183,
-0.10857923328876495,
0.14477859437465668,
0.016497576609253883,
0.1013975441455841,
-0.006207061931490898,
-0.007889035157859325,
-0.20577777922153473,
0.024890204891562462,
-0.05293011665344238,
-0.02073732763528824,
0.07814782857894897,
-0.09476397186517715,
0.22629831731319427,
-0.08276885002851486,
0.020940175279974937,
0.012659613974392414,
0.0870661810040474,
-0.030675338581204414,
0.09283176809549332,
-0.03660329803824425,
-0.12576518952846527,
-0.03620953485369682,
0.03001813031733036,
0.013904244638979435,
0.10071761906147003,
0.09772487729787827,
-0.03414725139737129,
0.03389119729399681,
0.09747414290904999,
0.04172342270612717,
-0.023843804374337196,
0.0360250361263752,
-0.17077107727527618,
0.02182629331946373,
-0.018498148769140244,
-0.06935930997133255,
0.03687669709324837,
-0.06603235751390457,
0.1639697551727295,
0.04022442549467087,
0.0670473501086235,
-0.036152735352516174,
0.0073931049555540085,
-0.014454689808189869,
-0.013775371946394444,
-0.026180334389209747,
-0.17259705066680908,
-0.10422050207853317,
-0.1347656100988388,
-0.012701659463346004,
-0.034971047192811966,
0.04591470584273338,
0.023234914988279343,
-0.0003200018545612693,
-0.014577031135559082,
-0.12090865522623062,
0.04360328987240791,
0.11146783083677292,
-0.04631396010518074,
-0.026193076744675636
] |
null | null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | null | mertllc/mms-tts-tur-inkilap | [
"transformers",
"safetensors",
"vits",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-09T11:25:05+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #vits #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #vits #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
34,
6,
3,
82,
28,
3,
4,
9,
9,
10,
42,
20,
3,
4,
5,
9,
11,
13,
3,
12,
5,
4,
5,
3,
4,
9,
53,
9,
8,
6,
3,
14,
8,
7,
9,
4
] | [
"passage: TAGS\n#transformers #safetensors #vits #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
] | [
-0.054659612476825714,
0.21414990723133087,
-0.0031807427294552326,
0.026865221560001373,
0.1250888854265213,
0.00032571866177022457,
0.04081440716981888,
0.12862813472747803,
-0.02167222462594509,
0.11129128932952881,
0.03218022361397743,
0.09727001935243607,
0.10339263826608658,
0.16586677730083466,
0.03691011667251587,
-0.21517004072666168,
0.009132993407547474,
-0.09292528033256531,
0.018077509477734566,
0.10867427289485931,
0.13162045180797577,
-0.10489460080862045,
0.07603627443313599,
-0.03790099918842316,
-0.017673974856734276,
-0.0003223843814339489,
-0.0923151820898056,
-0.070840984582901,
0.06550594419240952,
0.06909013539552689,
0.06122942641377449,
0.009997012093663216,
0.10145736485719681,
-0.29726552963256836,
0.01687687449157238,
0.08279260247945786,
-0.004506718832999468,
0.06148726865649223,
0.0646374449133873,
-0.08339887112379074,
0.1029256209731102,
-0.08559336513280869,
0.13652671873569489,
0.08214850723743439,
-0.06937385350465775,
-0.21391066908836365,
-0.06977995485067368,
0.0987061932682991,
0.12011658400297165,
0.06274435669183731,
-0.02326560579240322,
0.1522950381040573,
-0.06972704082727432,
0.012022249400615692,
0.1361677050590515,
-0.09713108092546463,
-0.05137801170349121,
0.049987345933914185,
0.11240657418966293,
0.10166463255882263,
-0.1353231519460678,
0.007596791721880436,
0.04457303136587143,
0.023097742348909378,
0.09194746613502502,
0.020738936960697174,
0.0916183590888977,
0.04564107209444046,
-0.13860996067523956,
-0.03957565128803253,
0.10889606922864914,
0.03478158637881279,
-0.05796414613723755,
-0.21188515424728394,
-0.0026691502425819635,
-0.026535477489233017,
-0.023307178169488907,
-0.05803702771663666,
0.045833978801965714,
-0.03317271173000336,
0.067923404276371,
-0.042256616055965424,
-0.10016343742609024,
-0.03838508576154709,
0.0836847797036171,
0.06997206062078476,
0.013808192685246468,
-0.026154542341828346,
0.03861820325255394,
0.11874474585056305,
0.037009406834840775,
-0.10824361443519592,
-0.0663856491446495,
-0.06518013030290604,
-0.09711762517690659,
-0.04532422870397568,
0.04776853322982788,
0.01869308575987816,
0.030892416834831238,
0.20719914138317108,
-0.0024066849146038294,
0.040300752967596054,
0.01544452179223299,
0.00820851232856512,
0.05608583986759186,
0.09020276367664337,
-0.057233426719903946,
-0.13989022374153137,
-0.04616677761077881,
0.08976847678422928,
-0.00493787182494998,
-0.03551584109663963,
-0.04997507110238075,
0.048379965126514435,
0.05169600620865822,
0.1267518699169159,
0.08646857738494873,
-0.012898874469101429,
-0.05273304134607315,
-0.025197435170412064,
0.22986702620983124,
-0.14503952860832214,
0.04801303148269653,
-0.016220765188336372,
-0.026413746178150177,
-0.04562145099043846,
0.037146687507629395,
0.02893291600048542,
-0.0071297562681138515,
0.09902069717645645,
-0.055000074207782745,
-0.03897455707192421,
-0.10056453198194504,
-0.03981734439730644,
0.04000834375619888,
-0.0014343701768666506,
-0.011925416998565197,
-0.07901987433433533,
-0.1033727377653122,
-0.04151687026023865,
0.0622556135058403,
-0.06062569096684456,
-0.03672588989138603,
0.014433487318456173,
-0.0646335631608963,
-0.011868113651871681,
-0.0046113538555800915,
0.10713792592287064,
-0.03111988678574562,
0.041085705161094666,
-0.03385680913925171,
0.05467362701892853,
0.10134078562259674,
0.03396330401301384,
-0.0692443996667862,
0.05283360555768013,
-0.2253323644399643,
0.0846395194530487,
-0.1103181466460228,
0.040045637637376785,
-0.1649162620306015,
-0.04362662881612778,
0.01545786950737238,
0.01223697792738676,
0.010682502761483192,
0.11813149601221085,
-0.18765069544315338,
-0.02040630392730236,
0.13456352055072784,
-0.09486816823482513,
-0.10925174504518509,
0.07470420002937317,
-0.04261988773941994,
0.14796192944049835,
0.04623936489224434,
-0.017894135788083076,
0.07337126135826111,
-0.16546636819839478,
-0.06534566730260849,
-0.015944186598062515,
-0.01140376552939415,
0.13805019855499268,
0.06177884340286255,
-0.05833873897790909,
0.06357681751251221,
0.02317901886999607,
-0.022351879626512527,
-0.04479735344648361,
-0.05049646645784378,
-0.10716529190540314,
-0.006589649710804224,
-0.0877491682767868,
0.049144841730594635,
-0.008710972033441067,
-0.07987060397863388,
-0.032660458236932755,
-0.18162156641483307,
0.03565994277596474,
0.08912748098373413,
0.006954456213861704,
-0.008257697336375713,
-0.07709750533103943,
0.012575463391840458,
-0.027584582567214966,
-0.010441360995173454,
-0.16807158291339874,
-0.045059818774461746,
0.045085642486810684,
-0.1683385670185089,
0.03666726127266884,
-0.05383622646331787,
0.057435907423496246,
0.04089425876736641,
-0.0608406662940979,
-0.012410139665007591,
-0.020455263555049896,
0.02037479542195797,
-0.03554835915565491,
-0.19715940952301025,
-0.04920884966850281,
-0.033720988780260086,
0.15323609113693237,
-0.2512565553188324,
0.03701164573431015,
0.04283377155661583,
0.1445688009262085,
-0.004499740432947874,
-0.041343484073877335,
0.021006079390645027,
-0.05124713480472565,
-0.04886976629495621,
-0.064845971763134,
-0.003489583032205701,
-0.029771825298666954,
-0.04689984768629074,
0.014419492334127426,
-0.17416127026081085,
-0.03588438406586647,
0.09719391912221909,
0.1012604832649231,
-0.15479636192321777,
-0.018018238246440887,
-0.046819429844617844,
-0.06501296907663345,
-0.08719377964735031,
-0.0634685754776001,
0.12365260720252991,
0.04887883737683296,
0.044603388756513596,
-0.07642911374568939,
-0.06516730040311813,
0.02209198847413063,
0.00037755590165033937,
-0.03342745080590248,
0.07709765434265137,
0.06420876830816269,
-0.09495706856250763,
0.07597044855356216,
0.0879693329334259,
0.07397416979074478,
0.09690815210342407,
0.017737112939357758,
-0.10766889899969101,
-0.025353191420435905,
0.025884538888931274,
0.02590569481253624,
0.14766225218772888,
-0.052133310586214066,
0.03766921907663345,
0.047928281128406525,
-0.048178963363170624,
0.018924955278635025,
-0.09172655642032623,
0.02477680705487728,
0.03108147345483303,
-0.0051895990036427975,
0.04569429159164429,
-0.04261132329702377,
0.0015583503991365433,
0.07553404569625854,
0.0439009927213192,
0.054722823202610016,
0.004550157580524683,
-0.014615098014473915,
-0.09760808199644089,
0.16303586959838867,
-0.09686829894781113,
-0.2844827473163605,
-0.15191766619682312,
0.025421515107154846,
0.038875505328178406,
-0.02202117070555687,
0.031196635216474533,
-0.0685606598854065,
-0.10619828850030899,
-0.10253546386957169,
-0.0007893215515650809,
0.021664658561348915,
-0.07999464124441147,
-0.07771245390176773,
0.07423610240221024,
0.04034431278705597,
-0.14601534605026245,
0.03843066841363907,
0.05174413323402405,
-0.05686575174331665,
-0.020990731194615364,
0.08788161724805832,
0.11919383704662323,
0.15064425766468048,
-0.01956579089164734,
-0.029653063043951988,
0.02179299294948578,
0.18913501501083374,
-0.13056331872940063,
0.10870491713285446,
0.1331699639558792,
-0.0433298796415329,
0.08741360157728195,
0.17486868798732758,
0.02946310304105282,
-0.08184187114238739,
0.04125521704554558,
0.04271497204899788,
-0.0446363128721714,
-0.2628204822540283,
-0.0587831549346447,
0.013565518893301487,
-0.07289978116750717,
0.09574431926012039,
0.09441626816987991,
0.13101495802402496,
0.03733300045132637,
-0.07704862952232361,
-0.042284153401851654,
-0.0007691121427342296,
0.11566338688135147,
-0.04729871824383736,
-0.00864650122821331,
0.08112052828073502,
-0.04204992949962616,
0.0042695761658251286,
0.101866215467453,
0.024085933342576027,
0.18680992722511292,
0.02045324817299843,
0.1325864940881729,
0.06266885250806808,
0.07362587004899979,
-0.00304698059335351,
0.021530818194150925,
0.04571235924959183,
0.016793522983789444,
-0.004352389834821224,
-0.10109587758779526,
0.004940509796142578,
0.14031140506267548,
0.044244058430194855,
0.029351718723773956,
0.0023038540966808796,
-0.025745723396539688,
0.059172797948122025,
0.16894783079624176,
-0.014623390510678291,
-0.20305828750133514,
-0.07212355732917786,
0.07476779818534851,
-0.05524183437228203,
-0.12190999835729599,
-0.03604535013437271,
0.03974858298897743,
-0.17753031849861145,
0.03411399945616722,
-0.020660564303398132,
0.09808827936649323,
-0.0960298478603363,
-0.025731271132826805,
0.017328539863228798,
0.08463997393846512,
-0.017630890011787415,
0.09686511754989624,
-0.15011048316955566,
0.12523487210273743,
0.03229980170726776,
0.0898485779762268,
-0.11468798667192459,
0.08304145932197571,
-0.009098101407289505,
0.016468055546283722,
0.18883956968784332,
-0.00914006493985653,
-0.043279051780700684,
-0.0765409916639328,
-0.09724772721529007,
-0.016675574705004692,
0.12457696348428726,
-0.11865599453449249,
0.08336363732814789,
-0.006434252485632896,
-0.05090279504656792,
0.010499227792024612,
-0.11436042934656143,
-0.17895425856113434,
-0.19684189558029175,
0.061690423637628555,
-0.10233647376298904,
0.01922602578997612,
-0.1105671152472496,
-0.06737665832042694,
-0.029828263446688652,
0.2358294576406479,
-0.14021140336990356,
-0.07348582148551941,
-0.1486395299434662,
-0.049397800117731094,
0.1688835471868515,
-0.039627790451049805,
0.07352027297019958,
-0.014237076044082642,
0.21156272292137146,
-0.0005727469106204808,
-0.0019497170578688383,
0.0662601962685585,
-0.09127254039049149,
-0.17042554914951324,
-0.0796523243188858,
0.1408538520336151,
0.1185344010591507,
0.05187511071562767,
-0.00005241960025159642,
0.008437353186309338,
-0.01933823712170124,
-0.11107131093740463,
-0.005973829887807369,
0.13854430615901947,
0.06674695014953613,
0.03547331318259239,
-0.05006469413638115,
-0.10860110819339752,
-0.06920936703681946,
-0.058358483016490936,
0.05175930634140968,
0.18184207379817963,
-0.1009909063577652,
0.17350798845291138,
0.15878215432167053,
-0.07211574912071228,
-0.21567314863204956,
0.039191193878650665,
0.04846473038196564,
-0.014512532390654087,
0.04614531248807907,
-0.1829945594072342,
0.09505120664834976,
0.015141540206968784,
-0.052736036479473114,
0.12199369817972183,
-0.15728448331356049,
-0.15639621019363403,
0.06087431684136391,
0.04970995709300041,
-0.23623821139335632,
-0.1441342532634735,
-0.08822641521692276,
-0.06784138828516006,
-0.14815589785575867,
0.07915012538433075,
-0.019972164183855057,
0.011897586286067963,
0.04091079905629158,
0.013740893453359604,
0.023185279220342636,
-0.055776987224817276,
0.18284909427165985,
-0.0035617330577224493,
0.014864614233374596,
-0.06912479549646378,
-0.058035630732774734,
0.0975092425942421,
-0.05838471278548241,
0.1184525191783905,
-0.003918026573956013,
0.013672815635800362,
-0.08212041109800339,
-0.05343952775001526,
-0.046617619693279266,
0.05752236396074295,
-0.08050531893968582,
-0.11092408001422882,
-0.04487094283103943,
0.08938708156347275,
0.07764840126037598,
-0.033286161720752716,
-0.010930746793746948,
-0.07634644955396652,
0.10063119232654572,
0.19033774733543396,
0.17030654847621918,
0.018113715574145317,
-0.07677590847015381,
0.015532949939370155,
-0.03924742713570595,
0.04019718989729881,
-0.2505480647087097,
0.03877655416727066,
0.0529145747423172,
0.0354921817779541,
0.1059221550822258,
-0.02500346675515175,
-0.17749741673469543,
-0.0438142865896225,
0.06573881208896637,
-0.045354213565588,
-0.22390563786029816,
-0.009726951830089092,
0.09943331032991409,
-0.1914641559123993,
-0.015451330691576004,
0.02838914282619953,
-0.04480560123920441,
-0.02868090756237507,
0.0007889526314102113,
0.0600614957511425,
0.015805870294570923,
0.09190283715724945,
0.07423794269561768,
0.09749054163694382,
-0.08805927634239197,
0.09811163693666458,
0.10723351687192917,
-0.09035424888134003,
0.03553062304854393,
0.06695880740880966,
-0.0467107780277729,
-0.04594837874174118,
0.05199020728468895,
0.04819667339324951,
0.01212578546255827,
-0.0561964213848114,
0.010319532826542854,
-0.04872706159949303,
0.04633839800953865,
0.10621411353349686,
0.028242740780115128,
-0.03058992512524128,
0.06704547256231308,
0.03252853453159332,
-0.1153404489159584,
0.09847725927829742,
0.012868257239460945,
0.03807265684008598,
-0.06272068619728088,
-0.015808504074811935,
0.04865187034010887,
0.027409857138991356,
-0.01764598675072193,
-0.025427930057048798,
-0.035527609288692474,
-0.015147317200899124,
-0.15422900021076202,
-0.012660279870033264,
-0.07294544577598572,
0.007333413697779179,
0.006807927042245865,
-0.03955657035112381,
-0.0043836915865540504,
0.029364487156271935,
-0.07081043720245361,
-0.06899864971637726,
-0.0017123379511758685,
0.10014908015727997,
-0.16123399138450623,
0.0016520773060619831,
0.07378670573234558,
-0.10700937360525131,
0.06776659190654755,
-0.009028629399836063,
0.006400149781256914,
0.021102426573634148,
-0.1615109145641327,
0.05426544323563576,
-0.010029333643615246,
0.02013414539396763,
0.032934170216321945,
-0.16248436272144318,
0.0024488656781613827,
-0.047329291701316833,
-0.022390197962522507,
-0.004845738876610994,
-0.04656189680099487,
-0.11974798142910004,
0.07715073227882385,
-0.01184067688882351,
-0.05094744265079498,
-0.01612357795238495,
0.05293868109583855,
0.08231643587350845,
-0.03882661834359169,
0.09632368385791779,
-0.005011113826185465,
0.05959545075893402,
-0.17253276705741882,
-0.02932477742433548,
-0.0432354174554348,
0.014331330545246601,
0.01743181422352791,
-0.009555062279105186,
0.03874485567212105,
-0.00935265514999628,
0.22544825077056885,
-0.03915993124246597,
0.16461394727230072,
0.055936723947525024,
-0.0032888432033360004,
0.0007776605198159814,
0.06758615374565125,
0.05568486079573631,
0.03412187471985817,
0.00899792555719614,
0.02200561948120594,
-0.023325180634856224,
-0.006471368949860334,
-0.1553903967142105,
0.02697177603840828,
0.14716137945652008,
0.0745159387588501,
0.006664956454187632,
0.07025619596242905,
-0.1267581284046173,
-0.11370917409658432,
0.09592846781015396,
-0.02568071521818638,
0.008476621471345425,
-0.07835444062948227,
0.12778781354427338,
0.14673273265361786,
-0.14686504006385803,
0.06517019122838974,
-0.053687721490859985,
-0.05600763112306595,
-0.09034380316734314,
-0.10879118740558624,
-0.06126067787408829,
-0.04308179020881653,
0.004678911529481411,
-0.042684826999902725,
0.055097613483667374,
0.04954573139548302,
-0.014461824670433998,
0.004931987728923559,
0.12391652166843414,
-0.006120255216956139,
0.001201988779939711,
0.03766126185655594,
0.03769403696060181,
0.024755796417593956,
-0.059261444956064224,
0.030717262998223305,
0.021477915346622467,
0.034908585250377655,
0.059853747487068176,
0.037230484187603,
-0.045039307326078415,
0.028804119676351547,
0.0020213082898408175,
-0.10957802832126617,
0.023749636486172676,
-0.012328135780990124,
-0.06936221569776535,
0.12969832122325897,
0.03471869230270386,
0.009512413293123245,
-0.037131089717149734,
0.23728759586811066,
-0.062090300023555756,
-0.08014962822198868,
-0.12913139164447784,
0.09616934508085251,
-0.013530191034078598,
0.057892732322216034,
0.03356536477804184,
-0.12210189551115036,
0.0036616562865674496,
0.13605539500713348,
0.11633196473121643,
-0.0003361425769980997,
0.012180927209556103,
0.044184453785419464,
0.004239979665726423,
-0.06263455748558044,
0.044461920857429504,
0.06619330495595932,
0.12273700535297394,
-0.07938622683286667,
0.07410858571529388,
0.00435013510286808,
-0.08385829627513885,
-0.0399140790104866,
0.1140546128153801,
-0.03326992690563202,
0.03303933143615723,
-0.041518088430166245,
0.10997304320335388,
-0.059399381279945374,
-0.3032641112804413,
0.03540288656949997,
-0.10066618025302887,
-0.1533578634262085,
-0.01690032333135605,
0.06605888903141022,
-0.02134985849261284,
0.01722477562725544,
0.06963877379894257,
-0.058587364852428436,
0.1905425637960434,
0.03258530795574188,
-0.07860512286424637,
-0.059183377772569656,
0.05133861303329468,
-0.0791650041937828,
0.302468478679657,
0.00626079086214304,
0.03169599175453186,
0.10508318990468979,
-0.028644336387515068,
-0.16361252963542938,
0.02362491562962532,
0.1140698790550232,
-0.08390003442764282,
0.08627496659755707,
0.19878266751766205,
-0.019539451226592064,
0.11435621976852417,
0.05704843997955322,
-0.06186779960989952,
0.0524447038769722,
-0.03936922550201416,
-0.052163589745759964,
-0.09776037186384201,
0.06190723925828934,
-0.06178663671016693,
0.15432539582252502,
0.09593082964420319,
-0.05059736222028732,
-0.006600235588848591,
-0.05587591603398323,
0.04507772624492645,
0.018967149779200554,
0.12800532579421997,
0.012484090402722359,
-0.17696550488471985,
0.032744914293289185,
0.0010579711524769664,
0.11208613961935043,
-0.24666742980480194,
-0.08353681117296219,
0.09015431255102158,
-0.019416818395256996,
-0.05258401483297348,
0.09870866686105728,
0.0722413882613182,
0.04240792989730835,
-0.04463369399309158,
-0.10492048412561417,
-0.019366342574357986,
0.1493324637413025,
-0.14043603837490082,
-0.014699541963636875
] |
null | null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | null | FrancB/phi2_DPO | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-09T11:27:25+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
31,
6,
3,
82,
28,
3,
4,
9,
9,
10,
42,
20,
3,
4,
5,
9,
11,
13,
3,
12,
5,
4,
5,
3,
4,
9,
53,
9,
8,
6,
3,
14,
8,
7,
9,
4
] | [
"passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
] | [
-0.06646376848220825,
0.2168014943599701,
-0.00225935154594481,
0.023818302899599075,
0.1271018385887146,
-0.001635765191167593,
0.04218708351254463,
0.13324736058712006,
-0.020175931975245476,
0.11144465953111649,
0.046588581055402756,
0.09377603232860565,
0.09928803145885468,
0.18404334783554077,
0.04859916493296623,
-0.2059975117444992,
0.007056170143187046,
-0.09090408682823181,
0.014076028019189835,
0.1116579994559288,
0.13719257712364197,
-0.10291384905576706,
0.08272874355316162,
-0.04045208916068077,
-0.02019004337489605,
0.00012576708104461432,
-0.09259183704853058,
-0.07032395154237747,
0.06885425746440887,
0.06264153122901917,
0.051234472543001175,
0.001456156256608665,
0.09140396863222122,
-0.2864592671394348,
0.017265573143959045,
0.08406311273574829,
0.0027674848679453135,
0.06290827691555023,
0.07236549258232117,
-0.07389893382787704,
0.11328595131635666,
-0.08021481335163116,
0.13019037246704102,
0.08625296503305435,
-0.062064990401268005,
-0.23071379959583282,
-0.07525765895843506,
0.0963398814201355,
0.12251301854848862,
0.06215599179267883,
-0.022921854630112648,
0.15455181896686554,
-0.06248689442873001,
0.012971068732440472,
0.1294165402650833,
-0.11526761949062347,
-0.05572471022605896,
0.061741601675748825,
0.11775490641593933,
0.10740239918231964,
-0.14110268652439117,
-0.0017287094378843904,
0.04900608956813812,
0.029121357947587967,
0.08589313924312592,
0.022661056369543076,
0.12003941088914871,
0.04652795568108559,
-0.13695219159126282,
-0.04037507623434067,
0.12011898308992386,
0.038862764835357666,
-0.06446044892072678,
-0.2168138176202774,
-0.006778308190405369,
-0.0601806715130806,
-0.014732478186488152,
-0.07019448280334473,
0.039128515869379044,
-0.02470310963690281,
0.07317749410867691,
-0.04465159401297569,
-0.1063927412033081,
-0.0421026237308979,
0.0892222449183464,
0.07748593389987946,
0.011527054943144321,
-0.02519804798066616,
0.04627908393740654,
0.13455867767333984,
0.05402068421244621,
-0.10399353504180908,
-0.07017925381660461,
-0.06942764669656754,
-0.09420394152402878,
-0.04035796597599983,
0.056760527193546295,
0.031942449510097504,
0.02665667235851288,
0.22703726589679718,
0.016653569415211678,
0.04155244305729866,
0.0224777739495039,
0.01032855175435543,
0.043662428855895996,
0.0955500528216362,
-0.05303520709276199,
-0.15660029649734497,
-0.04072032496333122,
0.09077946096658707,
-0.0027527001220732927,
-0.036689214408397675,
-0.03966725245118141,
0.03849169611930847,
0.06843466311693192,
0.13122352957725525,
0.07552056759595871,
-0.017929591238498688,
-0.04813180863857269,
-0.030096933245658875,
0.23523783683776855,
-0.1493375599384308,
0.04426715523004532,
-0.02271856553852558,
-0.01804111897945404,
-0.03908449783921242,
0.03597262129187584,
0.022118929773569107,
-0.000004518366949923802,
0.09706240892410278,
-0.058981191366910934,
-0.05378659814596176,
-0.10168042778968811,
-0.03272576630115509,
0.04088849574327469,
-0.013975566253066063,
-0.010589460842311382,
-0.09025166928768158,
-0.09490354359149933,
-0.04766594246029854,
0.05537205561995506,
-0.05123869329690933,
-0.03770573064684868,
0.009465423412621021,
-0.08151785284280777,
-0.005444355774670839,
-0.005417742300778627,
0.10699385404586792,
-0.03222226724028587,
0.04445803165435791,
-0.027600755915045738,
0.05225523188710213,
0.09919606149196625,
0.031576547771692276,
-0.0773419588804245,
0.0561848059296608,
-0.22559374570846558,
0.07503069192171097,
-0.11481974273920059,
0.04335082694888115,
-0.1704932004213333,
-0.042439818382263184,
0.005444696638733149,
0.0139949731528759,
0.013206101022660732,
0.12720820307731628,
-0.19255615770816803,
-0.01654396951198578,
0.13260798156261444,
-0.09212633967399597,
-0.118110790848732,
0.07884611934423447,
-0.029701577499508858,
0.1624738723039627,
0.04682036489248276,
-0.027025915682315826,
0.09224298596382141,
-0.16434773802757263,
-0.07092688232660294,
-0.00949116237461567,
-0.01727987825870514,
0.12109188735485077,
0.07512219995260239,
-0.05991523340344429,
0.046571120619773865,
0.02832140028476715,
-0.038078423589468,
-0.04424772411584854,
-0.050857074558734894,
-0.10884185880422592,
-0.01070026308298111,
-0.08987759798765182,
0.04065500199794769,
-0.01250192429870367,
-0.07916021347045898,
-0.029885273426771164,
-0.18612512946128845,
-0.0030564051121473312,
0.10038342326879501,
0.0035033065360039473,
-0.005652366206049919,
-0.08666291832923889,
0.026358824223279953,
-0.03112892620265484,
-0.008404186926782131,
-0.16764774918556213,
-0.04399421438574791,
0.046902090311050415,
-0.16094985604286194,
0.020117372274398804,
-0.06413903087377548,
0.06334125250577927,
0.03641495108604431,
-0.05590536445379257,
-0.0248766727745533,
-0.01730942726135254,
0.011945613659918308,
-0.05083848536014557,
-0.18994836509227753,
-0.056277405470609665,
-0.037882111966609955,
0.149809330701828,
-0.25956398248672485,
0.032966937869787216,
0.051140617579221725,
0.14649195969104767,
0.00406361510977149,
-0.05115427449345589,
0.01429014839231968,
-0.05360214412212372,
-0.054652128368616104,
-0.06746816635131836,
-0.006135428790003061,
-0.027576493099331856,
-0.05147203803062439,
0.019243421033024788,
-0.1755700707435608,
-0.021410830318927765,
0.09424154460430145,
0.12876708805561066,
-0.1486445665359497,
-0.018640631809830666,
-0.048725154250860214,
-0.06339836865663528,
-0.0715010017156601,
-0.07038594037294388,
0.10712739825248718,
0.0513901449739933,
0.04796046018600464,
-0.07435787469148636,
-0.07092321664094925,
0.02726263552904129,
0.006906150374561548,
-0.03382374346256256,
0.08727246522903442,
0.05199531093239784,
-0.09209315478801727,
0.0756213590502739,
0.1092359870672226,
0.07177663594484329,
0.09363535046577454,
0.01574566215276718,
-0.11756632477045059,
-0.028492970392107964,
0.036266472190618515,
0.02740776725113392,
0.1465986967086792,
-0.05952361226081848,
0.04016614332795143,
0.04494241625070572,
-0.04170418903231621,
0.022319864481687546,
-0.08787637203931808,
0.024075502529740334,
0.025203049182891846,
-0.0034381982404738665,
0.06284574419260025,
-0.02525499276816845,
-0.0050758360885083675,
0.07016654312610626,
0.047779910266399384,
0.04621000960469246,
0.009655474685132504,
-0.01720241829752922,
-0.1047825813293457,
0.16950392723083496,
-0.0951867327094078,
-0.269941508769989,
-0.17632324993610382,
0.026197833940386772,
0.04035249724984169,
-0.022378476336598396,
0.031619444489479065,
-0.07056326419115067,
-0.10630585998296738,
-0.1060405746102333,
-0.002429972169920802,
0.01714223250746727,
-0.06364088505506516,
-0.0741225928068161,
0.07348573952913284,
0.04382912442088127,
-0.14902326464653015,
0.038552410900592804,
0.055694397538900375,
-0.057955220341682434,
-0.0233661737293005,
0.09118817001581192,
0.12397737801074982,
0.14583967626094818,
-0.021366750821471214,
-0.028626007959246635,
0.029004426673054695,
0.19620531797409058,
-0.13469526171684265,
0.10371150821447372,
0.13814030587673187,
-0.04545360431075096,
0.08360563963651657,
0.1560150384902954,
0.029186224564909935,
-0.08317049592733383,
0.05044832453131676,
0.04082648828625679,
-0.043159641325473785,
-0.2666129767894745,
-0.0534592866897583,
0.012832709588110447,
-0.06255637854337692,
0.09786593168973923,
0.10183793306350708,
0.11542957276105881,
0.034910861402750015,
-0.07166364789009094,
-0.043925940990448,
-0.0058974819257855415,
0.11737963557243347,
-0.05490213260054588,
-0.012639665976166725,
0.07686592638492584,
-0.05086168646812439,
0.005355054512619972,
0.10266812145709991,
0.02973790094256401,
0.17442677915096283,
0.020399179309606552,
0.11231429129838943,
0.06195578724145889,
0.08633565157651901,
0.0007386076031252742,
0.02951662428677082,
0.05147615820169449,
0.017203815281391144,
-0.002300140680745244,
-0.10421168059110641,
-0.006156572140753269,
0.1449710875749588,
0.028103826567530632,
0.029669636860489845,
-0.0018948549404740334,
-0.005003341939300299,
0.05121048167347908,
0.1746254414319992,
-0.011592294089496136,
-0.22072425484657288,
-0.0845772922039032,
0.06936841458082199,
-0.06218599155545235,
-0.12968985736370087,
-0.026130788028240204,
0.045467354357242584,
-0.17519839107990265,
0.026703642681241035,
-0.027433741837739944,
0.0919293761253357,
-0.09345759451389313,
-0.02221956104040146,
0.03687324374914169,
0.084866963326931,
-0.014529162086546421,
0.08703910559415817,
-0.14498743414878845,
0.11886418610811234,
0.02978132851421833,
0.09024628251791,
-0.11081171780824661,
0.07909037172794342,
-0.007550720125436783,
0.009180475026369095,
0.19379350543022156,
-0.011335089802742004,
-0.03514958545565605,
-0.08774717897176743,
-0.11210042238235474,
-0.013537433929741383,
0.12687496840953827,
-0.1243172138929367,
0.08773399889469147,
-0.015198243781924248,
-0.044079482555389404,
0.00937260314822197,
-0.12100647389888763,
-0.17273177206516266,
-0.19628387689590454,
0.05585884302854538,
-0.09575839340686798,
0.025643249973654747,
-0.11914430558681488,
-0.07089093327522278,
-0.02952558360993862,
0.241120383143425,
-0.1745356321334839,
-0.06510113179683685,
-0.1468164622783661,
-0.046294767409563065,
0.1662203073501587,
-0.04437198117375374,
0.0718095526099205,
-0.0208172257989645,
0.20345525443553925,
0.005988610442727804,
-0.004939318168908358,
0.06724198162555695,
-0.08892562240362167,
-0.16873881220817566,
-0.06771010160446167,
0.1510489284992218,
0.11680185794830322,
0.04907919466495514,
-0.002248800592496991,
0.0011772146681323647,
-0.016943959519267082,
-0.1137804463505745,
-0.0033210667315870523,
0.16037839651107788,
0.03878779336810112,
0.025986969470977783,
-0.05243593826889992,
-0.08797456324100494,
-0.06899320334196091,
-0.06853509694337845,
0.06221301481127739,
0.19590823352336884,
-0.10376439243555069,
0.1700313836336136,
0.147536963224411,
-0.07305635511875153,
-0.23175598680973053,
0.035342130810022354,
0.04983805492520332,
0.0014306638622656465,
0.04886869341135025,
-0.18252557516098022,
0.10521943867206573,
0.019543392583727837,
-0.05505957826972008,
0.13485197722911835,
-0.1557481735944748,
-0.1552847921848297,
0.0722852572798729,
0.03904085233807564,
-0.22423844039440155,
-0.1354004591703415,
-0.09622503817081451,
-0.05825018882751465,
-0.14065024256706238,
0.06054598465561867,
-0.002136280992999673,
0.015948504209518433,
0.03500790148973465,
-0.0015643214574083686,
0.027123261243104935,
-0.058935679495334625,
0.18609118461608887,
-0.004065449349582195,
0.020676052197813988,
-0.060264769941568375,
-0.0478842556476593,
0.09839435666799545,
-0.06130504235625267,
0.12208222597837448,
0.004057085141539574,
0.01594383642077446,
-0.10362856835126877,
-0.048314861953258514,
-0.04328322783112526,
0.05154227837920189,
-0.07548051327466965,
-0.10070807486772537,
-0.043625857681035995,
0.08841723203659058,
0.07005169242620468,
-0.03383097052574158,
0.00549331633374095,
-0.07189501076936722,
0.10019614547491074,
0.17795267701148987,
0.17573626339435577,
0.009926567785441875,
-0.07241068035364151,
0.01677953451871872,
-0.04142116755247116,
0.044231921434402466,
-0.2513144314289093,
0.03756171092391014,
0.06098250672221184,
0.029438555240631104,
0.09217222779989243,
-0.020435843616724014,
-0.1820858269929886,
-0.04050002992153168,
0.08094815909862518,
-0.05452597141265869,
-0.22617179155349731,
-0.019085140898823738,
0.0954197570681572,
-0.2020406424999237,
-0.007372708059847355,
0.03995226323604584,
-0.048725228756666183,
-0.023169852793216705,
0.00010950004070764408,
0.06317184865474701,
0.002471912419423461,
0.09773622453212738,
0.0735151618719101,
0.09715340286493301,
-0.08337292820215225,
0.10562895983457565,
0.10150538384914398,
-0.09572599828243256,
0.03605884686112404,
0.06754924356937408,
-0.05300498008728027,
-0.043293699622154236,
0.03665391728281975,
0.033023297786712646,
0.005234600510448217,
-0.060321882367134094,
0.013913018628954887,
-0.036497246474027634,
0.044923391193151474,
0.08326134830713272,
0.03754979372024536,
-0.013354414142668247,
0.06462216377258301,
0.03401726484298706,
-0.10898099094629288,
0.10366570204496384,
0.01731540448963642,
0.04105307161808014,
-0.08384523540735245,
-0.019968897104263306,
0.035425446927547455,
0.030576206743717194,
-0.01765924133360386,
-0.02306121215224266,
-0.02860277332365513,
-0.01614218018949032,
-0.14299540221691132,
-0.023106401786208153,
-0.07243485748767853,
0.006181265693157911,
0.014656842686235905,
-0.031884219497442245,
-0.011233693920075893,
0.02475680410861969,
-0.06979699432849884,
-0.07426341623067856,
-0.006949664559215307,
0.09833318740129471,
-0.15115703642368317,
0.008848577737808228,
0.06907843053340912,
-0.11088496446609497,
0.08190931379795074,
-0.008411259390413761,
0.016245156526565552,
0.022527478635311127,
-0.15448406338691711,
0.05601610988378525,
0.0008648968650959432,
0.01916889287531376,
0.025886621326208115,
-0.16471809148788452,
0.004104440100491047,
-0.04661374166607857,
-0.02149827405810356,
-0.00004464812809601426,
-0.02647159807384014,
-0.12325995415449142,
0.06858719140291214,
-0.015622655861079693,
-0.035931166261434555,
-0.02701525390148163,
0.0539589487016201,
0.07888586074113846,
-0.027474910020828247,
0.10445091128349304,
-0.008690856397151947,
0.04941811040043831,
-0.16801609098911285,
-0.02470702864229679,
-0.04982255399227142,
0.019377702847123146,
0.009884213097393513,
-0.007693959400057793,
0.04183054715394974,
-0.00976533442735672,
0.21883612871170044,
-0.05075952783226967,
0.1607085019350052,
0.05847611650824547,
-0.017352959141135216,
-0.0007513365126214921,
0.06180921941995621,
0.05997028574347496,
0.04658793285489082,
0.009480604901909828,
0.023740366101264954,
-0.022450892254710197,
-0.006695089396089315,
-0.15932634472846985,
0.01890849508345127,
0.14999441802501678,
0.06301083415746689,
0.024745315313339233,
0.05866100639104843,
-0.12775006890296936,
-0.12135478109121323,
0.09311001747846603,
-0.026755332946777344,
0.00928465835750103,
-0.08245618641376495,
0.1358020007610321,
0.14980104565620422,
-0.14000412821769714,
0.05256148427724838,
-0.06134212389588356,
-0.05217423290014267,
-0.10388828068971634,
-0.12032219022512436,
-0.05887215584516525,
-0.053666237741708755,
0.002330566756427288,
-0.03760887682437897,
0.054546963423490524,
0.03344334661960602,
-0.009351172484457493,
-0.00022941511997487396,
0.13597318530082703,
-0.019751882180571556,
-0.0028988157864660025,
0.048313532024621964,
0.03693558648228645,
0.02373051457107067,
-0.05275435373187065,
0.02940409444272518,
0.02539868652820587,
0.032232340425252914,
0.06546790152788162,
0.033412106335163116,
-0.047448933124542236,
0.03804153576493263,
-0.0025254099164158106,
-0.11207924783229828,
0.019641218706965446,
-0.00460948096588254,
-0.0742158442735672,
0.1268945336341858,
0.0407399944961071,
0.010224059224128723,
-0.03741471841931343,
0.24361543357372284,
-0.06653323769569397,
-0.06378097087144852,
-0.13251738250255585,
0.10491154342889786,
-0.0027236645109951496,
0.06476365029811859,
0.023412218317389488,
-0.1284150779247284,
0.005243356805294752,
0.13858191668987274,
0.12181595712900162,
0.0045748427510261536,
0.009228081442415714,
0.0518609918653965,
0.0025186820421367884,
-0.06998204439878464,
0.054019294679164886,
0.06992026418447495,
0.12919506430625916,
-0.07847554981708527,
0.07680778950452805,
0.0006860480643808842,
-0.08370215445756912,
-0.02947772853076458,
0.11312682181596756,
-0.0409729965031147,
0.03491825982928276,
-0.047444481402635574,
0.10916327685117722,
-0.05787910893559456,
-0.29412412643432617,
0.02350960113108158,
-0.09588567912578583,
-0.15202060341835022,
-0.018367812037467957,
0.05944539234042168,
-0.02624768204987049,
0.018029648810625076,
0.06971040368080139,
-0.06011629104614258,
0.20098382234573364,
0.0335683599114418,
-0.07864278554916382,
-0.0664360448718071,
0.04837050288915634,
-0.06564252078533173,
0.2949807047843933,
0.008418165147304535,
0.02863333560526371,
0.10770907253026962,
-0.03253700211644173,
-0.18271861970424652,
0.010723991319537163,
0.1133992001414299,
-0.08056149631738663,
0.08200647681951523,
0.19000613689422607,
-0.012578671798110008,
0.1209007054567337,
0.05294662341475487,
-0.047376248985528946,
0.04217283055186272,
-0.03389401361346245,
-0.051268599927425385,
-0.10752558708190918,
0.058453381061553955,
-0.05909625440835953,
0.15447644889354706,
0.10152646154165268,
-0.05671518296003342,
-0.004550917539745569,
-0.05555408447980881,
0.04875178262591362,
0.01804669201374054,
0.12263146042823792,
0.02951994352042675,
-0.1865430772304535,
0.032826557755470276,
-0.01144319772720337,
0.10186848044395447,
-0.25588861107826233,
-0.08421015739440918,
0.08833149075508118,
-0.011924264021217823,
-0.05105875805020332,
0.10560628771781921,
0.057650718837976456,
0.04243382066488266,
-0.043439045548439026,
-0.10480839014053345,
-0.02186836116015911,
0.14663739502429962,
-0.1469624787569046,
-0.025013303384184837
] |
null | null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# openhermes-mistral-dpo-gptq
This model is a fine-tuned version of [TheBloke/OpenHermes-2-Mistral-7B-GPTQ](https://huggingface.co/TheBloke/OpenHermes-2-Mistral-7B-GPTQ) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6599
- Rewards/chosen: 0.0397
- Rewards/rejected: -0.0752
- Rewards/accuracies: 0.9375
- Rewards/margins: 0.1149
- Logps/rejected: -164.5962
- Logps/chosen: -292.6904
- Logits/rejected: -2.6901
- Logits/chosen: -2.3670
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- training_steps: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6819 | 0.01 | 10 | 0.6600 | 0.0491 | -0.0050 | 1.0 | 0.0540 | -163.8940 | -292.5971 | -2.6930 | -2.3675 |
| 0.7106 | 0.01 | 20 | 0.6787 | 0.0460 | 0.0162 | 0.5625 | 0.0298 | -163.6827 | -292.6277 | -2.6971 | -2.3713 |
| 0.6487 | 0.01 | 30 | 0.6889 | 0.0454 | -0.0002 | 0.8125 | 0.0456 | -163.8460 | -292.6334 | -2.6960 | -2.3700 |
| 0.5981 | 0.02 | 40 | 0.6718 | 0.0307 | -0.0583 | 0.9375 | 0.0890 | -164.4272 | -292.7806 | -2.6928 | -2.3685 |
| 0.6573 | 0.03 | 50 | 0.6599 | 0.0397 | -0.0752 | 0.9375 | 0.1149 | -164.5962 | -292.6904 | -2.6901 | -2.3670 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu117
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "TheBloke/OpenHermes-2-Mistral-7B-GPTQ", "model-index": [{"name": "openhermes-mistral-dpo-gptq", "results": []}]} | null | Saahil1801/openhermes-mistral-dpo-gptq | [
"tensorboard",
"safetensors",
"trl",
"dpo",
"generated_from_trainer",
"base_model:TheBloke/OpenHermes-2-Mistral-7B-GPTQ",
"license:apache-2.0",
"region:us"
] | 2024-02-09T11:29:09+00:00 | [] | [] | TAGS
#tensorboard #safetensors #trl #dpo #generated_from_trainer #base_model-TheBloke/OpenHermes-2-Mistral-7B-GPTQ #license-apache-2.0 #region-us
| openhermes-mistral-dpo-gptq
===========================
This model is a fine-tuned version of TheBloke/OpenHermes-2-Mistral-7B-GPTQ on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6599
* Rewards/chosen: 0.0397
* Rewards/rejected: -0.0752
* Rewards/accuracies: 0.9375
* Rewards/margins: 0.1149
* Logps/rejected: -164.5962
* Logps/chosen: -292.6904
* Logits/rejected: -2.6901
* Logits/chosen: -2.3670
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0002
* train\_batch\_size: 1
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2
* training\_steps: 50
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.0.1+cu117
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2\n* training\\_steps: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#tensorboard #safetensors #trl #dpo #generated_from_trainer #base_model-TheBloke/OpenHermes-2-Mistral-7B-GPTQ #license-apache-2.0 #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2\n* training\\_steps: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
58,
129,
4,
33
] | [
"passage: TAGS\n#tensorboard #safetensors #trl #dpo #generated_from_trainer #base_model-TheBloke/OpenHermes-2-Mistral-7B-GPTQ #license-apache-2.0 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2\n* training\\_steps: 50\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
-0.13103698194026947,
0.10529867559671402,
-0.0034347032196819782,
0.09178321808576584,
0.07071317732334137,
-0.012631220743060112,
0.12906812131404877,
0.15671807527542114,
-0.07430260628461838,
0.06674624979496002,
0.14720959961414337,
0.12853166460990906,
0.052652839571237564,
0.19125497341156006,
-0.045799169689416885,
-0.2555953562259674,
0.039745673537254333,
0.009297363460063934,
-0.07047785073518753,
0.10602942854166031,
0.09150093048810959,
-0.11368579417467117,
0.0658191666007042,
0.0036178510636091232,
-0.12793466448783875,
-0.022532520815730095,
-0.013954048976302147,
-0.05637010931968689,
0.11752396076917648,
-0.003990020602941513,
0.06477053463459015,
0.058372315019369125,
0.06985732167959213,
-0.19944481551647186,
0.018519656732678413,
0.06697572022676468,
0.017206579446792603,
0.08841349929571152,
0.06014299765229225,
0.008157261647284031,
0.08890755474567413,
-0.08460954576730728,
0.07915046066045761,
0.02454395964741707,
-0.12461891770362854,
-0.3176373243331909,
-0.12598969042301178,
0.05188300460577011,
0.08640763908624649,
0.05608200281858444,
0.0026938733644783497,
0.16839641332626343,
-0.035642899572849274,
0.09374316036701202,
0.2846405506134033,
-0.31118810176849365,
-0.058440741151571274,
0.05435998737812042,
0.0686093121767044,
0.08451256155967712,
-0.10722949355840683,
-0.030676130205392838,
0.039271146059036255,
0.02918645739555359,
0.14399376511573792,
-0.003246943699195981,
-0.007447070442140102,
-0.0011596976546570659,
-0.144899383187294,
-0.058795683085918427,
0.13804395496845245,
0.04332466050982475,
-0.05576183274388313,
-0.08527059108018875,
-0.054276563227176666,
-0.17571008205413818,
-0.053555428981781006,
-0.028204122558236122,
0.06452551484107971,
-0.03197408467531204,
-0.04560611769556999,
-0.024422189220786095,
-0.07471204549074173,
-0.08605068922042847,
-0.016143478453159332,
0.1739952564239502,
0.060145944356918335,
0.01483484823256731,
-0.02669423259794712,
0.11991284042596817,
-0.04875412955880165,
-0.15655863285064697,
-0.03528175875544548,
0.00816186424344778,
-0.00911181140691042,
-0.04340183362364769,
-0.020986998453736305,
-0.05388806760311127,
0.042462605983018875,
0.18934017419815063,
-0.13820971548557281,
0.07253902405500412,
0.0012907186755910516,
0.037272363901138306,
-0.09196777641773224,
0.12230264395475388,
-0.06607480347156525,
0.04877246543765068,
0.02121744118630886,
0.09693398326635361,
0.047620467841625214,
-0.008352103643119335,
-0.07683324068784714,
0.04622975364327431,
0.09626789391040802,
0.05053079500794411,
-0.022060003131628036,
0.04579784721136093,
-0.05339976027607918,
-0.014840880408883095,
0.08470822125673294,
-0.10788869112730026,
0.02078544907271862,
0.008345630951225758,
-0.08456886559724808,
-0.0979047417640686,
0.0032144272699952126,
0.015488685108721256,
0.007262228988111019,
0.08025158196687698,
-0.07244434952735901,
0.005201179534196854,
-0.07687599956989288,
-0.12156446278095245,
0.03635425120592117,
-0.10279584676027298,
0.02007634937763214,
-0.07903750240802765,
-0.15538457036018372,
-0.02376130037009716,
0.06517913937568665,
-0.04290739819407463,
-0.041867636144161224,
-0.04785177484154701,
-0.095158152282238,
0.02264625020325184,
-0.010879766196012497,
0.09526043385267258,
-0.08080526441335678,
0.10779138654470444,
-0.0016721539432182908,
0.07653424888849258,
-0.025083767250180244,
0.02670050784945488,
-0.09202457219362259,
0.05887628719210625,
-0.231302872300148,
0.025305794551968575,
-0.09546695649623871,
0.09004509449005127,
-0.11513397842645645,
-0.0785302221775055,
0.00522202393040061,
-0.026734381914138794,
0.11270257830619812,
0.12945732474327087,
-0.18947343528270721,
-0.036366235464811325,
0.22032281756401062,
-0.09811685234308243,
-0.1405944675207138,
0.11348744481801987,
-0.037504177540540695,
0.034312654286623,
0.04237028583884239,
0.2502022385597229,
0.0726776123046875,
-0.14222092926502228,
0.010267211124300957,
-0.0329628586769104,
0.04875403642654419,
-0.00968888122588396,
0.07513276487588882,
-0.019829770550131798,
0.05167475342750549,
0.01925084739923477,
-0.03228488937020302,
0.027865160256624222,
-0.08193153887987137,
-0.08228310197591782,
-0.042249105870723724,
-0.07792149484157562,
0.02046121098101139,
0.03908969834446907,
0.036863867193460464,
-0.1375754028558731,
-0.0963616743683815,
0.024839624762535095,
0.11172740906476974,
-0.06339123100042343,
0.03886593505740166,
-0.08989762514829636,
0.09016554057598114,
-0.03267733007669449,
-0.0022526467218995094,
-0.14661212265491486,
-0.031840208917856216,
0.04025337100028992,
-0.050888240337371826,
-0.007584338076412678,
-0.05956403911113739,
0.09582864493131638,
0.059328556060791016,
-0.05252258479595184,
-0.042647115886211395,
-0.04991266131401062,
0.0048412480391561985,
-0.0952095091342926,
-0.2539404332637787,
-0.024375470355153084,
-0.06330303102731705,
0.08296133577823639,
-0.18557725846767426,
0.053156279027462006,
0.07792676985263824,
0.10175363719463348,
0.06009509786963463,
-0.04563593119382858,
0.018424704670906067,
0.04324398189783096,
-0.008253729902207851,
-0.09335005283355713,
0.042790863662958145,
0.0369950607419014,
-0.08756829798221588,
0.02351704053580761,
-0.14284802973270416,
0.1614370048046112,
0.11994475871324539,
0.06817582249641418,
-0.09366631507873535,
-0.04832335188984871,
-0.06956008821725845,
-0.04669284075498581,
-0.01567278802394867,
0.05655422434210777,
0.10860416293144226,
0.010863621719181538,
0.11112359911203384,
-0.1171540841460228,
-0.06062966585159302,
0.05463186278939247,
-0.03272755444049835,
0.014669031836092472,
0.1222153902053833,
0.03951677680015564,
-0.08095931261777878,
0.13624469935894012,
0.1334211677312851,
-0.04305436089634895,
0.1358911693096161,
-0.08089050650596619,
-0.07033674418926239,
-0.03721576929092407,
0.06708914041519165,
0.021125804632902145,
0.18120382726192474,
-0.08222025632858276,
0.024784229695796967,
0.016010407358407974,
0.030979745090007782,
0.008739975281059742,
-0.21202142536640167,
-0.024730190634727478,
0.02963629737496376,
-0.08164077997207642,
-0.04912785440683365,
-0.00036112655652686954,
-0.00666801119223237,
0.10150741785764694,
0.01889464631676674,
-0.06743542850017548,
0.022212443873286247,
0.017252139747142792,
-0.0669994205236435,
0.20455984771251678,
-0.11385270953178406,
-0.09454046934843063,
-0.05451417341828346,
-0.016228843480348587,
-0.032343510538339615,
-0.011232852935791016,
0.07680186629295349,
-0.07518724352121353,
-0.02063162438571453,
-0.10635118931531906,
-0.06229899451136589,
0.013863198459148407,
0.026132332161068916,
0.01164825540035963,
0.004424121230840683,
0.07351674139499664,
-0.11364275962114334,
-0.005906793754547834,
-0.03550535812973976,
-0.005854531656950712,
0.059274766594171524,
0.03634297847747803,
0.09198297560214996,
0.12548352777957916,
-0.00263238069601357,
0.023126428946852684,
-0.0399414487183094,
0.19094741344451904,
-0.0853840783238411,
-0.010436386801302433,
0.08767856657505035,
0.016794778406620026,
0.07619436085224152,
0.14757981896400452,
0.05865052342414856,
-0.11597581207752228,
-0.0005241978215053678,
0.016164984554052353,
-0.0388658232986927,
-0.23563508689403534,
-0.011800586245954037,
-0.03484572097659111,
0.04076084867119789,
0.09131845831871033,
0.05691569298505783,
-0.007381699979305267,
0.04059388488531113,
-0.00046647057752124965,
-0.0037559885531663895,
0.011476858519017696,
0.09083271026611328,
0.029141342267394066,
0.03385016322135925,
0.10644878447055817,
-0.04540245980024338,
0.0011206931667402387,
0.05628734454512596,
0.052179768681526184,
0.23837698996067047,
-0.014888483099639416,
0.1548684686422348,
0.07193252444267273,
0.1865236759185791,
-0.00876121036708355,
0.05116056650876999,
-0.011045082472264767,
-0.036843471229076385,
0.0025773977395147085,
-0.06537312269210815,
0.0022130676079541445,
0.04412040114402771,
-0.07342221587896347,
0.016831504181027412,
-0.10343468189239502,
0.015619471669197083,
0.0545891672372818,
0.31761249899864197,
0.037225812673568726,
-0.3176933228969574,
-0.10339780896902084,
-0.006814845837652683,
-0.03015807829797268,
-0.043484628200531006,
0.026122376322746277,
0.11635757237672806,
-0.05021052062511444,
0.0665501207113266,
-0.07210332900285721,
0.07655493170022964,
-0.026365168392658234,
0.009749737568199635,
0.04012403264641762,
0.09690850228071213,
-0.030377274379134178,
0.04765466973185539,
-0.2902568280696869,
0.29615163803100586,
0.024559061974287033,
0.07957145571708679,
-0.04782447963953018,
0.0009741366375237703,
0.03355837240815163,
0.04573456943035126,
0.10573991388082504,
-0.005970918573439121,
-0.1252453327178955,
-0.1574326902627945,
-0.1219613328576088,
0.016607671976089478,
0.10012425482273102,
-0.007034025155007839,
0.09400270134210587,
0.012192235328257084,
0.011707672849297523,
0.04217064380645752,
-0.08860916644334793,
-0.09078527987003326,
-0.0583074688911438,
0.014806167222559452,
0.008426852524280548,
-0.010774543508887291,
-0.10484012961387634,
-0.08963323384523392,
-0.06601551920175552,
0.1540830135345459,
-0.03075246885418892,
-0.05989954248070717,
-0.12276554107666016,
0.020059501752257347,
0.0874352976679802,
-0.07067184895277023,
0.04942517727613449,
0.007218221668154001,
0.08960069715976715,
-0.002422676421701908,
-0.03593403100967407,
0.14649835228919983,
-0.06347952783107758,
-0.1908818632364273,
-0.046429604291915894,
0.12744002044200897,
0.0533469095826149,
0.04223213344812393,
-0.008138510398566723,
0.022990141063928604,
-0.00683984998613596,
-0.0888451486825943,
0.06219476833939552,
0.005947822704911232,
0.057086311280727386,
-0.03513240069150925,
-0.018003052100539207,
0.06188324838876724,
-0.05727311223745346,
-0.05391179025173187,
0.1264907270669937,
0.35044699907302856,
-0.09652271866798401,
0.09117667376995087,
0.05033878982067108,
-0.04432685300707817,
-0.1802499145269394,
0.03190954029560089,
0.055194854736328125,
-0.00849892944097519,
0.04017480090260506,
-0.19458046555519104,
0.006259776186197996,
0.1031842976808548,
-0.049227118492126465,
0.13861601054668427,
-0.3135727345943451,
-0.13300824165344238,
0.10715752840042114,
0.13535399734973907,
0.058019135147333145,
-0.16201679408550262,
-0.04953363165259361,
0.019198764115571976,
-0.10556501895189285,
0.08344128727912903,
-0.13311639428138733,
0.09738296270370483,
-0.03326684236526489,
0.047580014914274216,
0.02260972559452057,
-0.06730321049690247,
0.14997701346874237,
-0.029592294245958328,
0.11640515923500061,
-0.0575309619307518,
0.057708460837602615,
0.08108331263065338,
-0.08697480708360672,
0.057259753346443176,
-0.08963626623153687,
0.05401328578591347,
-0.07809211313724518,
-0.0100999241694808,
-0.06619136780500412,
0.020955072715878487,
-0.035694416612386703,
-0.05663171038031578,
-0.0578678734600544,
0.043083593249320984,
0.05243269354104996,
-0.019641315564513206,
0.16329780220985413,
0.014216502197086811,
0.14469894766807556,
0.14906589686870575,
0.06806685775518417,
-0.09289032220840454,
-0.02727891318500042,
0.02274686098098755,
-0.031176073476672173,
0.06017177179455757,
-0.18263867497444153,
0.030894294381141663,
0.12054461985826492,
0.03176063671708107,
0.08793175965547562,
0.04773092642426491,
-0.09018245339393616,
0.01375284232199192,
0.05309753119945526,
-0.16551168262958527,
-0.13826237618923187,
0.021928831934928894,
0.05302126333117485,
-0.1046343743801117,
0.0720459371805191,
0.0984039381146431,
-0.06498078256845474,
-0.013452130369842052,
0.008236300200223923,
0.043399155139923096,
-0.02352622151374817,
0.19582460820674896,
0.045179709792137146,
0.06864885985851288,
-0.09253115952014923,
0.10318464785814285,
0.030298076570034027,
-0.12322191894054413,
0.047885436564683914,
0.08157280087471008,
-0.09190186113119125,
-0.02528943493962288,
0.06571252644062042,
0.10628797113895416,
0.010331755504012108,
-0.06219523400068283,
-0.16244001686573029,
-0.11749066412448883,
0.07747037708759308,
0.16272097826004028,
0.06301727890968323,
0.030545754358172417,
0.023193079978227615,
0.018318064510822296,
-0.11073596030473709,
0.11245708912611008,
0.04283696040511131,
0.07896143943071365,
-0.1389336735010147,
0.09793709963560104,
-0.012314409017562866,
0.023813217878341675,
-0.008614451624453068,
0.02646532654762268,
-0.12796907126903534,
0.014170468784868717,
-0.12052179872989655,
-0.005245954729616642,
-0.05039899796247482,
0.00856851227581501,
-0.015112366527318954,
-0.02947070263326168,
-0.04139164835214615,
0.03345724195241928,
-0.1213582456111908,
-0.018600307404994965,
0.009139501489698887,
0.038319822400808334,
-0.12718181312084198,
-0.03761942312121391,
0.019766701385378838,
-0.0865117758512497,
0.08794330805540085,
0.038921456784009933,
0.01951250433921814,
0.010797617956995964,
-0.08976723998785019,
0.033054646104574203,
0.055909689515829086,
-0.014781300909817219,
0.027649737894535065,
-0.151350200176239,
-0.039349500089883804,
-0.016300180926918983,
-0.006501000840216875,
0.015677640214562416,
0.099986732006073,
-0.13596035540103912,
-0.00819013174623251,
-0.014320312067866325,
-0.03868366405367851,
-0.04611115902662277,
0.035477060824632645,
0.08102945983409882,
0.01884019561111927,
0.14442217350006104,
-0.08013366907835007,
0.005576292052865028,
-0.20302675664424896,
-0.012744512408971786,
-0.00039077471592463553,
-0.10252882540225983,
-0.05827980488538742,
0.01103349681943655,
0.07522445917129517,
-0.0708506777882576,
0.13224518299102783,
-0.023786606267094612,
-0.03413664922118187,
0.04539324343204498,
-0.09348943829536438,
0.01138455793261528,
0.031935349106788635,
0.18114683032035828,
-0.00044094864279031754,
-0.030819987878203392,
0.047750070691108704,
0.03544716164469719,
0.07304279506206512,
0.08245168626308441,
0.17649538815021515,
0.14976462721824646,
0.0574987530708313,
0.07663416862487793,
0.039439696818590164,
-0.0737619623541832,
-0.15272434055805206,
0.11047166585922241,
-0.01617802307009697,
0.11183422058820724,
-0.019538983702659607,
0.18861913681030273,
0.1614956110715866,
-0.16842131316661835,
0.04962518811225891,
-0.01734340377151966,
-0.07964137941598892,
-0.11870365589857101,
-0.09453365206718445,
-0.07547575980424881,
-0.18476974964141846,
0.0017153130611404777,
-0.10848492383956909,
0.04084233567118645,
0.053306709975004196,
0.020512761548161507,
0.025523660704493523,
0.1514289677143097,
0.03432474657893181,
0.03226090967655182,
0.05859436094760895,
0.00857046339660883,
-0.06364773958921432,
-0.037225086241960526,
-0.10718923807144165,
0.04309818521142006,
-0.047648608684539795,
0.027290083467960358,
-0.036845218390226364,
-0.032148394733667374,
0.05471502244472504,
-0.0011818234343081713,
-0.08859660476446152,
0.014054701663553715,
0.028059381991624832,
0.0642295703291893,
0.056536924093961716,
0.03263263404369354,
-0.013092280365526676,
-0.0025741180870682,
0.23752039670944214,
-0.06642276793718338,
-0.05816999450325966,
-0.10686875134706497,
0.23602086305618286,
0.024081997573375702,
-0.029929304495453835,
0.03409085050225258,
-0.08264211565256119,
0.014926009811460972,
0.13762880861759186,
0.16982243955135345,
-0.01753375120460987,
0.003557841293513775,
-0.03242351859807968,
-0.014516649767756462,
-0.05796125531196594,
0.09238894283771515,
0.11560279875993729,
0.01610848307609558,
-0.08484378457069397,
-0.054075874388217926,
-0.04684636741876602,
-0.017434218898415565,
-0.06227288022637367,
0.056136488914489746,
0.02888633869588375,
-0.008765945211052895,
-0.051684699952602386,
0.07064146548509598,
-0.0327342189848423,
-0.09505461901426315,
0.04446960240602493,
-0.16480353474617004,
-0.1569022387266159,
-0.022758038714528084,
0.04531753435730934,
-0.012576455250382423,
0.033453214913606644,
-0.007158446125686169,
0.0017157658003270626,
0.0848984345793724,
-0.02460961416363716,
-0.06409942358732224,
-0.116163469851017,
0.07577039301395416,
-0.09381025284528732,
0.2281518578529358,
-0.03641676530241966,
0.039765335619449615,
0.1288398653268814,
0.035129472613334656,
-0.1281401664018631,
0.0578596331179142,
0.05902653560042381,
-0.08566165715456009,
-0.008081994950771332,
0.11138962209224701,
-0.036910220980644226,
0.0807737335562706,
0.04840879887342453,
-0.08152224123477936,
-0.015324512496590614,
-0.0708361342549324,
-0.05700220167636871,
-0.0592346154153347,
0.0003678422071970999,
-0.04632502421736717,
0.11667614430189133,
0.17238949239253998,
-0.053849153220653534,
0.019492335617542267,
-0.06659383326768875,
0.034587547183036804,
0.07427995651960373,
-0.010085683315992355,
-0.029570311307907104,
-0.24334119260311127,
0.04089036583900452,
0.09552530199289322,
-0.007804397027939558,
-0.23163576424121857,
-0.10267163068056107,
0.015342715196311474,
-0.04427289962768555,
-0.07414337247610092,
0.07995127141475677,
0.09111111611127853,
0.058994073420763016,
-0.06726482510566711,
-0.04791228100657463,
-0.07207389175891876,
0.15374918282032013,
-0.13931524753570557,
-0.07390931993722916
] |
null | null | transformers |
# SciPhi-Mistral-7B-32k-sliced
This is purely an experiment in sliced layer extraction to find active layers.
SciPhi-Mistral-7B-32k-sliced is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [SciPhi/SciPhi-Mistral-7B-32k](https://huggingface.co/SciPhi/SciPhi-Mistral-7B-32k)
* [SciPhi/SciPhi-Mistral-7B-32k](https://huggingface.co/SciPhi/SciPhi-Mistral-7B-32k)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: SciPhi/SciPhi-Mistral-7B-32k
layer_range: [0, 6]
- model: SciPhi/SciPhi-Mistral-7B-32k
layer_range: [26, 32]
merge_method: slerp
base_model: SciPhi/SciPhi-Mistral-7B-32k
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5 # fallback for rest of tensors
tokenizer_source: union
dtype: float16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "jtatman/SciPhi-Mistral-7B-32k-sliced"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"tags": ["merge", "mergekit", "lazymergekit", "SciPhi/SciPhi-Mistral-7B-32k", "SciPhi/SciPhi-Mistral-7B-32k"], "base_model": ["SciPhi/SciPhi-Mistral-7B-32k", "SciPhi/SciPhi-Mistral-7B-32k"]} | text-generation | jtatman/SciPhi-Mistral-7B-32k-sliced | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"SciPhi/SciPhi-Mistral-7B-32k",
"base_model:SciPhi/SciPhi-Mistral-7B-32k",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T11:31:38+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #SciPhi/SciPhi-Mistral-7B-32k #base_model-SciPhi/SciPhi-Mistral-7B-32k #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# SciPhi-Mistral-7B-32k-sliced
This is purely an experiment in sliced layer extraction to find active layers.
SciPhi-Mistral-7B-32k-sliced is a merge of the following models using LazyMergekit:
* SciPhi/SciPhi-Mistral-7B-32k
* SciPhi/SciPhi-Mistral-7B-32k
## Configuration
## Usage
| [
"# SciPhi-Mistral-7B-32k-sliced\n\nThis is purely an experiment in sliced layer extraction to find active layers. \n\nSciPhi-Mistral-7B-32k-sliced is a merge of the following models using LazyMergekit:\n* SciPhi/SciPhi-Mistral-7B-32k\n* SciPhi/SciPhi-Mistral-7B-32k",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #SciPhi/SciPhi-Mistral-7B-32k #base_model-SciPhi/SciPhi-Mistral-7B-32k #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# SciPhi-Mistral-7B-32k-sliced\n\nThis is purely an experiment in sliced layer extraction to find active layers. \n\nSciPhi-Mistral-7B-32k-sliced is a merge of the following models using LazyMergekit:\n* SciPhi/SciPhi-Mistral-7B-32k\n* SciPhi/SciPhi-Mistral-7B-32k",
"## Configuration",
"## Usage"
] | [
98,
93,
4,
3
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #SciPhi/SciPhi-Mistral-7B-32k #base_model-SciPhi/SciPhi-Mistral-7B-32k #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# SciPhi-Mistral-7B-32k-sliced\n\nThis is purely an experiment in sliced layer extraction to find active layers. \n\nSciPhi-Mistral-7B-32k-sliced is a merge of the following models using LazyMergekit:\n* SciPhi/SciPhi-Mistral-7B-32k\n* SciPhi/SciPhi-Mistral-7B-32k## Configuration## Usage"
] | [
-0.07912129163742065,
-0.11075011640787125,
-0.001208890462294221,
0.008935350924730301,
0.05315978080034256,
0.02377985790371895,
0.1593417376279831,
0.05025436729192734,
0.12153027206659317,
0.08989319950342178,
0.06803830713033676,
0.153611421585083,
0.006708919536322355,
0.11741694062948227,
-0.09960201382637024,
-0.23919330537319183,
0.09450362622737885,
-0.015711599960923195,
-0.012883001938462257,
0.08754892647266388,
0.09828568249940872,
-0.05793408676981926,
0.08652481436729431,
-0.03619616478681564,
-0.10738059878349304,
-0.024879636242985725,
0.048452574759721756,
-0.006514209322631359,
0.12382537126541138,
0.10929960012435913,
0.08840945363044739,
0.011456146836280823,
0.012860473245382309,
-0.09233776479959488,
0.04635125398635864,
0.040704913437366486,
0.029626311734318733,
0.0919194445014,
0.00036501759313978255,
0.044816914945840836,
0.05192207172513008,
-0.07851380854845047,
-0.006077630911022425,
0.05315354838967323,
-0.08523982018232346,
-0.042682137340307236,
-0.09233475476503372,
0.06231241673231125,
0.08348296582698822,
0.0017014333279803395,
-0.0030064666643738747,
0.08382397890090942,
0.0018688898999243975,
0.10707991570234299,
0.17371933162212372,
-0.2602056562900543,
-0.038299381732940674,
0.0067339795641601086,
0.058682218194007874,
0.09342934936285019,
0.05061716213822365,
0.08596230298280716,
0.00022234622156247497,
0.011227019131183624,
0.048411812633275986,
-0.07850412279367447,
-0.08144660294055939,
-0.050851382315158844,
-0.12815432250499725,
0.02489333041012287,
0.11216754466295242,
-0.01693902164697647,
-0.05064304545521736,
-0.08754637837409973,
-0.136264368891716,
0.052902959287166595,
-0.03005322441458702,
-0.07985042780637741,
-0.009215263649821281,
0.017328979447484016,
0.048353228718042374,
-0.04306671768426895,
-0.016047772020101547,
-0.11421633511781693,
-0.08485681563615799,
0.21694865822792053,
-0.00510927801951766,
0.06460785865783691,
-0.01863938383758068,
0.05258733779191971,
-0.10070598125457764,
-0.06276247650384903,
-0.02807382121682167,
-0.018724460154771805,
-0.06390142440795898,
0.004347738344222307,
-0.07310181856155396,
-0.16916324198246002,
0.00393249373883009,
0.1800568848848343,
-0.0700189620256424,
0.04235205054283142,
0.03403814136981964,
0.06764847040176392,
-0.034646760672330856,
-0.05414606258273125,
-0.056825604289770126,
0.016038067638874054,
-0.002002992667257786,
0.016517389565706253,
0.11744458228349686,
-0.04632977396249771,
-0.047337185591459274,
-0.014741403982043266,
-0.020435351878404617,
-0.006875615566968918,
-0.002053857082501054,
0.0958583876490593,
-0.054196905344724655,
-0.04973052442073822,
0.19203247129917145,
-0.0798916444182396,
-0.01291567087173462,
-0.017756639048457146,
-0.010088160634040833,
0.041359793394804,
0.07968888431787491,
-0.0012839013943448663,
-0.02143135666847229,
0.11787109822034836,
-0.09055833518505096,
0.03650839254260063,
-0.018680918961763382,
-0.1292782574892044,
0.008026841096580029,
-0.030765414237976074,
-0.09695757180452347,
-0.10662335157394409,
-0.2487197071313858,
0.005396903958171606,
-0.013313041999936104,
-0.02835112065076828,
0.07220779359340668,
-0.010224114172160625,
0.03622803837060928,
0.005897155962884426,
0.0004597133956849575,
-0.06000238284468651,
0.00837648380547762,
0.024583294987678528,
-0.03724103048443794,
0.08553723990917206,
-0.24064499139785767,
0.015473532490432262,
-0.09273120760917664,
0.1115989089012146,
-0.17223180830478668,
0.08874461054801941,
-0.07327871024608612,
0.03343680128455162,
-0.12943322956562042,
-0.03484302759170532,
-0.052448295056819916,
0.031648553907871246,
0.05143042281270027,
0.10242393612861633,
-0.20093467831611633,
-0.06223061680793762,
0.06365562230348587,
-0.18758246302604675,
-0.09733342379331589,
0.1409340798854828,
0.016447078436613083,
0.06978588551282883,
0.04580652341246605,
0.20142114162445068,
0.1005103811621666,
0.08285275846719742,
-0.0642956867814064,
-0.005410226993262768,
-0.024067694321274757,
0.02408500202000141,
0.08090338110923767,
0.010310175828635693,
-0.06000261753797531,
0.03510112687945366,
-0.063393734395504,
0.011772085912525654,
-0.018629277125000954,
-0.04024313762784004,
-0.09032373130321503,
-0.03455459326505661,
0.13135874271392822,
-0.010345769114792347,
0.05499983951449394,
-0.03923896327614784,
-0.01826409250497818,
0.13170108199119568,
0.0647919699549675,
-0.054607003927230835,
0.03704747557640076,
-0.0675608292222023,
0.07130695879459381,
-0.019124064594507217,
0.03983668237924576,
-0.18813066184520721,
-0.14320825040340424,
-0.0006899010622873902,
0.04035471752285957,
-0.032051075249910355,
-0.06714732944965363,
0.049977779388427734,
0.08174139261245728,
-0.035779062658548355,
-0.008581065572798252,
0.12838967144489288,
0.028845973312854767,
-0.06877654045820236,
-0.09354732185602188,
-0.11637745797634125,
-0.05314060300588608,
0.034537605941295624,
-0.06980843842029572,
0.07839710265398026,
0.0047275591641664505,
0.13183683156967163,
-0.0011591373477131128,
-0.04068974778056145,
-0.012324076145887375,
0.06111836060881615,
-0.030320588499307632,
-0.0006446836050599813,
0.1019681841135025,
-0.03951195254921913,
-0.11214843392372131,
0.04143637418746948,
-0.12866853177547455,
0.06038821488618851,
0.006102276034653187,
-0.0073808906599879265,
-0.043407607823610306,
-0.10923238843679428,
0.012212422676384449,
-0.060763269662857056,
0.00990072637796402,
-0.12693257629871368,
-0.0014730329858139157,
0.009447174146771431,
0.0833030492067337,
-0.04083165526390076,
0.004188891034573317,
-0.03598737716674805,
-0.03080863133072853,
0.015087608247995377,
0.06546693295240402,
-0.04618378356099129,
-0.3102860450744629,
0.08959873020648956,
0.17769436538219452,
-0.007370830047875643,
0.07355277985334396,
-0.009563752450048923,
-0.0014237025752663612,
-0.04931148886680603,
0.048766519874334335,
0.02361011505126953,
-0.04524579644203186,
-0.009562099352478981,
0.007424343377351761,
0.05271018669009209,
0.03174054995179176,
-0.02898247353732586,
-0.06886462867259979,
0.019475791603326797,
0.016372835263609886,
0.0015247086994349957,
-0.017752334475517273,
0.07326562702655792,
-0.039088573306798935,
0.04347526654601097,
0.02288408763706684,
0.009447953663766384,
0.0347694493830204,
-0.011973735876381397,
-0.07319638878107071,
0.18275269865989685,
-0.10168527066707611,
-0.11139308661222458,
-0.20740005373954773,
-0.08463113754987717,
-0.043877013027668,
-0.005187688861042261,
0.05672430992126465,
-0.03674320504069328,
-0.05058201774954796,
-0.09263052046298981,
0.10971993952989578,
0.09714177250862122,
0.012485181912779808,
-0.01875928044319153,
-0.06036551296710968,
-0.01718597114086151,
-0.06560869514942169,
-0.01664017140865326,
0.005975379608571529,
0.051276300102472305,
0.055193934589624405,
-0.0397040881216526,
0.07476985454559326,
0.11709202080965042,
0.026038048788905144,
-0.023489639163017273,
0.00013458218018058687,
0.17958474159240723,
0.00041655346285551786,
0.07615932822227478,
0.19950702786445618,
-0.12960803508758545,
0.071343794465065,
0.1221756860613823,
0.08424002677202225,
-0.02082306519150734,
0.023704109713435173,
-0.031739287078380585,
-0.050081584602594376,
-0.141169011592865,
-0.11052467674016953,
-0.018703844398260117,
0.05163544788956642,
-0.00818668957799673,
0.04066547751426697,
0.04451186954975128,
0.0952383279800415,
-0.04155811294913292,
0.024521570652723312,
0.015934480354189873,
0.028435319662094116,
0.15520064532756805,
-0.011928610503673553,
0.14446501433849335,
-0.056391648948192596,
0.0020003151148557663,
0.051106397062540054,
-0.029204420745372772,
0.14818434417247772,
0.041401080787181854,
0.30371683835983276,
0.03294886648654938,
0.039501678198575974,
0.07603386044502258,
0.1305270493030548,
0.030526336282491684,
-0.030225643888115883,
-0.0010596142383292317,
-0.06855461746454239,
-0.028708186000585556,
0.03907933831214905,
-0.1561572104692459,
0.0958438590168953,
0.009789098985493183,
0.08152856677770615,
0.07384268194437027,
0.2601528465747833,
0.07350412756204605,
-0.2708573341369629,
-0.1672580987215042,
0.0471687838435173,
0.02688860148191452,
-0.02774270437657833,
-0.046698205173015594,
0.19760647416114807,
-0.02734978497028351,
0.02994970791041851,
-0.06938053667545319,
0.0788327232003212,
-0.01782751828432083,
0.05173790827393532,
0.018552357330918312,
0.08896316587924957,
-0.02386614680290222,
0.021057434380054474,
-0.08564598113298416,
0.11181402951478958,
0.058563876897096634,
-0.007125438656657934,
-0.025667356327176094,
0.012722527608275414,
0.041352368891239166,
0.2101088911294937,
0.09302268922328949,
0.02361607365310192,
-0.16201992332935333,
-0.13016660511493683,
-0.091246597468853,
-0.02421194687485695,
0.11422168463468552,
-0.026898179203271866,
0.0600745789706707,
-0.09022264927625656,
0.009556546807289124,
0.020656824111938477,
0.036393553018569946,
-0.0717303454875946,
-0.08977025002241135,
0.08737500756978989,
0.1266191005706787,
0.08699864149093628,
-0.06137402728199959,
-0.030854690819978714,
0.012645726092159748,
0.24599872529506683,
-0.12936879694461823,
-0.07100870460271835,
-0.12728875875473022,
0.0011202316964045167,
0.06967037916183472,
-0.08601072430610657,
0.049851007759571075,
-0.005858618300408125,
0.047306567430496216,
-0.017350465059280396,
-0.15750481188297272,
0.10145896673202515,
-0.06739582121372223,
-0.08677850663661957,
-0.04154987260699272,
0.06233274191617966,
-0.05371334031224251,
-0.00018871790962293744,
-0.019750405102968216,
0.06542203575372696,
0.005255581345409155,
-0.06347055733203888,
0.002421689685434103,
0.13878577947616577,
-0.03786909580230713,
0.13329541683197021,
-0.09196997433900833,
0.005865450482815504,
-0.0016673881327733397,
0.0017012832686305046,
0.15854871273040771,
0.12557919323444366,
-0.0420234240591526,
0.03051583282649517,
0.1231556236743927,
-0.022270722314715385,
-0.26908591389656067,
-0.07578258216381073,
-0.02277049608528614,
0.008492845110595226,
0.06440919637680054,
-0.06834438443183899,
0.09730863571166992,
0.19287686049938202,
0.0016856137663125992,
0.09483017772436142,
-0.33590197563171387,
-0.11189226806163788,
0.05399388074874878,
0.08188898116350174,
0.22153446078300476,
-0.12731705605983734,
-0.13617081940174103,
-0.09021911770105362,
-0.10503038018941879,
0.05175317823886871,
-0.09411676228046417,
0.058551475405693054,
-0.05546325445175171,
-0.03611292690038681,
0.007526503410190344,
-0.006563128437846899,
0.15296784043312073,
-0.07441820949316025,
0.054257601499557495,
-0.07463701069355011,
-0.023730307817459106,
0.08477149903774261,
-0.03338463604450226,
0.0904030054807663,
-0.12608540058135986,
0.05395771563053131,
0.05601835623383522,
-0.012550009414553642,
-0.05625677481293678,
0.027146311476826668,
0.007284504361450672,
-0.031076664105057716,
-0.03251815587282181,
-0.0033939462155103683,
-0.04971607029438019,
0.004029847681522369,
0.1747732311487198,
-0.04253431409597397,
0.12892688810825348,
0.148898646235466,
0.078806072473526,
-0.009401111863553524,
-0.09810560941696167,
-0.014557472430169582,
-0.050588399171829224,
0.0961000844836235,
-0.07582835853099823,
-0.015529971569776535,
0.08649181574583054,
0.034598834812641144,
0.09970011562108994,
0.07581475377082825,
0.030790923163294792,
0.03929739072918892,
0.05615518242120743,
-0.13403113186359406,
-0.2830732464790344,
0.008961796760559082,
0.08426753431558609,
-0.0868627056479454,
0.11852773278951645,
0.1976066529750824,
-0.025369985029101372,
0.0014323489740490913,
0.0175128523260355,
0.009580503217875957,
-0.06944245100021362,
0.1405382752418518,
-0.032603442668914795,
0.06945686042308807,
-0.0899517834186554,
0.04156969115138054,
0.04921763017773628,
-0.009176475927233696,
-0.049566615372896194,
0.11648111045360565,
-0.14090648293495178,
-0.10628923773765564,
-0.08874169737100601,
0.01528371125459671,
-0.0038898587226867676,
-0.05333735793828964,
-0.08719255775213242,
-0.14557799696922302,
-0.0037797584664076567,
0.17767766118049622,
0.044412482529878616,
0.033381469547748566,
-0.060972727835178375,
-0.05392129719257355,
0.040010999888181686,
0.05104673653841019,
-0.047362297773361206,
0.12449973821640015,
-0.09905828535556793,
0.11599742621183395,
-0.07952457666397095,
0.03834940120577812,
-0.05760009586811066,
0.020183498039841652,
-0.12777233123779297,
-0.04364485293626785,
-0.19878646731376648,
-0.04077981412410736,
-0.10394558310508728,
-0.06398431956768036,
0.002232995117083192,
0.028097333386540413,
0.0337674506008625,
0.021494969725608826,
-0.0010535488836467266,
-0.0601077675819397,
-0.008606268092989922,
0.02098548598587513,
-0.117184117436409,
0.004900807980448008,
0.03851889446377754,
-0.03068927675485611,
0.05654631927609444,
0.050201110541820526,
-0.03701050952076912,
0.012009802274405956,
-0.032706279307603836,
-0.02071724832057953,
0.09930277615785599,
-0.003619270632043481,
0.005457744933664799,
0.007547734305262566,
-0.07281360775232315,
-0.01700448803603649,
-0.019590474665164948,
0.029607709497213364,
0.1622588038444519,
-0.03945879265666008,
0.07247177511453629,
-0.04078266769647598,
0.0009805767331272364,
-0.08817405998706818,
-0.020493876188993454,
0.058497559279203415,
0.07916535437107086,
0.1135195940732956,
-0.06153109669685364,
0.01382805872708559,
-0.12590579688549042,
-0.005839386489242315,
0.04532403498888016,
-0.1542261838912964,
-0.05248374491930008,
-0.06955435127019882,
0.011457232758402824,
-0.004253501538187265,
-0.009442104026675224,
-0.09187818318605423,
-0.1203346848487854,
0.02699250727891922,
0.05646480247378349,
-0.016168491914868355,
0.039870020002126694,
0.18657594919204712,
0.06096634641289711,
-0.06672898679971695,
-0.0935652032494545,
0.031368885189294815,
-0.013842469081282616,
0.0733623206615448,
0.09048373252153397,
0.12493962049484253,
-0.05547795072197914,
0.08989722281694412,
0.010863358154892921,
0.017366182059049606,
-0.08924627304077148,
-0.048349376767873764,
-0.0442495234310627,
0.03930558264255524,
-0.018214011564850807,
0.105746790766716,
0.050478316843509674,
-0.07289081811904907,
0.0445668026804924,
0.026841817423701286,
-0.055621158331632614,
-0.08954128623008728,
-0.13028104603290558,
-0.06416050344705582,
-0.10478361696004868,
-0.10489005595445633,
-0.08338359743356705,
-0.015468460507690907,
0.14352162182331085,
-0.019200168550014496,
0.04340134933590889,
0.24147430062294006,
-0.09270960837602615,
0.012857835739850998,
0.009800062514841557,
-0.05528522655367851,
-0.01743902638554573,
-0.015786971896886826,
-0.06108550354838371,
0.07905614376068115,
-0.030987009406089783,
0.007660138886421919,
-0.002373240189626813,
0.007465779781341553,
0.022776860743761063,
-0.041288673877716064,
-0.11340294033288956,
-0.016172833740711212,
0.06341245770454407,
-0.05930400267243385,
0.055208414793014526,
0.06466978043317795,
-0.04249992221593857,
-0.020976949483156204,
0.08109184354543686,
-0.052274465560913086,
-0.026005616411566734,
-0.08266141265630722,
0.24475133419036865,
-0.08348242193460464,
0.05088735744357109,
-0.02033335715532303,
-0.0514211431145668,
-0.0008052933844737709,
0.04371039941906929,
0.10108790546655655,
-0.06713338196277618,
0.022315613925457,
-0.0019322484731674194,
-0.0002089793561026454,
0.0012368113966658711,
0.08844870328903198,
-0.009775599464774132,
0.09575121104717255,
-0.026118295267224312,
0.04053528979420662,
0.022636478766798973,
-0.1021699607372284,
-0.13595043122768402,
-0.0996052697300911,
0.00325587741099298,
0.015283020213246346,
-0.03168781101703644,
0.07086201757192612,
-0.05999220907688141,
0.036278728395700455,
0.06025547906756401,
-0.08226442337036133,
-0.0585004985332489,
-0.07890190184116364,
0.021781904622912407,
-0.024839693680405617,
0.0626259595155716,
-0.051680587232112885,
0.027015743777155876,
0.13687707483768463,
-0.03278421238064766,
-0.07037016749382019,
-0.04805063083767891,
0.04786459729075432,
-0.13224902749061584,
0.003148842602968216,
0.00621820567175746,
0.04700568690896034,
0.07305064052343369,
-0.01825609616935253,
-0.08491446077823639,
0.09174837172031403,
-0.007426385767757893,
-0.046725064516067505,
0.011629054322838783,
0.10324690490961075,
-0.03347437083721161,
0.08314663916826248,
0.02191864512860775,
-0.24116508662700653,
0.006278468295931816,
0.14519210159778595,
-0.0465305931866169,
-0.032737817615270615,
-0.014523617923259735,
-0.014724326319992542,
0.1280938684940338,
0.2042754888534546,
-0.0000901685343706049,
0.009531795978546143,
0.0022256257943809032,
0.015221833251416683,
0.09167971462011337,
0.038954462856054306,
-0.041317809373140335,
-0.1885918229818344,
-0.0048669492825865746,
0.04584011062979698,
0.011268561705946922,
-0.24183997511863708,
-0.12340306490659714,
-0.12059950083494186,
-0.01975071243941784,
-0.005621601361781359,
0.004616728983819485,
0.2150956690311432,
0.008196796290576458,
0.002398995216935873,
-0.18705840408802032,
0.011295686475932598,
0.07302986085414886,
-0.032150596380233765,
-0.06954506039619446
] |
null | null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | null | actorcritic/bloom-560m-alpaca1k-lora | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-09T11:32:40+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
31,
6,
3,
82,
28,
3,
4,
9,
9,
10,
42,
20,
3,
4,
5,
9,
11,
13,
3,
12,
5,
4,
5,
3,
4,
9,
53,
9,
8,
6,
3,
14,
8,
7,
9,
4
] | [
"passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
] | [
-0.06646376848220825,
0.2168014943599701,
-0.00225935154594481,
0.023818302899599075,
0.1271018385887146,
-0.001635765191167593,
0.04218708351254463,
0.13324736058712006,
-0.020175931975245476,
0.11144465953111649,
0.046588581055402756,
0.09377603232860565,
0.09928803145885468,
0.18404334783554077,
0.04859916493296623,
-0.2059975117444992,
0.007056170143187046,
-0.09090408682823181,
0.014076028019189835,
0.1116579994559288,
0.13719257712364197,
-0.10291384905576706,
0.08272874355316162,
-0.04045208916068077,
-0.02019004337489605,
0.00012576708104461432,
-0.09259183704853058,
-0.07032395154237747,
0.06885425746440887,
0.06264153122901917,
0.051234472543001175,
0.001456156256608665,
0.09140396863222122,
-0.2864592671394348,
0.017265573143959045,
0.08406311273574829,
0.0027674848679453135,
0.06290827691555023,
0.07236549258232117,
-0.07389893382787704,
0.11328595131635666,
-0.08021481335163116,
0.13019037246704102,
0.08625296503305435,
-0.062064990401268005,
-0.23071379959583282,
-0.07525765895843506,
0.0963398814201355,
0.12251301854848862,
0.06215599179267883,
-0.022921854630112648,
0.15455181896686554,
-0.06248689442873001,
0.012971068732440472,
0.1294165402650833,
-0.11526761949062347,
-0.05572471022605896,
0.061741601675748825,
0.11775490641593933,
0.10740239918231964,
-0.14110268652439117,
-0.0017287094378843904,
0.04900608956813812,
0.029121357947587967,
0.08589313924312592,
0.022661056369543076,
0.12003941088914871,
0.04652795568108559,
-0.13695219159126282,
-0.04037507623434067,
0.12011898308992386,
0.038862764835357666,
-0.06446044892072678,
-0.2168138176202774,
-0.006778308190405369,
-0.0601806715130806,
-0.014732478186488152,
-0.07019448280334473,
0.039128515869379044,
-0.02470310963690281,
0.07317749410867691,
-0.04465159401297569,
-0.1063927412033081,
-0.0421026237308979,
0.0892222449183464,
0.07748593389987946,
0.011527054943144321,
-0.02519804798066616,
0.04627908393740654,
0.13455867767333984,
0.05402068421244621,
-0.10399353504180908,
-0.07017925381660461,
-0.06942764669656754,
-0.09420394152402878,
-0.04035796597599983,
0.056760527193546295,
0.031942449510097504,
0.02665667235851288,
0.22703726589679718,
0.016653569415211678,
0.04155244305729866,
0.0224777739495039,
0.01032855175435543,
0.043662428855895996,
0.0955500528216362,
-0.05303520709276199,
-0.15660029649734497,
-0.04072032496333122,
0.09077946096658707,
-0.0027527001220732927,
-0.036689214408397675,
-0.03966725245118141,
0.03849169611930847,
0.06843466311693192,
0.13122352957725525,
0.07552056759595871,
-0.017929591238498688,
-0.04813180863857269,
-0.030096933245658875,
0.23523783683776855,
-0.1493375599384308,
0.04426715523004532,
-0.02271856553852558,
-0.01804111897945404,
-0.03908449783921242,
0.03597262129187584,
0.022118929773569107,
-0.000004518366949923802,
0.09706240892410278,
-0.058981191366910934,
-0.05378659814596176,
-0.10168042778968811,
-0.03272576630115509,
0.04088849574327469,
-0.013975566253066063,
-0.010589460842311382,
-0.09025166928768158,
-0.09490354359149933,
-0.04766594246029854,
0.05537205561995506,
-0.05123869329690933,
-0.03770573064684868,
0.009465423412621021,
-0.08151785284280777,
-0.005444355774670839,
-0.005417742300778627,
0.10699385404586792,
-0.03222226724028587,
0.04445803165435791,
-0.027600755915045738,
0.05225523188710213,
0.09919606149196625,
0.031576547771692276,
-0.0773419588804245,
0.0561848059296608,
-0.22559374570846558,
0.07503069192171097,
-0.11481974273920059,
0.04335082694888115,
-0.1704932004213333,
-0.042439818382263184,
0.005444696638733149,
0.0139949731528759,
0.013206101022660732,
0.12720820307731628,
-0.19255615770816803,
-0.01654396951198578,
0.13260798156261444,
-0.09212633967399597,
-0.118110790848732,
0.07884611934423447,
-0.029701577499508858,
0.1624738723039627,
0.04682036489248276,
-0.027025915682315826,
0.09224298596382141,
-0.16434773802757263,
-0.07092688232660294,
-0.00949116237461567,
-0.01727987825870514,
0.12109188735485077,
0.07512219995260239,
-0.05991523340344429,
0.046571120619773865,
0.02832140028476715,
-0.038078423589468,
-0.04424772411584854,
-0.050857074558734894,
-0.10884185880422592,
-0.01070026308298111,
-0.08987759798765182,
0.04065500199794769,
-0.01250192429870367,
-0.07916021347045898,
-0.029885273426771164,
-0.18612512946128845,
-0.0030564051121473312,
0.10038342326879501,
0.0035033065360039473,
-0.005652366206049919,
-0.08666291832923889,
0.026358824223279953,
-0.03112892620265484,
-0.008404186926782131,
-0.16764774918556213,
-0.04399421438574791,
0.046902090311050415,
-0.16094985604286194,
0.020117372274398804,
-0.06413903087377548,
0.06334125250577927,
0.03641495108604431,
-0.05590536445379257,
-0.0248766727745533,
-0.01730942726135254,
0.011945613659918308,
-0.05083848536014557,
-0.18994836509227753,
-0.056277405470609665,
-0.037882111966609955,
0.149809330701828,
-0.25956398248672485,
0.032966937869787216,
0.051140617579221725,
0.14649195969104767,
0.00406361510977149,
-0.05115427449345589,
0.01429014839231968,
-0.05360214412212372,
-0.054652128368616104,
-0.06746816635131836,
-0.006135428790003061,
-0.027576493099331856,
-0.05147203803062439,
0.019243421033024788,
-0.1755700707435608,
-0.021410830318927765,
0.09424154460430145,
0.12876708805561066,
-0.1486445665359497,
-0.018640631809830666,
-0.048725154250860214,
-0.06339836865663528,
-0.0715010017156601,
-0.07038594037294388,
0.10712739825248718,
0.0513901449739933,
0.04796046018600464,
-0.07435787469148636,
-0.07092321664094925,
0.02726263552904129,
0.006906150374561548,
-0.03382374346256256,
0.08727246522903442,
0.05199531093239784,
-0.09209315478801727,
0.0756213590502739,
0.1092359870672226,
0.07177663594484329,
0.09363535046577454,
0.01574566215276718,
-0.11756632477045059,
-0.028492970392107964,
0.036266472190618515,
0.02740776725113392,
0.1465986967086792,
-0.05952361226081848,
0.04016614332795143,
0.04494241625070572,
-0.04170418903231621,
0.022319864481687546,
-0.08787637203931808,
0.024075502529740334,
0.025203049182891846,
-0.0034381982404738665,
0.06284574419260025,
-0.02525499276816845,
-0.0050758360885083675,
0.07016654312610626,
0.047779910266399384,
0.04621000960469246,
0.009655474685132504,
-0.01720241829752922,
-0.1047825813293457,
0.16950392723083496,
-0.0951867327094078,
-0.269941508769989,
-0.17632324993610382,
0.026197833940386772,
0.04035249724984169,
-0.022378476336598396,
0.031619444489479065,
-0.07056326419115067,
-0.10630585998296738,
-0.1060405746102333,
-0.002429972169920802,
0.01714223250746727,
-0.06364088505506516,
-0.0741225928068161,
0.07348573952913284,
0.04382912442088127,
-0.14902326464653015,
0.038552410900592804,
0.055694397538900375,
-0.057955220341682434,
-0.0233661737293005,
0.09118817001581192,
0.12397737801074982,
0.14583967626094818,
-0.021366750821471214,
-0.028626007959246635,
0.029004426673054695,
0.19620531797409058,
-0.13469526171684265,
0.10371150821447372,
0.13814030587673187,
-0.04545360431075096,
0.08360563963651657,
0.1560150384902954,
0.029186224564909935,
-0.08317049592733383,
0.05044832453131676,
0.04082648828625679,
-0.043159641325473785,
-0.2666129767894745,
-0.0534592866897583,
0.012832709588110447,
-0.06255637854337692,
0.09786593168973923,
0.10183793306350708,
0.11542957276105881,
0.034910861402750015,
-0.07166364789009094,
-0.043925940990448,
-0.0058974819257855415,
0.11737963557243347,
-0.05490213260054588,
-0.012639665976166725,
0.07686592638492584,
-0.05086168646812439,
0.005355054512619972,
0.10266812145709991,
0.02973790094256401,
0.17442677915096283,
0.020399179309606552,
0.11231429129838943,
0.06195578724145889,
0.08633565157651901,
0.0007386076031252742,
0.02951662428677082,
0.05147615820169449,
0.017203815281391144,
-0.002300140680745244,
-0.10421168059110641,
-0.006156572140753269,
0.1449710875749588,
0.028103826567530632,
0.029669636860489845,
-0.0018948549404740334,
-0.005003341939300299,
0.05121048167347908,
0.1746254414319992,
-0.011592294089496136,
-0.22072425484657288,
-0.0845772922039032,
0.06936841458082199,
-0.06218599155545235,
-0.12968985736370087,
-0.026130788028240204,
0.045467354357242584,
-0.17519839107990265,
0.026703642681241035,
-0.027433741837739944,
0.0919293761253357,
-0.09345759451389313,
-0.02221956104040146,
0.03687324374914169,
0.084866963326931,
-0.014529162086546421,
0.08703910559415817,
-0.14498743414878845,
0.11886418610811234,
0.02978132851421833,
0.09024628251791,
-0.11081171780824661,
0.07909037172794342,
-0.007550720125436783,
0.009180475026369095,
0.19379350543022156,
-0.011335089802742004,
-0.03514958545565605,
-0.08774717897176743,
-0.11210042238235474,
-0.013537433929741383,
0.12687496840953827,
-0.1243172138929367,
0.08773399889469147,
-0.015198243781924248,
-0.044079482555389404,
0.00937260314822197,
-0.12100647389888763,
-0.17273177206516266,
-0.19628387689590454,
0.05585884302854538,
-0.09575839340686798,
0.025643249973654747,
-0.11914430558681488,
-0.07089093327522278,
-0.02952558360993862,
0.241120383143425,
-0.1745356321334839,
-0.06510113179683685,
-0.1468164622783661,
-0.046294767409563065,
0.1662203073501587,
-0.04437198117375374,
0.0718095526099205,
-0.0208172257989645,
0.20345525443553925,
0.005988610442727804,
-0.004939318168908358,
0.06724198162555695,
-0.08892562240362167,
-0.16873881220817566,
-0.06771010160446167,
0.1510489284992218,
0.11680185794830322,
0.04907919466495514,
-0.002248800592496991,
0.0011772146681323647,
-0.016943959519267082,
-0.1137804463505745,
-0.0033210667315870523,
0.16037839651107788,
0.03878779336810112,
0.025986969470977783,
-0.05243593826889992,
-0.08797456324100494,
-0.06899320334196091,
-0.06853509694337845,
0.06221301481127739,
0.19590823352336884,
-0.10376439243555069,
0.1700313836336136,
0.147536963224411,
-0.07305635511875153,
-0.23175598680973053,
0.035342130810022354,
0.04983805492520332,
0.0014306638622656465,
0.04886869341135025,
-0.18252557516098022,
0.10521943867206573,
0.019543392583727837,
-0.05505957826972008,
0.13485197722911835,
-0.1557481735944748,
-0.1552847921848297,
0.0722852572798729,
0.03904085233807564,
-0.22423844039440155,
-0.1354004591703415,
-0.09622503817081451,
-0.05825018882751465,
-0.14065024256706238,
0.06054598465561867,
-0.002136280992999673,
0.015948504209518433,
0.03500790148973465,
-0.0015643214574083686,
0.027123261243104935,
-0.058935679495334625,
0.18609118461608887,
-0.004065449349582195,
0.020676052197813988,
-0.060264769941568375,
-0.0478842556476593,
0.09839435666799545,
-0.06130504235625267,
0.12208222597837448,
0.004057085141539574,
0.01594383642077446,
-0.10362856835126877,
-0.048314861953258514,
-0.04328322783112526,
0.05154227837920189,
-0.07548051327466965,
-0.10070807486772537,
-0.043625857681035995,
0.08841723203659058,
0.07005169242620468,
-0.03383097052574158,
0.00549331633374095,
-0.07189501076936722,
0.10019614547491074,
0.17795267701148987,
0.17573626339435577,
0.009926567785441875,
-0.07241068035364151,
0.01677953451871872,
-0.04142116755247116,
0.044231921434402466,
-0.2513144314289093,
0.03756171092391014,
0.06098250672221184,
0.029438555240631104,
0.09217222779989243,
-0.020435843616724014,
-0.1820858269929886,
-0.04050002992153168,
0.08094815909862518,
-0.05452597141265869,
-0.22617179155349731,
-0.019085140898823738,
0.0954197570681572,
-0.2020406424999237,
-0.007372708059847355,
0.03995226323604584,
-0.048725228756666183,
-0.023169852793216705,
0.00010950004070764408,
0.06317184865474701,
0.002471912419423461,
0.09773622453212738,
0.0735151618719101,
0.09715340286493301,
-0.08337292820215225,
0.10562895983457565,
0.10150538384914398,
-0.09572599828243256,
0.03605884686112404,
0.06754924356937408,
-0.05300498008728027,
-0.043293699622154236,
0.03665391728281975,
0.033023297786712646,
0.005234600510448217,
-0.060321882367134094,
0.013913018628954887,
-0.036497246474027634,
0.044923391193151474,
0.08326134830713272,
0.03754979372024536,
-0.013354414142668247,
0.06462216377258301,
0.03401726484298706,
-0.10898099094629288,
0.10366570204496384,
0.01731540448963642,
0.04105307161808014,
-0.08384523540735245,
-0.019968897104263306,
0.035425446927547455,
0.030576206743717194,
-0.01765924133360386,
-0.02306121215224266,
-0.02860277332365513,
-0.01614218018949032,
-0.14299540221691132,
-0.023106401786208153,
-0.07243485748767853,
0.006181265693157911,
0.014656842686235905,
-0.031884219497442245,
-0.011233693920075893,
0.02475680410861969,
-0.06979699432849884,
-0.07426341623067856,
-0.006949664559215307,
0.09833318740129471,
-0.15115703642368317,
0.008848577737808228,
0.06907843053340912,
-0.11088496446609497,
0.08190931379795074,
-0.008411259390413761,
0.016245156526565552,
0.022527478635311127,
-0.15448406338691711,
0.05601610988378525,
0.0008648968650959432,
0.01916889287531376,
0.025886621326208115,
-0.16471809148788452,
0.004104440100491047,
-0.04661374166607857,
-0.02149827405810356,
-0.00004464812809601426,
-0.02647159807384014,
-0.12325995415449142,
0.06858719140291214,
-0.015622655861079693,
-0.035931166261434555,
-0.02701525390148163,
0.0539589487016201,
0.07888586074113846,
-0.027474910020828247,
0.10445091128349304,
-0.008690856397151947,
0.04941811040043831,
-0.16801609098911285,
-0.02470702864229679,
-0.04982255399227142,
0.019377702847123146,
0.009884213097393513,
-0.007693959400057793,
0.04183054715394974,
-0.00976533442735672,
0.21883612871170044,
-0.05075952783226967,
0.1607085019350052,
0.05847611650824547,
-0.017352959141135216,
-0.0007513365126214921,
0.06180921941995621,
0.05997028574347496,
0.04658793285489082,
0.009480604901909828,
0.023740366101264954,
-0.022450892254710197,
-0.006695089396089315,
-0.15932634472846985,
0.01890849508345127,
0.14999441802501678,
0.06301083415746689,
0.024745315313339233,
0.05866100639104843,
-0.12775006890296936,
-0.12135478109121323,
0.09311001747846603,
-0.026755332946777344,
0.00928465835750103,
-0.08245618641376495,
0.1358020007610321,
0.14980104565620422,
-0.14000412821769714,
0.05256148427724838,
-0.06134212389588356,
-0.05217423290014267,
-0.10388828068971634,
-0.12032219022512436,
-0.05887215584516525,
-0.053666237741708755,
0.002330566756427288,
-0.03760887682437897,
0.054546963423490524,
0.03344334661960602,
-0.009351172484457493,
-0.00022941511997487396,
0.13597318530082703,
-0.019751882180571556,
-0.0028988157864660025,
0.048313532024621964,
0.03693558648228645,
0.02373051457107067,
-0.05275435373187065,
0.02940409444272518,
0.02539868652820587,
0.032232340425252914,
0.06546790152788162,
0.033412106335163116,
-0.047448933124542236,
0.03804153576493263,
-0.0025254099164158106,
-0.11207924783229828,
0.019641218706965446,
-0.00460948096588254,
-0.0742158442735672,
0.1268945336341858,
0.0407399944961071,
0.010224059224128723,
-0.03741471841931343,
0.24361543357372284,
-0.06653323769569397,
-0.06378097087144852,
-0.13251738250255585,
0.10491154342889786,
-0.0027236645109951496,
0.06476365029811859,
0.023412218317389488,
-0.1284150779247284,
0.005243356805294752,
0.13858191668987274,
0.12181595712900162,
0.0045748427510261536,
0.009228081442415714,
0.0518609918653965,
0.0025186820421367884,
-0.06998204439878464,
0.054019294679164886,
0.06992026418447495,
0.12919506430625916,
-0.07847554981708527,
0.07680778950452805,
0.0006860480643808842,
-0.08370215445756912,
-0.02947772853076458,
0.11312682181596756,
-0.0409729965031147,
0.03491825982928276,
-0.047444481402635574,
0.10916327685117722,
-0.05787910893559456,
-0.29412412643432617,
0.02350960113108158,
-0.09588567912578583,
-0.15202060341835022,
-0.018367812037467957,
0.05944539234042168,
-0.02624768204987049,
0.018029648810625076,
0.06971040368080139,
-0.06011629104614258,
0.20098382234573364,
0.0335683599114418,
-0.07864278554916382,
-0.0664360448718071,
0.04837050288915634,
-0.06564252078533173,
0.2949807047843933,
0.008418165147304535,
0.02863333560526371,
0.10770907253026962,
-0.03253700211644173,
-0.18271861970424652,
0.010723991319537163,
0.1133992001414299,
-0.08056149631738663,
0.08200647681951523,
0.19000613689422607,
-0.012578671798110008,
0.1209007054567337,
0.05294662341475487,
-0.047376248985528946,
0.04217283055186272,
-0.03389401361346245,
-0.051268599927425385,
-0.10752558708190918,
0.058453381061553955,
-0.05909625440835953,
0.15447644889354706,
0.10152646154165268,
-0.05671518296003342,
-0.004550917539745569,
-0.05555408447980881,
0.04875178262591362,
0.01804669201374054,
0.12263146042823792,
0.02951994352042675,
-0.1865430772304535,
0.032826557755470276,
-0.01144319772720337,
0.10186848044395447,
-0.25588861107826233,
-0.08421015739440918,
0.08833149075508118,
-0.011924264021217823,
-0.05105875805020332,
0.10560628771781921,
0.057650718837976456,
0.04243382066488266,
-0.043439045548439026,
-0.10480839014053345,
-0.02186836116015911,
0.14663739502429962,
-0.1469624787569046,
-0.025013303384184837
] |
null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.8.2 | {"library_name": "peft", "base_model": "meta-llama/Llama-2-7b-hf"} | null | mohres/finetuned-llama-2-7b-code | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-hf",
"region:us"
] | 2024-02-09T11:33:17+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-hf #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
### Framework versions
- PEFT 0.8.2 | [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.8.2"
] | [
"TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-hf #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.8.2"
] | [
41,
6,
3,
54,
28,
3,
4,
9,
9,
10,
42,
20,
3,
4,
5,
9,
11,
13,
3,
12,
5,
4,
5,
3,
4,
9,
53,
9,
8,
6,
3,
14,
8,
7,
9,
4,
11
] | [
"passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-hf #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2"
] | [
-0.13237035274505615,
0.20393601059913635,
-0.002511046128347516,
0.02874687872827053,
0.07912357151508331,
0.019634027034044266,
0.04979075863957405,
0.13531364500522614,
0.020043307915329933,
0.10580451786518097,
0.0737132653594017,
0.11718367785215378,
0.10926163196563721,
0.20654499530792236,
0.003890186781063676,
-0.15440793335437775,
0.024214256554841995,
-0.08298544585704803,
0.006814117077738047,
0.1290476769208908,
0.14319083094596863,
-0.10468140989542007,
0.0831538662314415,
-0.014203370548784733,
0.0008161105797626078,
-0.03246506303548813,
-0.06674343347549438,
-0.015596466138958931,
0.04917285591363907,
0.02522817626595497,
0.05882670730352402,
-0.010089844465255737,
0.0929119735956192,
-0.26152917742729187,
0.018749000504612923,
0.04154228791594505,
0.009074261412024498,
0.08363344520330429,
0.0979103073477745,
-0.04074648395180702,
0.12078511714935303,
-0.024994686245918274,
0.13832204043865204,
0.09345067292451859,
-0.08226727694272995,
-0.233157217502594,
-0.06684722006320953,
0.07271547615528107,
0.18968668580055237,
0.08927863836288452,
-0.044125091284513474,
0.14097759127616882,
-0.07517150044441223,
0.02484818734228611,
0.04656748101115227,
-0.09290260076522827,
-0.06676048040390015,
0.0702265128493309,
0.13261590898036957,
0.0625041052699089,
-0.12113244831562042,
-0.03750992938876152,
0.03344248607754707,
0.044793009757995605,
0.06062353774905205,
0.005180627107620239,
0.16268815100193024,
0.034240271896123886,
-0.14592847228050232,
-0.05353321507573128,
0.14678435027599335,
0.01157673355191946,
-0.04636283218860626,
-0.21997328102588654,
-0.0027822081465274096,
-0.09489403665065765,
-0.022923149168491364,
-0.05228540673851967,
0.03324316069483757,
0.00603833794593811,
0.1196645051240921,
-0.042089227586984634,
-0.09635167568922043,
-0.029711460694670677,
0.0996040627360344,
0.05452839657664299,
0.02769845724105835,
-0.02099502831697464,
0.010653719305992126,
0.1290775090456009,
0.08296726644039154,
-0.1341402530670166,
-0.07021861523389816,
-0.0753326416015625,
-0.04316629841923714,
-0.03228989988565445,
0.03893959894776344,
0.019871119409799576,
0.07120058685541153,
0.2619621157646179,
-0.022196462377905846,
0.06401924788951874,
0.061033982783555984,
0.01709051802754402,
0.04062429443001747,
0.10795178264379501,
-0.03382651507854462,
-0.15705206990242004,
-0.007360270246863365,
0.10362072288990021,
-0.004135396331548691,
-0.02802850492298603,
-0.045986633747816086,
0.03152812272310257,
0.044165465980768204,
0.11501371115446091,
0.11203816533088684,
-0.019931387156248093,
-0.07717939466238022,
-0.05966082587838173,
0.19364216923713684,
-0.16149258613586426,
0.038572292774915695,
0.02467195875942707,
-0.006866174750030041,
-0.06484853476285934,
0.0073310090228915215,
0.016164373606443405,
-0.027354510501027107,
0.0603426918387413,
-0.0646006166934967,
-0.04179375246167183,
-0.1283673793077469,
-0.02387934736907482,
0.032629046589136124,
0.0170845165848732,
-0.0421639084815979,
-0.046661876142024994,
-0.08786044269800186,
-0.11000633984804153,
0.10926247388124466,
-0.05313732475042343,
-0.052913907915353775,
-0.02804330736398697,
-0.08941388875246048,
0.022293368354439735,
0.027490468695759773,
0.0755976140499115,
-0.02891632728278637,
0.052480049431324005,
0.003703000722452998,
0.059941843152046204,
0.0814133733510971,
0.027145687490701675,
-0.08097686618566513,
0.06685694307088852,
-0.19895170629024506,
0.07886288315057755,
-0.08557034283876419,
0.035526763647794724,
-0.16191443800926208,
-0.008882720954716206,
0.015485688112676144,
0.028551144525408745,
0.0418417863547802,
0.16628479957580566,
-0.21890771389007568,
-0.021091977134346962,
0.15901808440685272,
-0.10847076028585434,
-0.1374696046113968,
0.0436418242752552,
-0.04286689683794975,
0.18280568718910217,
0.028055870905518532,
0.010343263857066631,
0.09726855903863907,
-0.16840705275535583,
-0.02907063439488411,
-0.021288467571139336,
0.0036895605735480785,
0.07365763932466507,
0.09041544795036316,
-0.09089618921279907,
-0.0016403654590249062,
0.012144356034696102,
-0.06943254172801971,
-0.015110267326235771,
-0.04118245840072632,
-0.10628213733434677,
0.002018203027546406,
-0.09110194444656372,
0.023759065195918083,
0.0035124430432915688,
-0.09477277845144272,
-0.008542876690626144,
-0.1573835164308548,
-0.0652049109339714,
0.09409166127443314,
0.0002530320198275149,
-0.024702679365873337,
-0.10900412499904633,
0.06465248018503189,
-0.03883763402700424,
-0.026517964899539948,
-0.14125961065292358,
-0.023071611300110817,
0.01673055998980999,
-0.14134323596954346,
-0.01001854706555605,
-0.12183605134487152,
0.06567396223545074,
0.005137317348271608,
-0.0481104739010334,
-0.04708600044250488,
-0.004086394794285297,
0.0014921361580491066,
-0.05505292862653732,
-0.23444515466690063,
-0.028233496472239494,
-0.05085372179746628,
0.16539393365383148,
-0.2289838343858719,
0.044271692633628845,
0.014694449491798878,
0.11615854501724243,
-0.0018446118338033557,
-0.0661761611700058,
0.022094158455729485,
-0.07084274291992188,
-0.025033291429281235,
-0.07177132368087769,
-0.0071777342818677425,
0.00008959023398347199,
-0.029647991061210632,
0.015313859097659588,
-0.10952108353376389,
-0.053884293884038925,
0.100620798766613,
0.060472261160612106,
-0.14894865453243256,
0.008543584495782852,
-0.03779032453894615,
-0.06071627512574196,
-0.07427168637514114,
-0.0695083886384964,
0.0856412947177887,
0.052977994084358215,
0.03996400535106659,
-0.0812206119298935,
-0.07201940566301346,
0.005019875708967447,
-0.02742239646613598,
-0.005877636838704348,
0.11996077746152878,
0.07278608530759811,
-0.10015858709812164,
0.0890948474407196,
0.07567999511957169,
0.012905389070510864,
0.07863839715719223,
-0.028960783034563065,
-0.10615462064743042,
-0.03149069845676422,
0.05891314521431923,
0.0075002689845860004,
0.18196412920951843,
-0.07219336181879044,
0.05777830258011818,
0.046155888587236404,
-0.046635568141937256,
0.05089704319834709,
-0.09103982150554657,
0.0068960352800786495,
0.00045980032882653177,
-0.017081741243600845,
0.029599705711007118,
-0.020320137962698936,
0.006365274079144001,
0.07632698118686676,
0.05559656023979187,
0.02392573468387127,
0.023359429091215134,
-0.037590380758047104,
-0.1454712599515915,
0.18398217856884003,
-0.09283597022294998,
-0.235765740275383,
-0.15705986320972443,
0.0616452731192112,
0.049257904291152954,
-0.015689486637711525,
0.02697811834514141,
-0.055544715374708176,
-0.10059839487075806,
-0.08630408346652985,
-0.001965506933629513,
0.033574361354112625,
-0.05912783369421959,
-0.07473962754011154,
0.045523062348365784,
0.04523130878806114,
-0.11779510229825974,
0.02612960711121559,
0.06724361330270767,
-0.01014306303113699,
0.002122951438650489,
0.05421233922243118,
0.09625556319952011,
0.1871589571237564,
-0.0047584883868694305,
0.006493487861007452,
0.06463784724473953,
0.27302834391593933,
-0.16097134351730347,
0.10603976994752884,
0.1468280404806137,
-0.06509615480899811,
0.06928659975528717,
0.1811111718416214,
0.024897225201129913,
-0.0959320068359375,
0.024916043505072594,
0.02835996262729168,
-0.01960386149585247,
-0.2740720212459564,
-0.0512622706592083,
-0.015117009170353413,
-0.08622704446315765,
0.07128944247961044,
0.08718991279602051,
0.07891540229320526,
0.03938929736614227,
-0.05623466521501541,
-0.11011259257793427,
0.02521095983684063,
0.10682129859924316,
-0.01211885642260313,
0.003295447211712599,
0.08167944848537445,
-0.04613311216235161,
0.007927946746349335,
0.08699803054332733,
-0.01990879327058792,
0.1374768167734146,
0.04775961861014366,
0.09206060320138931,
0.08603846281766891,
0.10468525439500809,
-0.011216369457542896,
0.031460702419281006,
0.01713097095489502,
0.023083847016096115,
0.025577327236533165,
-0.0892123356461525,
0.00939508993178606,
0.11217135936021805,
0.02443520911037922,
0.02237142249941826,
0.016059260815382004,
-0.042084116488695145,
0.035355109721422195,
0.19778503477573395,
0.02863113395869732,
-0.21936152875423431,
-0.08315163850784302,
0.04950554668903351,
-0.07752750813961029,
-0.15846198797225952,
-0.0069001950323581696,
0.02585102617740631,
-0.16377925872802734,
0.015679948031902313,
-0.04114160314202309,
0.10047675669193268,
-0.07824478298425674,
-0.04026156663894653,
0.11029542237520218,
0.047400183975696564,
-0.01943347603082657,
0.05451195687055588,
-0.19536079466342926,
0.10843666642904282,
0.02992161363363266,
0.07536879926919937,
-0.08786998689174652,
0.09398660063743591,
0.006047630682587624,
-0.019160762429237366,
0.16931316256523132,
-0.0001144029592978768,
-0.049934081733226776,
-0.08560120314359665,
-0.09227954596281052,
0.0015766898868605494,
0.07818529009819031,
-0.12631447613239288,
0.0825691819190979,
-0.03569265082478523,
-0.024482207372784615,
-0.008127174340188503,
-0.08541606366634369,
-0.1325976550579071,
-0.14982733130455017,
0.05399367958307266,
-0.0976201519370079,
0.02554609440267086,
-0.08825770765542984,
-0.05347679927945137,
0.016768373548984528,
0.18224331736564636,
-0.21447692811489105,
-0.10864878445863724,
-0.14267513155937195,
-0.11213549226522446,
0.16079570353031158,
-0.042837124317884445,
0.08159231394529343,
0.00010400224709883332,
0.15704618394374847,
0.01110734511166811,
-0.015090357512235641,
0.08682332187891006,
-0.09437134861946106,
-0.19026298820972443,
-0.04887847229838371,
0.16311104595661163,
0.1444961428642273,
0.029530119150877,
-0.005065699107944965,
0.02549002133309841,
-0.06952440738677979,
-0.11216824501752853,
0.02609189972281456,
0.16361786425113678,
0.07300680130720139,
-0.012950204312801361,
-0.025871867313981056,
-0.0997539535164833,
-0.05963310971856117,
-0.04339827224612236,
-0.00898770522326231,
0.20425592362880707,
-0.06497634947299957,
0.14582973718643188,
0.10464579612016678,
-0.05606960505247116,
-0.21339629590511322,
0.03492094576358795,
0.04277806729078293,
0.026418045163154602,
0.04313372075557709,
-0.18166027963161469,
0.09741673618555069,
-0.014149999246001244,
-0.08650295436382294,
0.17498920857906342,
-0.17328102886676788,
-0.13439859449863434,
0.1159968227148056,
0.025544147938489914,
-0.21331895887851715,
-0.13972461223602295,
-0.10190334171056747,
-0.0198976993560791,
-0.126362144947052,
0.036111894994974136,
-0.0036879852414131165,
0.00850605871528387,
0.012948633171617985,
0.018173353746533394,
0.039593230932950974,
-0.05594787001609802,
0.21268853545188904,
-0.03937339782714844,
0.000047609177272534,
-0.050931964069604874,
-0.06770505011081696,
0.023772839456796646,
-0.0565045028924942,
0.12416863441467285,
-0.01210821233689785,
0.039195943623781204,
-0.17265570163726807,
-0.04285977780818939,
-0.058010976761579514,
0.03728554770350456,
-0.09242235124111176,
-0.0793662965297699,
-0.04483490809798241,
0.09155189245939255,
0.09041202813386917,
-0.018728721886873245,
0.0019666242878884077,
-0.09585212171077728,
0.07403325289487839,
0.20964933931827545,
0.20306745171546936,
0.0681707113981247,
-0.05247919633984566,
0.02836998738348484,
-0.03519117832183838,
0.04444263130426407,
-0.2148476094007492,
0.0430048331618309,
0.0631239265203476,
0.024400800466537476,
0.06267635524272919,
-0.01054441649466753,
-0.1590016484260559,
-0.07973737269639969,
0.08659059554338455,
-0.0608268640935421,
-0.16209019720554352,
-0.03262902423739433,
0.02129248157143593,
-0.2115628719329834,
-0.04105594381690025,
0.03599734604358673,
-0.014814808964729309,
-0.03840542584657669,
0.021407432854175568,
0.07970889657735825,
-0.028947602957487106,
0.1049608662724495,
0.09329938143491745,
0.09604475647211075,
-0.09774979948997498,
0.05453461781144142,
0.07179035246372223,
-0.031663764268159866,
0.03226640820503235,
0.1210775151848793,
-0.04315068572759628,
-0.046701591461896896,
0.08053972572088242,
0.11871292442083359,
-0.00035442441003397107,
-0.06335891038179398,
-0.0028557574842125177,
-0.0440225712954998,
0.054060470312833786,
0.10412941128015518,
0.036388467997312546,
0.0012024412862956524,
0.07687212526798248,
0.028011957183480263,
-0.09147296100854874,
0.12449978291988373,
0.06066809967160225,
0.02483541890978813,
-0.05523430183529854,
-0.038621995598077774,
-0.015819178894162178,
-0.0028008304070681334,
-0.01961326226592064,
-0.0014547118917107582,
-0.08309019356966019,
0.0061004795134067535,
-0.13227513432502747,
0.022323906421661377,
-0.07725922018289566,
0.00378548726439476,
0.036021001636981964,
-0.046576302498579025,
0.0013563713291659951,
-0.0008801636286079884,
-0.07430332899093628,
-0.05454954877495766,
-0.01629588007926941,
0.07790114730596542,
-0.13923588395118713,
0.03906119614839554,
0.07606222480535507,
-0.10726266354322433,
0.06878530234098434,
-0.007731399964541197,
0.008601504378020763,
0.0010856596054509282,
-0.13779860734939575,
0.05484551563858986,
-0.028775036334991455,
-0.006356567144393921,
0.005071246065199375,
-0.19585701823234558,
-0.00865773856639862,
-0.03182972967624664,
-0.0634872317314148,
0.019731810316443443,
-0.001073729363270104,
-0.11955288797616959,
0.1077868640422821,
0.004837313666939735,
-0.05712589994072914,
-0.0236744936555624,
0.042738161981105804,
0.0863419771194458,
-0.0053856209851801395,
0.12532570958137512,
-0.0293873380869627,
0.07612910121679306,
-0.17633569240570068,
-0.010070881806313992,
-0.015794692561030388,
0.05993741378188133,
-0.019834399223327637,
-0.03712667524814606,
0.06236843764781952,
-0.027145320549607277,
0.17265751957893372,
-0.004146610386669636,
0.07253459841012955,
0.0493277981877327,
0.008650471456348896,
0.04884583130478859,
0.07257263362407684,
0.06367837637662888,
-0.017801770940423012,
0.00016894470900297165,
0.04386947304010391,
-0.002970502246171236,
-0.051965516060590744,
-0.15762734413146973,
0.06277678161859512,
0.17842786014080048,
0.056998081505298615,
0.030175408348441124,
0.012138530611991882,
-0.12049488723278046,
-0.07329574972391129,
0.10845038294792175,
-0.021686408668756485,
-0.031095284968614578,
-0.06442723423242569,
0.21323516964912415,
0.1388614922761917,
-0.19825653731822968,
0.0702671930193901,
-0.06280558556318283,
-0.04658647999167442,
-0.14314492046833038,
-0.17366671562194824,
-0.059809304773807526,
-0.0547034814953804,
-0.026051264256238937,
-0.054700352251529694,
0.04570859298110008,
0.047346316277980804,
-0.0016739139100536704,
-0.02772514894604683,
0.1126171201467514,
0.02765420638024807,
-0.032165806740522385,
0.04451003298163414,
0.05619681254029274,
0.03682970255613327,
-0.09137814491987228,
0.007322985213249922,
0.0029695341363549232,
0.014342821203172207,
0.06777288764715195,
0.01613135077059269,
-0.06992621719837189,
0.02725713886320591,
-0.020467489957809448,
-0.12120343744754791,
0.042514219880104065,
-0.005491400603204966,
-0.02191038616001606,
0.14766326546669006,
0.039597559720277786,
0.008086306042969227,
-0.014769108034670353,
0.22978916764259338,
-0.079631008207798,
-0.08263124525547028,
-0.1393512636423111,
0.07894771546125412,
-0.07535439729690552,
0.020168637856841087,
0.02652786672115326,
-0.12502749264240265,
0.017455779016017914,
0.17437158524990082,
0.11967697739601135,
-0.01862110011279583,
0.005760727450251579,
0.04387581720948219,
0.003006097162142396,
-0.04732988774776459,
0.01692454144358635,
0.05290905013680458,
0.19558346271514893,
-0.0746847614645958,
0.054245725274086,
-0.01774757355451584,
-0.08059251308441162,
-0.020728278905153275,
0.09288354963064194,
-0.009933017194271088,
-0.004748775623738766,
-0.06074956804513931,
0.149005725979805,
-0.0759778842329979,
-0.20890262722969055,
0.06107410788536072,
-0.057474348694086075,
-0.13986754417419434,
-0.043588198721408844,
0.03270360454916954,
-0.02818191610276699,
-0.0004342520551290363,
0.05878293514251709,
-0.041880737990140915,
0.1787300854921341,
0.02775873802602291,
-0.04535049945116043,
-0.08805633336305618,
0.060195520520210266,
-0.15322564542293549,
0.28409940004348755,
0.02300625666975975,
0.06475372612476349,
0.11462150514125824,
-0.023716775700449944,
-0.14765876531600952,
0.016111766919493675,
0.11251717060804367,
-0.07146475464105606,
0.06923303008079529,
0.16616879403591156,
0.00888645276427269,
0.12871026992797852,
0.06517354398965836,
-0.04169101640582085,
0.03372213616967201,
-0.08477409183979034,
-0.04430316761136055,
-0.1301726996898651,
0.07585147768259048,
-0.09351208806037903,
0.15738072991371155,
0.11715016514062881,
-0.07169844210147858,
0.010452828370034695,
-0.02282477170228958,
0.09099912643432617,
0.012017005123198032,
0.10486294329166412,
0.01101954746991396,
-0.19380232691764832,
0.04388235881924629,
0.012521770782768726,
0.09230010956525803,
-0.21009819209575653,
-0.05027567222714424,
0.04558335989713669,
-0.022896859794855118,
-0.06855283677577972,
0.11809497326612473,
0.03357189893722534,
0.028112467378377914,
-0.037041857838630676,
-0.032784342765808105,
0.007307000923901796,
0.151776984333992,
-0.11639050394296646,
-0.019398227334022522
] |
null | null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | null | berkouille/mistral-PuttView-assistant | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-09T11:40:18+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
31,
6,
3,
82,
28,
3,
4,
9,
9,
10,
42,
20,
3,
4,
5,
9,
11,
13,
3,
12,
5,
4,
5,
3,
4,
9,
53,
9,
8,
6,
3,
14,
8,
7,
9,
4
] | [
"passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
] | [
-0.06646376848220825,
0.2168014943599701,
-0.00225935154594481,
0.023818302899599075,
0.1271018385887146,
-0.001635765191167593,
0.04218708351254463,
0.13324736058712006,
-0.020175931975245476,
0.11144465953111649,
0.046588581055402756,
0.09377603232860565,
0.09928803145885468,
0.18404334783554077,
0.04859916493296623,
-0.2059975117444992,
0.007056170143187046,
-0.09090408682823181,
0.014076028019189835,
0.1116579994559288,
0.13719257712364197,
-0.10291384905576706,
0.08272874355316162,
-0.04045208916068077,
-0.02019004337489605,
0.00012576708104461432,
-0.09259183704853058,
-0.07032395154237747,
0.06885425746440887,
0.06264153122901917,
0.051234472543001175,
0.001456156256608665,
0.09140396863222122,
-0.2864592671394348,
0.017265573143959045,
0.08406311273574829,
0.0027674848679453135,
0.06290827691555023,
0.07236549258232117,
-0.07389893382787704,
0.11328595131635666,
-0.08021481335163116,
0.13019037246704102,
0.08625296503305435,
-0.062064990401268005,
-0.23071379959583282,
-0.07525765895843506,
0.0963398814201355,
0.12251301854848862,
0.06215599179267883,
-0.022921854630112648,
0.15455181896686554,
-0.06248689442873001,
0.012971068732440472,
0.1294165402650833,
-0.11526761949062347,
-0.05572471022605896,
0.061741601675748825,
0.11775490641593933,
0.10740239918231964,
-0.14110268652439117,
-0.0017287094378843904,
0.04900608956813812,
0.029121357947587967,
0.08589313924312592,
0.022661056369543076,
0.12003941088914871,
0.04652795568108559,
-0.13695219159126282,
-0.04037507623434067,
0.12011898308992386,
0.038862764835357666,
-0.06446044892072678,
-0.2168138176202774,
-0.006778308190405369,
-0.0601806715130806,
-0.014732478186488152,
-0.07019448280334473,
0.039128515869379044,
-0.02470310963690281,
0.07317749410867691,
-0.04465159401297569,
-0.1063927412033081,
-0.0421026237308979,
0.0892222449183464,
0.07748593389987946,
0.011527054943144321,
-0.02519804798066616,
0.04627908393740654,
0.13455867767333984,
0.05402068421244621,
-0.10399353504180908,
-0.07017925381660461,
-0.06942764669656754,
-0.09420394152402878,
-0.04035796597599983,
0.056760527193546295,
0.031942449510097504,
0.02665667235851288,
0.22703726589679718,
0.016653569415211678,
0.04155244305729866,
0.0224777739495039,
0.01032855175435543,
0.043662428855895996,
0.0955500528216362,
-0.05303520709276199,
-0.15660029649734497,
-0.04072032496333122,
0.09077946096658707,
-0.0027527001220732927,
-0.036689214408397675,
-0.03966725245118141,
0.03849169611930847,
0.06843466311693192,
0.13122352957725525,
0.07552056759595871,
-0.017929591238498688,
-0.04813180863857269,
-0.030096933245658875,
0.23523783683776855,
-0.1493375599384308,
0.04426715523004532,
-0.02271856553852558,
-0.01804111897945404,
-0.03908449783921242,
0.03597262129187584,
0.022118929773569107,
-0.000004518366949923802,
0.09706240892410278,
-0.058981191366910934,
-0.05378659814596176,
-0.10168042778968811,
-0.03272576630115509,
0.04088849574327469,
-0.013975566253066063,
-0.010589460842311382,
-0.09025166928768158,
-0.09490354359149933,
-0.04766594246029854,
0.05537205561995506,
-0.05123869329690933,
-0.03770573064684868,
0.009465423412621021,
-0.08151785284280777,
-0.005444355774670839,
-0.005417742300778627,
0.10699385404586792,
-0.03222226724028587,
0.04445803165435791,
-0.027600755915045738,
0.05225523188710213,
0.09919606149196625,
0.031576547771692276,
-0.0773419588804245,
0.0561848059296608,
-0.22559374570846558,
0.07503069192171097,
-0.11481974273920059,
0.04335082694888115,
-0.1704932004213333,
-0.042439818382263184,
0.005444696638733149,
0.0139949731528759,
0.013206101022660732,
0.12720820307731628,
-0.19255615770816803,
-0.01654396951198578,
0.13260798156261444,
-0.09212633967399597,
-0.118110790848732,
0.07884611934423447,
-0.029701577499508858,
0.1624738723039627,
0.04682036489248276,
-0.027025915682315826,
0.09224298596382141,
-0.16434773802757263,
-0.07092688232660294,
-0.00949116237461567,
-0.01727987825870514,
0.12109188735485077,
0.07512219995260239,
-0.05991523340344429,
0.046571120619773865,
0.02832140028476715,
-0.038078423589468,
-0.04424772411584854,
-0.050857074558734894,
-0.10884185880422592,
-0.01070026308298111,
-0.08987759798765182,
0.04065500199794769,
-0.01250192429870367,
-0.07916021347045898,
-0.029885273426771164,
-0.18612512946128845,
-0.0030564051121473312,
0.10038342326879501,
0.0035033065360039473,
-0.005652366206049919,
-0.08666291832923889,
0.026358824223279953,
-0.03112892620265484,
-0.008404186926782131,
-0.16764774918556213,
-0.04399421438574791,
0.046902090311050415,
-0.16094985604286194,
0.020117372274398804,
-0.06413903087377548,
0.06334125250577927,
0.03641495108604431,
-0.05590536445379257,
-0.0248766727745533,
-0.01730942726135254,
0.011945613659918308,
-0.05083848536014557,
-0.18994836509227753,
-0.056277405470609665,
-0.037882111966609955,
0.149809330701828,
-0.25956398248672485,
0.032966937869787216,
0.051140617579221725,
0.14649195969104767,
0.00406361510977149,
-0.05115427449345589,
0.01429014839231968,
-0.05360214412212372,
-0.054652128368616104,
-0.06746816635131836,
-0.006135428790003061,
-0.027576493099331856,
-0.05147203803062439,
0.019243421033024788,
-0.1755700707435608,
-0.021410830318927765,
0.09424154460430145,
0.12876708805561066,
-0.1486445665359497,
-0.018640631809830666,
-0.048725154250860214,
-0.06339836865663528,
-0.0715010017156601,
-0.07038594037294388,
0.10712739825248718,
0.0513901449739933,
0.04796046018600464,
-0.07435787469148636,
-0.07092321664094925,
0.02726263552904129,
0.006906150374561548,
-0.03382374346256256,
0.08727246522903442,
0.05199531093239784,
-0.09209315478801727,
0.0756213590502739,
0.1092359870672226,
0.07177663594484329,
0.09363535046577454,
0.01574566215276718,
-0.11756632477045059,
-0.028492970392107964,
0.036266472190618515,
0.02740776725113392,
0.1465986967086792,
-0.05952361226081848,
0.04016614332795143,
0.04494241625070572,
-0.04170418903231621,
0.022319864481687546,
-0.08787637203931808,
0.024075502529740334,
0.025203049182891846,
-0.0034381982404738665,
0.06284574419260025,
-0.02525499276816845,
-0.0050758360885083675,
0.07016654312610626,
0.047779910266399384,
0.04621000960469246,
0.009655474685132504,
-0.01720241829752922,
-0.1047825813293457,
0.16950392723083496,
-0.0951867327094078,
-0.269941508769989,
-0.17632324993610382,
0.026197833940386772,
0.04035249724984169,
-0.022378476336598396,
0.031619444489479065,
-0.07056326419115067,
-0.10630585998296738,
-0.1060405746102333,
-0.002429972169920802,
0.01714223250746727,
-0.06364088505506516,
-0.0741225928068161,
0.07348573952913284,
0.04382912442088127,
-0.14902326464653015,
0.038552410900592804,
0.055694397538900375,
-0.057955220341682434,
-0.0233661737293005,
0.09118817001581192,
0.12397737801074982,
0.14583967626094818,
-0.021366750821471214,
-0.028626007959246635,
0.029004426673054695,
0.19620531797409058,
-0.13469526171684265,
0.10371150821447372,
0.13814030587673187,
-0.04545360431075096,
0.08360563963651657,
0.1560150384902954,
0.029186224564909935,
-0.08317049592733383,
0.05044832453131676,
0.04082648828625679,
-0.043159641325473785,
-0.2666129767894745,
-0.0534592866897583,
0.012832709588110447,
-0.06255637854337692,
0.09786593168973923,
0.10183793306350708,
0.11542957276105881,
0.034910861402750015,
-0.07166364789009094,
-0.043925940990448,
-0.0058974819257855415,
0.11737963557243347,
-0.05490213260054588,
-0.012639665976166725,
0.07686592638492584,
-0.05086168646812439,
0.005355054512619972,
0.10266812145709991,
0.02973790094256401,
0.17442677915096283,
0.020399179309606552,
0.11231429129838943,
0.06195578724145889,
0.08633565157651901,
0.0007386076031252742,
0.02951662428677082,
0.05147615820169449,
0.017203815281391144,
-0.002300140680745244,
-0.10421168059110641,
-0.006156572140753269,
0.1449710875749588,
0.028103826567530632,
0.029669636860489845,
-0.0018948549404740334,
-0.005003341939300299,
0.05121048167347908,
0.1746254414319992,
-0.011592294089496136,
-0.22072425484657288,
-0.0845772922039032,
0.06936841458082199,
-0.06218599155545235,
-0.12968985736370087,
-0.026130788028240204,
0.045467354357242584,
-0.17519839107990265,
0.026703642681241035,
-0.027433741837739944,
0.0919293761253357,
-0.09345759451389313,
-0.02221956104040146,
0.03687324374914169,
0.084866963326931,
-0.014529162086546421,
0.08703910559415817,
-0.14498743414878845,
0.11886418610811234,
0.02978132851421833,
0.09024628251791,
-0.11081171780824661,
0.07909037172794342,
-0.007550720125436783,
0.009180475026369095,
0.19379350543022156,
-0.011335089802742004,
-0.03514958545565605,
-0.08774717897176743,
-0.11210042238235474,
-0.013537433929741383,
0.12687496840953827,
-0.1243172138929367,
0.08773399889469147,
-0.015198243781924248,
-0.044079482555389404,
0.00937260314822197,
-0.12100647389888763,
-0.17273177206516266,
-0.19628387689590454,
0.05585884302854538,
-0.09575839340686798,
0.025643249973654747,
-0.11914430558681488,
-0.07089093327522278,
-0.02952558360993862,
0.241120383143425,
-0.1745356321334839,
-0.06510113179683685,
-0.1468164622783661,
-0.046294767409563065,
0.1662203073501587,
-0.04437198117375374,
0.0718095526099205,
-0.0208172257989645,
0.20345525443553925,
0.005988610442727804,
-0.004939318168908358,
0.06724198162555695,
-0.08892562240362167,
-0.16873881220817566,
-0.06771010160446167,
0.1510489284992218,
0.11680185794830322,
0.04907919466495514,
-0.002248800592496991,
0.0011772146681323647,
-0.016943959519267082,
-0.1137804463505745,
-0.0033210667315870523,
0.16037839651107788,
0.03878779336810112,
0.025986969470977783,
-0.05243593826889992,
-0.08797456324100494,
-0.06899320334196091,
-0.06853509694337845,
0.06221301481127739,
0.19590823352336884,
-0.10376439243555069,
0.1700313836336136,
0.147536963224411,
-0.07305635511875153,
-0.23175598680973053,
0.035342130810022354,
0.04983805492520332,
0.0014306638622656465,
0.04886869341135025,
-0.18252557516098022,
0.10521943867206573,
0.019543392583727837,
-0.05505957826972008,
0.13485197722911835,
-0.1557481735944748,
-0.1552847921848297,
0.0722852572798729,
0.03904085233807564,
-0.22423844039440155,
-0.1354004591703415,
-0.09622503817081451,
-0.05825018882751465,
-0.14065024256706238,
0.06054598465561867,
-0.002136280992999673,
0.015948504209518433,
0.03500790148973465,
-0.0015643214574083686,
0.027123261243104935,
-0.058935679495334625,
0.18609118461608887,
-0.004065449349582195,
0.020676052197813988,
-0.060264769941568375,
-0.0478842556476593,
0.09839435666799545,
-0.06130504235625267,
0.12208222597837448,
0.004057085141539574,
0.01594383642077446,
-0.10362856835126877,
-0.048314861953258514,
-0.04328322783112526,
0.05154227837920189,
-0.07548051327466965,
-0.10070807486772537,
-0.043625857681035995,
0.08841723203659058,
0.07005169242620468,
-0.03383097052574158,
0.00549331633374095,
-0.07189501076936722,
0.10019614547491074,
0.17795267701148987,
0.17573626339435577,
0.009926567785441875,
-0.07241068035364151,
0.01677953451871872,
-0.04142116755247116,
0.044231921434402466,
-0.2513144314289093,
0.03756171092391014,
0.06098250672221184,
0.029438555240631104,
0.09217222779989243,
-0.020435843616724014,
-0.1820858269929886,
-0.04050002992153168,
0.08094815909862518,
-0.05452597141265869,
-0.22617179155349731,
-0.019085140898823738,
0.0954197570681572,
-0.2020406424999237,
-0.007372708059847355,
0.03995226323604584,
-0.048725228756666183,
-0.023169852793216705,
0.00010950004070764408,
0.06317184865474701,
0.002471912419423461,
0.09773622453212738,
0.0735151618719101,
0.09715340286493301,
-0.08337292820215225,
0.10562895983457565,
0.10150538384914398,
-0.09572599828243256,
0.03605884686112404,
0.06754924356937408,
-0.05300498008728027,
-0.043293699622154236,
0.03665391728281975,
0.033023297786712646,
0.005234600510448217,
-0.060321882367134094,
0.013913018628954887,
-0.036497246474027634,
0.044923391193151474,
0.08326134830713272,
0.03754979372024536,
-0.013354414142668247,
0.06462216377258301,
0.03401726484298706,
-0.10898099094629288,
0.10366570204496384,
0.01731540448963642,
0.04105307161808014,
-0.08384523540735245,
-0.019968897104263306,
0.035425446927547455,
0.030576206743717194,
-0.01765924133360386,
-0.02306121215224266,
-0.02860277332365513,
-0.01614218018949032,
-0.14299540221691132,
-0.023106401786208153,
-0.07243485748767853,
0.006181265693157911,
0.014656842686235905,
-0.031884219497442245,
-0.011233693920075893,
0.02475680410861969,
-0.06979699432849884,
-0.07426341623067856,
-0.006949664559215307,
0.09833318740129471,
-0.15115703642368317,
0.008848577737808228,
0.06907843053340912,
-0.11088496446609497,
0.08190931379795074,
-0.008411259390413761,
0.016245156526565552,
0.022527478635311127,
-0.15448406338691711,
0.05601610988378525,
0.0008648968650959432,
0.01916889287531376,
0.025886621326208115,
-0.16471809148788452,
0.004104440100491047,
-0.04661374166607857,
-0.02149827405810356,
-0.00004464812809601426,
-0.02647159807384014,
-0.12325995415449142,
0.06858719140291214,
-0.015622655861079693,
-0.035931166261434555,
-0.02701525390148163,
0.0539589487016201,
0.07888586074113846,
-0.027474910020828247,
0.10445091128349304,
-0.008690856397151947,
0.04941811040043831,
-0.16801609098911285,
-0.02470702864229679,
-0.04982255399227142,
0.019377702847123146,
0.009884213097393513,
-0.007693959400057793,
0.04183054715394974,
-0.00976533442735672,
0.21883612871170044,
-0.05075952783226967,
0.1607085019350052,
0.05847611650824547,
-0.017352959141135216,
-0.0007513365126214921,
0.06180921941995621,
0.05997028574347496,
0.04658793285489082,
0.009480604901909828,
0.023740366101264954,
-0.022450892254710197,
-0.006695089396089315,
-0.15932634472846985,
0.01890849508345127,
0.14999441802501678,
0.06301083415746689,
0.024745315313339233,
0.05866100639104843,
-0.12775006890296936,
-0.12135478109121323,
0.09311001747846603,
-0.026755332946777344,
0.00928465835750103,
-0.08245618641376495,
0.1358020007610321,
0.14980104565620422,
-0.14000412821769714,
0.05256148427724838,
-0.06134212389588356,
-0.05217423290014267,
-0.10388828068971634,
-0.12032219022512436,
-0.05887215584516525,
-0.053666237741708755,
0.002330566756427288,
-0.03760887682437897,
0.054546963423490524,
0.03344334661960602,
-0.009351172484457493,
-0.00022941511997487396,
0.13597318530082703,
-0.019751882180571556,
-0.0028988157864660025,
0.048313532024621964,
0.03693558648228645,
0.02373051457107067,
-0.05275435373187065,
0.02940409444272518,
0.02539868652820587,
0.032232340425252914,
0.06546790152788162,
0.033412106335163116,
-0.047448933124542236,
0.03804153576493263,
-0.0025254099164158106,
-0.11207924783229828,
0.019641218706965446,
-0.00460948096588254,
-0.0742158442735672,
0.1268945336341858,
0.0407399944961071,
0.010224059224128723,
-0.03741471841931343,
0.24361543357372284,
-0.06653323769569397,
-0.06378097087144852,
-0.13251738250255585,
0.10491154342889786,
-0.0027236645109951496,
0.06476365029811859,
0.023412218317389488,
-0.1284150779247284,
0.005243356805294752,
0.13858191668987274,
0.12181595712900162,
0.0045748427510261536,
0.009228081442415714,
0.0518609918653965,
0.0025186820421367884,
-0.06998204439878464,
0.054019294679164886,
0.06992026418447495,
0.12919506430625916,
-0.07847554981708527,
0.07680778950452805,
0.0006860480643808842,
-0.08370215445756912,
-0.02947772853076458,
0.11312682181596756,
-0.0409729965031147,
0.03491825982928276,
-0.047444481402635574,
0.10916327685117722,
-0.05787910893559456,
-0.29412412643432617,
0.02350960113108158,
-0.09588567912578583,
-0.15202060341835022,
-0.018367812037467957,
0.05944539234042168,
-0.02624768204987049,
0.018029648810625076,
0.06971040368080139,
-0.06011629104614258,
0.20098382234573364,
0.0335683599114418,
-0.07864278554916382,
-0.0664360448718071,
0.04837050288915634,
-0.06564252078533173,
0.2949807047843933,
0.008418165147304535,
0.02863333560526371,
0.10770907253026962,
-0.03253700211644173,
-0.18271861970424652,
0.010723991319537163,
0.1133992001414299,
-0.08056149631738663,
0.08200647681951523,
0.19000613689422607,
-0.012578671798110008,
0.1209007054567337,
0.05294662341475487,
-0.047376248985528946,
0.04217283055186272,
-0.03389401361346245,
-0.051268599927425385,
-0.10752558708190918,
0.058453381061553955,
-0.05909625440835953,
0.15447644889354706,
0.10152646154165268,
-0.05671518296003342,
-0.004550917539745569,
-0.05555408447980881,
0.04875178262591362,
0.01804669201374054,
0.12263146042823792,
0.02951994352042675,
-0.1865430772304535,
0.032826557755470276,
-0.01144319772720337,
0.10186848044395447,
-0.25588861107826233,
-0.08421015739440918,
0.08833149075508118,
-0.011924264021217823,
-0.05105875805020332,
0.10560628771781921,
0.057650718837976456,
0.04243382066488266,
-0.043439045548439026,
-0.10480839014053345,
-0.02186836116015911,
0.14663739502429962,
-0.1469624787569046,
-0.025013303384184837
] |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-emotions-fp16
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2406
- Accuracy: 0.9487
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 50 | 1.1462 | 0.605 |
| No log | 2.0 | 100 | 0.9580 | 0.7175 |
| No log | 3.0 | 150 | 0.9513 | 0.68 |
| No log | 4.0 | 200 | 0.7491 | 0.7937 |
| No log | 5.0 | 250 | 0.6535 | 0.8387 |
| No log | 6.0 | 300 | 0.5758 | 0.87 |
| No log | 7.0 | 350 | 0.5042 | 0.8688 |
| No log | 8.0 | 400 | 0.4789 | 0.8775 |
| No log | 9.0 | 450 | 0.3602 | 0.9337 |
| 0.7188 | 10.0 | 500 | 0.3826 | 0.905 |
| 0.7188 | 11.0 | 550 | 0.3889 | 0.8938 |
| 0.7188 | 12.0 | 600 | 0.3590 | 0.9137 |
| 0.7188 | 13.0 | 650 | 0.2929 | 0.92 |
| 0.7188 | 14.0 | 700 | 0.2881 | 0.9213 |
| 0.7188 | 15.0 | 750 | 0.2652 | 0.9363 |
| 0.7188 | 16.0 | 800 | 0.2611 | 0.9463 |
| 0.7188 | 17.0 | 850 | 0.2267 | 0.9537 |
| 0.7188 | 18.0 | 900 | 0.2394 | 0.95 |
| 0.7188 | 19.0 | 950 | 0.1969 | 0.9525 |
| 0.2754 | 20.0 | 1000 | 0.2419 | 0.9413 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "vit-emotions-fp16", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.94875, "name": "Accuracy"}]}]}]} | image-classification | kerwinnl/vit-emotions-fp16 | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-09T11:41:21+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| vit-emotions-fp16
=================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2406
* Accuracy: 0.9487
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
86,
98,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
-0.11862684786319733,
0.13607098162174225,
-0.0025763940066099167,
0.11910273134708405,
0.1404537558555603,
0.004346479661762714,
0.14077715575695038,
0.1390070766210556,
-0.07172218710184097,
0.08159811049699783,
0.1491062492132187,
0.1303545981645584,
0.030898649245500565,
0.18905162811279297,
-0.049535807222127914,
-0.22352102398872375,
0.02580658718943596,
0.0477079339325428,
-0.04870045557618141,
0.12126699090003967,
0.08815955370664597,
-0.1382121741771698,
0.11751259118318558,
0.025848114863038063,
-0.2047291398048401,
-0.007606162689626217,
0.029538599774241447,
-0.05819394066929817,
0.1168045625090599,
0.0373651385307312,
0.09160982817411423,
0.028289178386330605,
0.05270405858755112,
-0.1481442153453827,
0.010206367820501328,
0.07640063017606735,
-0.009214755147695541,
0.09723441302776337,
0.05622915178537369,
0.011492506600916386,
0.016050094738602638,
-0.09463026374578476,
0.04004139453172684,
0.025265377014875412,
-0.11202917993068695,
-0.23511426150798798,
-0.08598961681127548,
0.059083208441734314,
0.0774804875254631,
0.07061222195625305,
-0.002693967893719673,
0.1436288058757782,
-0.005592017434537411,
0.09797509014606476,
0.22986344993114471,
-0.27418267726898193,
-0.07741672545671463,
0.03757063299417496,
0.017696822062134743,
0.07755748927593231,
-0.1007639467716217,
0.012463548220694065,
0.059283606708049774,
0.013740353286266327,
0.15326596796512604,
-0.00546793220564723,
-0.013231445103883743,
-0.026213020086288452,
-0.12508971989154816,
-0.06421228498220444,
0.19508886337280273,
0.0898618996143341,
-0.0470237098634243,
-0.08200053870677948,
-0.08072874695062637,
-0.13877563178539276,
-0.046653974801301956,
-0.011338525451719761,
0.057744529098272324,
-0.03410860896110535,
-0.06401600688695908,
-0.03429725766181946,
-0.09819400310516357,
-0.06963552534580231,
-0.015037794597446918,
0.09685873240232468,
0.053985342383384705,
0.01292209792882204,
-0.021191341802477837,
0.0808539018034935,
-0.04242787882685661,
-0.14507076144218445,
-0.006704701576381922,
0.01800202764570713,
0.025464244186878204,
-0.03126758337020874,
-0.025375982746481895,
-0.11615971475839615,
0.020918212831020355,
0.10874377191066742,
-0.06972605735063553,
0.05691898241639137,
-0.02163911797106266,
0.05179855599999428,
-0.11123023927211761,
0.19115161895751953,
-0.04929012805223465,
0.0159612987190485,
0.04046659544110298,
0.1017889603972435,
0.05127325654029846,
-0.002425429644063115,
-0.1056673526763916,
0.017791470512747765,
0.12042375653982162,
0.0049453722313046455,
-0.03495030477643013,
0.08120119571685791,
-0.0614188052713871,
-0.029111526906490326,
0.07602670043706894,
-0.08666333556175232,
0.026919234544038773,
-0.006897526793181896,
-0.05354881286621094,
-0.0565594881772995,
0.0469067320227623,
-0.011913116089999676,
-0.013371805660426617,
0.04143775254487991,
-0.10228675603866577,
0.012035829946398735,
-0.06763049215078354,
-0.1080244854092598,
0.01311678346246481,
-0.11561828851699829,
0.015319262631237507,
-0.12210355699062347,
-0.1382761150598526,
-0.013023408129811287,
0.05989771708846092,
-0.029016714543104172,
-0.05163951590657234,
-0.04167177900671959,
-0.08057110011577606,
0.026785289868712425,
0.005231655668467283,
0.04709792509675026,
-0.05685161426663399,
0.0881868526339531,
0.044517483562231064,
0.07619098573923111,
-0.019790910184383392,
0.04638770967721939,
-0.0868847668170929,
0.058167941868305206,
-0.2046217918395996,
0.03671747073531151,
-0.05808434262871742,
0.08664451539516449,
-0.1191207617521286,
-0.08627723902463913,
0.0009071230888366699,
-0.020160868763923645,
0.06494573503732681,
0.1077149286866188,
-0.14049723744392395,
-0.05874790623784065,
0.17436181008815765,
-0.10219626873731613,
-0.15532821416854858,
0.11266045272350311,
-0.03060438670217991,
0.029268037527799606,
0.05617973208427429,
0.19773077964782715,
0.07850029319524765,
-0.10863172262907028,
-0.006727805361151695,
-0.031635742634534836,
0.0340501144528389,
-0.053646519780159,
0.07568536698818207,
-0.0005131050711497664,
-0.01108004990965128,
0.022279467433691025,
-0.09523792564868927,
0.06257777661085129,
-0.07312074303627014,
-0.08432256430387497,
-0.0668586865067482,
-0.08747415244579315,
0.041866235435009,
0.05933330953121185,
0.06495782732963562,
-0.1016748771071434,
-0.09083876758813858,
0.028826074674725533,
0.08167547732591629,
-0.09448417276144028,
0.016852281987667084,
-0.08169770985841751,
0.11140599846839905,
-0.1074780747294426,
0.0014938017120584846,
-0.13464124500751495,
-0.02949785627424717,
0.049237966537475586,
-0.0625208243727684,
-0.008478378877043724,
-0.03656590357422829,
0.07331328839063644,
0.06236502155661583,
-0.06268363445997238,
-0.07329478859901428,
-0.03986898064613342,
-0.0025519083719700575,
-0.09935712814331055,
-0.19413237273693085,
-0.023727186024188995,
-0.026893194764852524,
0.10660623013973236,
-0.2199222296476364,
0.04163808375597,
0.05292150005698204,
0.10208848863840103,
0.05913138389587402,
-0.031235113739967346,
0.0039437394589185715,
0.017344877123832703,
-0.03981004282832146,
-0.09011241048574448,
0.061082541942596436,
0.014722965657711029,
-0.06827579438686371,
0.0051209270022809505,
-0.10053195804357529,
0.17472368478775024,
0.13116171956062317,
-0.033463917672634125,
-0.06332863867282867,
-0.0043567162938416,
-0.04356212913990021,
-0.03498488664627075,
-0.03569559007883072,
0.008986911736428738,
0.08051126450300217,
-0.008868166245520115,
0.16240540146827698,
-0.10505981743335724,
-0.026039814576506615,
0.058241575956344604,
-0.02974027581512928,
-0.03810049220919609,
0.08953040093183517,
0.06744997948408127,
-0.13986307382583618,
0.14738419651985168,
0.1662409007549286,
-0.06822194904088974,
0.12521155178546906,
-0.04773518443107605,
-0.06236952543258667,
-0.024040281772613525,
0.04132981598377228,
0.03265652805566788,
0.1292601376771927,
-0.1198551282286644,
-0.01395948976278305,
0.024260707199573517,
0.0016625267453491688,
-0.008375790901482105,
-0.20100361108779907,
-0.008371683768928051,
0.038532670587301254,
-0.05972742661833763,
0.02609979175031185,
-0.005517373792827129,
-0.021567916497588158,
0.08489884436130524,
0.008348884992301464,
-0.042639680206775665,
0.047265421599149704,
0.011100099422037601,
-0.06991036236286163,
0.19499625265598297,
-0.08492761105298996,
-0.21661029756069183,
-0.13107283413410187,
-0.020954333245754242,
-0.08055935055017471,
0.021638698875904083,
0.05819288641214371,
-0.09382759034633636,
-0.057645127177238464,
-0.10397281497716904,
-0.015600989572703838,
0.028978293761610985,
0.03961782902479172,
0.041902970522642136,
-0.0019354389514774084,
0.1307423710823059,
-0.09929072856903076,
-0.007536566816270351,
-0.010077708400785923,
-0.023466931656003,
0.048148881644010544,
0.01983301155269146,
0.12009347230195999,
0.08664698898792267,
-0.027577845379710197,
0.03478600084781647,
-0.020831966772675514,
0.2411278486251831,
-0.07336857914924622,
-0.0025067345704883337,
0.15122878551483154,
0.019550248980522156,
0.06790269911289215,
0.13012559711933136,
0.03827866166830063,
-0.10251547396183014,
0.007758599240332842,
0.021663567051291466,
-0.02579859271645546,
-0.1871739774942398,
-0.0171548780053854,
-0.03924945369362831,
-0.00444507272914052,
0.152572363615036,
0.05523106828331947,
0.061344489455223083,
0.09337121248245239,
0.00033120723674073815,
0.09185243397951126,
-0.004542561247944832,
0.08808216452598572,
0.11049571633338928,
0.045895807445049286,
0.10933946818113327,
-0.0441078245639801,
-0.028400860726833344,
0.03270525485277176,
0.014624936506152153,
0.2257072478532791,
0.0014615242835134268,
0.1745476871728897,
0.04722040519118309,
0.1896134614944458,
0.017876673489809036,
0.05607917532324791,
-0.020971206948161125,
-0.02658970281481743,
-0.009364157915115356,
-0.05445526912808418,
-0.021531760692596436,
0.03677821531891823,
-0.050751328468322754,
0.0655994564294815,
-0.0925455167889595,
0.04166332259774208,
0.06377926468849182,
0.2635495066642761,
0.0365661159157753,
-0.3789684474468231,
-0.09498053789138794,
-0.0053971679881215096,
-0.014488224871456623,
-0.06512360274791718,
0.0023779887706041336,
0.14586958289146423,
-0.0612533763051033,
0.060258202254772186,
-0.10404929518699646,
0.08211296051740646,
-0.051383692771196365,
0.021347982808947563,
0.07492318749427795,
0.09016896039247513,
0.008379991166293621,
0.056661225855350494,
-0.2485174536705017,
0.2579330503940582,
0.01591283641755581,
0.06340377777814865,
-0.04800954461097717,
0.01297358050942421,
0.03521382436156273,
0.10639113932847977,
0.1099725291132927,
-0.005465359892696142,
-0.01662856712937355,
-0.17460858821868896,
-0.08823971450328827,
0.006351474206894636,
0.07203894853591919,
-0.045675501227378845,
0.08031121641397476,
-0.030152365565299988,
-0.023747151717543602,
0.05103715881705284,
-0.0034925774671137333,
-0.08945973217487335,
-0.09370698034763336,
-0.007539461366832256,
0.04352934658527374,
0.015408708713948727,
-0.09620650857686996,
-0.09712299704551697,
-0.10361526906490326,
0.13423673808574677,
-0.01791711524128914,
-0.0405256412923336,
-0.12018390744924545,
0.0892866775393486,
0.05816558748483658,
-0.09259811788797379,
0.08043588697910309,
-0.028549257665872574,
0.13836847245693207,
0.029281003400683403,
-0.062355563044548035,
0.10987292975187302,
-0.059276655316352844,
-0.17301121354103088,
-0.046683523803949356,
0.10513435304164886,
-0.01869458332657814,
0.024247588589787483,
0.0005111363134346902,
0.028401998803019524,
-0.011718365363776684,
-0.058764077723026276,
0.05756045877933502,
0.014515225775539875,
0.05889863893389702,
-0.014903100207448006,
-0.019676700234413147,
0.007737073581665754,
-0.06332728266716003,
-0.029787488281726837,
0.13461267948150635,
0.2437446564435959,
-0.09745744615793228,
0.004943002015352249,
0.018968096002936363,
-0.051279183477163315,
-0.19513830542564392,
0.048698753118515015,
0.06568527966737747,
0.0020221148151904345,
0.0303266029804945,
-0.15475165843963623,
0.07322143018245697,
0.08173877745866776,
-0.030024560168385506,
0.09476637840270996,
-0.265503466129303,
-0.13263669610023499,
0.08120943605899811,
0.1838640421628952,
0.06726694107055664,
-0.14426320791244507,
-0.05513838306069374,
-0.012923520989716053,
-0.09017108380794525,
0.09474195539951324,
-0.06171131134033203,
0.10445062071084976,
-0.02821234054863453,
0.003340694820508361,
0.006336088292300701,
-0.0574214793741703,
0.1290079802274704,
-0.033671990036964417,
0.10798531025648117,
-0.05707699805498123,
-0.009296839125454426,
0.07792694121599197,
-0.07718640565872192,
0.06401468813419342,
-0.09070459008216858,
0.06322236359119415,
-0.061469778418540955,
-0.01584113948047161,
-0.0700555294752121,
0.03382529690861702,
-0.018966028466820717,
-0.025605415925383568,
-0.05134887993335724,
0.02348378114402294,
0.05129716917872429,
-0.0009205437381751835,
0.20003315806388855,
0.05019812658429146,
0.08837173134088516,
0.13760314881801605,
0.04343097656965256,
-0.07744259387254715,
-0.09973973035812378,
-0.027537761256098747,
-0.026997331529855728,
0.0875159427523613,
-0.18562975525856018,
0.04973244294524193,
0.09580063819885254,
0.011054624803364277,
0.1439087837934494,
0.045920830219984055,
-0.03386397287249565,
0.017781836912035942,
0.070749931037426,
-0.15560059249401093,
-0.1623297780752182,
-0.031811706721782684,
-0.016942474991083145,
-0.11406322568655014,
0.06307488679885864,
0.11163705587387085,
-0.08484364300966263,
0.0035264091566205025,
-0.008415672928094864,
0.014919687993824482,
-0.002409477951005101,
0.16151992976665497,
0.08037012070417404,
0.044436246156692505,
-0.09206259250640869,
0.09767723828554153,
0.054202839732170105,
-0.1045539379119873,
0.023341206833720207,
0.025801705196499825,
-0.10466807335615158,
-0.03715038672089577,
0.06702449917793274,
0.14704498648643494,
0.0009470285149291158,
-0.05198730155825615,
-0.14375616610050201,
-0.09377666562795639,
0.057217106223106384,
0.12533320486545563,
0.09250355511903763,
0.01556249987334013,
-0.011633376590907574,
-0.0002801600785460323,
-0.10414283722639084,
0.11900807917118073,
0.03160611540079117,
0.09795945137739182,
-0.2185405194759369,
0.06070994958281517,
0.01890115812420845,
0.031268004328012466,
-0.019250933080911636,
0.029586996883153915,
-0.09804419428110123,
-0.01634339988231659,
-0.05997609347105026,
0.04208562150597572,
-0.036590032279491425,
0.005531745962798595,
-0.0064903125166893005,
-0.06879284232854843,
-0.06098901107907295,
0.04104362055659294,
-0.10035879909992218,
-0.046047866344451904,
0.03615729510784149,
0.07032033801078796,
-0.10205142945051193,
-0.029235344380140305,
0.025446485728025436,
-0.08039696514606476,
0.0790892168879509,
0.014095950871706009,
0.0005695505533367395,
0.023463815450668335,
-0.10042203217744827,
0.010635514743626118,
0.08415917307138443,
0.0029707292560487986,
0.030986838042736053,
-0.10393767058849335,
0.007057030685245991,
-0.0011036454234272242,
0.0009315675706602633,
-0.008648671209812164,
0.10397418588399887,
-0.13311968743801117,
-0.025813480839133263,
-0.037991661578416824,
-0.03324704244732857,
-0.05948195978999138,
0.06284357607364655,
0.08606432378292084,
-0.0030004456639289856,
0.1998845636844635,
-0.08585313707590103,
0.00010894424485741183,
-0.2236366719007492,
0.004054774064570665,
-0.004355728160589933,
-0.1370699256658554,
-0.12498512864112854,
-0.028259091079235077,
0.05320440232753754,
-0.07354100048542023,
0.09616898745298386,
0.016123060137033463,
0.005351166240870953,
0.03564183786511421,
0.0026460830122232437,
-0.0028193816542625427,
0.02624340169131756,
0.18615828454494476,
-0.007491840049624443,
-0.02064543217420578,
0.07138451188802719,
0.018578987568616867,
0.11744797974824905,
0.08343684673309326,
0.0985216423869133,
0.1616794615983963,
-0.04428549483418465,
0.10382276773452759,
0.049792271107435226,
-0.0221316646784544,
-0.1735774725675583,
0.10315366834402084,
-0.07480514794588089,
0.1442563533782959,
-0.013153218664228916,
0.16521982848644257,
0.12247242778539658,
-0.1585603505373001,
0.028127895668148994,
-0.028060633689165115,
-0.07330657541751862,
-0.0704883560538292,
-0.14566665887832642,
-0.11752930283546448,
-0.18573613464832306,
0.014560677111148834,
-0.09806596487760544,
0.007403009105473757,
0.07112178951501846,
-0.009295541793107986,
-0.02352297119796276,
0.20558007061481476,
0.05049857869744301,
-0.002510844962671399,
0.07032909989356995,
0.0015775273786857724,
-0.06872397661209106,
-0.061035364866256714,
-0.08496210724115372,
0.03848041966557503,
-0.00851019099354744,
0.03335035592317581,
-0.02992033213376999,
-0.005304316058754921,
0.0488789863884449,
-0.00015379609249066561,
-0.1098908856511116,
0.018044419586658478,
0.014951071701943874,
0.010534374043345451,
0.0008333100704476237,
0.004809625446796417,
0.006202142685651779,
-0.009944885037839413,
0.1820433884859085,
-0.05484237149357796,
-0.006836243439465761,
-0.11771633476018906,
0.12500759959220886,
0.027567386627197266,
-0.01616005413234234,
0.029098497703671455,
-0.07949063926935196,
0.026830408722162247,
0.21858848631381989,
0.14383293688297272,
-0.0167448278516531,
-0.0014522324781864882,
-0.00857983622699976,
-0.020643005147576332,
-0.03108217939734459,
0.09174125641584396,
0.09339619427919388,
-0.0447763167321682,
-0.05456433817744255,
-0.022962506860494614,
-0.04835779592394829,
-0.016245421022176743,
-0.03796777129173279,
0.039009418338537216,
0.01815023086965084,
0.01443538349121809,
-0.06322929263114929,
0.042721226811409,
0.02114632911980152,
-0.07337304949760437,
0.0894613191485405,
-0.19521470367908478,
-0.13894040882587433,
-0.03288603574037552,
0.10051017254590988,
-0.006018717773258686,
0.02896568924188614,
-0.02127797342836857,
0.015905743464827538,
0.06856148689985275,
-0.02353663370013237,
-0.08276108652353287,
-0.09917619824409485,
0.05052541568875313,
-0.1333807408809662,
0.24502243101596832,
-0.032482389360666275,
0.00661885691806674,
0.11178344488143921,
0.018475130200386047,
-0.11790914833545685,
0.05208421126008034,
0.025536421686410904,
-0.03645871579647064,
0.025177190080285072,
0.11177454143762589,
-0.020675083622336388,
0.10754010826349258,
0.034746766090393066,
-0.08897331357002258,
-0.01814214512705803,
-0.054646085947752,
-0.04100141301751137,
-0.05697661265730858,
-0.02364540658891201,
-0.06854301691055298,
0.12410563230514526,
0.173186793923378,
-0.04242303967475891,
-0.02637280523777008,
-0.06457078456878662,
0.037550926208496094,
0.09137038141489029,
0.016641056165099144,
-0.012749847024679184,
-0.2280997335910797,
0.017656221985816956,
0.024374619126319885,
-0.003917396999895573,
-0.2118307650089264,
-0.11007525026798248,
-0.017804253846406937,
-0.053288642317056656,
-0.08763647079467773,
0.08584819734096527,
0.11768825352191925,
0.04970311373472214,
-0.060780398547649384,
-0.04441050440073013,
-0.06915197521448135,
0.1602003425359726,
-0.12230600416660309,
-0.08478182554244995
] |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vilt-finetuned-pathvqa
This model is a fine-tuned version of [dandelin/vilt-b32-mlm](https://huggingface.co/dandelin/vilt-b32-mlm) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "dandelin/vilt-b32-mlm", "model-index": [{"name": "vilt-finetuned-pathvqa", "results": []}]} | visual-question-answering | syammohan2103/vilt-finetuned-pathvqa | [
"transformers",
"tensorboard",
"safetensors",
"vilt",
"visual-question-answering",
"generated_from_trainer",
"base_model:dandelin/vilt-b32-mlm",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-09T11:41:59+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vilt #visual-question-answering #generated_from_trainer #base_model-dandelin/vilt-b32-mlm #license-apache-2.0 #endpoints_compatible #region-us
|
# vilt-finetuned-pathvqa
This model is a fine-tuned version of dandelin/vilt-b32-mlm on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| [
"# vilt-finetuned-pathvqa\n\nThis model is a fine-tuned version of dandelin/vilt-b32-mlm on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #vilt #visual-question-answering #generated_from_trainer #base_model-dandelin/vilt-b32-mlm #license-apache-2.0 #endpoints_compatible #region-us \n",
"# vilt-finetuned-pathvqa\n\nThis model is a fine-tuned version of dandelin/vilt-b32-mlm on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
70,
41,
6,
12,
8,
3,
90,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #vilt #visual-question-answering #generated_from_trainer #base_model-dandelin/vilt-b32-mlm #license-apache-2.0 #endpoints_compatible #region-us \n# vilt-finetuned-pathvqa\n\nThis model is a fine-tuned version of dandelin/vilt-b32-mlm on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
-0.11802927404642105,
0.14083416759967804,
-0.0009074553963728249,
0.11916462332010269,
0.13285374641418457,
0.0033546139020472765,
0.11329437047243118,
0.12536966800689697,
-0.0767483115196228,
0.05918985977768898,
0.08228401094675064,
0.05057074874639511,
0.031645696610212326,
0.1119651198387146,
-0.012656171806156635,
-0.21087348461151123,
-0.011498207226395607,
-0.010643801651895046,
-0.06299547851085663,
0.09482243657112122,
0.10747801512479782,
-0.10091914981603622,
0.08058244735002518,
-0.01748243160545826,
-0.12816739082336426,
0.041733045130968094,
-0.04415469244122505,
-0.03855267912149429,
0.10314346104860306,
0.012550503015518188,
0.06340204179286957,
-0.010084579698741436,
0.15616090595722198,
-0.22105303406715393,
0.001608384307473898,
0.06941557675600052,
0.03603992611169815,
0.07960104942321777,
0.026143668219447136,
-0.011744644492864609,
0.040760915726423264,
-0.18570975959300995,
0.10918431729078293,
0.02163984626531601,
-0.045515213161706924,
-0.149255633354187,
-0.08753024786710739,
0.09799991548061371,
0.09756574779748917,
0.1091935783624649,
0.02564183808863163,
0.1481383889913559,
-0.042703744024038315,
0.08418968319892883,
0.18454645574092865,
-0.23431982100009918,
-0.05183926597237587,
0.09053304046392441,
0.057788025587797165,
0.09636079519987106,
-0.11637494713068008,
0.012105482630431652,
0.06298006325960159,
0.019100559875369072,
0.08422218263149261,
-0.018032925203442574,
-0.09141948074102402,
0.020481176674365997,
-0.1354411542415619,
-0.013143092393875122,
0.19528762996196747,
0.06960362941026688,
-0.0428222194314003,
-0.10591049492359161,
-0.03462948650121689,
-0.06447549164295197,
-0.029128653928637505,
-0.0355888232588768,
0.04198794066905975,
-0.05047981068491936,
-0.04266566410660744,
-0.06895863264799118,
-0.08115240931510925,
-0.0922904834151268,
0.03367522358894348,
0.07042461633682251,
0.04486355930566788,
0.012548034079372883,
-0.04900125786662102,
0.0923706665635109,
-0.011246749199926853,
-0.10842645913362503,
-0.026309683918952942,
0.0040891580283641815,
-0.06025239825248718,
-0.04744279012084007,
-0.014379473403096199,
-0.029443712905049324,
0.010791180655360222,
0.14489144086837769,
-0.034989483654499054,
0.04763767495751381,
0.004399893339723349,
0.005708519369363785,
-0.036612264811992645,
0.14006313681602478,
-0.05524607375264168,
-0.03140101581811905,
0.032228220254182816,
0.10767807811498642,
0.027573933824896812,
-0.0067573003470897675,
-0.10392166674137115,
-0.018942078575491905,
0.08616671711206436,
0.08705251663923264,
-0.018517808988690376,
0.028730841353535652,
-0.015461975708603859,
-0.01877332478761673,
0.036808162927627563,
-0.14565736055374146,
0.049078475683927536,
-0.011333324015140533,
-0.08852416276931763,
-0.06190776079893112,
0.05256933346390724,
-0.0037257482763379812,
-0.014377590268850327,
0.024760477244853973,
-0.06485167145729065,
-0.00867270678281784,
-0.07912575453519821,
-0.08296588808298111,
0.03154462203383446,
-0.036727577447891235,
0.021271629258990288,
-0.08143772929906845,
-0.2568823993206024,
-0.03353755176067352,
0.03464635834097862,
-0.03863666579127312,
-0.03308334946632385,
-0.03746543824672699,
-0.05785225331783295,
-0.007380817085504532,
0.00505790812894702,
0.09280525147914886,
-0.045228805392980576,
0.058271121233701706,
0.005779392085969448,
0.020520295947790146,
0.02666342258453369,
0.03688109666109085,
-0.09106363356113434,
0.02597547136247158,
-0.08773248642683029,
0.06806716322898865,
-0.10662364214658737,
0.06622438132762909,
-0.1185583621263504,
-0.07610174268484116,
0.021032365038990974,
-0.029071377590298653,
0.049091119319200516,
0.14286373555660248,
-0.1909853219985962,
-0.00626950990408659,
0.12686879932880402,
-0.07477599382400513,
-0.12047004699707031,
0.09844685345888138,
-0.04190446436405182,
0.03395577520132065,
0.09536100924015045,
0.14088277518749237,
0.1510203629732132,
-0.18276479840278625,
-0.00032818721956573427,
0.046051353216171265,
0.07305508852005005,
-0.003512246534228325,
0.0737689957022667,
-0.0019421270117163658,
0.004740832839161158,
0.016082970425486565,
-0.10165660083293915,
0.012174876406788826,
-0.07693464308977127,
-0.10467671602964401,
-0.06654762476682663,
-0.10929662734270096,
0.0401141420006752,
0.043254196643829346,
0.02496238611638546,
-0.06165526807308197,
-0.11144621670246124,
0.08717714250087738,
0.1573168933391571,
-0.06816687434911728,
-0.0008114135125651956,
-0.07173434644937515,
0.023783639073371887,
-0.048274166882038116,
-0.01973068341612816,
-0.16957993805408478,
-0.11581271886825562,
0.026971586048603058,
-0.0959736704826355,
0.03135643154382706,
0.015444676391780376,
0.07400983572006226,
0.08367093652486801,
-0.03788161277770996,
-0.030185408890247345,
-0.08919954299926758,
0.013564657419919968,
-0.0889422595500946,
-0.20871393382549286,
-0.04885903373360634,
-0.0442674458026886,
0.14662425220012665,
-0.26493754982948303,
0.0331362783908844,
-0.027120327576994896,
0.10363404452800751,
0.02560938149690628,
-0.04037254676222801,
0.012527072802186012,
0.022314980626106262,
-0.01220959797501564,
-0.10841744393110275,
0.021283132955431938,
-0.017778761684894562,
-0.08463112264871597,
-0.07556051015853882,
-0.1050044596195221,
0.08124929666519165,
0.07513807713985443,
0.09443885833024979,
-0.07108192890882492,
-0.007658807560801506,
-0.05256970599293709,
-0.04593610763549805,
-0.08477857708930969,
0.00010815098357852548,
0.13902334868907928,
0.011170231737196445,
0.1107940524816513,
-0.043269604444503784,
-0.057282380759716034,
-0.007677506189793348,
0.00842424388974905,
-0.028259731829166412,
0.08422160893678665,
0.049431510269641876,
-0.09365620464086533,
0.0911875069141388,
0.08242358267307281,
-0.05191482976078987,
0.11219153553247452,
-0.05893881246447563,
-0.08544477075338364,
-0.026883915066719055,
0.02493826113641262,
-0.010912667028605938,
0.15684577822685242,
-0.09072071313858032,
-0.010072372853755951,
0.02131352387368679,
-0.00863209180533886,
0.016396937891840935,
-0.16750070452690125,
-0.030386729165911674,
0.03773834556341171,
-0.05214306712150574,
0.0009991747792810202,
-0.04227936267852783,
-0.005503928288817406,
0.06858284026384354,
0.008095747791230679,
-0.03245672583580017,
0.01700829714536667,
-0.004739182069897652,
-0.09621279686689377,
0.17464213073253632,
-0.10560858994722366,
-0.18040315806865692,
-0.12226494401693344,
0.059102971106767654,
-0.05301046743988991,
-0.027300940826535225,
0.011341184377670288,
-0.092135950922966,
-0.06162966042757034,
-0.09919040650129318,
-0.009496483020484447,
-0.03854553773999214,
-0.024928977712988853,
0.050146639347076416,
0.02837372198700905,
0.09254265576601028,
-0.14969605207443237,
0.03229738026857376,
0.0017832135781645775,
-0.07723963260650635,
-0.040870245546102524,
0.05053497105836868,
0.14210735261440277,
0.11980954557657242,
-0.010588560253381729,
0.019663779065012932,
-0.029740018770098686,
0.21447359025478363,
-0.05969410762190819,
0.0059212069027125835,
0.12943287193775177,
0.01621878519654274,
0.030244631692767143,
0.1289151906967163,
0.03855390474200249,
-0.09397578239440918,
0.028484709560871124,
0.061337344348430634,
-0.014540956355631351,
-0.2371019721031189,
-0.041739851236343384,
-0.02256108820438385,
-0.048216767609119415,
0.10578273236751556,
0.07509610056877136,
0.035412147641181946,
0.04973824694752693,
-0.013967503793537617,
0.058405932039022446,
0.005319071933627129,
0.07837234437465668,
0.08802665770053864,
0.010190409608185291,
0.077492356300354,
-0.043963976204395294,
-0.015958145260810852,
0.052678510546684265,
0.04025045782327652,
0.2496284544467926,
-0.01839243620634079,
0.1344369649887085,
0.031206894665956497,
0.16162320971488953,
-0.04205010086297989,
0.021719714626669884,
0.019257016479969025,
0.00036604306660592556,
0.022672057151794434,
-0.0801079273223877,
-0.00957613717764616,
0.04831215366721153,
-0.055020205676555634,
0.06882210075855255,
-0.08646998554468155,
0.06778990477323532,
0.027160484343767166,
0.19054920971393585,
0.043847013264894485,
-0.298419713973999,
-0.10106825083494186,
0.023302899673581123,
0.006857316941022873,
-0.05230264365673065,
0.018503714352846146,
0.11940620839595795,
-0.10660657286643982,
0.04857049137353897,
-0.045510005205869675,
0.09727746993303299,
-0.0510685108602047,
-0.007003732491284609,
0.026517149060964584,
0.10129430890083313,
0.0035495590418577194,
0.10884326696395874,
-0.2246786206960678,
0.19934025406837463,
0.03561115637421608,
0.13791409134864807,
-0.055417705327272415,
0.03431759402155876,
0.017276985570788383,
0.14255249500274658,
0.13733641803264618,
-0.016688363626599312,
0.019376225769519806,
-0.15696752071380615,
-0.09046278893947601,
0.045593369752168655,
0.07188369333744049,
-0.03248516097664833,
0.06352011859416962,
-0.04548630490899086,
-0.013558860868215561,
0.06573587656021118,
-0.09307737648487091,
-0.2222972810268402,
-0.13137245178222656,
0.010547582991421223,
0.008759637363255024,
-0.02648583985865116,
-0.08980822563171387,
-0.10458656400442123,
-0.047084130346775055,
0.161307692527771,
0.006388823967427015,
-0.048880673944950104,
-0.13833469152450562,
0.10170161724090576,
0.1157727912068367,
-0.06979754567146301,
0.010732651688158512,
0.023707350715994835,
0.14785055816173553,
0.02824954316020012,
-0.09193921834230423,
0.03268272802233696,
-0.08758600056171417,
-0.12731803953647614,
-0.04561186581850052,
0.1083768755197525,
0.06529880315065384,
0.043181274086236954,
0.01336948573589325,
0.0030884635634720325,
0.004818497225642204,
-0.09773261100053787,
0.011441516689956188,
0.09603314101696014,
0.04307340458035469,
0.039614807814359665,
-0.08881176263093948,
0.002530848141759634,
-0.04993683099746704,
-0.020014848560094833,
0.12429013103246689,
0.18522317707538605,
-0.09065663069486618,
0.07579698413610458,
0.10479601472616196,
-0.09742923080921173,
-0.1879352629184723,
0.0855024978518486,
0.05772106349468231,
0.008396904915571213,
0.06113216280937195,
-0.18238352239131927,
0.11212671548128128,
0.08219238370656967,
-0.03094065561890602,
0.06061503291130066,
-0.362182080745697,
-0.131129190325737,
0.1121070608496666,
0.12822911143302917,
-0.04744548350572586,
-0.1424531787633896,
-0.05044358968734741,
-0.03218914568424225,
-0.15554973483085632,
0.06656765192747116,
-0.09373416751623154,
0.07290863245725632,
-0.006769989151507616,
0.09318312257528305,
0.019260479137301445,
-0.034702759236097336,
0.15210503339767456,
0.021152721717953682,
0.10535171627998352,
-0.05871883034706116,
0.02084275148808956,
0.11824069172143936,
-0.08052884042263031,
0.11276684701442719,
-0.05881746485829353,
0.09472615271806717,
-0.13341404497623444,
-0.02649698592722416,
-0.04669187590479851,
0.08126145601272583,
-0.056134942919015884,
-0.0707346573472023,
-0.05393420159816742,
0.05386185273528099,
0.04360922798514366,
-0.030565425753593445,
0.10027021169662476,
0.057610780000686646,
0.07232696563005447,
0.05853164196014404,
0.07126698642969131,
-0.013914543204009533,
-0.11704239249229431,
-0.001325426041148603,
-0.027466069906949997,
0.07340420037508011,
-0.15050016343593597,
0.023713117465376854,
0.12402082979679108,
0.06251797825098038,
0.145316481590271,
0.009328773245215416,
-0.05074077844619751,
-0.015113204717636108,
0.014702407643198967,
-0.09097488969564438,
-0.1852731555700302,
-0.01583225093781948,
-0.04410899430513382,
-0.12554524838924408,
0.041706591844558716,
0.0948440432548523,
-0.10129868984222412,
-0.0029720047023147345,
-0.0324091762304306,
0.040448371320962906,
-0.022314317524433136,
0.181415855884552,
0.08174995332956314,
0.06330138444900513,
-0.07757294923067093,
0.1167682334780693,
0.0858202800154686,
-0.08099707216024399,
0.05455012246966362,
0.04539142921566963,
-0.08120901882648468,
-0.047404661774635315,
0.09653584659099579,
0.18469779193401337,
-0.025658993050456047,
-0.05604703351855278,
-0.07822395861148834,
-0.07293865829706192,
0.035783275961875916,
0.0921148806810379,
0.04344238340854645,
-0.019846206530928612,
-0.015072657726705074,
0.02183479815721512,
-0.15493181347846985,
0.13281260430812836,
0.020909508690238,
0.057980213314294815,
-0.1943882256746292,
0.08543729782104492,
0.016994796693325043,
0.048667848110198975,
-0.01346412394195795,
0.0254339799284935,
-0.07689154893159866,
-0.005163995549082756,
-0.1803516000509262,
0.0012797813396900892,
-0.027960943058133125,
0.03154097869992256,
-0.014971603639423847,
-0.061824243515729904,
-0.03114898130297661,
0.0774397999048233,
-0.050344888120889664,
-0.06629300117492676,
0.026698622852563858,
0.06862349063158035,
-0.17609170079231262,
-0.029903702437877655,
0.01758435368537903,
-0.07842551171779633,
0.05045339837670326,
0.03784994035959244,
0.028844771906733513,
0.023632697761058807,
-0.13144558668136597,
0.008908385410904884,
0.04346621409058571,
0.042655523866415024,
0.05501861497759819,
-0.11414431035518646,
-0.016198188066482544,
-0.020589429885149002,
0.02291737124323845,
0.012063125148415565,
0.07089805603027344,
-0.11471471190452576,
-0.04957197606563568,
-0.06820528954267502,
-0.021021973341703415,
-0.05000220984220505,
0.06304535269737244,
0.09201914817094803,
0.02994324453175068,
0.14393405616283417,
-0.0684022605419159,
0.0465567372739315,
-0.20358766615390778,
-0.042087212204933167,
-0.010632103309035301,
-0.015775948762893677,
-0.07734698802232742,
-0.015064344741404057,
0.07374994456768036,
-0.07045954465866089,
0.11532348394393921,
0.013558907434344292,
0.09623055905103683,
0.040915295481681824,
-0.011955835856497288,
-0.008464062586426735,
0.005100954789668322,
0.17606614530086517,
0.038631826639175415,
0.009005367755889893,
0.09151268750429153,
-0.008713950403034687,
0.09259170293807983,
0.031206225976347923,
0.12496959418058395,
0.13640165328979492,
-0.04544615000486374,
0.06104249879717827,
0.0921541079878807,
-0.1151430681347847,
-0.15766315162181854,
0.0696902945637703,
-0.00317306537181139,
0.11565199494361877,
-0.055537790060043335,
0.13940033316612244,
0.13342326879501343,
-0.15263071656227112,
0.03752654790878296,
-0.06009002402424812,
-0.12357427179813385,
-0.09423603862524033,
-0.07015898823738098,
-0.07960204035043716,
-0.10925357788801193,
0.015874171629548073,
-0.11555840075016022,
0.006990846246480942,
0.05457664281129837,
0.004185225814580917,
0.0077521344646811485,
0.16541379690170288,
0.004935821983963251,
0.030409380793571472,
0.0344611331820488,
0.006367519032210112,
-0.03370359539985657,
-0.06508912891149521,
-0.019412774592638016,
0.06636671721935272,
-0.005517143756151199,
0.05353100225329399,
-0.05325396731495857,
0.009183826856315136,
0.05890243873000145,
-0.013741995207965374,
-0.0663204938173294,
0.015829024836421013,
0.025972871109843254,
0.03283373638987541,
0.06495296210050583,
0.06733670085668564,
-0.015278961509466171,
-0.02668619528412819,
0.2977306842803955,
-0.0651969462633133,
-0.09882718324661255,
-0.11685331165790558,
0.12894690036773682,
0.02357310615479946,
-0.008394821546971798,
0.05988660827279091,
-0.12856614589691162,
0.016614897176623344,
0.17420245707035065,
0.15960638225078583,
-0.03695400431752205,
-0.01639128290116787,
-0.029757920652627945,
-0.016609327867627144,
-0.07016675174236298,
0.0903344675898552,
0.10993283241987228,
0.03927043825387955,
-0.05735214799642563,
-0.012756971642374992,
-0.01191564742475748,
-0.05269927531480789,
-0.09577970951795578,
0.06191706657409668,
-0.001931008417159319,
0.01433119922876358,
-0.022509820759296417,
0.09807408601045609,
0.013134249486029148,
-0.19007587432861328,
0.04866580292582512,
-0.13608238101005554,
-0.15660271048545837,
-0.03716089203953743,
0.13004864752292633,
-0.02092018350958824,
0.016331061720848083,
-0.021131794899702072,
0.0048316349275410175,
0.1353393942117691,
-0.022511817514896393,
-0.07697167992591858,
-0.1212691143155098,
0.07627813518047333,
-0.029718410223722458,
0.23916317522525787,
0.003750506090000272,
0.07256804406642914,
0.09342071413993835,
-0.005685372278094292,
-0.17149172723293304,
0.04051580652594566,
0.07730621099472046,
-0.031515419483184814,
0.03893902152776718,
0.13488395512104034,
-0.052171263843774796,
0.13006314635276794,
0.03662235662341118,
-0.07469034194946289,
-0.026638895273208618,
-0.0077851684764027596,
-0.021943988278508186,
-0.09385201334953308,
0.006354385521262884,
-0.06240474432706833,
0.13920892775058746,
0.1396845132112503,
-0.04607487469911575,
0.010531109757721424,
-0.08217524737119675,
0.04542599990963936,
0.06726406514644623,
0.061384230852127075,
-0.0038878917694091797,
-0.17151343822479248,
0.02486451156437397,
0.000744551420211792,
0.03684225678443909,
-0.23378899693489075,
-0.10479812324047089,
0.048332780599594116,
-0.058327462524175644,
-0.040486738085746765,
0.10674291849136353,
0.1242557242512703,
0.029313528910279274,
-0.043896764516830444,
-0.13546377420425415,
-0.042691025882959366,
0.12080400437116623,
-0.16543114185333252,
-0.05658915266394615
] |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cpu
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": []}]} | text-classification | adwinang/finetuning-sentiment-model-3000-samples | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-09T11:45:56+00:00 | [] | [] | TAGS
#transformers #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cpu
- Datasets 2.16.1
- Tokenizers 0.15.1
| [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2",
"### Training results",
"### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.2.0+cpu\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2",
"### Training results",
"### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.2.0+cpu\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
68,
41,
6,
12,
8,
3,
90,
4,
33
] | [
"passage: TAGS\n#transformers #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2### Training results### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.2.0+cpu\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
-0.080074243247509,
0.15847247838974,
-0.001303061842918396,
0.10206365585327148,
0.14750179648399353,
0.015619940124452114,
0.10974080860614777,
0.11162526160478592,
-0.09698382019996643,
0.054780758917331696,
0.0748550146818161,
0.06446279585361481,
0.02607683278620243,
0.11553999036550522,
-0.048939719796180725,
-0.22638681530952454,
0.014733717776834965,
-0.028536846861243248,
-0.036453139036893845,
0.0935562402009964,
0.10745132714509964,
-0.09111803025007248,
0.07896967232227325,
-0.01952650398015976,
-0.13888627290725708,
0.036022450774908066,
-0.026961810886859894,
-0.03943285718560219,
0.09020982682704926,
0.016626622527837753,
0.08625774830579758,
0.0048384093679487705,
0.14171883463859558,
-0.23810650408267975,
-0.006039158906787634,
0.07087975740432739,
0.02832852117717266,
0.05759378895163536,
0.03601029887795448,
-0.01839360222220421,
0.1012447252869606,
-0.15684127807617188,
0.11021360754966736,
0.029352739453315735,
-0.06540698558092117,
-0.10458085685968399,
-0.07361457496881485,
0.08183535933494568,
0.11173156648874283,
0.10425430536270142,
0.001727632014080882,
0.12929439544677734,
-0.09415753185749054,
0.08509624004364014,
0.14589115977287292,
-0.23855167627334595,
-0.05170898139476776,
0.049942195415496826,
0.041621435433626175,
0.09337569773197174,
-0.10529649257659912,
-0.01895085908472538,
0.06971858441829681,
0.019676286727190018,
0.0858246386051178,
-0.013505971990525723,
-0.11251737177371979,
-0.0030352361500263214,
-0.13506488502025604,
-0.012844696640968323,
0.20480972528457642,
0.05944362282752991,
-0.05584648251533508,
-0.10075303167104721,
-0.04781775921583176,
-0.07361095398664474,
-0.016105087473988533,
-0.03579305484890938,
0.042619187384843826,
-0.038207970559597015,
-0.018957702443003654,
-0.055522557348012924,
-0.06908904016017914,
-0.07081018388271332,
0.0244341641664505,
0.08523961156606674,
0.04935113713145256,
-0.002946374239400029,
-0.01261961180716753,
0.09231272339820862,
-0.02885909378528595,
-0.10977333784103394,
-0.014945686794817448,
-0.007191105745732784,
-0.056611161679029465,
-0.061405081301927567,
-0.033534400165081024,
-0.0159407090395689,
0.008958177641034126,
0.14850568771362305,
-0.0666457861661911,
0.06158927455544472,
0.010393703356385231,
0.007098312955349684,
-0.025887439027428627,
0.11635994911193848,
-0.020342733711004257,
-0.047522686421871185,
0.022922523319721222,
0.10739729553461075,
0.03283301368355751,
-0.0006477499264292419,
-0.10253189504146576,
-0.006302184425294399,
0.0905957743525505,
0.07908175885677338,
-0.05168679729104042,
0.044384878128767014,
-0.033117879182100296,
-0.014958137646317482,
0.03892240673303604,
-0.13922110199928284,
0.047748465090990067,
-0.012206903658807278,
-0.0786876231431961,
-0.07124024629592896,
0.04556415230035782,
0.0084432577714324,
-0.023626860231161118,
0.06953449547290802,
-0.07156175374984741,
-0.011180831119418144,
-0.08995714038610458,
-0.07881070673465729,
0.014431944116950035,
-0.060245662927627563,
0.004956308286637068,
-0.08674322068691254,
-0.24763807654380798,
-0.04278409481048584,
0.03371286392211914,
-0.03422939404845238,
-0.05324498564004898,
-0.07716002315282822,
-0.06439106166362762,
0.0023979395627975464,
0.005265159532427788,
0.058837998658418655,
-0.05493614450097084,
0.06256350129842758,
0.0012011771323159337,
0.02276543341577053,
0.012768558226525784,
0.03423164039850235,
-0.12034629285335541,
0.03512093052268028,
-0.11566691845655441,
0.07241281121969223,
-0.09277898818254471,
0.06115904077887535,
-0.0905548632144928,
-0.09010497480630875,
0.008102796971797943,
-0.01152894925326109,
0.0342908576130867,
0.14207366108894348,
-0.1790909767150879,
-0.016521956771612167,
0.1425381600856781,
-0.08262995630502701,
-0.08544258773326874,
0.10983163118362427,
-0.048459090292453766,
0.04319413751363754,
0.08175366371870041,
0.17458193004131317,
0.12373270094394684,
-0.12003859132528305,
-0.01020823884755373,
0.019142279401421547,
0.0584101602435112,
0.023927511647343636,
0.04611886292695999,
-0.005112646147608757,
-0.011531568132340908,
0.017140012234449387,
-0.07238146662712097,
-0.0070753078907728195,
-0.07189484685659409,
-0.09743737429380417,
-0.057202521711587906,
-0.11375344544649124,
0.052782777696847916,
0.02826441451907158,
0.032704152166843414,
-0.09110983461141586,
-0.10414846986532211,
0.11160457879304886,
0.1372034102678299,
-0.0622846893966198,
0.004823361057788134,
-0.05983131378889084,
0.03382224217057228,
0.004643551539629698,
-0.02122686617076397,
-0.17147189378738403,
-0.10991348326206207,
0.04202379286289215,
-0.05895933881402016,
0.051262736320495605,
-0.006595838349312544,
0.05156395956873894,
0.09476975351572037,
-0.055091094225645065,
-0.044230181723833084,
-0.06395969539880753,
0.014250705018639565,
-0.09471980482339859,
-0.19966097176074982,
-0.020975561812520027,
-0.032558176666498184,
0.17486393451690674,
-0.2764430046081543,
0.042397577315568924,
-0.06139837205410004,
0.10714399814605713,
0.022089436650276184,
-0.04370465874671936,
-0.004751964937895536,
0.047498464584350586,
-0.028470192104578018,
-0.09120704233646393,
0.03068697452545166,
-0.006932117976248264,
-0.08388698101043701,
-0.07097969949245453,
-0.14650462567806244,
0.06410063803195953,
0.07491526007652283,
0.059865210205316544,
-0.11049219965934753,
-0.008358663879334927,
-0.04636777192354202,
-0.04158579558134079,
-0.08686047047376633,
-0.001169473398476839,
0.15893784165382385,
-0.019858507439494133,
0.13244737684726715,
-0.05640352517366409,
-0.05166652053594589,
-0.012648286297917366,
-0.02511896751821041,
-0.01786162704229355,
0.09052678197622299,
0.057759858667850494,
-0.12769970297813416,
0.10963782668113708,
0.0987357348203659,
-0.08185399323701859,
0.13887281715869904,
-0.041701436042785645,
-0.0612555593252182,
-0.009115591645240784,
0.02716916799545288,
-0.010051390156149864,
0.09587395936250687,
-0.09917996823787689,
-0.0053603011183440685,
0.010310194455087185,
0.007918083108961582,
0.02453930862247944,
-0.1635875105857849,
-0.01233490277081728,
0.03797902539372444,
-0.04465525224804878,
-0.012205676175653934,
-0.043698303401470184,
0.010835649445652962,
0.08520562946796417,
0.0023155694361776114,
-0.034598208963871,
0.030390877276659012,
-0.005214492324739695,
-0.10053633153438568,
0.1808106005191803,
-0.13645078241825104,
-0.14852628111839294,
-0.1300274133682251,
0.06319983303546906,
-0.06869206577539444,
-0.002769042272120714,
0.030410660430788994,
-0.08926656097173691,
-0.060157809406518936,
-0.12092941999435425,
-0.03675643354654312,
-0.02226097881793976,
-0.027738912031054497,
0.019044047221541405,
0.03006499633193016,
0.08909650892019272,
-0.1296483725309372,
0.004636615980416536,
0.006225387565791607,
-0.10002779215574265,
-0.015919502824544907,
0.0358794704079628,
0.12819267809391022,
0.13168326020240784,
-0.01990928314626217,
0.003991041332483292,
-0.020411213859915733,
0.22024022042751312,
-0.05142662301659584,
-0.002297712489962578,
0.11357587575912476,
0.008893677964806557,
0.049619410187006,
0.13911034166812897,
0.021373603492975235,
-0.1061752438545227,
0.05568574368953705,
0.07047455757856369,
-0.01044316403567791,
-0.22749941051006317,
-0.0628167912364006,
-0.016893496736884117,
-0.05629340931773186,
0.08958182483911514,
0.062494538724422455,
0.03781287372112274,
0.04078681394457817,
-0.01597033254802227,
0.049585361033678055,
0.00821557268500328,
0.0912390947341919,
0.07997632026672363,
0.023845581337809563,
0.08989334106445312,
-0.03720184788107872,
-0.03342846781015396,
0.05650123208761215,
-0.016825679689645767,
0.2467087358236313,
-0.010747384279966354,
0.09371258318424225,
0.034627389162778854,
0.1473248153924942,
-0.02486402541399002,
0.021093502640724182,
0.027093609794974327,
0.003267368534579873,
0.01154505368322134,
-0.06374448537826538,
-0.03266216069459915,
0.05754617974162102,
-0.04614086449146271,
0.06498134881258011,
-0.11008672416210175,
0.05213930085301399,
0.03780018910765648,
0.23603138327598572,
0.035749949514865875,
-0.30664730072021484,
-0.08565905690193176,
0.03601941093802452,
-0.027074603363871574,
-0.06069452315568924,
0.02413821406662464,
0.11678393185138702,
-0.11661593616008759,
0.0538550429046154,
-0.024472815915942192,
0.09510601311922073,
-0.033857379108667374,
0.009921233169734478,
0.024036698043346405,
0.10188647359609604,
-0.00951394997537136,
0.10196231305599213,
-0.2073318213224411,
0.20096352696418762,
0.021589387208223343,
0.12310346215963364,
-0.0503498800098896,
0.02449779585003853,
0.02269727736711502,
0.15245944261550903,
0.12492375075817108,
-0.0033194145653396845,
-0.04129322990775108,
-0.18243619799613953,
-0.055584050714969635,
0.0322209969162941,
0.10781390219926834,
-0.024739079177379608,
0.07633215188980103,
-0.05324173718690872,
0.002187466947361827,
0.06080896034836769,
-0.08666563779115677,
-0.20725634694099426,
-0.1318998634815216,
-0.015135821886360645,
0.0202354583889246,
-0.012902219779789448,
-0.08467705547809601,
-0.08451604098081589,
-0.029863541945815086,
0.200094535946846,
-0.012554089538753033,
-0.04410073161125183,
-0.12205492705106735,
0.0413188561797142,
0.08800856024026871,
-0.06230270117521286,
0.011524462141096592,
0.022442052140831947,
0.12503641843795776,
0.03126652538776398,
-0.07790184766054153,
0.05065092071890831,
-0.0829165130853653,
-0.15299934148788452,
-0.05837463214993477,
0.11128906905651093,
0.067390576004982,
0.04698784276843071,
0.008752929978072643,
0.008871971629559994,
0.020382048562169075,
-0.08517570793628693,
-0.009505839087069035,
0.12094202637672424,
0.08693885803222656,
0.08345866203308105,
-0.1121891662478447,
-0.001130011398345232,
-0.05533207580447197,
-0.026048189029097557,
0.12232656031847,
0.21790780127048492,
-0.0825088620185852,
0.07652991265058517,
0.09832720458507538,
-0.10418229550123215,
-0.19994591176509857,
0.06855262815952301,
0.07411389797925949,
0.01148963812738657,
0.050047196447849274,
-0.16680993139743805,
0.12373959273099899,
0.11382929980754852,
-0.02587662637233734,
0.04930451884865761,
-0.3211841881275177,
-0.13673220574855804,
0.10337688773870468,
0.10997189581394196,
-0.007454656530171633,
-0.14068956673145294,
-0.037453725934028625,
-0.06647436320781708,
-0.09731259942054749,
0.0954512506723404,
-0.09590451419353485,
0.09208551794290543,
0.00713846692815423,
0.08539057523012161,
0.024529974907636642,
-0.028196152299642563,
0.15550705790519714,
0.029108582064509392,
0.10522879660129547,
-0.07381697744131088,
0.027095800265669823,
0.07886777818202972,
-0.08017134666442871,
0.08803239464759827,
-0.07673744857311249,
0.07982921600341797,
-0.11914987862110138,
-0.030027275905013084,
-0.04969700053334236,
0.08767613023519516,
-0.053353201597929,
-0.06956382840871811,
-0.05889291688799858,
0.05034752935171127,
0.06962016224861145,
-0.027853598818182945,
0.0573166161775589,
0.016192927956581116,
0.09803278744220734,
0.09753382205963135,
0.11306609958410263,
-0.008125720545649529,
-0.06129080429673195,
0.005318214185535908,
-0.0266738161444664,
0.06436828523874283,
-0.12192846089601517,
0.0264578927308321,
0.1196814775466919,
0.038269441574811935,
0.16389478743076324,
0.02736441232264042,
-0.03597939386963844,
-0.013174031861126423,
0.03498231992125511,
-0.11584118008613586,
-0.14755195379257202,
-0.02290017530322075,
-0.04483660310506821,
-0.1320037692785263,
0.011535551398992538,
0.08977008610963821,
-0.0886792242527008,
-0.0016949290875345469,
-0.03302440792322159,
0.04068955034017563,
-0.027542680501937866,
0.17258389294147491,
0.04639580100774765,
0.05556360259652138,
-0.07151506096124649,
0.1275370866060257,
0.07780855894088745,
-0.07975903153419495,
0.06137663498520851,
0.06930005550384521,
-0.08949854969978333,
-0.04206657037138939,
0.08835570514202118,
0.19257572293281555,
-0.0317685604095459,
-0.05660083889961243,
-0.0790582150220871,
-0.09114614129066467,
0.036702122539281845,
0.10263608396053314,
0.05234616622328758,
-0.019490443170070648,
-0.02567349746823311,
0.02665030024945736,
-0.15428704023361206,
0.10762297362089157,
0.05143975093960762,
0.06930433213710785,
-0.17315413057804108,
0.09870181977748871,
0.007400142960250378,
0.024161387234926224,
-0.012543863616883755,
0.03174171596765518,
-0.09843268990516663,
-0.016929777339100838,
-0.14749158918857574,
-0.0015517317224293947,
-0.03287842497229576,
0.02262711338698864,
-0.004770674277096987,
-0.029531942680478096,
-0.03098578006029129,
0.04794874042272568,
-0.05466185137629509,
-0.06699927896261215,
0.01941242814064026,
0.07614347338676453,
-0.15358228981494904,
-0.018492598086595535,
0.02574915438890457,
-0.08962664008140564,
0.055411916226148605,
0.04906972497701645,
0.01867513917386532,
0.038650888949632645,
-0.1445537656545639,
0.011480898596346378,
0.05228804051876068,
0.032436713576316833,
0.04596361145377159,
-0.08193992078304291,
-0.02038467489182949,
-0.013769668526947498,
0.033021073788404465,
0.02068902738392353,
0.06506125628948212,
-0.1138819083571434,
-0.013975400477647781,
-0.056097883731126785,
-0.06554196029901505,
-0.06541711837053299,
0.04542616009712219,
0.11054576933383942,
0.013252658769488335,
0.1813400387763977,
-0.09832783043384552,
0.035705581307411194,
-0.1754782646894455,
-0.03534254804253578,
0.0036712707951664925,
-0.028708744794130325,
-0.08368252217769623,
-0.031094802543520927,
0.06564315408468246,
-0.057856250554323196,
0.11915753781795502,
0.00995021965354681,
0.08141352236270905,
0.03654547780752182,
-0.02974497154355049,
0.006824852898716927,
0.012517684139311314,
0.18509021401405334,
0.05060279741883278,
-0.02422228641808033,
0.06089736893773079,
-0.015825262293219566,
0.0825035348534584,
0.015353535301983356,
0.1610155999660492,
0.15051394701004028,
-0.06534844636917114,
0.060555677860975266,
0.06492387503385544,
-0.11155622452497482,
-0.1386750489473343,
0.08281967788934708,
-0.014593859203159809,
0.11332324892282486,
-0.04675754904747009,
0.16721908748149872,
0.11212746053934097,
-0.16519832611083984,
0.04455028101801872,
-0.06242625042796135,
-0.1276087462902069,
-0.1193469688296318,
-0.07555995881557465,
-0.09284574538469315,
-0.10879772156476974,
0.017069274559617043,
-0.1259114146232605,
0.029193688184022903,
0.037419792264699936,
-0.005254633259028196,
-0.004523629322648048,
0.16302494704723358,
-0.05231097340583801,
0.024562405422329903,
0.043770525604486465,
-0.0030551000963896513,
-0.030539441853761673,
-0.06146211922168732,
-0.06658682227134705,
0.05556764826178551,
-0.0069196224212646484,
0.05421477183699608,
-0.040472596883773804,
-0.00813717395067215,
0.03997237607836723,
-0.024135036394000053,
-0.0576184019446373,
0.0287360567599535,
0.028209587559103966,
0.024151990190148354,
0.05456370860338211,
0.061541035771369934,
-0.030400464311242104,
-0.022604335099458694,
0.2909006178379059,
-0.08565115928649902,
-0.09953199326992035,
-0.120137520134449,
0.22789418697357178,
0.04075995087623596,
0.005598465446382761,
0.06227610632777214,
-0.12631666660308838,
0.014414558187127113,
0.1629083752632141,
0.17750662565231323,
-0.055159810930490494,
-0.009603031910955906,
-0.05306834354996681,
-0.016571857035160065,
-0.06355646997690201,
0.10746127367019653,
0.11064468324184418,
0.019004954025149345,
-0.0418669730424881,
0.010784336365759373,
-0.012870516628026962,
-0.03040359728038311,
-0.11035013198852539,
0.07738460600376129,
0.010643268004059792,
0.009087871760129929,
-0.023682616651058197,
0.0822170153260231,
-0.013722400180995464,
-0.15715637803077698,
0.03498883172869682,
-0.12844017148017883,
-0.15057817101478577,
-0.025775181129574776,
0.08414316177368164,
-0.020094262436032295,
0.033643938601017,
-0.01613932102918625,
-0.01377655379474163,
0.12153460085391998,
-0.024156711995601654,
-0.07074355334043503,
-0.10423073172569275,
0.05539049208164215,
-0.05611592158675194,
0.24999839067459106,
0.011072474531829357,
0.06243865564465523,
0.10160687565803528,
0.009146997705101967,
-0.12818345427513123,
0.08350507915019989,
0.06758230924606323,
-0.035777438431978226,
0.0472077839076519,
0.14869190752506256,
-0.0574638806283474,
0.12300122529268265,
0.038198065012693405,
-0.13629110157489777,
-0.010561944916844368,
-0.029224812984466553,
-0.04878569021821022,
-0.09373456984758377,
0.005265405401587486,
-0.07203861325979233,
0.15398895740509033,
0.18919655680656433,
-0.035399291664361954,
0.02245304360985756,
-0.06899634748697281,
0.031557004898786545,
0.06135024502873421,
0.06986650079488754,
-0.002716073300689459,
-0.20384494960308075,
0.020975220948457718,
0.02133217267692089,
0.03776273876428604,
-0.268104612827301,
-0.09422935545444489,
0.020555824041366577,
-0.037482745945453644,
-0.05799322575330734,
0.10852376371622086,
0.10201987624168396,
0.02296788990497589,
-0.046741727739572525,
-0.15159304440021515,
-0.04499520733952522,
0.15519970655441284,
-0.1347922533750534,
-0.06244826689362526
] |
null | null | null |
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` | {"license": "other", "tags": ["autotrain", "text-generation"], "widget": [{"text": "I love AutoTrain because "}]} | text-generation | kouki13/newopenhermes2 | [
"safetensors",
"autotrain",
"text-generation",
"license:other",
"endpoints_compatible",
"region:us"
] | 2024-02-09T11:47:37+00:00 | [] | [] | TAGS
#safetensors #autotrain #text-generation #license-other #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit AutoTrain.
# Usage
| [
"# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.",
"# Usage"
] | [
"TAGS\n#safetensors #autotrain #text-generation #license-other #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.",
"# Usage"
] | [
33,
29,
3
] | [
"passage: TAGS\n#safetensors #autotrain #text-generation #license-other #endpoints_compatible #region-us \n# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.# Usage"
] | [
-0.03320549428462982,
0.03780708089470863,
-0.0005784488166682422,
0.037439193576574326,
0.13256101310253143,
-0.02594633586704731,
0.22870999574661255,
0.04971681907773018,
-0.04270017519593239,
-0.08776232600212097,
0.19642603397369385,
0.16802352666854858,
-0.04566871374845505,
0.18935616314411163,
-0.02990073338150978,
-0.2414124757051468,
0.021885043010115623,
-0.025850016623735428,
0.1327640414237976,
0.11522045731544495,
0.14238014817237854,
-0.07779128849506378,
0.06120644509792328,
0.04086628183722496,
-0.20404933393001556,
0.03463415056467056,
0.07968573272228241,
-0.11895040422677994,
0.18004877865314484,
0.032886918634176254,
0.13635416328907013,
0.01931498385965824,
0.14652439951896667,
-0.12186150997877121,
0.014377960003912449,
0.01464270893484354,
-0.015491045080125332,
0.055415596812963486,
0.08804452419281006,
-0.038794226944446564,
0.09763352572917938,
0.177653506398201,
0.10883878171443939,
0.04911845549941063,
-0.10558086633682251,
-0.014727416448295116,
-0.03310466557741165,
0.018835384398698807,
0.12075160443782806,
0.1193094402551651,
-0.01845790445804596,
0.20021599531173706,
-0.14986595511436462,
0.07329507917165756,
-0.0995626449584961,
-0.27255508303642273,
-0.0038277229759842157,
0.21143054962158203,
0.07346842437982559,
-0.025004452094435692,
-0.12620827555656433,
0.06475763022899628,
0.12761425971984863,
0.0030757547356188297,
0.06504988670349121,
-0.015198786742985249,
-0.055105701088905334,
-0.0015243350062519312,
-0.07397002726793289,
-0.004598719999194145,
0.18640007078647614,
-0.07974611967802048,
-0.031184203922748566,
-0.12737500667572021,
-0.019428882747888565,
0.04709514603018761,
0.011552144773304462,
-0.09352482110261917,
-0.0217994824051857,
0.11079124361276627,
-0.007622338831424713,
-0.02531961165368557,
-0.15207529067993164,
-0.05755603685975075,
-0.08864409476518631,
0.04077286645770073,
0.0017509139142930508,
0.011538662947714329,
-0.09947098046541214,
0.12073534727096558,
-0.029350996017456055,
-0.0943499282002449,
0.052897434681653976,
-0.1107030138373375,
0.04635190963745117,
-0.11982002854347229,
-0.03970254212617874,
-0.10856737196445465,
0.013430505990982056,
0.22841021418571472,
0.1669083684682846,
-0.015314205549657345,
-0.08587565273046494,
0.039016176015138626,
0.02371702343225479,
0.09614221751689911,
0.06376225501298904,
-0.015822242945432663,
0.06775996834039688,
-0.04785482585430145,
-0.017039362341165543,
-0.025495992973446846,
-0.1726902425289154,
0.032083623111248016,
0.01997307874262333,
0.07117509841918945,
-0.0760226845741272,
0.06040170043706894,
-0.01951628364622593,
0.055283352732658386,
0.05161101743578911,
-0.031190861016511917,
0.03744623437523842,
-0.052504897117614746,
0.01617865450680256,
-0.09791388362646103,
0.0286922138184309,
0.1180110052227974,
0.03286140412092209,
0.1336720734834671,
-0.09649777412414551,
-0.026225421577692032,
-0.1056324690580368,
-0.03878350928425789,
0.018166208639740944,
-0.0019215025240555406,
0.0628642737865448,
-0.19663763046264648,
-0.30395275354385376,
-0.027070891112089157,
0.053043100982904434,
-0.019671862944960594,
-0.05561401695013046,
-0.07015043497085571,
0.016289202496409416,
0.059536442160606384,
-0.02920805849134922,
0.054385289549827576,
-0.022419849410653114,
0.03813159465789795,
-0.07676586508750916,
-0.02052054926753044,
-0.06291672587394714,
0.006658008787781,
-0.14841435849666595,
-0.03448035567998886,
-0.030017102137207985,
0.006548900622874498,
-0.03775618225336075,
0.16895608603954315,
-0.011088937520980835,
0.047757651656866074,
-0.05747115612030029,
0.05074193328619003,
0.007877329364418983,
0.1440490484237671,
-0.1335235834121704,
0.005429679993540049,
0.1511751264333725,
-0.11302075535058975,
-0.10663392394781113,
0.09467647224664688,
-0.10317569971084595,
0.23649843037128448,
0.10416192561388016,
0.13955152034759521,
0.05125761032104492,
-0.12630151212215424,
0.11601320654153824,
0.03282208740711212,
-0.08780468255281448,
-0.062369491904973984,
-0.0006791196065023541,
-0.034443121403455734,
-0.22099432349205017,
0.031658004969358444,
0.11068084836006165,
0.07476310431957245,
-0.03403317928314209,
-0.08304393291473389,
-0.02895026095211506,
-0.058612581342458725,
0.03986813873052597,
0.016017582267522812,
0.12599535286426544,
-0.07699156552553177,
-0.02858225256204605,
0.032077912241220474,
0.038467586040496826,
0.07923582941293716,
-0.054815541952848434,
-0.057291675359010696,
-0.01996961608529091,
-0.023569827899336815,
-0.00915558822453022,
-0.0898597314953804,
-0.0620407834649086,
-0.006840218789875507,
0.1304454207420349,
0.03466487303376198,
0.07167287915945053,
0.0362425372004509,
0.052633073180913925,
-0.028641145676374435,
0.002677651820704341,
0.1629824936389923,
0.04459667578339577,
-0.12675853073596954,
-0.08582112193107605,
0.10815013945102692,
-0.07446087151765823,
0.1071702167391777,
-0.2590586841106415,
0.028333326801657677,
-0.11371348798274994,
0.08611167222261429,
-0.013308924622833729,
0.06491301208734512,
-0.08320876955986023,
0.024355897679924965,
-0.08930765837430954,
-0.008432179689407349,
0.05678462237119675,
0.04953930526971817,
-0.02282531000673771,
0.12372811883687973,
-0.1432238668203354,
0.21934939920902252,
0.1198250874876976,
-0.09310522675514221,
-0.11077594012022018,
-0.0739443302154541,
0.009118417277932167,
-0.005148864816874266,
-0.1179550290107727,
0.005491754971444607,
0.076014444231987,
-0.04686584323644638,
0.1847466230392456,
-0.034107014536857605,
-0.03428659960627556,
-0.015382813289761543,
-0.08532355725765228,
-0.009268855676054955,
-0.02073976956307888,
0.09649215638637543,
-0.2238936424255371,
0.1325010061264038,
0.16212041676044464,
-0.015046309679746628,
0.1718226969242096,
0.01847519353032112,
0.013679388910531998,
0.006052343640476465,
-0.04082776978611946,
-0.00007846848893677816,
0.02128027006983757,
0.0015916629927232862,
0.0011914868373423815,
0.007707077544182539,
0.02131907269358635,
0.030305195599794388,
-0.14438240230083466,
-0.05413905158638954,
0.010167223401367664,
0.052466847002506256,
0.00018202696810476482,
0.0614926852285862,
-0.08105885237455368,
0.05735839903354645,
-0.0333511158823967,
-0.11407014727592468,
0.12527471780776978,
0.0140310637652874,
-0.12375999987125397,
0.1809239387512207,
-0.09875242412090302,
-0.177916020154953,
-0.19897617399692535,
-0.11664178967475891,
0.025174645707011223,
0.09509945660829544,
0.06778308749198914,
-0.06591268628835678,
-0.0677633062005043,
-0.013884147629141808,
-0.13205823302268982,
0.015237858518958092,
-0.0303916335105896,
-0.10815607011318207,
0.06643082201480865,
0.002197817200794816,
-0.1106930822134018,
-0.04751880466938019,
0.012397545389831066,
-0.05212624743580818,
0.06534521281719208,
-0.032029394060373306,
0.06015416979789734,
0.12733860313892365,
-0.009645693004131317,
0.014830506406724453,
-0.03892328962683678,
0.1736617386341095,
-0.07863081991672516,
0.0028175772167742252,
0.11224561184644699,
-0.04382455348968506,
0.03531843051314354,
0.2027312070131302,
0.03458266332745552,
-0.07247956842184067,
0.06938916444778442,
-0.03509911522269249,
-0.05979844182729721,
-0.202435702085495,
-0.10123657435178757,
-0.007523522712290287,
-0.02823515795171261,
0.08373580127954483,
0.0565473809838295,
0.25448861718177795,
0.1288231760263443,
0.060374923050403595,
0.03997355327010155,
0.024889161810278893,
0.0913970097899437,
0.1029813289642334,
-0.027027886360883713,
0.16222402453422546,
-0.08429007232189178,
-0.14650671184062958,
0.048164136707782745,
-0.022769063711166382,
0.07281020283699036,
0.17174853384494781,
-0.06210782378911972,
0.04705783352255821,
0.11571547389030457,
0.13094793260097504,
0.12702703475952148,
0.07746905833482742,
-0.061997704207897186,
-0.006629003677517176,
0.0010869213147088885,
-0.04415592923760414,
0.14652740955352783,
-0.060009948909282684,
-0.06889448314905167,
-0.04306207224726677,
-0.003198902355507016,
0.04323491454124451,
0.05818231403827667,
0.026216039434075356,
-0.28657910227775574,
0.042942874133586884,
0.04888097196817398,
-0.05969006195664406,
-0.11467164009809494,
0.09232109785079956,
-0.027857046574354172,
-0.18361465632915497,
0.03563778102397919,
-0.033283449709415436,
0.09147034585475922,
0.062072351574897766,
0.04841171205043793,
-0.06585943698883057,
-0.0609852597117424,
-0.045712124556303024,
0.15376420319080353,
-0.33846980333328247,
0.20756816864013672,
-0.011205663904547691,
0.08115556091070175,
-0.10785048454999924,
0.010794016532599926,
0.08773794025182724,
0.19103488326072693,
0.12050216645002365,
-0.049261946231126785,
-0.19848455488681793,
-0.11937171965837479,
-0.08363119512796402,
-0.015415008179843426,
0.02001480758190155,
-0.008096402511000633,
0.0008919041720218956,
-0.11757626384496689,
0.0014032695908099413,
0.04126403480768204,
-0.0069845812395215034,
-0.17894983291625977,
-0.15384836494922638,
-0.03538630157709122,
0.030474675819277763,
0.10934672504663467,
-0.04776112735271454,
-0.0534328930079937,
-0.06292759627103806,
0.13548673689365387,
0.026695549488067627,
0.008182995021343231,
-0.1301279366016388,
-0.053804632276296616,
-0.044131867587566376,
-0.023950019851326942,
0.07710648328065872,
0.009424211457371712,
0.11959850043058395,
-0.08615647256374359,
-0.06447352468967438,
0.09218238294124603,
-0.12910714745521545,
-0.042984966188669205,
-0.12177132815122604,
0.03449074551463127,
-0.045684002339839935,
-0.01073586754500866,
0.11459703743457794,
0.04736353084445,
-0.07455705851316452,
-0.06686578691005707,
-0.016151487827301025,
-0.0162202138453722,
0.052238523960113525,
-0.10140960663557053,
-0.11989933252334595,
-0.12391869723796844,
-0.023699220269918442,
-0.11985665559768677,
0.1933230459690094,
0.14995472133159637,
-0.08873795717954636,
0.15256796777248383,
0.2099498212337494,
-0.11413656920194626,
-0.29302918910980225,
-0.05128840357065201,
-0.06601350009441376,
0.004299632739275694,
0.06156041473150253,
-0.10058135539293289,
0.1023014560341835,
0.016915474086999893,
-0.08869403600692749,
-0.016260353848338127,
-0.10926515609025955,
-0.16224952042102814,
0.22960300743579865,
-0.0020108406897634268,
0.18459931015968323,
-0.07568172365427017,
-0.05459576100111008,
-0.12268339842557907,
0.05030543729662895,
0.043312136083841324,
-0.06949128210544586,
0.04921199381351471,
0.045118432492017746,
0.04848489910364151,
0.02309754677116871,
-0.04944291338324547,
0.05402865633368492,
-0.07527824491262436,
0.09563448280096054,
-0.16834798455238342,
-0.019022751599550247,
0.05676575005054474,
-0.027846379205584526,
0.11607834696769714,
-0.040225449949502945,
0.045501600950956345,
-0.05838647112250328,
-0.07079911977052689,
0.02105431631207466,
0.07136379927396774,
-0.007516450714319944,
-0.11632271111011505,
0.009460309520363808,
0.0020681610330939293,
-0.007515698205679655,
-0.07468903809785843,
0.01720641367137432,
-0.009510648436844349,
0.14864802360534668,
0.13830016553401947,
0.2062399536371231,
-0.06995580345392227,
0.06706579029560089,
-0.03199863061308861,
-0.11711113899946213,
0.07805433124303818,
-0.07166967540979385,
0.004296483471989632,
0.05220668390393257,
-0.0538930743932724,
0.14611311256885529,
0.06082209199666977,
0.003751826472580433,
-0.01890469156205654,
0.16250212490558624,
-0.16876746714115143,
0.04684048146009445,
-0.0843876302242279,
0.1279323697090149,
0.04778100550174713,
-0.03293748199939728,
0.09026376903057098,
-0.07791304588317871,
-0.03329215198755264,
-0.0002585914626251906,
0.006090222392231226,
-0.038581836968660355,
0.06518552452325821,
0.04536600783467293,
0.02252393215894699,
-0.06704199314117432,
0.0445764996111393,
0.07239795476198196,
0.016518399119377136,
0.041721411049366,
0.015846284106373787,
-0.09952405095100403,
-0.09522253274917603,
0.04372299090027809,
0.26397231221199036,
-0.1863422393798828,
-0.09990737587213516,
0.004564397502690554,
-0.09345841407775879,
0.004960347898304462,
0.08620705455541611,
0.0809662714600563,
0.04341237619519234,
-0.03603934869170189,
-0.02565331570804119,
-0.11602527648210526,
0.08217493444681168,
-0.015696978196501732,
0.05509110167622566,
-0.16319575905799866,
0.06676459312438965,
-0.030968010425567627,
-0.008549565449357033,
-0.08279257267713547,
-0.010031647980213165,
-0.11571928858757019,
0.026098787784576416,
-0.10430167615413666,
-0.03189973905682564,
-0.041006896644830704,
-0.011233619414269924,
0.05850789323449135,
-0.011018243618309498,
-0.013110441155731678,
-0.01927962154150009,
-0.08805359154939651,
0.02887921780347824,
-0.0008198951254598796,
0.04547540098428726,
-0.05460818111896515,
-0.024217726662755013,
0.037278566509485245,
0.004562355112284422,
0.046250831335783005,
0.012032478116452694,
-0.0011190201621502638,
0.049139540642499924,
-0.14732354879379272,
0.009436994791030884,
0.06159417703747749,
-0.0016145178815349936,
0.0070913624949753284,
-0.028678715229034424,
0.005330502521246672,
0.09783722460269928,
0.018718764185905457,
0.04128317907452583,
-0.0048657008446753025,
-0.1091027706861496,
0.014511657878756523,
0.10307195782661438,
-0.14174701273441315,
-0.03145497664809227,
-0.052812907844781876,
0.01100962609052658,
-0.05524790287017822,
0.23351503908634186,
-0.11669892817735672,
0.04470064863562584,
-0.02692001312971115,
0.030550040304660797,
-0.05822846665978432,
-0.10757116973400116,
-0.12190251797437668,
-0.0954190194606781,
-0.042861051857471466,
0.007703589275479317,
0.2689315676689148,
0.1459355354309082,
-0.008143693208694458,
0.0415508970618248,
0.07256698608398438,
0.09993022680282593,
0.001325596240349114,
0.22187061607837677,
0.09407079964876175,
-0.011255222372710705,
-0.12900875508785248,
0.0802748054265976,
0.027718892320990562,
-0.10550516843795776,
0.0003671931044664234,
0.017833324149250984,
-0.07709381729364395,
0.05998256057500839,
0.04779348149895668,
-0.04618219658732414,
-0.11530262231826782,
-0.1887446641921997,
-0.1010153517127037,
0.01362328790128231,
-0.09494820982217789,
-0.00841664057224989,
0.17340072989463806,
-0.07381404936313629,
-0.020257510244846344,
-0.08453129231929779,
-0.042230453342199326,
-0.21403644979000092,
-0.1685105264186859,
-0.09951409697532654,
-0.07172851264476776,
0.054574232548475266,
-0.01444533746689558,
0.051937036216259,
0.0384058877825737,
0.03334033116698265,
-0.0690227821469307,
0.10118697583675385,
-0.11317354440689087,
0.006825347896665335,
-0.007538147736340761,
-0.042660877108573914,
0.007157159503549337,
-0.17031751573085785,
-0.023363124579191208,
-0.1397811770439148,
-0.04669688642024994,
-0.031707603484392166,
-0.04375086724758148,
0.0007692996296100318,
-0.003963754046708345,
-0.03139100596308708,
-0.009807240217924118,
-0.01006900705397129,
0.03744599595665932,
0.023235660046339035,
0.05043753236532211,
0.022183645516633987,
0.01541586872190237,
0.043549589812755585,
0.21836970746517181,
-0.03527946025133133,
-0.18426218628883362,
-0.12376350164413452,
0.24631790816783905,
0.03293769061565399,
0.11490416526794434,
-0.07057193666696548,
-0.01361043006181717,
0.07598087936639786,
0.31235218048095703,
0.2598150074481964,
-0.03414434567093849,
0.010121017694473267,
-0.03132476285099983,
-0.014958096668124199,
-0.0064048562198877335,
0.18490195274353027,
0.008828791789710522,
0.16826002299785614,
-0.0621221587061882,
0.059055350720882416,
-0.016177164390683174,
-0.07808512449264526,
-0.06689254939556122,
0.14256809651851654,
-0.036333873867988586,
-0.02151089534163475,
-0.01796986348927021,
0.08792226016521454,
-0.0589551106095314,
0.17949369549751282,
-0.09007178992033005,
-0.009130639024078846,
-0.04809116572141647,
0.053617071360349655,
0.11827872693538666,
-0.02074413187801838,
0.03285614401102066,
-0.03567332774400711,
-0.018393725156784058,
0.0029441264923661947,
-0.04050283133983612,
-0.07413910329341888,
-0.04345672205090523,
0.06311136484146118,
0.02551795169711113,
0.25671228766441345,
-0.009337767027318478,
0.05477561056613922,
0.07988451421260834,
-0.0020537625532597303,
-0.10351628065109253,
0.11267323791980743,
0.00224103475920856,
-0.029008302837610245,
0.12491703033447266,
-0.015443749725818634,
0.007564615458250046,
-0.01867114193737507,
-0.01239294558763504,
-0.15698960423469543,
0.14728498458862305,
-0.10142818093299866,
-0.08940913528203964,
-0.05584051460027695,
0.12545742094516754,
-0.032320525497198105,
0.16258437931537628,
0.05726946145296097,
-0.026426637545228004,
0.0021389273460954428,
-0.0331779383122921,
0.08067825436592102,
0.009919043630361557,
-0.09914126992225647,
-0.02203422784805298,
-0.17707498371601105,
-0.016973769292235374,
0.12876249849796295,
-0.02544221095740795,
-0.24601322412490845,
-0.07971391826868057,
-0.06824030727148056,
-0.04311496391892433,
-0.1386985182762146,
0.07398401945829391,
0.2028772532939911,
0.019287997856736183,
-0.01476763840764761,
-0.1369636058807373,
-0.021961720660328865,
0.019149890169501305,
-0.026857441291213036,
-0.10799262672662735
] |
null | null | null | #ToDo
need more datasets and no gpt subscription . sorry | {} | null | fhai50032/RolePlayLake-7B-Toxic-v2-lora | [
"region:us"
] | 2024-02-09T11:48:17+00:00 | [] | [] | TAGS
#region-us
| #ToDo
need more datasets and no gpt subscription . sorry | [] | [
"TAGS\n#region-us \n"
] | [
6
] | [
"passage: TAGS\n#region-us \n"
] | [
0.024608636274933815,
-0.026205500587821007,
-0.009666500613093376,
-0.10395516455173492,
0.08638657629489899,
0.059816278517246246,
0.01882290467619896,
0.020661840215325356,
0.23975107073783875,
-0.005599027033895254,
0.1219947561621666,
0.0015615287702530622,
-0.037353623658418655,
0.03733762726187706,
-0.0035912662278860807,
-0.17583473026752472,
0.03876631706953049,
-0.018274923786520958,
0.01843859627842903,
0.026470553129911423,
-0.07776834815740585,
-0.07564429938793182,
0.015296397730708122,
-0.10247814655303955,
-0.083692267537117,
0.11002834886312485,
0.031466204673051834,
-0.019670886918902397,
0.10779199749231339,
-0.04243955761194229,
0.18699054419994354,
-0.011512263678014278,
-0.11213519424200058,
-0.2536850869655609,
0.021806683391332626,
-0.01765260472893715,
-0.08747660368680954,
0.01506110467016697,
0.0665089413523674,
-0.09014441072940826,
-0.0588928684592247,
0.0795099288225174,
-0.01132340170443058,
0.04246443510055542,
-0.27593839168548584,
-0.12684126198291779,
-0.05297930911183357,
-0.1421966552734375,
0.08651168644428253,
0.04035491496324539,
0.008764253929257393,
0.15506891906261444,
-0.20897391438484192,
0.004104613792151213,
0.08255259692668915,
-0.2538507878780365,
0.05591634660959244,
0.17671173810958862,
0.03623908758163452,
0.18037272989749908,
0.0060391901060938835,
0.11029672622680664,
0.0716743916273117,
-0.024263937026262283,
-0.17590197920799255,
-0.08127854019403458,
-0.04696211963891983,
0.16642488539218903,
-0.06727185100317001,
-0.14248386025428772,
0.34701237082481384,
0.00015008423360995948,
0.009657775051891804,
0.16921205818653107,
-0.059524230659008026,
-0.09972117841243744,
0.07259953022003174,
0.016484731808304787,
0.018492350354790688,
0.1471305936574936,
0.16307872533798218,
-0.0458691343665123,
-0.13837823271751404,
-0.018630273640155792,
-0.22798998653888702,
0.17510560154914856,
-0.03248048573732376,
0.13137903809547424,
-0.27447956800460815,
0.01684025302529335,
-0.2570667266845703,
0.0032130838371813297,
0.04178816080093384,
-0.06004921346902847,
-0.0226522795855999,
-0.013265985064208508,
-0.08018817007541656,
0.004899587947875261,
0.06192673370242119,
0.1266920566558838,
-0.06128726154565811,
0.06128238886594772,
-0.09319206327199936,
0.141696035861969,
0.07166698575019836,
0.07868369668722153,
0.13037432730197906,
0.041205424815416336,
-0.07187089323997498,
-0.21872246265411377,
-0.0026476888451725245,
-0.06275863200426102,
-0.09502086788415909,
-0.0020165652967989445,
-0.11606067419052124,
0.17244569957256317,
-0.030802514404058456,
-0.09825427830219269,
-0.11208184063434601,
0.09148659557104111,
-0.032992321997880936,
-0.03437839448451996,
-0.03552987426519394,
-0.020977836102247238,
0.019381176680326462,
0.04704452306032181,
-0.1548958420753479,
-0.005131472367793322,
0.07039852440357208,
0.11502562463283539,
-0.1346137970685959,
-0.003783059772104025,
-0.07908964157104492,
0.03039063885807991,
0.07654735445976257,
-0.16510222852230072,
0.03158547356724739,
-0.1124754324555397,
-0.07531405985355377,
0.002912673633545637,
-0.015710093080997467,
-0.016202643513679504,
0.166526660323143,
-0.0020451415330171585,
0.0714716836810112,
-0.026345307007431984,
-0.05890209600329399,
-0.11243434250354767,
-0.08489254862070084,
0.05390460044145584,
0.03670717030763626,
0.03266148269176483,
-0.2193479984998703,
0.014805203303694725,
-0.12762966752052307,
0.1360815018415451,
-0.10566820204257965,
-0.04705966264009476,
-0.022842247039079666,
0.20562705397605896,
0.037286072969436646,
0.08762791007757187,
-0.22171171009540558,
0.039756543934345245,
-0.05404696613550186,
0.18480908870697021,
-0.1502426266670227,
-0.0799463614821434,
0.20813211798667908,
-0.07964949309825897,
-0.10115210711956024,
0.021235812455415726,
0.020391687750816345,
0.026287272572517395,
0.0766737088561058,
0.4564172327518463,
-0.09766800701618195,
-0.09146861732006073,
0.10178250074386597,
0.17055274546146393,
-0.12427149713039398,
-0.1827561855316162,
0.06446871906518936,
-0.16666454076766968,
-0.1973118633031845,
0.0018917324487119913,
0.09222044050693512,
0.038269978016614914,
-0.07875611633062363,
-0.020746968686580658,
0.06325206160545349,
-0.0007678253459744155,
0.09095914661884308,
0.03755716234445572,
0.09034032374620438,
-0.08716782182455063,
0.11115926504135132,
-0.05017651244997978,
0.004037132486701012,
0.1343354731798172,
0.027325427159667015,
-0.03223329409956932,
0.08694463223218918,
-0.0485352948307991,
0.05295134335756302,
-0.1662379503250122,
-0.15068690478801727,
0.03398871049284935,
0.06283251196146011,
0.03186952322721481,
0.1280253529548645,
0.08141885697841644,
-0.10732853412628174,
0.022690722718834877,
-0.004228927195072174,
0.058398615568876266,
0.03891623765230179,
0.006107209715992212,
0.008764320984482765,
0.0961301177740097,
-0.10607069730758667,
-0.13589619100093842,
-0.07336436957120895,
-0.014715781435370445,
0.14371353387832642,
-0.0302802175283432,
0.07690227776765823,
-0.004240254405885935,
0.00013200697139836848,
0.06930823624134064,
0.08137880265712738,
0.016412746161222458,
0.08971183747053146,
-0.05237193778157234,
-0.05160155147314072,
0.10863113403320312,
-0.13533565402030945,
0.17837053537368774,
0.14053137600421906,
-0.20532016456127167,
0.029453208670020103,
-0.06838275492191315,
0.03670361638069153,
-0.008162540383636951,
0.0975119024515152,
-0.08272241055965424,
-0.02106042578816414,
0.013134466484189034,
0.0052274600602686405,
-0.013007243163883686,
0.017682146281003952,
-0.07295988500118256,
-0.07787393033504486,
-0.10233919322490692,
0.08436838537454605,
0.11562882363796234,
-0.10282530635595322,
0.14214380085468292,
0.4384984076023102,
0.11495281755924225,
0.21582984924316406,
-0.09581480920314789,
-0.0412987545132637,
0.007486371789127588,
0.0001535322517156601,
-0.04476691037416458,
0.08031861484050751,
-0.15973517298698425,
-0.038901735097169876,
0.027348900213837624,
0.07128690183162689,
0.11475157737731934,
-0.14959022402763367,
-0.09639324247837067,
-0.00793045200407505,
0.0022841424215584993,
-0.1249532699584961,
0.023905446752905846,
-0.03974650055170059,
0.04015624523162842,
0.07232289016246796,
-0.021535737439990044,
0.13939237594604492,
-0.04166141897439957,
-0.0639561116695404,
0.07585346698760986,
-0.2017085999250412,
-0.23179671168327332,
-0.12309670448303223,
-0.14680525660514832,
0.04366797208786011,
0.05154111236333847,
0.01726446859538555,
-0.17635835707187653,
-0.015074856579303741,
0.07706750929355621,
0.07820965349674225,
-0.20886357128620148,
-0.022814949974417686,
-0.004290030337870121,
0.0895976573228836,
-0.10227091610431671,
-0.0017130117630586028,
-0.04419664293527603,
-0.10150232166051865,
0.0017003051470965147,
0.07279510796070099,
-0.137485533952713,
0.13807645440101624,
0.21589438617229462,
0.07225540280342102,
0.07359948754310608,
-0.019093448296189308,
0.09936179965734482,
-0.10856141895055771,
-0.16549113392829895,
0.08348225057125092,
-0.06234746053814888,
0.047262318432331085,
0.17534415423870087,
0.03307317942380905,
-0.13904969394207,
-0.015682822093367577,
-0.0402069091796875,
-0.15603256225585938,
-0.238995760679245,
-0.09178274869918823,
-0.1182505264878273,
0.16442428529262543,
0.0009358620154671371,
0.06651917099952698,
0.08258313685655594,
-0.022042419761419296,
0.16447891294956207,
-0.07379321753978729,
-0.07578866183757782,
-0.006978808436542749,
0.12375060468912125,
-0.056660156697034836,
-0.03080669604241848,
-0.10566964000463486,
-0.008295975625514984,
0.1151021271944046,
0.15304014086723328,
0.12214863300323486,
0.2957419455051422,
0.08268889784812927,
0.026645636186003685,
0.08958091586828232,
0.17622539401054382,
0.09495089203119278,
0.07838419824838638,
-0.045413073152303696,
-0.014814783819019794,
0.014317171648144722,
-0.04022889584302902,
0.010141594335436821,
0.14683100581169128,
-0.2679629921913147,
-0.006678564939647913,
-0.2710230350494385,
0.0965198427438736,
-0.10913380235433578,
0.11837165057659149,
-0.01015760749578476,
0.10194015502929688,
0.11082887649536133,
0.03233652561903,
-0.03858073800802231,
0.16613617539405823,
0.08450309932231903,
-0.11277695000171661,
0.001758623169735074,
0.03737903758883476,
0.09715615212917328,
-0.02818971499800682,
0.12721189856529236,
-0.11048974841833115,
-0.1464834064245224,
0.013753619976341724,
0.07152791321277618,
-0.15373679995536804,
0.3138748109340668,
0.012069208547472954,
-0.13481520116329193,
-0.01481647603213787,
-0.09957809001207352,
-0.006440147757530212,
0.1254177987575531,
0.09333524852991104,
0.07935678958892822,
-0.2185502052307129,
-0.13339371979236603,
0.05872276425361633,
-0.00575496768578887,
0.22408108413219452,
-0.034034017473459244,
-0.11356475204229355,
-0.027013886719942093,
0.04241163283586502,
-0.06043251231312752,
0.08524788916110992,
0.023536119610071182,
-0.08113526552915573,
-0.032957352697849274,
0.05323701351881027,
0.012368366122245789,
0.00524376705288887,
0.09360801428556442,
0.020107939839363098,
-0.0009265501867048442,
0.01785753294825554,
0.047885000705718994,
-0.0675911232829094,
-0.1984109878540039,
0.09357594698667526,
-0.05215044692158699,
0.0015536568826064467,
-0.08013670891523361,
-0.15122665464878082,
-0.08837161958217621,
-0.16009655594825745,
0.12540200352668762,
-0.034406669437885284,
0.12700119614601135,
-0.06619787961244583,
0.17341409623622894,
-0.07871770113706589,
0.04481020197272301,
-0.047349292784929276,
0.050332702696323395,
-0.007268077693879604,
-0.07756082713603973,
0.16585899889469147,
-0.15564003586769104,
0.01809087023139,
0.19572502374649048,
-0.018915493041276932,
0.07177707552909851,
0.021322092041373253,
-0.0636206790804863,
0.23147478699684143,
0.3014698624610901,
0.008138049393892288,
0.1665448248386383,
0.3018903136253357,
-0.07466315478086472,
-0.2642788887023926,
-0.05505012720823288,
-0.2841376066207886,
-0.05371501296758652,
0.10716094076633453,
-0.22523896396160126,
0.06986407935619354,
0.14383509755134583,
-0.06471995264291763,
0.30228954553604126,
-0.21825523674488068,
0.012589273042976856,
0.15434536337852478,
-0.08868814259767532,
0.5515313148498535,
-0.1133413165807724,
-0.17677772045135498,
-0.008122089318931103,
-0.08741296827793121,
0.10602109134197235,
-0.0340677872300148,
0.06877441704273224,
0.013465235009789467,
0.04797380417585373,
0.048932258039712906,
-0.03111894056200981,
0.22701001167297363,
0.008710170164704323,
0.09015397727489471,
-0.07378865778446198,
-0.18624304234981537,
0.11639340221881866,
-0.04359482601284981,
-0.08891059458255768,
0.0849778801202774,
-0.05942516401410103,
-0.11078983545303345,
0.04663389176130295,
-0.07950539886951447,
-0.024862350896000862,
0.08423490077257156,
-0.04678233340382576,
-0.042606171220541,
-0.008054176345467567,
-0.1618063747882843,
-0.0002289071271661669,
0.31360217928886414,
-0.07096036523580551,
0.16695955395698547,
0.03677211329340935,
0.00038613268407061696,
-0.11027684062719345,
0.030288029462099075,
-0.05203165486454964,
-0.021576624363660812,
0.09578979015350342,
-0.11096979677677155,
0.03204701095819473,
0.14160704612731934,
-0.04864364117383957,
0.05846960097551346,
0.09256096184253693,
-0.0849417969584465,
0.007583672646433115,
0.17753590643405914,
-0.17537221312522888,
-0.1273445188999176,
-0.006135711446404457,
-0.09862716495990753,
0.14055661857128143,
0.04394126310944557,
0.05191568285226822,
0.16669964790344238,
0.03967129811644554,
-0.029474308714270592,
-0.02817419543862343,
-0.1153380498290062,
-0.0201893113553524,
0.040153320878744125,
0.00045633706031367183,
-0.08791285753250122,
0.2262638509273529,
0.06409153342247009,
-0.1328488290309906,
-0.051157206296920776,
0.2161225974559784,
-0.06805316358804703,
-0.04911920800805092,
-0.223562553524971,
0.10752306133508682,
-0.07112517952919006,
-0.0965060144662857,
0.05453834682703018,
-0.02270081453025341,
0.005106312222778797,
0.181985542178154,
0.03941008821129799,
0.11070270836353302,
0.03738937899470329,
-0.02448922023177147,
0.15798696875572205,
-0.142850860953331,
-0.14191335439682007,
-0.025354057550430298,
-0.08757315576076508,
-0.13844476640224457,
-0.026804137974977493,
0.1617041826248169,
-0.09177309274673462,
-0.14772607386112213,
-0.2621181011199951,
0.10968475043773651,
-0.16432365775108337,
-0.10192688554525375,
-0.03469514101743698,
-0.08968492597341537,
0.0696166530251503,
0.030301768332719803,
-0.03093348816037178,
-0.06706760823726654,
-0.18593791127204895,
0.0816768929362297,
0.06349513679742813,
0.045533183962106705,
-0.017847947776317596,
0.0067379772663116455,
0.1720137596130371,
0.025955144315958023,
0.10040043294429779,
0.16762186586856842,
0.011397695168852806,
0.2246655523777008,
-0.1671202927827835,
-0.11496317386627197,
0.1336962729692459,
-0.026543032377958298,
0.06762003898620605,
0.16792191565036774,
-0.0772583931684494,
0.015526676550507545,
-0.028136352077126503,
0.07066910713911057,
-0.11003983020782471,
-0.105624258518219,
0.007937257178127766,
0.02567129209637642,
-0.2755882740020752,
-0.005599735304713249,
-0.19717298448085785,
0.14788752794265747,
0.02579621411859989,
0.03297143429517746,
0.10257530212402344,
0.10404334217309952,
0.08312062919139862,
-0.0017710148822516203,
0.03226327523589134,
-0.1176818460226059,
0.02753005363047123,
-0.059239376336336136,
-0.020663779228925705,
0.017624232918024063,
0.36952024698257446,
-0.03603357449173927,
-0.046802736818790436,
0.003710439894348383,
0.1307835876941681,
-0.02139742486178875,
0.017395347356796265,
0.13209912180900574,
0.12607666850090027,
-0.08595693111419678,
-0.1504845917224884,
0.04888554662466049,
-0.04565655067563057,
-0.02836887165904045,
0.1464131623506546,
0.05905961990356445,
0.1050296202301979,
0.0908031314611435,
-0.014463032595813274,
-0.00318976235575974,
0.012856799177825451,
-0.15486004948616028,
0.06223496049642563,
-0.010558074340224266,
0.012565906159579754,
0.017934376373887062,
0.15238402783870697,
-0.005540105979889631,
0.07739730179309845,
-0.09889880567789078,
0.004208535887300968,
-0.13498884439468384,
-0.07913459837436676,
0.03617347031831741,
-0.13393273949623108,
0.04141177982091904,
-0.01871878281235695,
0.029611799865961075,
0.30386561155319214,
0.02558239921927452,
-0.020639164373278618,
0.12512871623039246,
-0.1214587539434433,
-0.12050267308950424,
-0.001594188273884356,
-0.029960084706544876,
0.0791488066315651,
-0.02633434161543846,
-0.0997740775346756,
-0.1001306027173996,
-0.15166029334068298,
-0.09759195148944855,
0.05182836204767227,
-0.04993441700935364,
-0.059362251311540604,
-0.17634081840515137,
-0.05707859992980957,
-0.05147340148687363,
0.14025864005088806,
-0.12263951450586319,
0.15159130096435547,
-0.014490418136119843,
0.004084470681846142,
0.04405883327126503,
0.1950942426919937,
-0.03644494712352753,
0.08714226633310318,
0.0154351145029068,
0.1522706001996994,
-0.05119588226079941,
0.14720745384693146,
-0.10931728035211563,
-0.04014137014746666,
-0.06710435450077057,
0.21513493359088898,
0.25630924105644226,
-0.06136954948306084,
-0.008937356993556023,
-0.012760217301547527,
0.058654606342315674,
0.1073930487036705,
0.16049085557460785,
0.002326392102986574,
0.2802925705909729,
-0.03133585304021835,
0.04815128445625305,
0.02901598811149597,
0.013607407920062542,
-0.06336209923028946,
0.03397751972079277,
0.07539387792348862,
-0.035039983689785004,
-0.1412304788827896,
0.15837742388248444,
-0.21980468928813934,
0.18157227337360382,
0.11640069633722305,
-0.19996967911720276,
-0.013728445395827293,
-0.04882071167230606,
0.1689416468143463,
-0.0856364443898201,
0.1637246012687683,
-0.0903693437576294,
-0.2108195722103119,
-0.2056000679731369,
0.03867346793413162,
-0.34623071551322937,
-0.254462867975235,
0.10422009229660034,
0.1488201916217804,
0.04015883058309555,
-0.018507536500692368,
-0.019967829808592796,
-0.018367022275924683,
0.04877542704343796,
-0.0067357709631323814,
0.06014643982052803,
0.031397558748722076,
-0.02988368645310402,
-0.24127542972564697,
-0.029804671183228493,
0.023964406922459602,
-0.07093082368373871,
0.07464958727359772,
-0.06874357163906097,
-0.022495782002806664,
0.08059766888618469,
-0.03066304884850979,
0.03298592567443848,
-0.035373736172914505,
-0.16326889395713806,
0.027529051527380943,
0.03900543600320816,
0.036012712866067886,
0.00634160777553916,
0.0008072225609794259,
-0.03455270454287529,
0.0644603744149208,
-0.16716794669628143,
-0.16015739738941193,
0.14140215516090393,
-0.06745140254497528,
0.2779497504234314,
-0.05812826007604599,
-0.0809100940823555,
0.04766704887151718,
-0.03426874056458473,
0.1807648241519928,
-0.07756473124027252,
0.047254521399736404,
0.12766779959201813,
0.011127962730824947,
0.03121316432952881,
-0.3092964291572571,
0.11082969605922699,
-0.000795336440205574,
-0.006093299947679043,
-0.07581598311662674
] |
null | null | null | # X-LoRA: Mixture of Low-Rank Adapter Experts, a Flexible Framework for Large Language Models
X-LoRA works by learning scaling values for LoRA adapters. These learned scalings values are used to
gate the LoRA experts in a dense fashion. Additionally, all LoRA adapters and the base model are frozen, allowing efficient fine tuning due to a low parameter count.
X-LoRA is easily applied to any HuggingFace Transformers model.
## Features
- Effective: Dense gating of experts allows effective mixing
- Efficient fine-tuning: low trainable parameter count
- Hierarchical encapsulated strategy: Re-use existing trained models or model section and re-use them to address complex tasks that cut across experts, following a bio-inspired strategy
- Easy-to-use API: `add_xlora_to_model`, broad compatibility
- Dynamically mix LoRA adapters: Deep layer-wise combinations of adapters.
## X-LoRA source code
Install directly from source
```
pip install git+https://github.com/EricLBuehler/xlora.git -U
```
Further details on installation, packages with source code, API details and more examples:
[https://github.com/EricLBuehler/xlora](https://github.com/EricLBuehler/xlora)
## Converting and loading a model
Example for model conversation:
```python
import torch
import xlora
from transformers import AutoConfig, AutoModelForCausalLM # type: ignore
model = AutoModelForCausalLM.from_pretrained(
"mistralai/Mistral-7B-Instruct-v0.1",
trust_remote_code=True,
use_flash_attention_2=False,
device_map="cuda:0",
torch_dtype=torch.bfloat16,
)
config = AutoConfig.from_pretrained(
"mistralai/Mistral-7B-Instruct-v0.1",
trust_remote_code=True,
use_flash_attention_2=False,
device_map="auto",
)
### Convert the model to X-LoRA
model_created = xlora.add_xlora_to_model(
model=model,
xlora_config=xlora.xLoRAConfig(config.hidden_size, xlora_depth=8, device=torch.device("cuda")),
verbose=True,
adapters={
"adapter_1": "./path/to/the/checkpoint_adapter_1/",
"adapter_2": "./path/to/the/checkpoint_adapter_2/",
"adapter_n": "./path/to/the/checkpoint_adapter_3/",
},
)
```
## Loading a trained X-LoRA model from scratch
```python
import torch
import xlora
from transformers import AutoConfig, AutoModelForCausalLM # type: ignore
model = AutoModelForCausalLM.from_pretrained(
"mistralai/Mistral-7B-Instruct-v0.1",
trust_remote_code=True,
use_flash_attention_2=False,
device_map="cuda:0",
torch_dtype=torch.bfloat16,
)
config = AutoConfig.from_pretrained(
"mistralai/Mistral-7B-Instruct-v0.1",
trust_remote_code=True,
use_flash_attention_2=False,
device_map="auto",
)
model = xlora.from_pretrained(
"./path/to/saved/model",
model,
{
"adapter_1": "./path/to/the/checkpoint/",
"adapter_2": "./path/to/the/checkpoint/",
"adapter_n": "./path/to/the/checkpoint/",
},
"cuda",
)
```
## Loading pre-trained X-LoRA model
```python
import torch
from xlora.xlora_utils import load_model # type: ignore
XLoRA_model_name = "lamm-mit/x-lora/X-LoRA"
model, tokenizer = load_model(
model_name="HuggingFaceH4/zephyr-7b-beta",
device="cuda:0",
dtype=torch.bfloat16,
fine_tune_model_name=XLoRA_model_name,
adapters={
"adapter_1": "lamm-mit/x-lora/X-LoRA_adapters/1/",
"adapter_2": "lamm-mit/x-lora/X-LoRA_adapters/2/",
"adapter_3": "lamm-mit/x-lora/X-LoRA_adapters/3/",
"adapter_4": "lamm-mit/x-lora/X-LoRA_adapters/4/",
"adapter_5": "lamm-mit/x-lora/X-LoRA_adapters/5/",
"adapter_6": "lamm-mit/x-lora/X-LoRA_adapters/6/",
"adapter_7": "lamm-mit/x-lora/X-LoRA_adapters/7/",
"adapter_8": "lamm-mit/x-lora/X-LoRA_adapters/8/",
"adapter_9": "lamm-mit/x-lora/X-LoRA_adapters/9/",
},
)
```
Inference:
```python
def generate_response (model, tokenizer,
text_input="What is the best biomaterial for superior strength?",
num_return_sequences = 1,
temperature = 0.75,
max_new_tokens = 127,
num_beams = 1,
top_k = 50,
top_p = 0.9,
repetition_penalty=1.,
eos_token_id=2,
add_special_tokens=True,
):
inputs = tokenizer(text_input, add_special_tokens=add_special_tokens)
with torch.no_grad():
outputs = model.generate(input_ids = inputs["input_ids"],
attention_mask = inputs["attention_mask"] ,
max_new_tokens=max_new_tokens,
temperature=temperature,
num_beams=num_beams,
top_k = top_k,
top_p = top_p,
num_return_sequences = num_return_sequences,
eos_token_id=eos_token_id,
pad_token_id = eos_token_id,
do_sample =True,
repetition_penalty=repetition_penalty,
)
return tokenizer.batch_decode(outputs[:,inputs["input_ids"].shape[1]:].detach().cpu().numpy(), skip_special_tokens=True)
output_text=generate_response (model, tokenizer, text_input=txt,eos_token_id=eos_token,
num_return_sequences=1, repetition_penalty=1.1,
top_p=0.9, top_k=512,
temperature=0.5,
max_new_tokens=256)
print (output_text[0])
```
## Dataset
See [lamm-mit/x-lora-dataset](https://huggingface.co/datasets/lamm-mit/x-lora-dataset) for the dataset used to train the X-LoRA model. Details on the datasets used to train the original adapters are included in the paper (see reference below).
## Sample results
## Acknowledgements
This work is built on the Hugging Face [PEFT library](https://github.com/huggingface/peft/tree/main/) and other components in the Hugging Face ecosystem. We acknowledge the authors of this excellent library and related methods.
## Original paper and citation
Cite this work as:
```bibtex
@article{Buehler_XLoRA_2024,
title = {X-LoRA: Mixture of Low-Rank Adapter Experts, a Flexible Framework for Large Language Models with Applications in Protein Mechanics and Design},
author = {E.L. Buehler, M.J. Buehler},
journal = {},
year = {2024},
volume = {},
pages = {},
url = {https://arxiv.org/abs/2402.07148}
}
```
| {"license": "apache-2.0"} | null | lamm-mit/x-lora | [
"safetensors",
"arxiv:2402.07148",
"license:apache-2.0",
"region:us"
] | 2024-02-09T11:52:31+00:00 | [
"2402.07148"
] | [] | TAGS
#safetensors #arxiv-2402.07148 #license-apache-2.0 #region-us
| # X-LoRA: Mixture of Low-Rank Adapter Experts, a Flexible Framework for Large Language Models
X-LoRA works by learning scaling values for LoRA adapters. These learned scalings values are used to
gate the LoRA experts in a dense fashion. Additionally, all LoRA adapters and the base model are frozen, allowing efficient fine tuning due to a low parameter count.
X-LoRA is easily applied to any HuggingFace Transformers model.
## Features
- Effective: Dense gating of experts allows effective mixing
- Efficient fine-tuning: low trainable parameter count
- Hierarchical encapsulated strategy: Re-use existing trained models or model section and re-use them to address complex tasks that cut across experts, following a bio-inspired strategy
- Easy-to-use API: 'add_xlora_to_model', broad compatibility
- Dynamically mix LoRA adapters: Deep layer-wise combinations of adapters.
## X-LoRA source code
Install directly from source
!image/png
Further details on installation, packages with source code, API details and more examples:
URL
## Converting and loading a model
Example for model conversation:
## Loading a trained X-LoRA model from scratch
## Loading pre-trained X-LoRA model
Inference:
## Dataset
See lamm-mit/x-lora-dataset for the dataset used to train the X-LoRA model. Details on the datasets used to train the original adapters are included in the paper (see reference below).
## Sample results
!image/png
## Acknowledgements
This work is built on the Hugging Face PEFT library and other components in the Hugging Face ecosystem. We acknowledge the authors of this excellent library and related methods.
## Original paper and citation
Cite this work as:
| [
"# X-LoRA: Mixture of Low-Rank Adapter Experts, a Flexible Framework for Large Language Models \n\nX-LoRA works by learning scaling values for LoRA adapters. These learned scalings values are used to\ngate the LoRA experts in a dense fashion. Additionally, all LoRA adapters and the base model are frozen, allowing efficient fine tuning due to a low parameter count.\n\nX-LoRA is easily applied to any HuggingFace Transformers model.",
"## Features\n- Effective: Dense gating of experts allows effective mixing\n- Efficient fine-tuning: low trainable parameter count\n- Hierarchical encapsulated strategy: Re-use existing trained models or model section and re-use them to address complex tasks that cut across experts, following a bio-inspired strategy \n- Easy-to-use API: 'add_xlora_to_model', broad compatibility \n- Dynamically mix LoRA adapters: Deep layer-wise combinations of adapters.",
"## X-LoRA source code\n\nInstall directly from source\n\n\n\n!image/png\n\nFurther details on installation, packages with source code, API details and more examples:\n\nURL",
"## Converting and loading a model\n\nExample for model conversation:",
"## Loading a trained X-LoRA model from scratch",
"## Loading pre-trained X-LoRA model\n\n\nInference:",
"## Dataset\n\nSee lamm-mit/x-lora-dataset for the dataset used to train the X-LoRA model. Details on the datasets used to train the original adapters are included in the paper (see reference below).",
"## Sample results\n\n!image/png",
"## Acknowledgements\n\nThis work is built on the Hugging Face PEFT library and other components in the Hugging Face ecosystem. We acknowledge the authors of this excellent library and related methods.",
"## Original paper and citation\n\nCite this work as:"
] | [
"TAGS\n#safetensors #arxiv-2402.07148 #license-apache-2.0 #region-us \n",
"# X-LoRA: Mixture of Low-Rank Adapter Experts, a Flexible Framework for Large Language Models \n\nX-LoRA works by learning scaling values for LoRA adapters. These learned scalings values are used to\ngate the LoRA experts in a dense fashion. Additionally, all LoRA adapters and the base model are frozen, allowing efficient fine tuning due to a low parameter count.\n\nX-LoRA is easily applied to any HuggingFace Transformers model.",
"## Features\n- Effective: Dense gating of experts allows effective mixing\n- Efficient fine-tuning: low trainable parameter count\n- Hierarchical encapsulated strategy: Re-use existing trained models or model section and re-use them to address complex tasks that cut across experts, following a bio-inspired strategy \n- Easy-to-use API: 'add_xlora_to_model', broad compatibility \n- Dynamically mix LoRA adapters: Deep layer-wise combinations of adapters.",
"## X-LoRA source code\n\nInstall directly from source\n\n\n\n!image/png\n\nFurther details on installation, packages with source code, API details and more examples:\n\nURL",
"## Converting and loading a model\n\nExample for model conversation:",
"## Loading a trained X-LoRA model from scratch",
"## Loading pre-trained X-LoRA model\n\n\nInference:",
"## Dataset\n\nSee lamm-mit/x-lora-dataset for the dataset used to train the X-LoRA model. Details on the datasets used to train the original adapters are included in the paper (see reference below).",
"## Sample results\n\n!image/png",
"## Acknowledgements\n\nThis work is built on the Hugging Face PEFT library and other components in the Hugging Face ecosystem. We acknowledge the authors of this excellent library and related methods.",
"## Original paper and citation\n\nCite this work as:"
] | [
27,
111,
117,
35,
15,
12,
15,
53,
8,
44,
12
] | [
"passage: TAGS\n#safetensors #arxiv-2402.07148 #license-apache-2.0 #region-us \n# X-LoRA: Mixture of Low-Rank Adapter Experts, a Flexible Framework for Large Language Models \n\nX-LoRA works by learning scaling values for LoRA adapters. These learned scalings values are used to\ngate the LoRA experts in a dense fashion. Additionally, all LoRA adapters and the base model are frozen, allowing efficient fine tuning due to a low parameter count.\n\nX-LoRA is easily applied to any HuggingFace Transformers model.## Features\n- Effective: Dense gating of experts allows effective mixing\n- Efficient fine-tuning: low trainable parameter count\n- Hierarchical encapsulated strategy: Re-use existing trained models or model section and re-use them to address complex tasks that cut across experts, following a bio-inspired strategy \n- Easy-to-use API: 'add_xlora_to_model', broad compatibility \n- Dynamically mix LoRA adapters: Deep layer-wise combinations of adapters.## X-LoRA source code\n\nInstall directly from source\n\n\n\n!image/png\n\nFurther details on installation, packages with source code, API details and more examples:\n\nURL## Converting and loading a model\n\nExample for model conversation:## Loading a trained X-LoRA model from scratch## Loading pre-trained X-LoRA model\n\n\nInference:## Dataset\n\nSee lamm-mit/x-lora-dataset for the dataset used to train the X-LoRA model. Details on the datasets used to train the original adapters are included in the paper (see reference below).## Sample results\n\n!image/png## Acknowledgements\n\nThis work is built on the Hugging Face PEFT library and other components in the Hugging Face ecosystem. We acknowledge the authors of this excellent library and related methods.## Original paper and citation\n\nCite this work as:"
] | [
-0.030196545645594597,
0.1256631761789322,
-0.0039214384742081165,
0.023557553067803383,
0.1041518896818161,
0.019582312554121017,
0.10284494608640671,
0.10190269351005554,
0.03770919516682625,
0.08141259104013443,
-0.05385730788111687,
0.05842588096857071,
0.08006271719932556,
0.189003124833107,
0.03483593836426735,
-0.16082417964935303,
-0.007886159233748913,
-0.08473778516054153,
0.01271890476346016,
0.04991813004016876,
0.1252298802137375,
-0.05731554701924324,
0.10030463337898254,
0.010673326440155506,
-0.05065391957759857,
0.03571134805679321,
-0.03607664629817009,
-0.033465299755334854,
0.08788489550352097,
0.10200296342372894,
0.07668975740671158,
-0.023410392925143242,
0.0351477786898613,
-0.27724283933639526,
0.023605048656463623,
0.0829518586397171,
0.009780092164874077,
0.052554965019226074,
0.06284765899181366,
0.007136359810829163,
0.07431948184967041,
-0.10934765636920929,
0.06300637871026993,
0.06887082755565643,
-0.02941312827169895,
-0.18842187523841858,
-0.08807515352964401,
0.04000774398446083,
0.05056476965546608,
0.041346292942762375,
0.016104605048894882,
0.06094558537006378,
-0.0016481681959703565,
0.041920170187950134,
0.14398464560508728,
-0.18282859027385712,
-0.020511357113718987,
-0.04594351351261139,
0.0027029188349843025,
0.09481088072061539,
-0.06509589403867722,
0.015742085874080658,
0.018193047493696213,
0.0002506601740606129,
0.07458522170782089,
-0.025184886530041695,
-0.029633576050400734,
-0.014908292330801487,
-0.07479584217071533,
0.013291304931044579,
0.13039515912532806,
-0.001977195031940937,
-0.07319974154233932,
-0.2553156018257141,
-0.04374358057975769,
0.08637334406375885,
-0.02393856644630432,
-0.047301795333623886,
0.05249213054776192,
0.01747049391269684,
0.062276799231767654,
-0.09698957204818726,
-0.08324692398309708,
0.0365518182516098,
0.03731488063931465,
0.07794838398694992,
0.03303978219628334,
0.03163200616836548,
0.03917251154780388,
0.09536049515008926,
-0.013187793083488941,
-0.08533169329166412,
-0.0374290905892849,
-0.050888415426015854,
-0.06170687451958656,
0.004867967683821917,
0.007868972606956959,
0.00021823537827003747,
0.07208439707756042,
0.14263689517974854,
0.04142456501722336,
0.0349862277507782,
-0.02703273668885231,
0.02214346081018448,
0.01818874664604664,
0.1535646915435791,
0.022683506831526756,
-0.20652012526988983,
0.08095158636569977,
0.017695821821689606,
0.08895540237426758,
-0.019330449402332306,
-0.05177317187190056,
-0.03529892489314079,
-0.017565080896019936,
0.07030785083770752,
0.015031025744974613,
-0.030033212155103683,
-0.0762714073061943,
-0.012867309153079987,
0.15993571281433105,
-0.13969068229198456,
0.06998065114021301,
-0.009289969690144062,
-0.017831778153777122,
0.055202972143888474,
0.07217218726873398,
0.0017445633420720696,
-0.06299512833356857,
0.02982008270919323,
-0.03574070706963539,
-0.012229949235916138,
-0.06645896285772324,
-0.0891018807888031,
0.014449261128902435,
-0.022639010101556778,
-0.020209042355418205,
-0.10494980961084366,
-0.08432264626026154,
-0.04243946447968483,
0.019970491528511047,
-0.04506954178214073,
-0.016140660271048546,
-0.016827309504151344,
0.024605916813015938,
-0.02156629040837288,
0.025900956243276596,
0.008574689738452435,
-0.024509793147444725,
0.02017596922814846,
-0.011998463422060013,
0.03192601725459099,
0.0020672527607530355,
0.026008717715740204,
-0.07524362206459045,
0.02693982981145382,
-0.16483376920223236,
0.12249880284070969,
-0.060593947768211365,
0.007877475582063198,
-0.10819896310567856,
0.012377303093671799,
0.021111074835062027,
0.042665380984544754,
0.009140579961240292,
0.08701091259717941,
-0.11882925778627396,
-0.04359446465969086,
0.09661805629730225,
-0.1343744993209839,
-0.04110505059361458,
0.11846022307872772,
-0.01860344223678112,
0.11407316476106644,
0.098293237388134,
0.026270750910043716,
0.16990689933300018,
-0.15898773074150085,
-0.043577421456575394,
0.01869857870042324,
0.02684641256928444,
0.05968901515007019,
0.022002944722771645,
-0.02416694350540638,
0.07106918841600418,
0.05114104598760605,
-0.03217397257685661,
0.03716855123639107,
0.027206478640437126,
-0.03895363584160805,
-0.04356753081083298,
-0.056662894785404205,
-0.03874790295958519,
-0.04630817100405693,
-0.053597576916217804,
-0.02113991603255272,
-0.12206294387578964,
-0.02088414691388607,
0.12692619860172272,
-0.05158507078886032,
0.01951770856976509,
-0.08678282797336578,
0.08490695059299469,
-0.0477297380566597,
-0.04105376452207565,
-0.10629873722791672,
-0.10470428317785263,
0.07842608541250229,
-0.06940948963165283,
0.06222429871559143,
-0.012361586093902588,
0.046674925833940506,
0.06224608048796654,
-0.0183392446488142,
-0.030939612537622452,
-0.01842687278985977,
-0.023803649470210075,
-0.05240201577544212,
-0.10307565331459045,
-0.047370102256536484,
-0.06393635272979736,
0.1684412658214569,
-0.1123698502779007,
-0.001959421904757619,
0.026211610063910484,
0.11961768567562103,
0.06719343364238739,
-0.03472975641489029,
0.019731223583221436,
-0.019666722044348717,
-0.007273841183632612,
-0.070904441177845,
0.011432775296270847,
-0.003675158368423581,
-0.05599210411310196,
0.07025735825300217,
-0.1375795304775238,
-0.029017645865678787,
0.04846930503845215,
0.1034604087471962,
-0.06573498994112015,
-0.12053072452545166,
-0.017842862755060196,
-0.020213790237903595,
-0.11054486036300659,
-0.10125143080949783,
0.19663304090499878,
0.026016978546977043,
0.09026654064655304,
-0.0567103810608387,
-0.04279663413763046,
-0.03342306986451149,
-0.0032101059332489967,
-0.025968538597226143,
0.05940110608935356,
0.01740211807191372,
-0.07502640038728714,
0.06518309563398361,
0.06787014752626419,
0.01027801726013422,
0.12189554423093796,
-0.008733316324651241,
-0.08013356477022171,
-0.009159749373793602,
0.03443077206611633,
0.020440949127078056,
0.06162799149751663,
0.04205654188990593,
0.0013700657291337848,
0.05160936713218689,
-0.0686051994562149,
0.038465987890958786,
-0.11822917312383652,
0.06127483770251274,
0.041967108845710754,
-0.011670729145407677,
0.13287749886512756,
0.021931910887360573,
0.016518428921699524,
0.07138680666685104,
-0.005509667098522186,
0.017400387674570084,
-0.013588832691311836,
-0.03880275413393974,
-0.08322539180517197,
0.08328211307525635,
-0.13145174086093903,
-0.1954851895570755,
-0.21392914652824402,
0.022084273397922516,
-0.010016814805567265,
-0.05494626238942146,
0.013842390850186348,
-0.059284765273332596,
-0.12092568725347519,
-0.09853445738554001,
0.05421847477555275,
0.06770887225866318,
-0.08240983635187149,
-0.03888097032904625,
0.02226715348660946,
0.022207684814929962,
-0.15933699905872345,
-0.006816425360739231,
0.051939018070697784,
-0.06506311893463135,
-0.03836141154170036,
0.03420814871788025,
0.04304082319140434,
0.06124822422862053,
0.016296610236167908,
-0.0266744215041399,
-0.0005769257550127804,
0.2093559205532074,
-0.08672620356082916,
0.13439077138900757,
0.21300175786018372,
-0.04931839555501938,
0.08641224354505539,
0.07740527391433716,
0.014080876484513283,
-0.04387668892741203,
0.03171874210238457,
0.016601087525486946,
-0.018210655078291893,
-0.1700958013534546,
-0.07057078927755356,
-0.0608370341360569,
-0.054703328758478165,
0.05710277333855629,
0.05156804248690605,
-0.023469317704439163,
0.05051135644316673,
-0.061371903866529465,
-0.003991446923464537,
0.07317937165498734,
0.0679779052734375,
0.10659255087375641,
0.005881867837160826,
0.0331907719373703,
-0.09523212909698486,
-0.029087426140904427,
0.1065962016582489,
0.0814543142914772,
0.18109464645385742,
-0.04178735986351967,
0.1387995332479477,
0.010492553934454918,
0.041937824338674545,
0.03269074484705925,
0.05775495246052742,
-0.0469658225774765,
0.04528306424617767,
-0.023859098553657532,
-0.09004583209753036,
-0.037752579897642136,
0.11887992173433304,
0.05681624263525009,
-0.02819947339594364,
0.04853280633687973,
-0.017742911353707314,
0.049504801630973816,
0.15528060495853424,
0.025173617526888847,
-0.11375903338193893,
-0.012157625518739223,
0.08179733157157898,
-0.06637266278266907,
-0.07106409221887589,
0.004501122981309891,
0.16545487940311432,
-0.1305791139602661,
0.06227094307541847,
-0.029643554240465164,
0.0775306224822998,
-0.0890541598200798,
-0.05036665499210358,
0.017820030450820923,
0.19094213843345642,
0.0060333749279379845,
0.0454644039273262,
-0.10818072408437729,
-0.015923716127872467,
0.028905192390084267,
0.05681818723678589,
-0.07642924040555954,
0.09526947140693665,
0.06814500689506531,
-0.02826508693397045,
0.10124677419662476,
-0.0030277238693088293,
-0.056804195046424866,
-0.053672321140766144,
-0.10806157439947128,
0.046945296227931976,
0.04570170119404793,
-0.06320957094430923,
0.057990655303001404,
-0.08806733787059784,
-0.03328320011496544,
-0.02462296187877655,
0.004942993633449078,
-0.08432536572217941,
-0.2769402861595154,
0.006254501640796661,
0.0036040875129401684,
0.0030088326893746853,
-0.08227516710758209,
-0.016841355711221695,
-0.022183841094374657,
0.1444363296031952,
-0.049668192863464355,
-0.1270415037870407,
-0.1175309419631958,
-0.05914043262600899,
0.14731769263744354,
-0.03182598575949669,
0.051495131105184555,
-0.010145103558897972,
0.15120412409305573,
-0.07917899638414383,
-0.08559994399547577,
-0.011335612274706364,
-0.05701638013124466,
-0.12346871197223663,
-0.024481499567627907,
0.1274559497833252,
0.05245736613869667,
0.039800841361284256,
0.010397948324680328,
0.01759977824985981,
-0.017406294122338295,
-0.06665267795324326,
-0.03582249581813812,
0.21760067343711853,
-0.02429135888814926,
0.09121084958314896,
-0.12437376379966736,
-0.0768306776881218,
-0.09737233072519302,
-0.047325484454631805,
0.13189642131328583,
0.13422644138336182,
-0.06833034753799438,
0.12476103007793427,
0.056163474917411804,
-0.09886651486158371,
-0.16829364001750946,
-0.032754555344581604,
0.035117413848638535,
0.05165265500545502,
0.06527282297611237,
-0.16134165227413177,
0.09709416329860687,
0.035257481038570404,
-0.0077081359922885895,
0.12430542707443237,
-0.21166209876537323,
-0.10741067677736282,
0.034813009202480316,
0.06983579695224762,
-0.027110323309898376,
-0.11443082988262177,
-0.02118590474128723,
-0.050608932971954346,
-0.05146649107336998,
0.08539862930774689,
-0.017276518046855927,
0.049089621752500534,
0.017162784934043884,
0.05593547597527504,
0.05374974384903908,
-0.015465408563613892,
0.13065671920776367,
-0.09834672510623932,
0.06489935517311096,
-0.030402543023228645,
0.03139229863882065,
-0.03651491552591324,
-0.0634102076292038,
0.06650687009096146,
-0.12386947870254517,
-0.007798469625413418,
-0.10910557955503464,
-0.04125964269042015,
0.000779665366280824,
0.07025459408760071,
-0.037473440170288086,
-0.04264449700713158,
-0.09699009358882904,
0.05448824167251587,
0.07630117237567902,
-0.007993963547050953,
0.03244591876864433,
-0.05260070785880089,
0.057895295321941376,
0.1264876425266266,
0.12351563572883606,
0.09299061447381973,
-0.11358970403671265,
-0.019680263474583626,
-0.0029229263309389353,
0.0482197143137455,
-0.10339956730604172,
0.052870042622089386,
0.0726650282740593,
-0.010530387051403522,
0.11648648977279663,
-0.031211581081151962,
-0.13445420563220978,
0.0037175798788666725,
0.059645116329193115,
-0.029779726639389992,
-0.17067000269889832,
-0.030925868079066277,
0.04733637347817421,
-0.08027951419353485,
-0.0890909731388092,
0.14041519165039062,
-0.04341741278767586,
-0.05110461637377739,
-0.004992006346583366,
0.0975189059972763,
-0.02312910370528698,
0.1031288355588913,
0.03284488618373871,
0.013922745361924171,
-0.0534832701086998,
0.07882683724164963,
0.06011993810534477,
-0.012281370349228382,
0.01980103924870491,
0.09879343956708908,
-0.04240088909864426,
-0.02397228591144085,
0.0012634453596547246,
0.09346350282430649,
-0.003054080531001091,
-0.07572410255670547,
-0.01405918039381504,
-0.09933698177337646,
-0.027523133903741837,
0.059606246650218964,
0.0012115660356357694,
0.028935151174664497,
-0.005369917489588261,
-0.02007375657558441,
-0.06808435171842575,
0.09149085730314255,
0.03323335200548172,
0.02167489193379879,
-0.0939302146434784,
0.045668598264455795,
0.031125353649258614,
-0.017608700320124626,
-0.007337314076721668,
-0.018114563077688217,
-0.07213827222585678,
-0.04020657762885094,
-0.15905718505382538,
0.0577334463596344,
-0.04161782190203667,
0.012918030843138695,
0.010457350872457027,
0.01144470926374197,
0.009156412445008755,
0.06554056704044342,
-0.045446064323186874,
-0.03835839405655861,
-0.030837401747703552,
0.07121795415878296,
-0.08816810697317123,
0.0045066592283546925,
0.060339055955410004,
-0.12011690437793732,
0.10026472806930542,
-0.03939388319849968,
-0.04443603381514549,
-0.012079359032213688,
-0.15464405715465546,
-0.02221735380589962,
0.01444230042397976,
0.05394654721021652,
0.009113352745771408,
-0.1572973132133484,
0.0334678590297699,
-0.027232754975557327,
-0.06209712103009224,
-0.046372201293706894,
0.16619731485843658,
-0.07243519276380539,
0.07856053858995438,
-0.046458180993795395,
-0.04608302190899849,
-0.04981847479939461,
0.015479635447263718,
0.116920106112957,
0.02462342381477356,
0.09409245103597641,
-0.06125515326857567,
0.0629996806383133,
-0.11805279552936554,
-0.014065628871321678,
-0.013116138055920601,
-0.006734222639352083,
-0.0002151292865164578,
-0.07655613869428635,
0.027286190539598465,
0.021883737295866013,
0.1747303158044815,
-0.05615536868572235,
-0.021094946190714836,
0.02652864344418049,
-0.01581229642033577,
-0.0847342386841774,
0.024445384740829468,
-0.00537454430013895,
-0.027852432802319527,
0.05631648376584053,
-0.004508060868829489,
-0.0465833954513073,
0.02454793080687523,
-0.0115288607776165,
0.061391789466142654,
0.12516942620277405,
0.03132687136530876,
0.040086012333631516,
0.05117103457450867,
-0.10806360840797424,
-0.027727264910936356,
0.09155874699354172,
-0.013802658766508102,
0.036978572607040405,
-0.10130204260349274,
0.09744267910718918,
0.12223107367753983,
-0.1359773725271225,
0.10366515070199966,
0.01875603199005127,
-0.010666695423424244,
-0.09392610937356949,
-0.09238766878843307,
-0.05582146719098091,
-0.046525873243808746,
-0.016856757923960686,
-0.07228721678256989,
0.03643235191702843,
0.03785187005996704,
-0.00325925275683403,
-0.009369617328047752,
0.06630119681358337,
-0.1134997308254242,
-0.09826764464378357,
0.06640014052391052,
0.04055514931678772,
0.033284347504377365,
0.024265926331281662,
0.004822188522666693,
-0.011490212753415108,
0.04879726096987724,
0.05633431673049927,
0.06692275404930115,
0.07573949545621872,
0.037695545703172684,
-0.046366531401872635,
-0.02948467805981636,
-0.010245578363537788,
-0.02508588694036007,
-0.021066119894385338,
0.15392298996448517,
0.053999681025743484,
-0.036826491355895996,
-0.023526418954133987,
0.17568524181842804,
-0.06131935864686966,
-0.09276565909385681,
-0.1517113745212555,
0.0735078975558281,
-0.0736972913146019,
-0.0028742789290845394,
0.019272614270448685,
-0.09281660616397858,
0.004846842028200626,
0.14528536796569824,
0.12473820894956589,
-0.058277517557144165,
0.03284211829304695,
-0.007346300408244133,
0.0029139581602066755,
-0.050837356597185135,
0.09325521439313889,
0.04721847549080849,
0.26830166578292847,
-0.03396384418010712,
0.07207870483398438,
-0.02535165473818779,
-0.03268452361226082,
-0.08536078035831451,
0.11057251691818237,
-0.0929398164153099,
0.00961862038820982,
-0.02111814357340336,
0.030366554856300354,
-0.00198595249094069,
-0.2853637933731079,
0.08057185262441635,
-0.1403924524784088,
-0.10538357496261597,
-0.028318706899881363,
0.025202395394444466,
-0.010175441391766071,
0.0394953228533268,
0.0240420363843441,
-0.02509482018649578,
0.1931087225675583,
-0.005073967389762402,
-0.07155215740203857,
-0.00033678172621876,
0.032293111085891724,
-0.15723565220832825,
0.14385418593883514,
0.03313431143760681,
0.019542314112186432,
0.07881846278905869,
-0.016007835045456886,
-0.09562746435403824,
0.04786576330661774,
0.040377963334321976,
-0.09174401313066483,
0.0029921995010226965,
0.08280640840530396,
-0.012035289779305458,
0.1572466939687729,
0.07318875193595886,
-0.01727314293384552,
0.042881473898887634,
0.05569273233413696,
-0.04583302512764931,
-0.050281036645174026,
0.01495046354830265,
-0.137154683470726,
0.1538245528936386,
0.12252233177423477,
-0.021560175344347954,
-0.02026161178946495,
-0.03811213746666908,
0.048241958022117615,
0.04207579046487808,
0.12600812315940857,
-0.011344983242452145,
-0.11685866117477417,
-0.01845567673444748,
-0.04631684347987175,
0.05922064930200577,
-0.16987207531929016,
-0.07144597172737122,
-0.044296808540821075,
-0.015603686682879925,
-0.07091300934553146,
0.10115304589271545,
0.11892122775316238,
0.006768289487808943,
-0.012680656276643276,
-0.16211530566215515,
0.02099565975368023,
0.06717187166213989,
-0.06496643275022507,
0.017414117231965065
] |
null | null | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# shruthicapstone-bert-qa
This model is a fine-tuned version of [Shruthi-S/capstone-project-bert-ten](https://huggingface.co/Shruthi-S/capstone-project-bert-ten) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 5.9574
- Validation Loss: 5.9507
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 0.001, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 5.9574 | 5.9507 | 0 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"tags": ["generated_from_keras_callback"], "base_model": "Shruthi-S/capstone-project-bert-ten", "model-index": [{"name": "shruthicapstone-bert-qa", "results": []}]} | question-answering | Shruthi-S/shruthicapstone-bert-qa | [
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"base_model:Shruthi-S/capstone-project-bert-ten",
"endpoints_compatible",
"region:us"
] | 2024-02-09T11:52:33+00:00 | [] | [] | TAGS
#transformers #tf #bert #question-answering #generated_from_keras_callback #base_model-Shruthi-S/capstone-project-bert-ten #endpoints_compatible #region-us
| shruthicapstone-bert-qa
=======================
This model is a fine-tuned version of Shruthi-S/capstone-project-bert-ten on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 5.9574
* Validation Loss: 5.9507
* Epoch: 0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* optimizer: {'name': 'Adam', 'weight\_decay': None, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': True, 'is\_legacy\_optimizer': False, 'learning\_rate': 0.001, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: mixed\_float16
### Training results
### Framework versions
* Transformers 4.35.2
* TensorFlow 2.15.0
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': 0.001, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: mixed\\_float16",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tf #bert #question-answering #generated_from_keras_callback #base_model-Shruthi-S/capstone-project-bert-ten #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': 0.001, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: mixed\\_float16",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
58,
198,
4,
31
] | [
"passage: TAGS\n#transformers #tf #bert #question-answering #generated_from_keras_callback #base_model-Shruthi-S/capstone-project-bert-ten #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': 0.001, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: mixed\\_float16### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
-0.07904591411352158,
0.0425376258790493,
-0.00614326074719429,
0.056860893964767456,
0.09824380278587341,
0.051594771444797516,
0.08892139792442322,
0.12905743718147278,
-0.06738915294408798,
0.1441151350736618,
0.13547220826148987,
0.13996069133281708,
0.05265194922685623,
0.13358326256275177,
-0.097813680768013,
-0.13343513011932373,
0.039919015020132065,
-0.017544804140925407,
-0.05745857208967209,
0.07470285147428513,
0.054097626358270645,
-0.07722724974155426,
0.07320194691419601,
-0.025285160169005394,
-0.08817007392644882,
0.02213401533663273,
0.03333378955721855,
-0.049876999109983444,
0.08221825957298279,
0.08449891209602356,
0.04213785380125046,
0.010352954268455505,
-0.0010200354736298323,
-0.18589767813682556,
0.0037291627377271652,
0.07965214550495148,
-0.007165093440562487,
0.07526704668998718,
0.04061491787433624,
0.002472123596817255,
0.062334027141332626,
-0.11394275724887848,
0.03672097995877266,
0.028804399073123932,
-0.13512073457241058,
-0.2409215122461319,
-0.09558594971895218,
0.038387201726436615,
0.10588615387678146,
0.07717138528823853,
-0.031488943845033646,
0.19979949295520782,
-0.04796665161848068,
0.07805239409208298,
0.2015330046415329,
-0.2952558398246765,
-0.052607208490371704,
0.006711568217724562,
0.06584180891513824,
0.03557248041033745,
-0.06885123252868652,
0.01720551773905754,
0.04422204568982124,
0.01682453788816929,
-0.004750700201839209,
-0.023260729387402534,
-0.0007009026012383401,
-0.03146430850028992,
-0.08427354693412781,
-0.017550598829984665,
0.18763229250907898,
0.07673162966966629,
-0.0538654699921608,
-0.05787115916609764,
-0.05565294250845909,
-0.15890224277973175,
0.018363259732723236,
-0.05500292032957077,
0.024037552997469902,
-0.011542214080691338,
-0.01763627864420414,
0.0020526167936623096,
-0.05906800925731659,
-0.04825359582901001,
-0.008551690727472305,
0.09267333149909973,
0.04312172532081604,
0.029309025034308434,
-0.01705699972808361,
0.06033650040626526,
-0.1004800796508789,
-0.14313073456287384,
-0.008636215701699257,
-0.011159546673297882,
-0.08123630285263062,
-0.020909519866108894,
-0.06350076198577881,
-0.03076488897204399,
0.09492266178131104,
0.1998920887708664,
-0.04147486388683319,
0.11590297520160675,
-0.00987926870584488,
0.01938774064183235,
-0.06136080622673035,
0.07875926792621613,
-0.039604105055332184,
-0.0523635558784008,
-0.01512468233704567,
0.05485377088189125,
0.0280597023665905,
-0.02138793095946312,
-0.03171423077583313,
0.0070382533594965935,
0.07979138195514679,
0.024663204327225685,
-0.030558615922927856,
0.05308832600712776,
-0.07118839025497437,
-0.0170286912471056,
-0.014957946725189686,
-0.09596770256757736,
0.028174003586173058,
0.0071156504563987255,
-0.08300450444221497,
0.03027700074017048,
0.03632510080933571,
-0.00409065093845129,
-0.06199047714471817,
0.050225164741277695,
-0.07787813991308212,
-0.0367191806435585,
-0.06941209733486176,
-0.10244978219270706,
0.02730998769402504,
-0.11640141159296036,
-0.004694611765444279,
-0.04038286209106445,
-0.13076727092266083,
-0.052081700414419174,
0.06660810858011246,
-0.07630572468042374,
-0.018608275800943375,
-0.05991246923804283,
-0.14794088900089264,
0.06017031520605087,
-0.013826392590999603,
0.1292111724615097,
-0.04755530506372452,
0.05228971689939499,
0.02429148368537426,
0.04355987533926964,
-0.012903970666229725,
0.02459469623863697,
-0.042041510343551636,
0.05645358934998512,
-0.15842339396476746,
0.07716397941112518,
-0.06418643146753311,
0.017336616292595863,
-0.16407431662082672,
-0.04723627120256424,
0.018014660105109215,
0.000441760232206434,
0.11967723071575165,
0.13910801708698273,
-0.18785779178142548,
-0.04421256110072136,
0.13650406897068024,
-0.07392296195030212,
-0.11108467727899551,
0.09688545763492584,
-0.035020723938941956,
-0.03213281184434891,
0.06718886643648148,
0.11825251579284668,
-0.031510911881923676,
-0.08229351788759232,
-0.010557033121585846,
-0.08121456205844879,
0.019058797508478165,
0.0836305096745491,
0.061622098088264465,
-0.05929473787546158,
-0.03630353882908821,
-0.004264552611857653,
-0.016757186502218246,
-0.019360432401299477,
-0.08942388743162155,
-0.043033502995967865,
-0.034383803606033325,
-0.051734697073698044,
0.027651669457554817,
0.01749662309885025,
0.02602214552462101,
-0.11494618654251099,
-0.1715758591890335,
0.01123341079801321,
0.039081789553165436,
-0.04458022862672806,
0.007342874072492123,
-0.07136748731136322,
0.06399393826723099,
-0.011990264989435673,
-0.003023426979780197,
-0.16313455998897552,
-0.0664520412683487,
0.029446959495544434,
-0.023541999980807304,
0.03657357394695282,
-0.019884953275322914,
0.0577240064740181,
0.04286983236670494,
-0.05029777064919472,
-0.02728322520852089,
-0.013770432211458683,
0.012291988357901573,
-0.0801084041595459,
-0.24618029594421387,
-0.0029947685543447733,
-0.011067559942603111,
0.06971883773803711,
-0.2739720642566681,
-0.000028149870558991097,
0.018636001273989677,
0.13258524239063263,
0.021390400826931,
-0.03679390251636505,
-0.034506842494010925,
0.021386411041021347,
-0.04143119603395462,
-0.04840066656470299,
0.01707867532968521,
-0.007064728066325188,
-0.1549062728881836,
-0.03963211923837662,
-0.18454371392726898,
0.07720290124416351,
0.11177336424589157,
-0.06527431309223175,
-0.14147740602493286,
0.02044133096933365,
-0.014204765670001507,
-0.04383402317762375,
-0.0009148073149845004,
-0.012982039712369442,
0.15683400630950928,
0.028785116970539093,
0.10086336731910706,
-0.056787725538015366,
-0.05697062984108925,
0.03547142818570137,
-0.03484412282705307,
-0.014290640130639076,
0.13568389415740967,
0.021293053403496742,
-0.1876174807548523,
0.09683506190776825,
0.11229687929153442,
-0.07613172382116318,
0.12564150989055634,
-0.047276198863983154,
-0.08046635240316391,
-0.10338651388883591,
0.0646565854549408,
0.044626638293266296,
0.08180622011423111,
-0.13225555419921875,
0.014558937400579453,
0.0220697782933712,
0.02654811553657055,
-0.01805141195654869,
-0.13910512626171112,
0.004323416389524937,
-0.0034601802472025156,
-0.036770712584257126,
0.03171743080019951,
0.03563929721713066,
0.014241943135857582,
0.13050232827663422,
0.01667759008705616,
0.014127781614661217,
0.02302461676299572,
-0.029407456517219543,
-0.09156405180692673,
0.22598963975906372,
-0.12658441066741943,
-0.0741514265537262,
-0.0774320513010025,
0.0036519765853881836,
-0.05476760119199753,
-0.02415364421904087,
0.042711831629276276,
-0.0993620976805687,
-0.056767117232084274,
-0.05867747962474823,
0.014731387607753277,
-0.014654194004833698,
0.02689877338707447,
0.028798002749681473,
-0.02346939407289028,
0.1224973276257515,
-0.10472778975963593,
-0.03992575779557228,
-0.024143818765878677,
-0.05174332112073898,
0.015224219299852848,
0.020490499213337898,
0.02094123139977455,
0.0806470438838005,
0.008954542689025402,
0.021823005750775337,
-0.027957733720541,
0.2413884401321411,
-0.050255659967660904,
0.004928726237267256,
0.0880662202835083,
-0.042023930698633194,
0.06550583988428116,
0.12674325704574585,
0.03729844093322754,
-0.11555223166942596,
0.012304979376494884,
0.10397069156169891,
0.006573839113116264,
-0.27472203969955444,
-0.010694505646824837,
-0.03942478075623512,
-0.09100769460201263,
0.03821498155593872,
0.03593849390745163,
0.10412153601646423,
0.03667835891246796,
-0.01265601348131895,
0.04346849396824837,
0.02723168581724167,
0.0798129141330719,
0.12351864576339722,
0.06846936792135239,
0.1074436828494072,
-0.01777208223938942,
-0.002622431144118309,
0.04762799292802811,
-0.034591615200042725,
0.24912254512310028,
0.0329081229865551,
0.10229197144508362,
0.10258196294307709,
0.05711144208908081,
-0.02971586026251316,
0.007651943247765303,
0.01616845093667507,
-0.012163793668150902,
0.0014905714197084308,
-0.05139625445008278,
-0.012017056345939636,
0.03244730085134506,
0.013715624809265137,
0.07461292296648026,
-0.08975227177143097,
0.02885107696056366,
0.08022449165582657,
0.18070872128009796,
0.09479008615016937,
-0.3103083372116089,
-0.06953044980764389,
0.013668426312506199,
-0.04263219237327576,
-0.05615459010004997,
-0.015181802213191986,
0.08892052620649338,
-0.062070369720458984,
0.11217709630727768,
-0.05573642626404762,
0.0655406191945076,
-0.014947871677577496,
0.039668548852205276,
0.07328587770462036,
0.07067494094371796,
0.009533004835247993,
0.022501926869153976,
-0.2461467683315277,
0.28511810302734375,
0.028772693127393723,
0.09923294186592102,
-0.04943139851093292,
0.03881819173693657,
0.01675284095108509,
-0.06781353801488876,
0.11999666690826416,
-0.01240081712603569,
-0.07181929051876068,
-0.14421096444129944,
-0.0594295859336853,
0.0043573034927248955,
0.14263419806957245,
-0.042224135249853134,
0.12329418212175369,
-0.025425143539905548,
0.0021735066547989845,
0.04687323421239853,
0.05238413065671921,
-0.19030863046646118,
-0.0678899958729744,
0.055127400904893875,
-0.00252899038605392,
-0.04356894642114639,
-0.043892599642276764,
-0.0635949894785881,
-0.047454290091991425,
0.1816723346710205,
-0.14234833419322968,
-0.042691171169281006,
-0.1147138699889183,
0.10983213782310486,
0.15920379757881165,
-0.0797039121389389,
0.027779731899499893,
0.004817022010684013,
0.03878258541226387,
0.06408802419900894,
-0.07522032409906387,
0.15161773562431335,
-0.027652639895677567,
-0.20803795754909515,
-0.0802689790725708,
0.1267024427652359,
0.0700000748038292,
0.03726872056722641,
-0.016692444682121277,
0.07515193521976471,
0.03113190084695816,
-0.09795331954956055,
0.09699634462594986,
0.01858001947402954,
0.08174025267362595,
0.04232611507177353,
-0.030636582523584366,
-0.01810801401734352,
-0.043094802647829056,
-0.025756575167179108,
0.09752415865659714,
0.36301949620246887,
-0.06767244637012482,
0.0019422484328970313,
0.029683569446206093,
-0.10301186144351959,
-0.13048610091209412,
0.03570914641022682,
0.12707950174808502,
0.0015432334039360285,
-0.01799224689602852,
-0.14064599573612213,
0.07554391026496887,
0.1437787115573883,
0.017629077658057213,
0.0749482661485672,
-0.2917570173740387,
-0.14960667490959167,
0.07488559931516647,
0.09164571762084961,
0.1052771657705307,
-0.21627238392829895,
-0.06632260978221893,
-0.021436400711536407,
-0.06042196601629257,
0.13355255126953125,
-0.0823274701833725,
0.10182858258485794,
0.01229356974363327,
-0.016975287348031998,
0.014510439708828926,
-0.03668256849050522,
0.16074444353580475,
0.00699639180675149,
0.07578572630882263,
-0.046177998185157776,
-0.06441722065210342,
0.10694783180952072,
-0.07579308748245239,
0.03313741832971573,
-0.05823065713047981,
0.013820123858749866,
-0.11276829987764359,
-0.003623571014031768,
-0.07466612756252289,
0.04216933622956276,
-0.06370490044355392,
-0.007015748415142298,
0.0001872102584457025,
0.032016318291425705,
0.07573253661394119,
-0.02473611943423748,
0.11015765368938446,
-0.021013904362916946,
0.17574019730091095,
0.10236316919326782,
0.09245633333921432,
0.028174709528684616,
-0.09421182423830032,
0.06971987336874008,
-0.011118786409497261,
0.05711137503385544,
-0.11440891772508621,
0.05050471052527428,
0.15351974964141846,
0.0009523487533442676,
0.14651192724704742,
0.059524133801460266,
-0.06117049232125282,
0.02644987404346466,
0.05236537754535675,
-0.1393188089132309,
-0.08604148030281067,
0.013838463462889194,
-0.029629133641719818,
-0.0744607076048851,
0.02916254661977291,
0.14191123843193054,
-0.013922545127570629,
0.006639623083174229,
0.004509872291237116,
0.05197600647807121,
-0.0558302216231823,
0.188009113073349,
-0.01616579107940197,
0.06342172622680664,
-0.08973990380764008,
0.12120354175567627,
0.0480109304189682,
-0.12115450203418732,
0.12341140955686569,
0.077079638838768,
-0.05139707773923874,
-0.0199993047863245,
0.045547958463430405,
0.15897931158542633,
0.0014740568585693836,
-0.03646749630570412,
-0.08977364748716354,
-0.12842904031276703,
0.08639807254076004,
0.1905716061592102,
0.03185827285051346,
0.029659075662493706,
0.0018864853773266077,
-0.008870146237313747,
-0.05734424665570259,
0.0907740443944931,
0.12668822705745697,
0.05802730470895767,
-0.11750815808773041,
0.08624250441789627,
0.01109914481639862,
-0.038428135216236115,
-0.004499312024563551,
0.017537927255034447,
-0.19252081215381622,
-0.01517078559845686,
-0.14916130900382996,
0.03909202292561531,
0.010716544464230537,
-0.025888875126838684,
0.033888280391693115,
-0.0553276464343071,
-0.06915777921676636,
0.015592462383210659,
-0.08824696391820908,
-0.05498361587524414,
0.04517074674367905,
0.06729602813720703,
-0.13041046261787415,
-0.06085623800754547,
0.016545984894037247,
-0.09973523020744324,
0.04929100349545479,
0.0648588091135025,
0.01664172112941742,
0.016787637025117874,
-0.08863037079572678,
0.00502805644646287,
0.039648935198783875,
0.004748167470097542,
0.04428146034479141,
-0.18060974776744843,
0.015240161679685116,
-0.04111345857381821,
0.0426335483789444,
0.007102120202034712,
0.07123198360204697,
-0.09731482714414597,
-0.07907550781965256,
-0.020443106070160866,
-0.05165786296129227,
-0.052405741065740585,
0.0142471082508564,
0.14062900841236115,
0.00014296061999630183,
0.16097591817378998,
-0.09480298310518265,
0.03855755552649498,
-0.200120210647583,
-0.001839023781940341,
0.02748294733464718,
-0.07084017246961594,
-0.04453711584210396,
-0.0021118782460689545,
0.10881499946117401,
-0.10005863755941391,
0.08313872665166855,
-0.09031954407691956,
0.1005612313747406,
0.0364411398768425,
-0.06919770687818527,
-0.0798938199877739,
0.06278816610574722,
0.18113596737384796,
0.03266008570790291,
-0.02093564160168171,
-0.013166559860110283,
-0.007164481561630964,
0.07222465425729752,
0.002671159105375409,
0.20295090973377228,
0.10481613874435425,
-0.003090260084718466,
0.08046132326126099,
0.0653170570731163,
-0.11033458262681961,
-0.10139777511358261,
0.1536758542060852,
-0.05670982971787453,
0.14855322241783142,
-0.008094863034784794,
0.10424730181694031,
0.08479662984609604,
-0.1804138720035553,
0.02813437581062317,
-0.0787649154663086,
-0.100185327231884,
-0.1152133196592331,
-0.1389084905385971,
-0.09525047242641449,
-0.0932471975684166,
0.02071850374341011,
-0.12841427326202393,
0.0636696070432663,
0.07871337980031967,
0.054087940603494644,
0.019443312659859657,
0.09981739521026611,
-0.03416605666279793,
-0.013578353449702263,
0.06901893764734268,
0.026497092097997665,
-0.026070455089211464,
-0.030063655227422714,
-0.04562705010175705,
0.054270949214696884,
0.0027121861930936575,
0.03400440514087677,
0.01914277672767639,
-0.03379518911242485,
0.041646808385849,
-0.025511128827929497,
-0.10117710381746292,
0.037265460938215256,
0.02823609672486782,
0.012878247536718845,
0.044745974242687225,
0.026817841455340385,
-0.02874922752380371,
-0.03005957417190075,
0.145880788564682,
-0.08984023332595825,
-0.018039105460047722,
-0.14087162911891937,
0.18998079001903534,
0.013676848262548447,
0.03600791096687317,
0.028229108080267906,
-0.0867006704211235,
-0.01114859338849783,
0.14543329179286957,
0.1426135152578354,
-0.0098660783842206,
-0.026436133310198784,
0.08498738706111908,
-0.007393755484372377,
-0.02442139945924282,
0.08441999554634094,
0.08526837825775146,
0.010159997269511223,
-0.06281158328056335,
0.007134361658245325,
-0.018136825412511826,
-0.012650853954255581,
-0.040961913764476776,
0.07945473492145538,
0.03935052454471588,
-0.0023765426594763994,
-0.014097555540502071,
0.07780412584543228,
-0.041300296783447266,
-0.15039871633052826,
0.0775168389081955,
-0.19413767755031586,
-0.17061445116996765,
-0.03617808595299721,
0.02800283581018448,
0.005906241945922375,
0.05176254361867905,
-0.0016225051367655396,
-0.03771410882472992,
0.1067524179816246,
-0.01765136420726776,
-0.053224049508571625,
-0.12526211142539978,
0.044789817184209824,
-0.06251255422830582,
0.17753593623638153,
0.0017381266225129366,
0.06216916814446449,
0.13546526432037354,
0.006965175736695528,
-0.07698855549097061,
0.04489576444029808,
0.07871409505605698,
-0.0937182679772377,
0.03301869332790375,
0.11430636048316956,
-0.018666600808501244,
0.14390528202056885,
0.06714115291833878,
-0.12040860205888748,
-0.0015107602812349796,
-0.023326100781559944,
-0.054712407290935516,
-0.03833020105957985,
0.018544528633356094,
-0.0928267315030098,
0.15675699710845947,
0.2162448912858963,
-0.04396084323525429,
0.013777806423604488,
-0.04454817250370979,
0.02047276496887207,
0.0710669681429863,
0.05653667077422142,
-0.02288067527115345,
-0.23467707633972168,
0.08957169204950333,
0.05269193649291992,
0.06660260260105133,
-0.15039515495300293,
-0.08563966304063797,
0.014415459707379341,
-0.01079192478209734,
-0.09380650520324707,
0.08506491035223007,
0.08015210181474686,
0.0478220209479332,
-0.07061878591775894,
-0.16195034980773926,
-0.02416837029159069,
0.17583231627941132,
-0.10279132425785065,
-0.07641210407018661
] |
null | null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bn-adapter-895K-squad-model2
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 4
- seed: 49
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["varun-v-rao/squad"], "base_model": "roberta-base", "model-index": [{"name": "roberta-base-bn-adapter-895K-squad-model2", "results": []}]} | null | varun-v-rao/roberta-base-bn-adapter-895K-squad-model2 | [
"tensorboard",
"generated_from_trainer",
"dataset:varun-v-rao/squad",
"base_model:roberta-base",
"license:mit",
"region:us"
] | 2024-02-09T11:53:03+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #dataset-varun-v-rao/squad #base_model-roberta-base #license-mit #region-us
|
# roberta-base-bn-adapter-895K-squad-model2
This model is a fine-tuned version of roberta-base on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 4
- seed: 49
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| [
"# roberta-base-bn-adapter-895K-squad-model2\n\nThis model is a fine-tuned version of roberta-base on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 4\n- seed: 49\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
"TAGS\n#tensorboard #generated_from_trainer #dataset-varun-v-rao/squad #base_model-roberta-base #license-mit #region-us \n",
"# roberta-base-bn-adapter-895K-squad-model2\n\nThis model is a fine-tuned version of roberta-base on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 4\n- seed: 49\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
44,
39,
6,
12,
8,
3,
90,
4,
33
] | [
"passage: TAGS\n#tensorboard #generated_from_trainer #dataset-varun-v-rao/squad #base_model-roberta-base #license-mit #region-us \n# roberta-base-bn-adapter-895K-squad-model2\n\nThis model is a fine-tuned version of roberta-base on the squad dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 4\n- seed: 49\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
-0.10914035141468048,
0.10354112088680267,
-0.0021230389829725027,
0.08219581097364426,
0.16555500030517578,
0.028558222576975822,
0.1340385377407074,
0.10522915422916412,
-0.12594833970069885,
0.04751986265182495,
0.09445278346538544,
0.028800252825021744,
0.04219989851117134,
0.12622778117656708,
-0.00481037562713027,
-0.23222051560878754,
0.01000641006976366,
0.013903860934078693,
-0.10910166800022125,
0.11137079447507858,
0.1174115240573883,
-0.0883626788854599,
0.06349076330661774,
0.014156834222376347,
-0.22382017970085144,
0.025744561105966568,
0.0008728919201530516,
-0.05921560153365135,
0.10430942475795746,
0.005189715884625912,
0.13313068449497223,
0.009336713701486588,
0.12853263318538666,
-0.1305740475654602,
0.012559314258396626,
0.052373453974723816,
0.024376139044761658,
0.11149415373802185,
0.003027666360139847,
0.012689484283328056,
0.13020290434360504,
-0.08132428675889969,
0.07177866250276566,
0.029004720970988274,
-0.09524863958358765,
-0.1881958544254303,
-0.09845209121704102,
0.09715458005666733,
0.05432630330324173,
0.08285099267959595,
0.0011559167178347707,
0.15314006805419922,
-0.094782754778862,
0.058026887476444244,
0.21384695172309875,
-0.3056032657623291,
-0.08057533949613571,
0.055962543934583664,
0.058104243129491806,
0.07107890397310257,
-0.09735659509897232,
-0.03133491054177284,
0.04298558831214905,
0.036835454404354095,
0.08213939517736435,
-0.005901683121919632,
-0.06027492508292198,
-0.007202107924968004,
-0.13038036227226257,
-0.04062649980187416,
0.20410451292991638,
0.031470946967601776,
-0.06139521300792694,
-0.007028626743704081,
-0.051900263875722885,
-0.09908222407102585,
-0.018102945759892464,
-0.017577778548002243,
0.009313764050602913,
-0.05094064027070999,
-0.12463521957397461,
-0.06981351971626282,
-0.08916394412517548,
-0.08974898606538773,
-0.007449431344866753,
0.14605513215065002,
0.05793821066617966,
0.04267982393503189,
-0.04738751798868179,
0.1349693089723587,
-0.0077070496045053005,
-0.10963140428066254,
0.005048563703894615,
-0.01234299037605524,
-0.06340108066797256,
-0.032604847103357315,
-0.04644838348031044,
-0.007871228270232677,
0.012958399951457977,
0.19321668148040771,
-0.034737687557935715,
0.015145531855523586,
0.02474784292280674,
0.03275451809167862,
-0.040819551795721054,
0.14714160561561584,
-0.0914500504732132,
-0.023952070623636246,
0.04281281307339668,
0.09500876069068909,
0.01997385546565056,
0.002469327300786972,
-0.10233438014984131,
-0.05865508317947388,
0.10596206784248352,
0.04764683172106743,
-0.039135225117206573,
0.01627448946237564,
-0.0025033934507519007,
-0.03549494221806526,
0.04437991976737976,
-0.1001177504658699,
-0.0004899831837974489,
-0.004349809605628252,
-0.09614541381597519,
-0.03922802209854126,
0.03616927191615105,
-0.00710283825173974,
-0.013805289752781391,
0.03188139572739601,
-0.10602393001317978,
-0.017580555751919746,
-0.08834977447986603,
-0.08735965937376022,
-0.001861376571469009,
-0.12864729762077332,
0.010413208045065403,
-0.09854917973279953,
-0.14162804186344147,
-0.004590275697410107,
0.01895809918642044,
-0.06948612630367279,
-0.06588421761989594,
-0.020142288878560066,
-0.07534616440534592,
0.0057501234114170074,
-0.005833407398313284,
0.15130048990249634,
-0.020907845348119736,
0.11111276596784592,
0.058904774487018585,
0.028169086202979088,
-0.04213592782616615,
0.03921141475439072,
-0.08071225881576538,
0.036711499094963074,
-0.15266238152980804,
0.043298158794641495,
-0.07513900846242905,
0.029945893213152885,
-0.11809252947568893,
-0.09990903735160828,
0.009400690905749798,
-0.03389614820480347,
0.112120121717453,
0.09119657427072525,
-0.11085428297519684,
-0.036653634160757065,
0.14082454144954681,
-0.08728762716054916,
-0.06754574924707413,
0.10147465020418167,
-0.049299243837594986,
0.0552971251308918,
0.0419202595949173,
0.13861487805843353,
0.09821465611457825,
-0.08624483644962311,
-0.028810206800699234,
0.008704941719770432,
0.04469076171517372,
-0.07876967638731003,
0.1041337326169014,
0.020495811477303505,
-0.01238209381699562,
0.031815122812986374,
-0.07466757297515869,
0.04779232665896416,
-0.11189994215965271,
-0.07962573319673538,
-0.052340079098939896,
-0.09353195875883102,
0.04493817314505577,
0.030768821015954018,
0.05333675816655159,
-0.05548156425356865,
-0.10464583337306976,
0.08389568328857422,
0.16439327597618103,
-0.0407053679227829,
0.01350206695497036,
-0.09585989266633987,
0.11546887457370758,
-0.12127251923084259,
-0.022766394540667534,
-0.20611442625522614,
-0.07079736888408661,
0.023867851123213768,
0.025997012853622437,
0.062260281294584274,
0.01459977775812149,
0.056375958025455475,
0.06812001764774323,
-0.03290120139718056,
-0.017801998183131218,
-0.1225883886218071,
-0.029339613392949104,
-0.09916888177394867,
-0.17561040818691254,
-0.09268221259117126,
-0.03327878564596176,
0.18206875026226044,
-0.19897209107875824,
0.026304565370082855,
-0.01888449862599373,
0.13278168439865112,
0.02211727388203144,
-0.03550068661570549,
-0.015272116288542747,
0.04783643037080765,
-0.014687716960906982,
-0.0817384123802185,
0.04271097481250763,
0.018629752099514008,
-0.08261644095182419,
-0.0543014295399189,
-0.08843988925218582,
0.05701620131731033,
0.09510697424411774,
0.05689492076635361,
-0.08500484377145767,
0.019886543974280357,
-0.08269074559211731,
-0.03783425688743591,
-0.019541386514902115,
-0.002622079337015748,
0.13680444657802582,
0.005887935403734446,
0.14155687391757965,
-0.07509146630764008,
-0.038644399493932724,
0.03259005770087242,
-0.013333676382899284,
0.02476273849606514,
0.08394446223974228,
0.102106973528862,
-0.12503695487976074,
0.08157666772603989,
0.08445832878351212,
-0.09174028784036636,
0.12721993029117584,
-0.04509644955396652,
-0.09736430644989014,
-0.04833463579416275,
-0.004862814210355282,
0.008656120859086514,
0.1689935028553009,
-0.07897626608610153,
-0.01056760922074318,
0.03762570396065712,
-0.003428843803703785,
0.04034828394651413,
-0.1801164746284485,
-0.020785102620720863,
0.0043717497028410435,
-0.030233053490519524,
-0.016186464577913284,
0.0039854091592133045,
0.008401677012443542,
0.10275676846504211,
0.032518304884433746,
-0.047686055302619934,
0.022801445797085762,
0.01136739831417799,
-0.05615396052598953,
0.18775349855422974,
-0.06826269626617432,
-0.09630371630191803,
-0.1521642655134201,
0.02874276414513588,
-0.048635706305503845,
-0.022012464702129364,
0.008669178932905197,
-0.07802880555391312,
-0.015104064717888832,
-0.06110289320349693,
0.017148850485682487,
-0.051707010716199875,
0.011707747355103493,
0.0076196761801838875,
0.036577172577381134,
0.10160723328590393,
-0.135466068983078,
0.03134822100400925,
-0.058216966688632965,
-0.12896592915058136,
0.00144953525159508,
0.05019728094339371,
0.11257995665073395,
0.09338614344596863,
-0.038823019713163376,
0.013271646574139595,
-0.030163992196321487,
0.23015083372592926,
-0.05696042254567146,
-0.026889419183135033,
0.14311309158802032,
0.00876821018755436,
0.0386304073035717,
0.10218190401792526,
0.037534601986408234,
-0.06051129475235939,
0.011693078093230724,
0.03948871046304703,
-0.044873323291540146,
-0.25985100865364075,
-0.01843714714050293,
-0.0410497710108757,
-0.05688875541090965,
0.09036512672901154,
0.04743202403187752,
-0.01695293001830578,
0.0865820124745369,
-0.018201973289251328,
0.0904669314622879,
-0.06693734973669052,
0.08719557523727417,
0.07576718926429749,
0.03127024695277214,
0.10353687405586243,
-0.05383972078561783,
-0.047417908906936646,
0.04736490175127983,
0.0409504733979702,
0.30738914012908936,
-0.05259156599640846,
0.10439425706863403,
0.05260203406214714,
0.19541022181510925,
-0.006900185253471136,
0.05174669250845909,
-0.005471659358590841,
0.005225242581218481,
-0.008320013992488384,
-0.040981512516736984,
-0.027878010645508766,
-0.00919286161661148,
-0.029679371044039726,
0.07522648572921753,
-0.09040450304746628,
0.04144233837723732,
0.0007049740524962544,
0.2530879080295563,
0.03578115627169609,
-0.30130887031555176,
-0.12005563080310822,
-0.01210973970592022,
-0.006979702040553093,
-0.0462050661444664,
0.0038289856165647507,
0.11238729953765869,
-0.11144749820232391,
-0.02869751863181591,
-0.061112985014915466,
0.08398836106061935,
-0.0346607007086277,
-0.016305912286043167,
0.026103343814611435,
0.16370975971221924,
0.00637333607301116,
0.09259234368801117,
-0.18331032991409302,
0.23790554702281952,
0.012158936820924282,
0.09927919507026672,
-0.02274208515882492,
0.013070542365312576,
0.008696148172020912,
0.060875412076711655,
0.0662173330783844,
-0.004542829934507608,
-0.025664761662483215,
-0.17754647135734558,
-0.10006938874721527,
0.04356322064995766,
0.08586179465055466,
-0.04792964085936546,
0.11189758032560349,
-0.048330143094062805,
0.02303505316376686,
0.0344746895134449,
-0.059981849044561386,
-0.14924411475658417,
-0.09717198461294174,
-0.0043980833142995834,
-0.006068922113627195,
-0.0687846839427948,
-0.09286203980445862,
-0.09639526158571243,
0.018289456143975258,
0.1526184231042862,
-0.032369550317525864,
-0.03272218629717827,
-0.12145062536001205,
0.136914923787117,
0.14326229691505432,
-0.08295255899429321,
0.014643508940935135,
-0.01076233945786953,
0.10532719641923904,
0.013369440101087093,
-0.07788102328777313,
0.040263205766677856,
-0.05315106734633446,
-0.16568048298358917,
-0.04015646502375603,
0.12378043681383133,
0.03638862445950508,
0.05257309973239899,
-0.007865723222494125,
0.0076416051015257835,
-0.0053808423690497875,
-0.08110343664884567,
0.01416089478880167,
0.015462140552699566,
0.029352938756346703,
0.03770723193883896,
-0.05973958969116211,
0.015540049411356449,
-0.04926547780632973,
-0.002811033045873046,
0.13903942704200745,
0.22642412781715393,
-0.0948743149638176,
0.04901006817817688,
0.0870998427271843,
-0.064052514731884,
-0.16928930580615997,
0.03272378444671631,
0.10297513753175735,
0.039581649005413055,
0.01990501396358013,
-0.20243456959724426,
0.0851941853761673,
0.09516950696706772,
-0.02864663489162922,
0.05010419711470604,
-0.28990697860717773,
-0.11283977329730988,
0.08373933285474777,
0.13375814259052277,
0.11341046541929245,
-0.12312668561935425,
-0.033938273787498474,
-0.011308910325169563,
-0.15006251633167267,
0.10060498118400574,
-0.13663868606090546,
0.12141123414039612,
-0.03195265308022499,
0.10563837736845016,
0.013111036270856857,
-0.0435330830514431,
0.13154514133930206,
0.010671339929103851,
0.06938625872135162,
-0.03975565358996391,
-0.04812033101916313,
0.17259755730628967,
-0.045611292123794556,
0.03604339063167572,
-0.018023140728473663,
0.07266892492771149,
-0.13877569139003754,
-0.011093180626630783,
-0.07530350983142853,
0.05401667580008507,
-0.04724623262882233,
-0.051365356892347336,
-0.05319075658917427,
0.06343603134155273,
-0.020094133913517,
-0.02098393812775612,
0.06829344481229782,
0.04132046550512314,
0.0904933363199234,
0.07848009467124939,
0.05165083333849907,
0.052331894636154175,
-0.055787548422813416,
0.0015290254959836602,
-0.01985868625342846,
0.08453401178121567,
-0.1534540355205536,
-0.006880858913064003,
0.10444275289773941,
0.03879944980144501,
0.11131080240011215,
0.04740990698337555,
-0.09325296431779861,
0.053472377359867096,
0.05726631358265877,
-0.10656663775444031,
-0.12280125170946121,
-0.014340550638735294,
-0.030911998823285103,
-0.15883158147335052,
0.06015168875455856,
0.12682472169399261,
-0.0883244127035141,
-0.04271138831973076,
-0.01769089885056019,
-0.017073210328817368,
-0.06376118212938309,
0.17480254173278809,
0.09022694081068039,
0.0631718710064888,
-0.08618558198213577,
0.09762780368328094,
0.08583415299654007,
-0.007026882842183113,
0.03774037957191467,
0.056656740605831146,
-0.09408144652843475,
-0.015648284927010536,
0.005585885141044855,
0.16150259971618652,
-0.08393711596727371,
-0.03554513677954674,
-0.13635903596878052,
-0.07214324176311493,
0.045427560806274414,
0.09217394888401031,
0.06544406712055206,
0.0035891446750611067,
-0.046840354800224304,
0.03640122711658478,
-0.16274796426296234,
0.0855686292052269,
0.04182024300098419,
0.07163695991039276,
-0.13939549028873444,
0.09648405760526657,
0.011618330143392086,
0.0616801381111145,
-0.01816597580909729,
0.005410877522081137,
-0.10019092261791229,
-0.005299300421029329,
-0.12782904505729675,
-0.013503553345799446,
-0.00019143930694554,
0.0049409037455916405,
-0.01738370954990387,
-0.06734001636505127,
-0.06490252166986465,
0.055201634764671326,
-0.0788222923874855,
-0.042151302099227905,
0.01983339712023735,
0.033235203474760056,
-0.126275897026062,
0.010983029380440712,
0.02139904908835888,
-0.08274609595537186,
0.09984537959098816,
0.07810129225254059,
0.060921333730220795,
0.04030245542526245,
-0.07893065363168716,
-0.015525197610259056,
-0.012602773495018482,
0.010813631117343903,
0.06798966228961945,
-0.07032154500484467,
0.017804263159632683,
-0.03674563020467758,
0.05749371647834778,
0.0009333918569609523,
0.06283753365278244,
-0.13245171308517456,
-0.031441763043403625,
-0.06163911521434784,
-0.02925335243344307,
-0.07286091893911362,
0.0555940605700016,
0.08295580744743347,
0.08366337418556213,
0.13551126420497894,
-0.06586425006389618,
0.023679615929722786,
-0.20865412056446075,
-0.019338231533765793,
-0.025226959958672523,
-0.04301999509334564,
-0.11676757037639618,
-0.048152439296245575,
0.05377032235264778,
-0.04772605746984482,
0.07639069855213165,
-0.011215291917324066,
0.07969920337200165,
0.023647600784897804,
-0.0269073024392128,
-0.006169139873236418,
0.009750830009579659,
0.17710722982883453,
0.07001626491546631,
-0.0016528047854080796,
0.06020185723900795,
0.0012143499916419387,
0.053718697279691696,
0.05987538769841194,
0.12964367866516113,
0.13537220656871796,
0.02633986622095108,
0.06699550896883011,
0.10638533532619476,
-0.059758223593235016,
-0.11032060533761978,
0.11175103485584259,
-0.010257057845592499,
0.04899127781391144,
-0.029643479734659195,
0.1625342220067978,
0.1679764986038208,
-0.13750310242176056,
0.02120976708829403,
-0.03721357882022858,
-0.08956372737884521,
-0.08526156842708588,
-0.03924944996833801,
-0.07364480197429657,
-0.11189558357000351,
0.04043584689497948,
-0.11232636868953705,
-0.03228288143873215,
0.10504952073097229,
-0.004438987001776695,
-0.005798438563942909,
0.12513427436351776,
0.049753837287425995,
0.01987941563129425,
0.035107728093862534,
0.002880654064938426,
-0.020167622715234756,
-0.04086771979928017,
-0.05385381355881691,
0.07136674970388412,
0.0011082610581070185,
0.08479329198598862,
-0.04815753549337387,
-0.003864886472001672,
0.05043832212686539,
-0.00193544733338058,
-0.07697104662656784,
0.017813529819250107,
0.024405842646956444,
0.060124192386865616,
0.05139249935746193,
0.028714274987578392,
0.0024383964482694864,
-0.04421117901802063,
0.2577349543571472,
-0.04820360615849495,
-0.08297358453273773,
-0.1358591616153717,
0.19238832592964172,
0.016783880069851875,
-0.04421417787671089,
0.04528728872537613,
-0.10291738063097,
-0.005667198449373245,
0.1641988307237625,
0.19159306585788727,
-0.05218447744846344,
-0.029167458415031433,
0.01479692105203867,
-0.030106890946626663,
-0.06893117725849152,
0.1368296891450882,
0.09104392677545547,
0.03582852706313133,
-0.07738712430000305,
-0.030660072341561317,
-0.04635957255959511,
-0.01052408292889595,
-0.030989455059170723,
0.05028346925973892,
0.02424425072968006,
0.009576714597642422,
-0.058517348021268845,
0.04978659749031067,
-0.01800469309091568,
-0.1988857239484787,
0.05996009707450867,
-0.1309783011674881,
-0.18970809876918793,
-0.037030160427093506,
0.06625264137983322,
-0.0032216503750532866,
0.07157192379236221,
-0.049754731357097626,
0.008822965435683727,
0.10958431661128998,
-0.01980629190802574,
-0.07220414280891418,
-0.09820836782455444,
0.11834174394607544,
-0.02962692454457283,
0.19209398329257965,
-0.04192906990647316,
0.08040257543325424,
0.12073412537574768,
0.023660412058234215,
-0.1535012274980545,
0.012113198637962341,
0.05082011967897415,
-0.03494551032781601,
0.01250985637307167,
0.15947340428829193,
-0.03131214901804924,
0.08770771324634552,
0.024873800575733185,
-0.09793185442686081,
-0.017586739733815193,
-0.06289801001548767,
0.0006739382515661418,
-0.07273916900157928,
-0.00013378896983340383,
-0.06555930525064468,
0.1535016894340515,
0.19545802474021912,
-0.05554750934243202,
-0.015070794150233269,
-0.10366487503051758,
0.027877913787961006,
0.08462539315223694,
0.06944148242473602,
-0.014907324686646461,
-0.19156691431999207,
-0.0026255808770656586,
0.05281967669725418,
-0.0016010475810617208,
-0.29483741521835327,
-0.06736864149570465,
0.0316828116774559,
-0.05744282156229019,
-0.06536878645420074,
0.1074465811252594,
0.07328172028064728,
0.06873378902673721,
-0.03680163994431496,
-0.07655102759599686,
-0.08659177273511887,
0.12202633917331696,
-0.1700153648853302,
-0.07482954859733582
] |
null | null | transformers |
# wav2vec2-bert-CV16-de
This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the MOZILLA-FOUNDATION/COMMON_VOICE_16_0 - DE dataset. It achieves the following results on the evaluation set:
- Loss: 0.095182
- Wer: 0.066672
- Steps: 25000
- epoch: 10
| {"language": ["de"], "datasets": ["mozilla-foundation/common_voice_16_0"]} | automatic-speech-recognition | sharrnah/wav2vec2-bert-CV16-de | [
"transformers",
"safetensors",
"wav2vec2-bert",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_16_0",
"endpoints_compatible",
"region:us"
] | 2024-02-09T11:53:04+00:00 | [] | [
"de"
] | TAGS
#transformers #safetensors #wav2vec2-bert #automatic-speech-recognition #de #dataset-mozilla-foundation/common_voice_16_0 #endpoints_compatible #region-us
|
# wav2vec2-bert-CV16-de
This model is a fine-tuned version of facebook/w2v-bert-2.0 on the MOZILLA-FOUNDATION/COMMON_VOICE_16_0 - DE dataset. It achieves the following results on the evaluation set:
- Loss: 0.095182
- Wer: 0.066672
- Steps: 25000
- epoch: 10
| [
"# wav2vec2-bert-CV16-de\n\nThis model is a fine-tuned version of facebook/w2v-bert-2.0 on the MOZILLA-FOUNDATION/COMMON_VOICE_16_0 - DE dataset. It achieves the following results on the evaluation set:\n- Loss: 0.095182\n- Wer: 0.066672\n- Steps: 25000\n- epoch: 10"
] | [
"TAGS\n#transformers #safetensors #wav2vec2-bert #automatic-speech-recognition #de #dataset-mozilla-foundation/common_voice_16_0 #endpoints_compatible #region-us \n",
"# wav2vec2-bert-CV16-de\n\nThis model is a fine-tuned version of facebook/w2v-bert-2.0 on the MOZILLA-FOUNDATION/COMMON_VOICE_16_0 - DE dataset. It achieves the following results on the evaluation set:\n- Loss: 0.095182\n- Wer: 0.066672\n- Steps: 25000\n- epoch: 10"
] | [
60,
92
] | [
"passage: TAGS\n#transformers #safetensors #wav2vec2-bert #automatic-speech-recognition #de #dataset-mozilla-foundation/common_voice_16_0 #endpoints_compatible #region-us \n# wav2vec2-bert-CV16-de\n\nThis model is a fine-tuned version of facebook/w2v-bert-2.0 on the MOZILLA-FOUNDATION/COMMON_VOICE_16_0 - DE dataset. It achieves the following results on the evaluation set:\n- Loss: 0.095182\n- Wer: 0.066672\n- Steps: 25000\n- epoch: 10"
] | [
-0.18796692788600922,
0.013196521438658237,
-0.004093165043741465,
-0.07428719103336334,
0.0663621723651886,
-0.05933539196848869,
0.06378016620874405,
0.05084461346268654,
0.02770853042602539,
0.07479991763830185,
0.09755967557430267,
0.06063031032681465,
0.06647177785634995,
0.07990910857915878,
-0.10853186994791031,
-0.09462457150220871,
0.09822465479373932,
-0.01806236244738102,
0.07881609350442886,
0.05745735019445419,
0.0697922557592392,
-0.09560061991214752,
0.043466147035360336,
0.053235284984111786,
-0.06617745012044907,
0.01933758147060871,
0.09069975465536118,
-0.08012082427740097,
0.09882659465074539,
0.08414992690086365,
-0.02374313212931156,
0.07094801962375641,
0.0018282615346834064,
-0.14673340320587158,
0.014059791341423988,
0.03375355526804924,
0.047137148678302765,
-0.016574585810303688,
0.07168553024530411,
-0.021280009299516678,
-0.03359152004122734,
-0.007323193363845348,
-0.1277274191379547,
0.09499920904636383,
-0.028705166652798653,
-0.13002558052539825,
-0.048394083976745605,
-0.05084729194641113,
0.09962727129459381,
0.05708196386694908,
-0.0680164247751236,
0.0907670333981514,
-0.03451112285256386,
0.08331935852766037,
0.2045823633670807,
-0.2980231046676636,
0.0013093841262161732,
0.0022507368121296167,
0.046885184943675995,
-0.07397879660129547,
-0.036479268223047256,
0.15507584810256958,
0.09515843540430069,
-0.02668508142232895,
-0.062149640172719955,
-0.0486995130777359,
-0.017898349091410637,
-0.062086381018161774,
-0.08790566027164459,
0.027961472049355507,
0.2829923927783966,
0.0804845467209816,
-0.07221441715955734,
-0.020867113023996353,
-0.03054388426244259,
0.06570927798748016,
-0.019919371232390404,
-0.059886638075113297,
-0.03083661012351513,
0.02100403420627117,
-0.05723387748003006,
0.007627177517861128,
-0.07493993639945984,
-0.09609100967645645,
-0.06938975304365158,
0.20133504271507263,
-0.04581523314118385,
0.03828796371817589,
-0.08874452114105225,
-0.018878642469644547,
-0.0840228721499443,
-0.04933824762701988,
-0.02779986895620823,
-0.032512400299310684,
-0.055837735533714294,
0.04923773929476738,
-0.09882906824350357,
-0.026555094867944717,
0.12557734549045563,
-0.045616138726472855,
0.05805535241961479,
0.048300426453351974,
-0.10264983773231506,
0.024857057258486748,
-0.0036648677196353674,
0.059709757566452026,
-0.1375512331724167,
-0.12997029721736908,
0.03703746572136879,
0.009609101340174675,
0.043574560433626175,
-0.046187322586774826,
-0.045517534017562866,
-0.07592182606458664,
0.0848783627152443,
0.06183958053588867,
-0.0021834620274603367,
0.04268231615424156,
-0.04352077096700668,
0.006155029404908419,
-0.09646911174058914,
-0.08124428987503052,
-0.0035626613534986973,
0.07287057489156723,
-0.007482864893972874,
0.16035312414169312,
-0.026665963232517242,
0.046913739293813705,
-0.11186998337507248,
-0.04770251736044884,
0.0023208712227642536,
-0.013865938410162926,
0.043089352548122406,
-0.08646748214960098,
0.042677562683820724,
-0.021100295707583427,
0.0023875294718891382,
-0.15684263408184052,
-0.02974453940987587,
-0.02439095266163349,
-0.03923405706882477,
-0.0276950653642416,
0.012773423455655575,
-0.1099611297249794,
-0.04263344034552574,
0.02169746346771717,
-0.08034932613372803,
0.002205075230449438,
-0.02385404147207737,
0.029643092304468155,
0.04593706130981445,
0.08213872462511063,
-0.07813379168510437,
0.039773352444171906,
0.013340654782950878,
0.0230261217802763,
0.053548868745565414,
0.09671764820814133,
-0.054888766258955,
-0.010991627350449562,
-0.09409835189580917,
0.0031120229978114367,
-0.0825548768043518,
0.0538947656750679,
0.08569828420877457,
0.17118129134178162,
-0.1874806135892868,
-0.079595185816288,
0.09483780711889267,
-0.08752784132957458,
-0.06838112324476242,
0.15965594351291656,
0.04174512252211571,
-0.04440793767571449,
0.1251685917377472,
0.26020872592926025,
0.0023300570901483297,
-0.08651792258024216,
-0.05457194522023201,
0.008840346708893776,
-0.04730595275759697,
-0.06610409915447235,
0.04429745301604271,
-0.07626193761825562,
-0.15071575343608856,
0.0006917451391927898,
0.03710922226309776,
-0.016731606796383858,
-0.10569935292005539,
-0.026335200294852257,
-0.0015412374632433057,
-0.11501722037792206,
0.10350996255874634,
-0.060717638581991196,
-0.021025720983743668,
-0.10883443802595139,
0.016049984842538834,
-0.06476584821939468,
0.07510526478290558,
-0.11070982366800308,
0.04480142146348953,
-0.16821402311325073,
0.1302369236946106,
-0.15523791313171387,
0.025048021227121353,
-0.1043747141957283,
0.12654195725917816,
0.010398977436125278,
0.09395842254161835,
-0.010890757665038109,
0.07427091151475906,
0.03721245378255844,
-0.03766326606273651,
-0.013474240899085999,
-0.023056011646986008,
0.09675262868404388,
0.033423926681280136,
-0.03571018949151039,
-0.14055606722831726,
-0.030266132205724716,
-0.05678366497159004,
0.01911839470267296,
-0.09365805983543396,
-0.0592828169465065,
0.1285455971956253,
0.08465716987848282,
-0.03070051781833172,
0.004385939799249172,
0.03795595094561577,
0.012654769234359264,
0.0006716587813571095,
0.018476637080311775,
0.039802778512239456,
0.01423609908670187,
-0.09355935454368591,
0.19189047813415527,
-0.13081547617912292,
0.15939630568027496,
0.15155357122421265,
-0.11260343343019485,
0.08427400141954422,
0.0649522915482521,
0.021745499223470688,
-0.017781773582100868,
-0.002386021427810192,
-0.11378772556781769,
0.11046941578388214,
-0.049799054861068726,
0.09173369407653809,
-0.0999751165509224,
0.05570423975586891,
0.08715738356113434,
-0.07049541920423508,
-0.07051792740821838,
0.029816290363669395,
-0.06972365826368332,
-0.12656863033771515,
-0.016537899151444435,
0.03789748251438141,
-0.00802957359701395,
0.15348443388938904,
-0.009762787260115147,
-0.03597389906644821,
-0.031897690147161484,
-0.0022648717276751995,
-0.0015282055828720331,
0.10200270265340805,
-0.2696523070335388,
-0.07082360982894897,
0.028146209195256233,
0.04801017418503761,
0.08443422615528107,
-0.0890803337097168,
0.05310914292931557,
-0.0029334761202335358,
-0.08962545543909073,
-0.048189904540777206,
0.0744364932179451,
-0.05576565861701965,
0.10094781219959259,
-0.05254300311207771,
-0.14058837294578552,
0.025752820074558258,
-0.06559967994689941,
-0.13550223410129547,
0.06998928636312485,
-0.09169948846101761,
-0.26535940170288086,
-0.07859470695257187,
-0.07612336426973343,
-0.09249229729175568,
0.06579557806253433,
0.07060587406158447,
-0.09600209444761276,
-0.035356003791093826,
-0.04993881285190582,
0.055151548236608505,
-0.051600381731987,
0.10142765194177628,
0.18179941177368164,
-0.036805663257837296,
0.06333669275045395,
-0.07450893521308899,
-0.03129703551530838,
-0.08669637143611908,
0.007640040013939142,
-0.014731691218912601,
-0.059127453714609146,
0.08584251999855042,
0.11678945273160934,
0.026842517778277397,
0.04902637004852295,
-0.042953912168741226,
0.12998774647712708,
-0.04715900495648384,
-0.03551674634218216,
0.17908509075641632,
0.006592447403818369,
-0.03281588479876518,
0.11239543557167053,
-0.032129280269145966,
-0.1196710616350174,
-0.007209727540612221,
-0.008848803117871284,
-0.04386476054787636,
-0.18381519615650177,
-0.10990738123655319,
-0.05604160204529762,
0.026066718623042107,
-0.03530232980847359,
-0.01983507163822651,
0.043584875762462616,
-0.014730670489370823,
-0.02772902138531208,
-0.023770838975906372,
0.055590830743312836,
-0.00612544035539031,
0.0982665866613388,
-0.05160946771502495,
0.1076117679476738,
-0.025406645610928535,
0.0018956834683194757,
0.07299633324146271,
-0.03750556334853172,
0.11777403205633163,
0.12354225665330887,
0.02692667953670025,
0.046347178518772125,
0.07495595514774323,
0.07498648762702942,
0.11204591393470764,
0.013691814616322517,
-0.010187757201492786,
0.005387694574892521,
-0.06895746290683746,
-0.06907942146062851,
0.017648087814450264,
0.15887053310871124,
-0.004223364405333996,
-0.1456126570701599,
0.05673336237668991,
0.06381861120462418,
0.22483471035957336,
0.20980484783649445,
-0.1965954303741455,
-0.06111006811261177,
0.015560923144221306,
-0.034305959939956665,
-0.032972924411296844,
0.10070373117923737,
0.19941115379333496,
0.011597502045333385,
0.11221224069595337,
0.022936884313821793,
0.04531562328338623,
-0.05216013640165329,
0.10054248571395874,
-0.15456345677375793,
-0.013444487936794758,
-0.0013073849258944392,
0.02761082537472248,
-0.2216244786977768,
0.2288866490125656,
0.04429546743631363,
0.097505584359169,
-0.051698893308639526,
-0.018684228882193565,
0.013211852870881557,
0.0708136335015297,
0.13869668543338776,
0.010442260652780533,
0.016157671809196472,
0.013522019609808922,
-0.061592672020196915,
0.06594792753458023,
0.11521636694669724,
0.07946356385946274,
0.037428610026836395,
-0.024979976937174797,
-0.05093654617667198,
0.038696590811014175,
0.03423266112804413,
-0.2150440216064453,
-0.018278421834111214,
0.025640640407800674,
0.29223862290382385,
0.121444471180439,
-0.0462934784591198,
-0.055166203528642654,
-0.06774606555700302,
0.15566295385360718,
-0.12955833971500397,
0.040480732917785645,
-0.05346585065126419,
0.0008235417190007865,
0.07472392171621323,
0.00009993893763748929,
-0.0016198769444599748,
0.04311670362949371,
0.1298609972000122,
-0.013569092378020287,
-0.08050286769866943,
0.10451744496822357,
-0.14620214700698853,
-0.10633686184883118,
-0.022925658151507378,
0.255516916513443,
-0.010252262465655804,
-0.004968102090060711,
0.1030210480093956,
-0.06796008348464966,
0.0564141646027565,
-0.023765496909618378,
0.10555575788021088,
0.14559634029865265,
-0.11957739293575287,
0.05561961978673935,
0.1005304828286171,
-0.22311386466026306,
-0.08991602808237076,
0.0003558678145054728,
0.1309475600719452,
0.15705718100070953,
-0.02279755473136902,
0.05395914986729622,
0.2865790128707886,
-0.02702406421303749,
-0.2730216383934021,
-0.06681245565414429,
-0.03476371616125107,
0.0898740217089653,
-0.1253543347120285,
0.016834957525134087,
0.014987340196967125,
-0.01882074400782585,
-0.043084803968667984,
-0.04225429147481918,
-0.19143334031105042,
-0.0997774675488472,
0.14300070703029633,
-0.030466128140687943,
0.462454229593277,
-0.019237639382481575,
-0.09817738085985184,
-0.14503715932369232,
0.014553285203874111,
0.0024137485306710005,
-0.14948394894599915,
0.10788296163082123,
0.07165591418743134,
-0.03522714599967003,
0.026040945202112198,
-0.0074056051671504974,
0.13633912801742554,
0.06101072579622269,
-0.0006221826770342886,
0.012286510318517685,
0.00185707479249686,
0.05495905131101608,
-0.014977507293224335,
0.04793265089392662,
0.00906216911971569,
0.037166185677051544,
-0.00894787348806858,
-0.057014066725969315,
-0.05669934302568436,
0.06992212682962418,
0.03400029242038727,
0.008970294147729874,
0.030696285888552666,
-0.05857935920357704,
-0.003857466159388423,
0.07165554910898209,
-0.021495776250958443,
-0.13243447244167328,
-0.035753823816776276,
0.21849852800369263,
0.21380096673965454,
-0.17894379794597626,
0.09699680656194687,
0.028275789692997932,
-0.07430284470319748,
0.12347787618637085,
0.02713148482143879,
0.09854687750339508,
0.04708324000239372,
0.005338262300938368,
0.06422323733568192,
0.03433356061577797,
-0.1108209639787674,
0.05083382874727249,
0.03897017613053322,
-0.10096482932567596,
-0.15620306134223938,
-0.03508717939257622,
0.042355623096227646,
0.025473177433013916,
0.2067013531923294,
0.22961890697479248,
-0.015898672863841057,
0.03694410249590874,
-0.07310695946216583,
-0.0017394570168107748,
-0.10868345201015472,
0.19583699107170105,
-0.011780639179050922,
0.05854889005422592,
-0.13725081086158752,
0.10949578881263733,
-0.09295295923948288,
-0.015436363406479359,
0.07088112831115723,
-0.1032203808426857,
-0.07740775495767593,
-0.08470609784126282,
-0.16499178111553192,
0.06210298463702202,
0.10173404961824417,
-0.17295622825622559,
-0.023906748741865158,
-0.16384024918079376,
0.064297616481781,
0.18551945686340332,
0.0042422376573085785,
0.09867244958877563,
-0.03029553033411503,
-0.07130767405033112,
-0.07499165832996368,
-0.011988515965640545,
0.06682782620191574,
-0.029341071844100952,
-0.15507462620735168,
0.02015741355717182,
-0.02804487757384777,
0.06133275851607323,
-0.06190859526395798,
-0.061898939311504364,
-0.07510656118392944,
0.05595066770911217,
-0.18210254609584808,
0.010816345922648907,
-0.07888321578502655,
-0.047616321593523026,
0.04329722374677658,
-0.0549493134021759,
-0.07250706106424332,
0.07656566053628922,
-0.030244264751672745,
0.06335978955030441,
0.0035572939086705446,
0.02499331533908844,
-0.0623750202357769,
0.004935234785079956,
-0.03052395209670067,
-0.055461857467889786,
0.11391275376081467,
0.13099044561386108,
-0.0887165293097496,
0.06898418813943863,
-0.1876302808523178,
-0.11986152082681656,
0.11328226327896118,
0.009879352524876595,
0.008393569849431515,
-0.06056633219122887,
-0.013529494404792786,
0.11428320407867432,
0.04764894023537636,
-0.02911759726703167,
0.10138510167598724,
-0.0007112217135727406,
-0.0350542850792408,
-0.09109997004270554,
0.010861244052648544,
0.007074856664985418,
-0.03576858341693878,
0.1438540816307068,
0.081760473549366,
0.06765329092741013,
-0.08962513506412506,
-0.006358422338962555,
-0.06076469272375107,
0.05511854961514473,
-0.04946700856089592,
-0.07197203487157822,
-0.04312669858336449,
-0.026713579893112183,
0.09595957398414612,
-0.027854233980178833,
0.07276096940040588,
-0.08617111295461655,
0.0700865164399147,
-0.023817144334316254,
-0.1573169231414795,
-0.04120330139994621,
0.05879901722073555,
0.18198223412036896,
0.05556335672736168,
-0.006440393626689911,
-0.05813504010438919,
-0.02001742087304592,
0.13232333958148956,
0.21681079268455505,
0.03436799719929695,
0.08564435690641403,
0.07269544899463654,
0.18342962861061096,
0.09469327330589294,
-0.07668932527303696,
-0.05418560281395912,
-0.01652575470507145,
-0.1674499213695526,
0.08865261822938919,
-0.012956919148564339,
-0.008762462995946407,
0.16982604563236237,
-0.002065670909360051,
0.00879520270973444,
-0.03509526327252388,
-0.046019528061151505,
-0.1411023736000061,
-0.17978493869304657,
-0.12881574034690857,
-0.12267244607210159,
-0.006784387864172459,
-0.08867571502923965,
0.02110220305621624,
0.02401447296142578,
0.06026773527264595,
-0.02077745832502842,
0.12215232849121094,
-0.06474942713975906,
-0.06590522825717926,
0.18313294649124146,
-0.07808367162942886,
-0.04196784272789955,
0.05022876709699631,
-0.0846174955368042,
0.12811218202114105,
0.019577836617827415,
0.0273393914103508,
0.03573482483625412,
-0.05465501919388771,
0.05604803189635277,
-0.011165761388838291,
-0.09643259644508362,
-0.028458332642912865,
-0.05009116977453232,
0.04262981191277504,
0.0939057245850563,
0.09583520889282227,
-0.09233738481998444,
0.029724817723035812,
0.10687363147735596,
-0.09106846898794174,
-0.039988014847040176,
-0.09972914308309555,
0.046964798122644424,
0.015465161763131618,
0.15414726734161377,
-0.0695747509598732,
-0.045026350766420364,
-0.02807500958442688,
0.23225441575050354,
0.1332065463066101,
0.004809699021279812,
0.02840246818959713,
-0.07486796379089355,
0.013562168926000595,
-0.03561033681035042,
0.04699869826436043,
0.06836917251348495,
0.08415254950523376,
0.061203692108392715,
-0.014489809982478619,
-0.024633072316646576,
-0.04156780615448952,
-0.0032850278075784445,
0.11539026349782944,
-0.038095202296972275,
-0.08763667196035385,
0.015282751992344856,
0.09258367866277695,
-0.02741001546382904,
-0.08206433057785034,
-0.05365710332989693,
-0.09053361415863037,
-0.05447246506810188,
0.011809904128313065,
-0.01554966252297163,
0.11212369799613953,
0.08654432743787766,
-0.09929709136486053,
-0.011262776330113411,
0.09159644693136215,
0.008709486573934555,
-0.10483139008283615,
-0.016618957743048668,
-0.05260390415787697,
-0.09837760031223297,
0.03992617875337601,
0.01977497711777687,
0.06169046461582184,
0.044883210211992264,
0.06567100435495377,
-0.05224664509296417,
0.13833600282669067,
-0.009857785888016224,
-0.08069319278001785,
0.061515893787145615,
0.1874612271785736,
-0.028628310188651085,
0.1839839071035385,
0.03161425143480301,
-0.1541714072227478,
-0.007873798720538616,
0.003249275265261531,
-0.023239225149154663,
-0.0452938936650753,
0.017018524929881096,
-0.04914020374417305,
0.06794911623001099,
-0.04418925940990448,
-0.08595319837331772,
0.0032932735048234463,
0.026951640844345093,
0.0441899411380291,
-0.01696755364537239,
-0.01638570986688137,
-0.013819963671267033,
-0.2387121617794037,
0.00998162105679512,
-0.051680393517017365,
0.023009806871414185,
-0.14874088764190674,
0.006807140074670315,
-0.060379065573215485,
-0.03345348685979843,
-0.013371763750910759,
0.03156829997897148,
0.049516379833221436,
-0.007105490192770958,
-0.03942086175084114,
-0.052711885422468185,
0.0803123340010643,
0.1510475128889084,
-0.08055927604436874,
-0.1438511461019516
] |
null | null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | null | Prajvi/Llama2_7B_qlora_FT_bush_crisis_10 | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-09T12:00:28+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
31,
6,
3,
82,
28,
3,
4,
9,
9,
10,
42,
20,
3,
4,
5,
9,
11,
13,
3,
12,
5,
4,
5,
3,
4,
9,
53,
9,
8,
6,
3,
14,
8,
7,
9,
4
] | [
"passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
] | [
-0.06646376848220825,
0.2168014943599701,
-0.00225935154594481,
0.023818302899599075,
0.1271018385887146,
-0.001635765191167593,
0.04218708351254463,
0.13324736058712006,
-0.020175931975245476,
0.11144465953111649,
0.046588581055402756,
0.09377603232860565,
0.09928803145885468,
0.18404334783554077,
0.04859916493296623,
-0.2059975117444992,
0.007056170143187046,
-0.09090408682823181,
0.014076028019189835,
0.1116579994559288,
0.13719257712364197,
-0.10291384905576706,
0.08272874355316162,
-0.04045208916068077,
-0.02019004337489605,
0.00012576708104461432,
-0.09259183704853058,
-0.07032395154237747,
0.06885425746440887,
0.06264153122901917,
0.051234472543001175,
0.001456156256608665,
0.09140396863222122,
-0.2864592671394348,
0.017265573143959045,
0.08406311273574829,
0.0027674848679453135,
0.06290827691555023,
0.07236549258232117,
-0.07389893382787704,
0.11328595131635666,
-0.08021481335163116,
0.13019037246704102,
0.08625296503305435,
-0.062064990401268005,
-0.23071379959583282,
-0.07525765895843506,
0.0963398814201355,
0.12251301854848862,
0.06215599179267883,
-0.022921854630112648,
0.15455181896686554,
-0.06248689442873001,
0.012971068732440472,
0.1294165402650833,
-0.11526761949062347,
-0.05572471022605896,
0.061741601675748825,
0.11775490641593933,
0.10740239918231964,
-0.14110268652439117,
-0.0017287094378843904,
0.04900608956813812,
0.029121357947587967,
0.08589313924312592,
0.022661056369543076,
0.12003941088914871,
0.04652795568108559,
-0.13695219159126282,
-0.04037507623434067,
0.12011898308992386,
0.038862764835357666,
-0.06446044892072678,
-0.2168138176202774,
-0.006778308190405369,
-0.0601806715130806,
-0.014732478186488152,
-0.07019448280334473,
0.039128515869379044,
-0.02470310963690281,
0.07317749410867691,
-0.04465159401297569,
-0.1063927412033081,
-0.0421026237308979,
0.0892222449183464,
0.07748593389987946,
0.011527054943144321,
-0.02519804798066616,
0.04627908393740654,
0.13455867767333984,
0.05402068421244621,
-0.10399353504180908,
-0.07017925381660461,
-0.06942764669656754,
-0.09420394152402878,
-0.04035796597599983,
0.056760527193546295,
0.031942449510097504,
0.02665667235851288,
0.22703726589679718,
0.016653569415211678,
0.04155244305729866,
0.0224777739495039,
0.01032855175435543,
0.043662428855895996,
0.0955500528216362,
-0.05303520709276199,
-0.15660029649734497,
-0.04072032496333122,
0.09077946096658707,
-0.0027527001220732927,
-0.036689214408397675,
-0.03966725245118141,
0.03849169611930847,
0.06843466311693192,
0.13122352957725525,
0.07552056759595871,
-0.017929591238498688,
-0.04813180863857269,
-0.030096933245658875,
0.23523783683776855,
-0.1493375599384308,
0.04426715523004532,
-0.02271856553852558,
-0.01804111897945404,
-0.03908449783921242,
0.03597262129187584,
0.022118929773569107,
-0.000004518366949923802,
0.09706240892410278,
-0.058981191366910934,
-0.05378659814596176,
-0.10168042778968811,
-0.03272576630115509,
0.04088849574327469,
-0.013975566253066063,
-0.010589460842311382,
-0.09025166928768158,
-0.09490354359149933,
-0.04766594246029854,
0.05537205561995506,
-0.05123869329690933,
-0.03770573064684868,
0.009465423412621021,
-0.08151785284280777,
-0.005444355774670839,
-0.005417742300778627,
0.10699385404586792,
-0.03222226724028587,
0.04445803165435791,
-0.027600755915045738,
0.05225523188710213,
0.09919606149196625,
0.031576547771692276,
-0.0773419588804245,
0.0561848059296608,
-0.22559374570846558,
0.07503069192171097,
-0.11481974273920059,
0.04335082694888115,
-0.1704932004213333,
-0.042439818382263184,
0.005444696638733149,
0.0139949731528759,
0.013206101022660732,
0.12720820307731628,
-0.19255615770816803,
-0.01654396951198578,
0.13260798156261444,
-0.09212633967399597,
-0.118110790848732,
0.07884611934423447,
-0.029701577499508858,
0.1624738723039627,
0.04682036489248276,
-0.027025915682315826,
0.09224298596382141,
-0.16434773802757263,
-0.07092688232660294,
-0.00949116237461567,
-0.01727987825870514,
0.12109188735485077,
0.07512219995260239,
-0.05991523340344429,
0.046571120619773865,
0.02832140028476715,
-0.038078423589468,
-0.04424772411584854,
-0.050857074558734894,
-0.10884185880422592,
-0.01070026308298111,
-0.08987759798765182,
0.04065500199794769,
-0.01250192429870367,
-0.07916021347045898,
-0.029885273426771164,
-0.18612512946128845,
-0.0030564051121473312,
0.10038342326879501,
0.0035033065360039473,
-0.005652366206049919,
-0.08666291832923889,
0.026358824223279953,
-0.03112892620265484,
-0.008404186926782131,
-0.16764774918556213,
-0.04399421438574791,
0.046902090311050415,
-0.16094985604286194,
0.020117372274398804,
-0.06413903087377548,
0.06334125250577927,
0.03641495108604431,
-0.05590536445379257,
-0.0248766727745533,
-0.01730942726135254,
0.011945613659918308,
-0.05083848536014557,
-0.18994836509227753,
-0.056277405470609665,
-0.037882111966609955,
0.149809330701828,
-0.25956398248672485,
0.032966937869787216,
0.051140617579221725,
0.14649195969104767,
0.00406361510977149,
-0.05115427449345589,
0.01429014839231968,
-0.05360214412212372,
-0.054652128368616104,
-0.06746816635131836,
-0.006135428790003061,
-0.027576493099331856,
-0.05147203803062439,
0.019243421033024788,
-0.1755700707435608,
-0.021410830318927765,
0.09424154460430145,
0.12876708805561066,
-0.1486445665359497,
-0.018640631809830666,
-0.048725154250860214,
-0.06339836865663528,
-0.0715010017156601,
-0.07038594037294388,
0.10712739825248718,
0.0513901449739933,
0.04796046018600464,
-0.07435787469148636,
-0.07092321664094925,
0.02726263552904129,
0.006906150374561548,
-0.03382374346256256,
0.08727246522903442,
0.05199531093239784,
-0.09209315478801727,
0.0756213590502739,
0.1092359870672226,
0.07177663594484329,
0.09363535046577454,
0.01574566215276718,
-0.11756632477045059,
-0.028492970392107964,
0.036266472190618515,
0.02740776725113392,
0.1465986967086792,
-0.05952361226081848,
0.04016614332795143,
0.04494241625070572,
-0.04170418903231621,
0.022319864481687546,
-0.08787637203931808,
0.024075502529740334,
0.025203049182891846,
-0.0034381982404738665,
0.06284574419260025,
-0.02525499276816845,
-0.0050758360885083675,
0.07016654312610626,
0.047779910266399384,
0.04621000960469246,
0.009655474685132504,
-0.01720241829752922,
-0.1047825813293457,
0.16950392723083496,
-0.0951867327094078,
-0.269941508769989,
-0.17632324993610382,
0.026197833940386772,
0.04035249724984169,
-0.022378476336598396,
0.031619444489479065,
-0.07056326419115067,
-0.10630585998296738,
-0.1060405746102333,
-0.002429972169920802,
0.01714223250746727,
-0.06364088505506516,
-0.0741225928068161,
0.07348573952913284,
0.04382912442088127,
-0.14902326464653015,
0.038552410900592804,
0.055694397538900375,
-0.057955220341682434,
-0.0233661737293005,
0.09118817001581192,
0.12397737801074982,
0.14583967626094818,
-0.021366750821471214,
-0.028626007959246635,
0.029004426673054695,
0.19620531797409058,
-0.13469526171684265,
0.10371150821447372,
0.13814030587673187,
-0.04545360431075096,
0.08360563963651657,
0.1560150384902954,
0.029186224564909935,
-0.08317049592733383,
0.05044832453131676,
0.04082648828625679,
-0.043159641325473785,
-0.2666129767894745,
-0.0534592866897583,
0.012832709588110447,
-0.06255637854337692,
0.09786593168973923,
0.10183793306350708,
0.11542957276105881,
0.034910861402750015,
-0.07166364789009094,
-0.043925940990448,
-0.0058974819257855415,
0.11737963557243347,
-0.05490213260054588,
-0.012639665976166725,
0.07686592638492584,
-0.05086168646812439,
0.005355054512619972,
0.10266812145709991,
0.02973790094256401,
0.17442677915096283,
0.020399179309606552,
0.11231429129838943,
0.06195578724145889,
0.08633565157651901,
0.0007386076031252742,
0.02951662428677082,
0.05147615820169449,
0.017203815281391144,
-0.002300140680745244,
-0.10421168059110641,
-0.006156572140753269,
0.1449710875749588,
0.028103826567530632,
0.029669636860489845,
-0.0018948549404740334,
-0.005003341939300299,
0.05121048167347908,
0.1746254414319992,
-0.011592294089496136,
-0.22072425484657288,
-0.0845772922039032,
0.06936841458082199,
-0.06218599155545235,
-0.12968985736370087,
-0.026130788028240204,
0.045467354357242584,
-0.17519839107990265,
0.026703642681241035,
-0.027433741837739944,
0.0919293761253357,
-0.09345759451389313,
-0.02221956104040146,
0.03687324374914169,
0.084866963326931,
-0.014529162086546421,
0.08703910559415817,
-0.14498743414878845,
0.11886418610811234,
0.02978132851421833,
0.09024628251791,
-0.11081171780824661,
0.07909037172794342,
-0.007550720125436783,
0.009180475026369095,
0.19379350543022156,
-0.011335089802742004,
-0.03514958545565605,
-0.08774717897176743,
-0.11210042238235474,
-0.013537433929741383,
0.12687496840953827,
-0.1243172138929367,
0.08773399889469147,
-0.015198243781924248,
-0.044079482555389404,
0.00937260314822197,
-0.12100647389888763,
-0.17273177206516266,
-0.19628387689590454,
0.05585884302854538,
-0.09575839340686798,
0.025643249973654747,
-0.11914430558681488,
-0.07089093327522278,
-0.02952558360993862,
0.241120383143425,
-0.1745356321334839,
-0.06510113179683685,
-0.1468164622783661,
-0.046294767409563065,
0.1662203073501587,
-0.04437198117375374,
0.0718095526099205,
-0.0208172257989645,
0.20345525443553925,
0.005988610442727804,
-0.004939318168908358,
0.06724198162555695,
-0.08892562240362167,
-0.16873881220817566,
-0.06771010160446167,
0.1510489284992218,
0.11680185794830322,
0.04907919466495514,
-0.002248800592496991,
0.0011772146681323647,
-0.016943959519267082,
-0.1137804463505745,
-0.0033210667315870523,
0.16037839651107788,
0.03878779336810112,
0.025986969470977783,
-0.05243593826889992,
-0.08797456324100494,
-0.06899320334196091,
-0.06853509694337845,
0.06221301481127739,
0.19590823352336884,
-0.10376439243555069,
0.1700313836336136,
0.147536963224411,
-0.07305635511875153,
-0.23175598680973053,
0.035342130810022354,
0.04983805492520332,
0.0014306638622656465,
0.04886869341135025,
-0.18252557516098022,
0.10521943867206573,
0.019543392583727837,
-0.05505957826972008,
0.13485197722911835,
-0.1557481735944748,
-0.1552847921848297,
0.0722852572798729,
0.03904085233807564,
-0.22423844039440155,
-0.1354004591703415,
-0.09622503817081451,
-0.05825018882751465,
-0.14065024256706238,
0.06054598465561867,
-0.002136280992999673,
0.015948504209518433,
0.03500790148973465,
-0.0015643214574083686,
0.027123261243104935,
-0.058935679495334625,
0.18609118461608887,
-0.004065449349582195,
0.020676052197813988,
-0.060264769941568375,
-0.0478842556476593,
0.09839435666799545,
-0.06130504235625267,
0.12208222597837448,
0.004057085141539574,
0.01594383642077446,
-0.10362856835126877,
-0.048314861953258514,
-0.04328322783112526,
0.05154227837920189,
-0.07548051327466965,
-0.10070807486772537,
-0.043625857681035995,
0.08841723203659058,
0.07005169242620468,
-0.03383097052574158,
0.00549331633374095,
-0.07189501076936722,
0.10019614547491074,
0.17795267701148987,
0.17573626339435577,
0.009926567785441875,
-0.07241068035364151,
0.01677953451871872,
-0.04142116755247116,
0.044231921434402466,
-0.2513144314289093,
0.03756171092391014,
0.06098250672221184,
0.029438555240631104,
0.09217222779989243,
-0.020435843616724014,
-0.1820858269929886,
-0.04050002992153168,
0.08094815909862518,
-0.05452597141265869,
-0.22617179155349731,
-0.019085140898823738,
0.0954197570681572,
-0.2020406424999237,
-0.007372708059847355,
0.03995226323604584,
-0.048725228756666183,
-0.023169852793216705,
0.00010950004070764408,
0.06317184865474701,
0.002471912419423461,
0.09773622453212738,
0.0735151618719101,
0.09715340286493301,
-0.08337292820215225,
0.10562895983457565,
0.10150538384914398,
-0.09572599828243256,
0.03605884686112404,
0.06754924356937408,
-0.05300498008728027,
-0.043293699622154236,
0.03665391728281975,
0.033023297786712646,
0.005234600510448217,
-0.060321882367134094,
0.013913018628954887,
-0.036497246474027634,
0.044923391193151474,
0.08326134830713272,
0.03754979372024536,
-0.013354414142668247,
0.06462216377258301,
0.03401726484298706,
-0.10898099094629288,
0.10366570204496384,
0.01731540448963642,
0.04105307161808014,
-0.08384523540735245,
-0.019968897104263306,
0.035425446927547455,
0.030576206743717194,
-0.01765924133360386,
-0.02306121215224266,
-0.02860277332365513,
-0.01614218018949032,
-0.14299540221691132,
-0.023106401786208153,
-0.07243485748767853,
0.006181265693157911,
0.014656842686235905,
-0.031884219497442245,
-0.011233693920075893,
0.02475680410861969,
-0.06979699432849884,
-0.07426341623067856,
-0.006949664559215307,
0.09833318740129471,
-0.15115703642368317,
0.008848577737808228,
0.06907843053340912,
-0.11088496446609497,
0.08190931379795074,
-0.008411259390413761,
0.016245156526565552,
0.022527478635311127,
-0.15448406338691711,
0.05601610988378525,
0.0008648968650959432,
0.01916889287531376,
0.025886621326208115,
-0.16471809148788452,
0.004104440100491047,
-0.04661374166607857,
-0.02149827405810356,
-0.00004464812809601426,
-0.02647159807384014,
-0.12325995415449142,
0.06858719140291214,
-0.015622655861079693,
-0.035931166261434555,
-0.02701525390148163,
0.0539589487016201,
0.07888586074113846,
-0.027474910020828247,
0.10445091128349304,
-0.008690856397151947,
0.04941811040043831,
-0.16801609098911285,
-0.02470702864229679,
-0.04982255399227142,
0.019377702847123146,
0.009884213097393513,
-0.007693959400057793,
0.04183054715394974,
-0.00976533442735672,
0.21883612871170044,
-0.05075952783226967,
0.1607085019350052,
0.05847611650824547,
-0.017352959141135216,
-0.0007513365126214921,
0.06180921941995621,
0.05997028574347496,
0.04658793285489082,
0.009480604901909828,
0.023740366101264954,
-0.022450892254710197,
-0.006695089396089315,
-0.15932634472846985,
0.01890849508345127,
0.14999441802501678,
0.06301083415746689,
0.024745315313339233,
0.05866100639104843,
-0.12775006890296936,
-0.12135478109121323,
0.09311001747846603,
-0.026755332946777344,
0.00928465835750103,
-0.08245618641376495,
0.1358020007610321,
0.14980104565620422,
-0.14000412821769714,
0.05256148427724838,
-0.06134212389588356,
-0.05217423290014267,
-0.10388828068971634,
-0.12032219022512436,
-0.05887215584516525,
-0.053666237741708755,
0.002330566756427288,
-0.03760887682437897,
0.054546963423490524,
0.03344334661960602,
-0.009351172484457493,
-0.00022941511997487396,
0.13597318530082703,
-0.019751882180571556,
-0.0028988157864660025,
0.048313532024621964,
0.03693558648228645,
0.02373051457107067,
-0.05275435373187065,
0.02940409444272518,
0.02539868652820587,
0.032232340425252914,
0.06546790152788162,
0.033412106335163116,
-0.047448933124542236,
0.03804153576493263,
-0.0025254099164158106,
-0.11207924783229828,
0.019641218706965446,
-0.00460948096588254,
-0.0742158442735672,
0.1268945336341858,
0.0407399944961071,
0.010224059224128723,
-0.03741471841931343,
0.24361543357372284,
-0.06653323769569397,
-0.06378097087144852,
-0.13251738250255585,
0.10491154342889786,
-0.0027236645109951496,
0.06476365029811859,
0.023412218317389488,
-0.1284150779247284,
0.005243356805294752,
0.13858191668987274,
0.12181595712900162,
0.0045748427510261536,
0.009228081442415714,
0.0518609918653965,
0.0025186820421367884,
-0.06998204439878464,
0.054019294679164886,
0.06992026418447495,
0.12919506430625916,
-0.07847554981708527,
0.07680778950452805,
0.0006860480643808842,
-0.08370215445756912,
-0.02947772853076458,
0.11312682181596756,
-0.0409729965031147,
0.03491825982928276,
-0.047444481402635574,
0.10916327685117722,
-0.05787910893559456,
-0.29412412643432617,
0.02350960113108158,
-0.09588567912578583,
-0.15202060341835022,
-0.018367812037467957,
0.05944539234042168,
-0.02624768204987049,
0.018029648810625076,
0.06971040368080139,
-0.06011629104614258,
0.20098382234573364,
0.0335683599114418,
-0.07864278554916382,
-0.0664360448718071,
0.04837050288915634,
-0.06564252078533173,
0.2949807047843933,
0.008418165147304535,
0.02863333560526371,
0.10770907253026962,
-0.03253700211644173,
-0.18271861970424652,
0.010723991319537163,
0.1133992001414299,
-0.08056149631738663,
0.08200647681951523,
0.19000613689422607,
-0.012578671798110008,
0.1209007054567337,
0.05294662341475487,
-0.047376248985528946,
0.04217283055186272,
-0.03389401361346245,
-0.051268599927425385,
-0.10752558708190918,
0.058453381061553955,
-0.05909625440835953,
0.15447644889354706,
0.10152646154165268,
-0.05671518296003342,
-0.004550917539745569,
-0.05555408447980881,
0.04875178262591362,
0.01804669201374054,
0.12263146042823792,
0.02951994352042675,
-0.1865430772304535,
0.032826557755470276,
-0.01144319772720337,
0.10186848044395447,
-0.25588861107826233,
-0.08421015739440918,
0.08833149075508118,
-0.011924264021217823,
-0.05105875805020332,
0.10560628771781921,
0.057650718837976456,
0.04243382066488266,
-0.043439045548439026,
-0.10480839014053345,
-0.02186836116015911,
0.14663739502429962,
-0.1469624787569046,
-0.025013303384184837
] |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# safety-utcustom-train-SF30-RGBD-b5
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/safety-utcustom-TRAIN-30 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1952
- Mean Iou: 0.6486
- Mean Accuracy: 0.7199
- Overall Accuracy: 0.9704
- Accuracy Unlabeled: nan
- Accuracy Safe: 0.4523
- Accuracy Unsafe: 0.9874
- Iou Unlabeled: nan
- Iou Safe: 0.3271
- Iou Unsafe: 0.9700
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Safe | Accuracy Unsafe | Iou Unlabeled | Iou Safe | Iou Unsafe |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:---------------:|:-------------:|:--------:|:----------:|
| 0.8758 | 5.0 | 10 | 0.9831 | 0.3415 | 0.6100 | 0.9154 | nan | 0.2839 | 0.9362 | 0.0 | 0.1099 | 0.9147 |
| 0.7637 | 10.0 | 20 | 0.7236 | 0.3771 | 0.6275 | 0.9582 | nan | 0.2745 | 0.9806 | 0.0 | 0.1735 | 0.9578 |
| 0.6698 | 15.0 | 30 | 0.5510 | 0.3789 | 0.6286 | 0.9593 | nan | 0.2755 | 0.9818 | 0.0 | 0.1776 | 0.9590 |
| 0.5935 | 20.0 | 40 | 0.4632 | 0.3822 | 0.6388 | 0.9591 | nan | 0.2967 | 0.9809 | 0.0 | 0.1877 | 0.9588 |
| 0.5108 | 25.0 | 50 | 0.4239 | 0.3814 | 0.6492 | 0.9560 | nan | 0.3214 | 0.9769 | 0.0 | 0.1887 | 0.9556 |
| 0.4597 | 30.0 | 60 | 0.4134 | 0.3845 | 0.6422 | 0.9596 | nan | 0.3034 | 0.9811 | 0.0 | 0.1943 | 0.9592 |
| 0.4307 | 35.0 | 70 | 0.3918 | 0.3900 | 0.6516 | 0.9594 | nan | 0.3229 | 0.9803 | 0.0 | 0.2111 | 0.9590 |
| 0.367 | 40.0 | 80 | 0.3578 | 0.3885 | 0.6600 | 0.9582 | nan | 0.3415 | 0.9784 | 0.0 | 0.2077 | 0.9577 |
| 0.3249 | 45.0 | 90 | 0.3395 | 0.3921 | 0.6587 | 0.9607 | nan | 0.3360 | 0.9813 | 0.0 | 0.2161 | 0.9603 |
| 0.292 | 50.0 | 100 | 0.3124 | 0.3969 | 0.6622 | 0.9633 | nan | 0.3408 | 0.9837 | 0.0 | 0.2280 | 0.9629 |
| 0.2766 | 55.0 | 110 | 0.2820 | 0.4078 | 0.6878 | 0.9644 | nan | 0.3925 | 0.9831 | 0.0 | 0.2594 | 0.9639 |
| 0.2347 | 60.0 | 120 | 0.2673 | 0.6169 | 0.7000 | 0.9641 | nan | 0.4181 | 0.9820 | nan | 0.2701 | 0.9636 |
| 0.226 | 65.0 | 130 | 0.2350 | 0.6280 | 0.6854 | 0.9698 | nan | 0.3818 | 0.9891 | nan | 0.2865 | 0.9694 |
| 0.3262 | 70.0 | 140 | 0.2354 | 0.6338 | 0.7125 | 0.9674 | nan | 0.4402 | 0.9848 | nan | 0.3006 | 0.9670 |
| 0.1991 | 75.0 | 150 | 0.2231 | 0.6363 | 0.7169 | 0.9676 | nan | 0.4492 | 0.9846 | nan | 0.3056 | 0.9671 |
| 0.2106 | 80.0 | 160 | 0.2089 | 0.6399 | 0.7152 | 0.9688 | nan | 0.4444 | 0.9860 | nan | 0.3114 | 0.9683 |
| 0.1995 | 85.0 | 170 | 0.1969 | 0.6493 | 0.7179 | 0.9709 | nan | 0.4478 | 0.9880 | nan | 0.3281 | 0.9704 |
| 0.1981 | 90.0 | 180 | 0.1909 | 0.6503 | 0.7136 | 0.9716 | nan | 0.4381 | 0.9892 | nan | 0.3293 | 0.9712 |
| 0.1875 | 95.0 | 190 | 0.1965 | 0.6473 | 0.7231 | 0.9697 | nan | 0.4598 | 0.9864 | nan | 0.3254 | 0.9692 |
| 0.2088 | 100.0 | 200 | 0.1952 | 0.6486 | 0.7199 | 0.9704 | nan | 0.4523 | 0.9874 | nan | 0.3271 | 0.9700 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
| {"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "safety-utcustom-train-SF30-RGBD-b5", "results": []}]} | image-segmentation | sam1120/safety-utcustom-train-SF30-RGBD-b5 | [
"transformers",
"pytorch",
"tensorboard",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"license:other",
"endpoints_compatible",
"region:us"
] | 2024-02-09T12:02:25+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us
| safety-utcustom-train-SF30-RGBD-b5
==================================
This model is a fine-tuned version of nvidia/mit-b5 on the sam1120/safety-utcustom-TRAIN-30 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1952
* Mean Iou: 0.6486
* Mean Accuracy: 0.7199
* Overall Accuracy: 0.9704
* Accuracy Unlabeled: nan
* Accuracy Safe: 0.4523
* Accuracy Unsafe: 0.9874
* Iou Unlabeled: nan
* Iou Safe: 0.3271
* Iou Unsafe: 0.9700
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 100
### Training results
### Framework versions
* Transformers 4.30.2
* Pytorch 2.0.1+cu117
* Datasets 2.13.1
* Tokenizers 0.13.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 100",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3"
] | [
"TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 100",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3"
] | [
48,
117,
4,
33
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 100### Training results### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3"
] | [
-0.1062762588262558,
0.03180491179227829,
-0.0020265523344278336,
0.11488214880228043,
0.1714872419834137,
0.027942916378378868,
0.11693540960550308,
0.1149817630648613,
-0.1055101677775383,
0.03223733231425285,
0.10563058406114578,
0.1446245163679123,
0.016651920974254608,
0.09891024976968765,
-0.019798511639237404,
-0.3046700954437256,
-0.024964535608887672,
0.03180314972996712,
-0.0830923467874527,
0.1256706416606903,
0.06444676220417023,
-0.16314738988876343,
0.09225337952375412,
-0.004405954387038946,
-0.2203545868396759,
0.014857142232358456,
-0.004570122808218002,
-0.032513465732336044,
0.16055569052696228,
0.02237292379140854,
0.11631030589342117,
0.008660655468702316,
0.11536862701177597,
-0.20462395250797272,
0.017332037910819054,
0.05645623058080673,
-0.0032244801986962557,
0.06708730757236481,
0.06358225643634796,
0.00040782286669127643,
0.14795427024364471,
-0.10561015456914902,
0.06882792711257935,
0.002512105042114854,
-0.14550523459911346,
-0.2128603756427765,
-0.07436975091695786,
0.02233281545341015,
0.07944818586111069,
0.09523368626832962,
-0.005918548908084631,
0.11236105114221573,
-0.09087568521499634,
0.11268625408411026,
0.26951274275779724,
-0.24253332614898682,
-0.08586844056844711,
0.0410427488386631,
0.002967492677271366,
0.06754080206155777,
-0.13189271092414856,
0.008960027247667313,
0.03381012752652168,
0.046782951802015305,
0.11930929124355316,
-0.034012146294116974,
-0.09868236631155014,
0.025814063847064972,
-0.13746066391468048,
-0.03412563353776932,
0.05545201897621155,
0.05328775569796562,
-0.021275624632835388,
-0.03401516005396843,
-0.06769571453332901,
-0.18560799956321716,
-0.06657490134239197,
0.011572206392884254,
0.06678598374128342,
-0.05912870168685913,
-0.11616339534521103,
-0.014313511550426483,
-0.11004127562046051,
-0.08515316247940063,
-0.04900451377034187,
0.12922456860542297,
0.03356526792049408,
0.018962427973747253,
-0.0333496518433094,
0.1266951709985733,
-0.02729910984635353,
-0.1399071216583252,
0.017359275370836258,
0.03039136342704296,
-0.04416310042142868,
-0.032456111162900925,
-0.04915246739983559,
-0.06477101892232895,
-0.011133676394820213,
0.10984738916158676,
-0.05814938619732857,
0.06742125749588013,
0.03746471926569939,
0.05138295888900757,
-0.11198769509792328,
0.19070403277873993,
-0.06695730984210968,
-0.010112504474818707,
-0.03689881041646004,
0.05777011811733246,
0.005239830818027258,
-0.023567261174321175,
-0.10580854117870331,
0.006040660664439201,
0.07215054333209991,
-0.008046231232583523,
-0.08796045929193497,
0.06979069858789444,
-0.03974883630871773,
-0.010885217227041721,
-0.004117639735341072,
-0.07617881149053574,
0.04657537490129471,
-0.0009122662013396621,
-0.08194732666015625,
-0.030406849458813667,
0.048799458891153336,
0.014914295636117458,
0.01286794152110815,
0.1658531278371811,
-0.0861496552824974,
0.06372915953397751,
-0.11199190467596054,
-0.10133306682109833,
0.0001936600892804563,
-0.08747304975986481,
0.035692986100912094,
-0.07831090688705444,
-0.14846277236938477,
-0.009947095066308975,
0.07222790271043777,
-0.04077272489666939,
0.002467192942276597,
-0.05415486916899681,
-0.09158105403184891,
0.00203449884429574,
-0.009107164107263088,
0.16360357403755188,
-0.06539756804704666,
0.12221027910709381,
0.03617791458964348,
0.07295254617929459,
-0.06295195966959,
0.039166439324617386,
-0.08520416170358658,
0.020235959440469742,
-0.22141589224338531,
0.0446796789765358,
-0.050371475517749786,
0.0678204819560051,
-0.060556765645742416,
-0.12124992907047272,
0.0067116981372237206,
0.0028598224744200706,
0.09201633185148239,
0.10711704194545746,
-0.225285604596138,
-0.0770995020866394,
0.15116892755031586,
-0.07281807065010071,
-0.09980719536542892,
0.11284511536359787,
-0.064201720058918,
0.013226481154561043,
0.06185471639037132,
0.19891546666622162,
0.05064018443226814,
-0.13872428238391876,
0.021954234689474106,
-0.01568746753036976,
0.048840682953596115,
-0.027748912572860718,
0.04919520020484924,
0.021740660071372986,
0.0890972763299942,
0.019191129133105278,
-0.06486959010362625,
0.0673610121011734,
-0.12511248886585236,
-0.09749598801136017,
-0.0261884443461895,
-0.08597024530172348,
0.04067249968647957,
0.08868933469057083,
0.06180626153945923,
-0.10471505671739578,
-0.0786893293261528,
0.08835694938898087,
0.07446414977312088,
-0.06911313533782959,
0.040129464119672775,
-0.06502477079629898,
0.044429995119571686,
-0.018205363303422928,
-0.035411588847637177,
-0.17520353198051453,
-0.023749299347400665,
-0.020342517644166946,
0.03376643732190132,
0.028607405722141266,
0.02070034109055996,
0.09129003435373306,
0.08789271861314774,
-0.07040560245513916,
-0.02476905845105648,
-0.06548870354890823,
0.0030152557883411646,
-0.12271034717559814,
-0.22799594700336456,
-0.04455568641424179,
-0.008504110388457775,
0.08846969902515411,
-0.21100516617298126,
0.024924490600824356,
0.022944575175642967,
0.08628983795642853,
0.02459721267223358,
-0.032658763229846954,
-0.05330634489655495,
0.07774894684553146,
-0.011118695139884949,
-0.06571497768163681,
0.06852778047323227,
-0.005365102086216211,
-0.06990133970975876,
-0.056128453463315964,
-0.11278644949197769,
0.16286790370941162,
0.13359351456165314,
-0.1489158719778061,
-0.09435568749904633,
-0.01191939227283001,
-0.06379661709070206,
-0.03240511566400528,
-0.0446312315762043,
0.038430776447057724,
0.18146289885044098,
0.002338939579203725,
0.1325148195028305,
-0.059507425874471664,
-0.03604547306895256,
0.030263548716902733,
-0.02744917757809162,
0.028185255825519562,
0.1278345286846161,
0.12543828785419464,
-0.05909246206283569,
0.12416525185108185,
0.1236182227730751,
-0.079574815928936,
0.14607824385166168,
-0.03365793079137802,
-0.08016326278448105,
-0.019381074234843254,
-0.014642464928328991,
-0.008956913836300373,
0.17668873071670532,
-0.15235702693462372,
-0.01876070722937584,
-0.004644289147108793,
0.012978784739971161,
0.013469614088535309,
-0.2510194778442383,
-0.05627782642841339,
0.039644625037908554,
-0.0457548163831234,
-0.008565731346607208,
-0.025612208992242813,
-0.0035661845467984676,
0.103941909968853,
-0.00717173470184207,
-0.07418662309646606,
0.0005000920500606298,
-0.008132281713187695,
-0.04908804967999458,
0.2078830748796463,
-0.05656309798359871,
-0.11874203383922577,
-0.0888475552201271,
-0.07687526941299438,
-0.03540562838315964,
0.003312439424917102,
0.05829938128590584,
-0.10826986283063889,
-0.018079956993460655,
-0.060165885835886,
0.02006346546113491,
0.006547420751303434,
0.03611709922552109,
0.0008715864387340844,
-0.009013461880385876,
0.05426604673266411,
-0.09689241647720337,
-0.011010132730007172,
-0.0661090686917305,
-0.05387724190950394,
0.0562068335711956,
0.060396548360586166,
0.1476803421974182,
0.1364363431930542,
-0.02518540807068348,
0.02166100963950157,
-0.03292275592684746,
0.25965243577957153,
-0.09596740454435349,
-0.026613125577569008,
0.11526237428188324,
-0.010875185020267963,
0.05543675273656845,
0.10892395675182343,
0.08354087173938751,
-0.1099393293261528,
-0.0029823887161910534,
0.06413880735635757,
-0.05247080698609352,
-0.15301398932933807,
-0.014471200294792652,
-0.057120416313409805,
-0.030415598303079605,
0.07806937396526337,
0.02731643058359623,
-0.0013853487325832248,
0.057050030678510666,
0.04909895360469818,
0.04264372959733009,
-0.02305159904062748,
0.050846826285123825,
0.0872529074549675,
0.03109779953956604,
0.10956577956676483,
-0.04319720342755318,
-0.0675651952624321,
0.03156034275889397,
0.0024882315192371607,
0.24654115736484528,
-0.014094951562583447,
0.09805863350629807,
0.07394170016050339,
0.16033215820789337,
-0.012864423915743828,
0.04806949198246002,
-0.01352583896368742,
-0.06730464100837708,
-0.018943728879094124,
-0.04448121786117554,
-0.016574939712882042,
0.009940491057932377,
-0.05302334949374199,
0.038453083485364914,
-0.1260606050491333,
0.006800672970712185,
0.06862087547779083,
0.2515527307987213,
0.028790513053536415,
-0.3169243633747101,
-0.064580038189888,
-0.005179132334887981,
-0.011700832284986973,
-0.009111884981393814,
0.0060203103348612785,
0.1526600420475006,
-0.08304860442876816,
0.056516725569963455,
-0.08546167612075806,
0.08628370612859726,
-0.03792786970734596,
0.05088786035776138,
0.07526114583015442,
0.07289113104343414,
-0.004493719898164272,
0.05451153591275215,
-0.28623247146606445,
0.3022659122943878,
0.0011375393951311707,
0.08569665998220444,
-0.06520658731460571,
-0.03142275661230087,
0.03199116513133049,
0.08060221374034882,
0.08765627443790436,
-0.014960774220526218,
-0.021692996844649315,
-0.2182338982820511,
-0.022457852959632874,
0.030609378591179848,
0.12872153520584106,
-0.018955057486891747,
0.10484777390956879,
-0.009205411188304424,
-0.004893523640930653,
0.07370437681674957,
0.0018622524803504348,
-0.03345048055052757,
-0.08988900482654572,
-0.026975955814123154,
-0.02344941906630993,
-0.04719666764140129,
-0.05900386720895767,
-0.10573604702949524,
-0.11351454257965088,
0.11245793849229813,
0.016212746500968933,
-0.014616256579756737,
-0.12012504041194916,
0.09969014674425125,
0.07929100096225739,
-0.07592757046222687,
0.041307233273983,
0.03173583745956421,
0.057786714285612106,
0.03126770257949829,
-0.0572199709713459,
0.11685200035572052,
-0.059540748596191406,
-0.15940357744693756,
-0.05619044974446297,
0.09169582277536392,
0.05086896941065788,
0.05693741515278816,
-0.023278994485735893,
0.0163753479719162,
-0.01793042942881584,
-0.09359962493181229,
0.05555788800120354,
-0.042327072471380234,
0.06306339055299759,
0.009030255489051342,
-0.02036634087562561,
0.052815910428762436,
-0.05705913528800011,
-0.012613571248948574,
0.148376002907753,
0.286318838596344,
-0.09032127261161804,
0.013233629986643791,
0.016987938433885574,
-0.06627031415700912,
-0.18948806822299957,
0.07684556394815445,
0.05846034735441208,
0.000198392677702941,
0.08484649658203125,
-0.16741356253623962,
0.0985059067606926,
0.10461292415857315,
0.0005174805410206318,
0.11648163199424744,
-0.37067556381225586,
-0.1287514865398407,
0.08194172382354736,
0.19178113341331482,
0.07912397384643555,
-0.1557561457157135,
0.00009313853661296889,
-0.002872403711080551,
-0.14727254211902618,
0.09143593907356262,
-0.07454821467399597,
0.13568741083145142,
-0.018605342134833336,
0.08673810958862305,
0.016615791246294975,
-0.061233021318912506,
0.12196020781993866,
-0.0020607169717550278,
0.14119750261306763,
-0.06917189806699753,
-0.04139022156596184,
0.05495383217930794,
-0.03713738173246384,
-0.01241977233439684,
-0.04681752622127533,
0.02520948462188244,
-0.05796236917376518,
-0.013111296109855175,
-0.10409355163574219,
0.012671730481088161,
-0.03856562077999115,
-0.06872669607400894,
-0.046457819640636444,
0.04187127202749252,
0.046173904091119766,
-0.004347665701061487,
0.1517941951751709,
-0.010208487510681152,
0.1145961582660675,
0.0487038679420948,
0.05759699270129204,
-0.06535650044679642,
-0.10781130194664001,
-0.01662050187587738,
0.007644966710358858,
0.04801776260137558,
-0.13068506121635437,
0.01519148238003254,
0.15252044796943665,
0.0508299358189106,
0.1230609118938446,
0.08693701028823853,
-0.03189811110496521,
0.03187684714794159,
0.06877289712429047,
-0.1550123244524002,
-0.1159525141119957,
0.00307133374735713,
-0.0689646378159523,
-0.07268694043159485,
0.052438750863075256,
0.07767747342586517,
-0.07549475878477097,
0.011450055986642838,
-0.007632447872310877,
0.007043138612061739,
-0.06772784143686295,
0.20606794953346252,
0.05526842176914215,
0.04173769801855087,
-0.10405143350362778,
0.07321818917989731,
0.019597399979829788,
-0.08580797165632248,
-0.0009066509082913399,
0.09010099619626999,
-0.06923580169677734,
-0.024730700999498367,
0.08104240149259567,
0.192729651927948,
-0.07612200081348419,
-0.020841890946030617,
-0.14980177581310272,
-0.10851622372865677,
0.06970205157995224,
0.1851431280374527,
0.09989190846681595,
-0.005781654734164476,
-0.05271788313984871,
0.04727230966091156,
-0.11694306135177612,
0.07721419632434845,
0.02606264501810074,
0.08025883883237839,
-0.14856992661952972,
0.18545791506767273,
0.0116440923884511,
0.05517760291695595,
-0.026506876572966576,
0.03133274242281914,
-0.11967498064041138,
0.04171361029148102,
-0.11294488608837128,
-0.035630542784929276,
-0.017102666199207306,
0.003525670850649476,
-0.012774837203323841,
-0.06196821853518486,
-0.06310302019119263,
0.004322623368352652,
-0.1270684450864792,
-0.02302238531410694,
0.04441089555621147,
0.02194337360560894,
-0.1272217482328415,
-0.03941956162452698,
0.026843398809432983,
-0.06422698497772217,
0.055940162390470505,
0.0359339565038681,
0.013140649534761906,
0.06457527726888657,
-0.1751832365989685,
-0.020436665043234825,
0.07107419520616531,
-0.007801709230989218,
0.06308873742818832,
-0.037690419703722,
-0.026051035150885582,
-0.029896479099988937,
0.08722539991140366,
0.013100674375891685,
0.062112364917993546,
-0.13730162382125854,
0.004048645030707121,
-0.0319988913834095,
-0.0940546914935112,
-0.05909106880426407,
0.05334068834781647,
0.06284331530332565,
0.036480508744716644,
0.16238419711589813,
-0.0829518735408783,
0.04564983397722244,
-0.21975377202033997,
-0.016091637313365936,
0.002252950333058834,
-0.1063220277428627,
-0.08176814764738083,
-0.07316777110099792,
0.0830390676856041,
-0.0748259425163269,
0.11371082067489624,
0.037186428904533386,
0.06332332640886307,
0.03074459545314312,
-0.03358282893896103,
-0.003392525715753436,
0.035055775195360184,
0.2140370011329651,
0.012751772068440914,
-0.03422921523451805,
0.08692289143800735,
0.0795869454741478,
0.10055039823055267,
0.1353200078010559,
0.2260706126689911,
0.15415650606155396,
-0.02574523538351059,
0.0894816517829895,
0.050701092928647995,
-0.06616480648517609,
-0.16951915621757507,
0.03672156482934952,
-0.05264041945338249,
0.09805795550346375,
-0.061137836426496506,
0.2058541178703308,
0.08459548652172089,
-0.1838388442993164,
0.06726786494255066,
-0.043808769434690475,
-0.10169360041618347,
-0.08149950206279755,
-0.03957514837384224,
-0.07038678973913193,
-0.14792197942733765,
0.0254342220723629,
-0.1028243824839592,
0.04365086555480957,
0.14840292930603027,
0.010573025792837143,
-0.012043884955346584,
0.2128751277923584,
0.03381815180182457,
0.035756442695856094,
0.05829598382115364,
0.01503654196858406,
-0.029705652967095375,
-0.09447493404150009,
-0.06003592908382416,
0.016951339319348335,
-0.029349857941269875,
0.01889607682824135,
-0.06738051027059555,
-0.07761002331972122,
0.02696983702480793,
0.004491243977099657,
-0.0937914028763771,
0.02370997704565525,
0.019151290878653526,
0.09028057754039764,
0.02619517408311367,
0.006615263409912586,
0.015298736281692982,
-0.028468340635299683,
0.24601446092128754,
-0.09261999279260635,
-0.08368151634931564,
-0.08216787874698639,
0.21599151194095612,
0.03183118999004364,
0.00041712471283972263,
0.008226998150348663,
-0.08104052394628525,
0.008533041924238205,
0.230509951710701,
0.173725888133049,
-0.13057464361190796,
-0.010407119058072567,
-0.000253234087722376,
0.000749577593524009,
-0.029086042195558548,
0.11775923520326614,
0.11927026510238647,
0.053149156272411346,
-0.1147167831659317,
-0.048563774675130844,
-0.05160810425877571,
-0.019782118499279022,
-0.02862590365111828,
0.051942307502031326,
0.06686025112867355,
0.02180231362581253,
-0.07148759067058563,
0.07590923458337784,
-0.058285124599933624,
-0.1409795880317688,
0.10801422595977783,
-0.22707054018974304,
-0.15499135851860046,
-0.007152719423174858,
0.11885535717010498,
0.00433693453669548,
0.060410890728235245,
-0.04058306664228439,
0.0019430540269240737,
0.05067381635308266,
-0.005306743085384369,
-0.07916779816150665,
-0.10420049726963043,
0.08407063782215118,
-0.11643970012664795,
0.21808339655399323,
-0.05898395553231239,
0.033930789679288864,
0.11319959908723831,
0.06309398263692856,
-0.04961023107171059,
0.05781909078359604,
0.04168254882097244,
-0.12097784131765366,
-0.0036957012489438057,
0.1232391819357872,
-0.03874809294939041,
0.05268893390893936,
0.03263397142291069,
-0.1328454166650772,
0.03314812853932381,
-0.055968545377254486,
-0.04154778644442558,
-0.028269734233617783,
-0.05251046642661095,
-0.06356414407491684,
0.11494989693164825,
0.20811079442501068,
-0.009032335132360458,
0.025161880999803543,
-0.08741147071123123,
0.01575438119471073,
0.06533987075090408,
0.047917094081640244,
-0.07867252081632614,
-0.216937854886055,
0.007845279760658741,
0.07333408296108246,
-0.04157581925392151,
-0.20354820787906647,
-0.11203299462795258,
0.037289075553417206,
-0.05339254438877106,
-0.0709451287984848,
0.0902678370475769,
0.09048021584749222,
0.057064056396484375,
-0.05485730618238449,
-0.10467628389596939,
-0.0571453794836998,
0.171553835272789,
-0.14593668282032013,
-0.07679115235805511
] |
null | null | transformers |
# SciPhi-Mistral-7B-32k-sliced-smol
SciPhi-Mistral-7B-32k-sliced-smol is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [SciPhi/SciPhi-Mistral-7B-32k](https://huggingface.co/SciPhi/SciPhi-Mistral-7B-32k)
* [SciPhi/SciPhi-Mistral-7B-32k](https://huggingface.co/SciPhi/SciPhi-Mistral-7B-32k)
* [SciPhi/SciPhi-Mistral-7B-32k](https://huggingface.co/SciPhi/SciPhi-Mistral-7B-32k)
* [SciPhi/SciPhi-Mistral-7B-32k](https://huggingface.co/SciPhi/SciPhi-Mistral-7B-32k)
* [SciPhi/SciPhi-Mistral-7B-32k](https://huggingface.co/SciPhi/SciPhi-Mistral-7B-32k)
* [SciPhi/SciPhi-Mistral-7B-32k](https://huggingface.co/SciPhi/SciPhi-Mistral-7B-32k)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: SciPhi/SciPhi-Mistral-7B-32k
layer_range: [0, 2]
- sources:
- model: SciPhi/SciPhi-Mistral-7B-32k
layer_range: [4, 6]
- sources:
- model: SciPhi/SciPhi-Mistral-7B-32k
layer_range: [8, 10]
- sources:
- model: SciPhi/SciPhi-Mistral-7B-32k
layer_range: [12, 14]
- sources:
- model: SciPhi/SciPhi-Mistral-7B-32k
layer_range: [16, 18]
- sources:
- model: SciPhi/SciPhi-Mistral-7B-32k
layer_range: [30, 32]
merge_method: passthrough
tokenizer_source: union
dtype: float16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "jtatman/SciPhi-Mistral-7B-32k-sliced-smol"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"tags": ["merge", "mergekit", "lazymergekit", "SciPhi/SciPhi-Mistral-7B-32k", "SciPhi/SciPhi-Mistral-7B-32k", "SciPhi/SciPhi-Mistral-7B-32k", "SciPhi/SciPhi-Mistral-7B-32k", "SciPhi/SciPhi-Mistral-7B-32k", "SciPhi/SciPhi-Mistral-7B-32k"], "base_model": ["SciPhi/SciPhi-Mistral-7B-32k", "SciPhi/SciPhi-Mistral-7B-32k", "SciPhi/SciPhi-Mistral-7B-32k", "SciPhi/SciPhi-Mistral-7B-32k", "SciPhi/SciPhi-Mistral-7B-32k", "SciPhi/SciPhi-Mistral-7B-32k"]} | text-generation | jtatman/SciPhi-Mistral-7B-32k-sliced-smol | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"SciPhi/SciPhi-Mistral-7B-32k",
"base_model:SciPhi/SciPhi-Mistral-7B-32k",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T12:07:13+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #SciPhi/SciPhi-Mistral-7B-32k #base_model-SciPhi/SciPhi-Mistral-7B-32k #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# SciPhi-Mistral-7B-32k-sliced-smol
SciPhi-Mistral-7B-32k-sliced-smol is a merge of the following models using LazyMergekit:
* SciPhi/SciPhi-Mistral-7B-32k
* SciPhi/SciPhi-Mistral-7B-32k
* SciPhi/SciPhi-Mistral-7B-32k
* SciPhi/SciPhi-Mistral-7B-32k
* SciPhi/SciPhi-Mistral-7B-32k
* SciPhi/SciPhi-Mistral-7B-32k
## Configuration
## Usage
| [
"# SciPhi-Mistral-7B-32k-sliced-smol\n\nSciPhi-Mistral-7B-32k-sliced-smol is a merge of the following models using LazyMergekit:\n* SciPhi/SciPhi-Mistral-7B-32k\n* SciPhi/SciPhi-Mistral-7B-32k\n* SciPhi/SciPhi-Mistral-7B-32k\n* SciPhi/SciPhi-Mistral-7B-32k\n* SciPhi/SciPhi-Mistral-7B-32k\n* SciPhi/SciPhi-Mistral-7B-32k",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #SciPhi/SciPhi-Mistral-7B-32k #base_model-SciPhi/SciPhi-Mistral-7B-32k #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# SciPhi-Mistral-7B-32k-sliced-smol\n\nSciPhi-Mistral-7B-32k-sliced-smol is a merge of the following models using LazyMergekit:\n* SciPhi/SciPhi-Mistral-7B-32k\n* SciPhi/SciPhi-Mistral-7B-32k\n* SciPhi/SciPhi-Mistral-7B-32k\n* SciPhi/SciPhi-Mistral-7B-32k\n* SciPhi/SciPhi-Mistral-7B-32k\n* SciPhi/SciPhi-Mistral-7B-32k",
"## Configuration",
"## Usage"
] | [
98,
145,
4,
3
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #SciPhi/SciPhi-Mistral-7B-32k #base_model-SciPhi/SciPhi-Mistral-7B-32k #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# SciPhi-Mistral-7B-32k-sliced-smol\n\nSciPhi-Mistral-7B-32k-sliced-smol is a merge of the following models using LazyMergekit:\n* SciPhi/SciPhi-Mistral-7B-32k\n* SciPhi/SciPhi-Mistral-7B-32k\n* SciPhi/SciPhi-Mistral-7B-32k\n* SciPhi/SciPhi-Mistral-7B-32k\n* SciPhi/SciPhi-Mistral-7B-32k\n* SciPhi/SciPhi-Mistral-7B-32k## Configuration## Usage"
] | [
-0.0851176530122757,
-0.03403473272919655,
-0.002077172277495265,
-0.0113361906260252,
0.07179566472768784,
0.09791747480630875,
0.1805695742368698,
0.0793156549334526,
0.20202171802520752,
0.10785498470067978,
0.06102975830435753,
0.17100116610527039,
0.06868654489517212,
0.08980846405029297,
-0.0638289749622345,
-0.26213768124580383,
0.0985167846083641,
-0.02459922805428505,
-0.05891675874590874,
0.052144136279821396,
0.09816224873065948,
-0.03319692239165306,
0.10516686737537384,
-0.025280024856328964,
-0.03264378756284714,
-0.030834931880235672,
0.04157131537795067,
0.017079684883356094,
0.10685516148805618,
0.11656278371810913,
0.0542328804731369,
0.04576556757092476,
0.06987487524747849,
-0.14926093816757202,
0.028391102328896523,
0.0015693334862589836,
0.01315239630639553,
0.0892736092209816,
0.04395361989736557,
0.013735954649746418,
0.1172080710530281,
-0.068030945956707,
0.013905790634453297,
0.04849984496831894,
-0.07167113572359085,
-0.038393378257751465,
-0.09936562180519104,
0.13557367026805878,
0.10494890809059143,
-0.011107038706541061,
0.007911065593361855,
0.12313146889209747,
0.03895791620016098,
0.07727205008268356,
0.1575019657611847,
-0.3160645365715027,
-0.038450077176094055,
0.0807449147105217,
0.052894849330186844,
-0.012845933437347412,
0.05026752129197121,
0.07150272279977798,
0.0029277880676090717,
0.005799042526632547,
0.03472977876663208,
-0.06780171394348145,
0.08543436229228973,
-0.04545683413743973,
-0.1265176683664322,
0.014385462738573551,
0.009005753323435783,
0.00723729282617569,
-0.037467993795871735,
-0.11408434808254242,
-0.0858926922082901,
0.02368505671620369,
-0.0792260617017746,
-0.07287128269672394,
0.0219812523573637,
-0.0025532401632517576,
0.14172254502773285,
-0.021845145151019096,
-0.028822382912039757,
-0.09439446777105331,
-0.04887563735246658,
0.1596173793077469,
0.020279983058571815,
0.04849638044834137,
0.016565371304750443,
0.050611115992069244,
-0.08589015901088715,
-0.11074952036142349,
-0.007871650159358978,
-0.055387094616889954,
-0.09850029647350311,
0.008490055799484253,
-0.07259087264537811,
-0.04793393984436989,
0.04757505655288696,
0.19386830925941467,
-0.017436394467949867,
0.08419227600097656,
0.11000242829322815,
0.03970233350992203,
-0.010910853743553162,
-0.041298478841781616,
-0.1427648812532425,
-0.04882330074906349,
-0.03396032750606537,
0.06208059936761856,
0.0982876718044281,
-0.007838591001927853,
-0.01781649701297283,
-0.019626205787062645,
-0.046583134680986404,
-0.022357288748025894,
-0.005879472009837627,
0.08500797301530838,
-0.07367244362831116,
-0.06269291043281555,
0.24270853400230408,
-0.06403522938489914,
-0.014178339391946793,
-0.006444959435611963,
-0.04126895219087601,
0.11397456377744675,
0.047374170273542404,
0.0410185344517231,
-0.029056724160909653,
0.14694903790950775,
-0.08156044781208038,
-0.0032002751249819994,
-0.044770024716854095,
-0.052669115364551544,
-0.010554265230894089,
-0.0182817243039608,
-0.07418990135192871,
-0.06719247251749039,
-0.21076147258281708,
-0.048948779702186584,
0.061464667320251465,
-0.07871758937835693,
0.023784726858139038,
0.014892837963998318,
0.0279160737991333,
0.04850071296095848,
0.0004993480397388339,
0.028731442987918854,
0.00657725939527154,
-0.015587075613439083,
-0.04653824120759964,
0.07738074660301208,
-0.16899609565734863,
0.006956078577786684,
-0.05971505120396614,
0.09929724037647247,
-0.1582311987876892,
0.04662071168422699,
-0.12135659903287888,
0.019620366394519806,
-0.14030365645885468,
-0.04651564732193947,
-0.03332618996500969,
0.055368755012750626,
0.06274200230836868,
0.13167926669120789,
-0.14212238788604736,
-0.044328801333904266,
0.11580473184585571,
-0.1552485227584839,
-0.12619352340698242,
0.1036348044872284,
0.040026985108852386,
0.009395552799105644,
0.018377363681793213,
0.15216012299060822,
0.1455385982990265,
-0.015336338430643082,
-0.07522665709257126,
-0.015292447060346603,
0.04675399884581566,
0.010435802862048149,
0.09261371940374374,
0.029291421175003052,
-0.028515895828604698,
0.050565917044878006,
-0.0364706888794899,
0.00659027136862278,
-0.02040802128612995,
-0.04725804179906845,
-0.08978662639856339,
-0.09069428592920303,
0.04962850362062454,
0.028173912316560745,
0.05191890150308609,
-0.030305128544569016,
-0.052595801651477814,
0.08372222632169724,
0.11675360798835754,
-0.03317862004041672,
-0.028103312477469444,
-0.08686523884534836,
0.07615449279546738,
0.052685003727674484,
0.019919617101550102,
-0.16143330931663513,
-0.1264055371284485,
-0.0011045874562114477,
-0.02134375274181366,
-0.017982613295316696,
-0.04468020051717758,
0.07781054824590683,
0.06298339366912842,
-0.04969741031527519,
-0.02234111726284027,
0.16772326827049255,
0.010557051748037338,
-0.06823205947875977,
-0.16238388419151306,
-0.14222808182239532,
-0.04098045080900192,
0.1736367642879486,
-0.06449969857931137,
0.05924570560455322,
-0.05125706270337105,
0.22140297293663025,
-0.03395973518490791,
-0.026402655988931656,
0.021736346185207367,
0.031178101897239685,
-0.016132108867168427,
0.014127778820693493,
0.08839266747236252,
-0.05547802895307541,
-0.10225258767604828,
0.06104778125882149,
-0.10761583596467972,
0.0841750055551529,
0.051403921097517014,
0.08116976171731949,
-0.05677612125873566,
-0.06920868158340454,
0.017217036336660385,
-0.06090967729687691,
0.07715548574924469,
-0.10090944916009903,
0.0027277141343802214,
0.0005816181073896587,
0.07623837143182755,
-0.0209118090569973,
-0.030325645580887794,
-0.006217453628778458,
-0.020732825621962547,
-0.029907334595918655,
0.09356532245874405,
0.04373403638601303,
-0.3270527720451355,
0.10706454515457153,
0.0801340788602829,
0.02887588180601597,
0.05239192396402359,
-0.0062385182827711105,
-0.03676227480173111,
-0.07033594697713852,
-0.012754288502037525,
0.030406760051846504,
0.004041935782879591,
0.016766922548413277,
0.020121026784181595,
0.0832648053765297,
-0.009118501096963882,
0.00018579047173261642,
-0.07014678418636322,
0.034684598445892334,
-0.008515642955899239,
-0.0011184096802026033,
0.04064750671386719,
0.07143820822238922,
-0.01514257863163948,
0.10702481120824814,
0.001235778909176588,
-0.06333297491073608,
-0.034781355410814285,
-0.029333416372537613,
-0.09950119256973267,
0.16960391402244568,
-0.09144160151481628,
-0.08777470886707306,
-0.19776934385299683,
-0.008170044049620628,
-0.022562723606824875,
-0.023824000731110573,
0.005143941845744848,
0.008464997634291649,
-0.06293822079896927,
-0.08735747635364532,
0.004260482266545296,
0.08180557936429977,
-0.002022937871515751,
0.008550749160349369,
-0.012504474259912968,
-0.014072630554437637,
-0.05759063735604286,
-0.05209571123123169,
-0.008447440341114998,
0.056030578911304474,
0.02684226632118225,
-0.03606535121798515,
0.04491693153977394,
0.11112437397241592,
0.041460227221250534,
-0.03226327896118164,
0.011313163675367832,
0.19881628453731537,
-0.013911215588450432,
0.11056698858737946,
0.22276340425014496,
-0.08637184649705887,
0.08053258061408997,
0.13899511098861694,
0.07270561903715134,
-0.03312668576836586,
0.004193139262497425,
-0.02404504269361496,
-0.030365942046046257,
-0.19844825565814972,
-0.10986410826444626,
-0.027549412101507187,
-0.00649140402674675,
-0.006767385173588991,
0.03118276037275791,
0.029016360640525818,
0.1262069046497345,
-0.08671391010284424,
-0.03210257738828659,
-0.016360685229301453,
0.030053861439228058,
0.09221693873405457,
0.023487994447350502,
0.15061357617378235,
-0.07685939222574234,
0.001501979655586183,
0.046024080365896225,
-0.027747010812163353,
0.08607975393533707,
0.040902551263570786,
0.18755151331424713,
0.0045305704697966576,
0.08718690276145935,
0.033777616918087006,
0.09565886110067368,
-0.0031113140285015106,
-0.031189488247036934,
-0.017234763130545616,
-0.08053169399499893,
-0.052910950034856796,
0.09968233853578568,
-0.06075603514909744,
0.04747474566102028,
0.0013005128130316734,
0.05616709962487221,
0.022836390882730484,
0.2837716042995453,
0.10082268714904785,
-0.2830749452114105,
-0.12914885580539703,
0.08984306454658508,
0.01748635247349739,
-0.022510197013616562,
-0.03557680919766426,
0.08735935389995575,
-0.05090123042464256,
0.13632547855377197,
-0.040322404354810715,
0.06323300302028656,
-0.015256484039127827,
0.02771982178092003,
0.08102098852396011,
0.051437150686979294,
0.01846516877412796,
0.014658864587545395,
-0.09564670920372009,
0.14896905422210693,
0.030387451872229576,
-0.023495890200138092,
-0.05782487243413925,
0.05605307221412659,
0.049674853682518005,
0.08959925919771194,
0.09545950591564178,
0.018812913447618484,
-0.21852996945381165,
-0.11544995754957199,
-0.10301528126001358,
-0.030759958550333977,
0.11237505823373795,
-0.06515166163444519,
0.09553014487028122,
-0.07460066676139832,
-0.049269188195466995,
-0.0033457386307418346,
0.012841340154409409,
-0.09498092532157898,
-0.11947707831859589,
0.1255612075328827,
0.07521512359380722,
-0.025350065901875496,
-0.06843729317188263,
-0.03174332529306412,
-0.0732841044664383,
0.2259645015001297,
-0.1369805485010147,
-0.09694097936153412,
-0.1314268708229065,
-0.03360714763402939,
0.14315535128116608,
-0.08692941814661026,
0.02502143755555153,
-0.0011498802341520786,
0.03628535568714142,
0.015733199194073677,
-0.1880911886692047,
0.09736078977584839,
-0.11272980272769928,
-0.12824629247188568,
-0.06646295636892319,
0.12429504096508026,
-0.06913544237613678,
0.046700675040483475,
-0.01774623990058899,
0.08201117068529129,
0.034341808408498764,
-0.061057284474372864,
0.05584370717406273,
0.143648162484169,
0.014992114156484604,
0.09338419884443283,
-0.11946246773004532,
-0.06445236504077911,
-0.01411470491439104,
-0.006354566663503647,
0.0903213694691658,
0.2250499129295349,
-0.04518144577741623,
0.03944946825504303,
0.09230554103851318,
-0.05048591271042824,
-0.22965306043624878,
-0.088925302028656,
0.048361413180828094,
0.04476858302950859,
0.07869630306959152,
-0.12466812878847122,
0.09011176973581314,
0.18011914193630219,
-0.021280163899064064,
0.06482715159654617,
-0.31756648421287537,
-0.12789061665534973,
0.042591486126184464,
0.07943029701709747,
0.10094616562128067,
-0.18658801913261414,
-0.12128737568855286,
-0.10016001760959625,
-0.18875019252300262,
0.09479609876871109,
0.007996100932359695,
0.05604683235287666,
-0.057642173022031784,
-0.03425275906920433,
0.02934335544705391,
-0.0377998948097229,
0.18874818086624146,
-0.07072935253381729,
0.012167097069323063,
-0.0743248462677002,
-0.015410978347063065,
0.043238185346126556,
-0.02158854901790619,
0.105658158659935,
-0.11277715861797333,
0.04932709410786629,
-0.05915592983365059,
-0.01801617071032524,
-0.06054619699716568,
-0.027843456715345383,
-0.01902109757065773,
-0.024966076016426086,
-0.002313904697075486,
0.053688894957304,
-0.0038349460810422897,
-0.0104603860527277,
0.0919080600142479,
-0.047410570085048676,
0.12035694718360901,
0.18724486231803894,
0.10409124195575714,
0.03898797184228897,
-0.09146612137556076,
-0.029508743435144424,
-0.044212449342012405,
0.028331784531474113,
-0.0385889858007431,
-0.037072766572237015,
0.1211584284901619,
0.024066926911473274,
0.057854585349559784,
0.046288587152957916,
0.01647838018834591,
-0.0081968754529953,
0.09197025746107101,
-0.12019862234592438,
-0.27107903361320496,
0.0026458066422492266,
0.09698281437158585,
-0.07525639235973358,
0.07624503970146179,
0.2143714725971222,
-0.023334823548793793,
-0.016085218638181686,
0.013841395266354084,
0.038692258298397064,
-0.057827435433864594,
0.176424041390419,
-0.03071010671555996,
0.07229059934616089,
-0.09285995364189148,
0.009685700759291649,
0.03520604968070984,
-0.013471412472426891,
-0.032218702137470245,
0.12222535908222198,
-0.10807374864816666,
-0.08515660464763641,
-0.09650612622499466,
-0.02468194253742695,
-0.04509271681308746,
-0.046343252062797546,
-0.1007552519440651,
-0.1456400603055954,
-0.0005059549584984779,
0.1415192037820816,
0.05536358803510666,
0.00037978217005729675,
0.008338016457855701,
-0.06041354686021805,
0.019774211570620537,
0.026891330257058144,
-0.014393291436135769,
0.13933485746383667,
-0.11832396686077118,
0.05750833824276924,
-0.0792493224143982,
0.0036999352741986513,
-0.05004851147532463,
0.050653234124183655,
-0.1740543246269226,
-0.06115575507283211,
-0.2071467787027359,
-0.03678329288959503,
-0.138353168964386,
-0.07859016954898834,
0.023580094799399376,
-0.01546355802565813,
0.00622025178745389,
-0.012905074283480644,
-0.03121408075094223,
-0.05816735327243805,
-0.03388555347919464,
0.06822247058153152,
-0.10678879171609879,
0.004960899241268635,
0.04879258573055267,
-0.06614254415035248,
0.04193997383117676,
0.07738132774829865,
-0.006053510587662458,
-0.004156853072345257,
-0.06751163303852081,
-0.010537469759583473,
0.04605778679251671,
0.049076277762651443,
0.027772793546319008,
-0.061243437230587006,
-0.033821720629930496,
-0.025267640128731728,
-0.02419053390622139,
0.016967525705695152,
0.12175855040550232,
-0.05777936801314354,
0.06247999519109726,
-0.04584820196032524,
-0.009052684530615807,
-0.06317228078842163,
-0.03808140382170677,
0.09290117025375366,
0.021603157743811607,
0.09096112102270126,
-0.04528734087944031,
0.021438637748360634,
-0.1842004656791687,
0.012298092246055603,
0.008563188835978508,
-0.11434227973222733,
-0.05773371830582619,
-0.06392589956521988,
0.03862264007329941,
0.008493641391396523,
0.03185494244098663,
-0.12081054598093033,
-0.10591571778059006,
0.037037238478660583,
-0.028165321797132492,
0.09550180286169052,
0.0802125632762909,
0.11461329460144043,
0.08280984312295914,
-0.0367274209856987,
-0.07483667135238647,
0.04146623983979225,
-0.07063204795122147,
0.030360491946339607,
0.11227644979953766,
0.11710627377033234,
0.010890243574976921,
0.107209712266922,
0.06054312363266945,
-0.01999417506158352,
-0.045981600880622864,
0.0005626566708087921,
-0.09206348657608032,
0.04991853982210159,
-0.051446251571178436,
0.16969606280326843,
0.04994760826230049,
-0.1568962186574936,
0.05352116748690605,
-0.05398106575012207,
-0.035643257200717926,
-0.10093115270137787,
-0.09909442067146301,
-0.07766500860452652,
-0.05239982530474663,
-0.06436824053525925,
-0.10183580964803696,
-0.002854489954188466,
0.17288197576999664,
0.007402533665299416,
0.04609914869070053,
0.11552216112613678,
-0.07550930231809616,
0.06154586374759674,
0.0034837271086871624,
0.002023151610046625,
-0.022747794166207314,
-0.003488819347694516,
-0.06756798923015594,
0.03731531277298927,
-0.009547626599669456,
-0.008793200366199017,
-0.006580387707799673,
-0.05317185819149017,
0.022442499175667763,
0.018528126180171967,
-0.1036846712231636,
0.004661375656723976,
0.015875879675149918,
-0.007444724906235933,
0.01615719124674797,
0.04842382296919823,
-0.02367383800446987,
-0.05170147120952606,
0.06747590005397797,
-0.09220647066831589,
-0.029825136065483093,
-0.09785175323486328,
0.27800241112709045,
-0.06591124832630157,
0.05511932447552681,
0.03820706903934479,
-0.04536886513233185,
-0.038515109568834305,
0.08460386097431183,
0.1928272843360901,
-0.0873718187212944,
-0.0024674339219927788,
0.03911777585744858,
0.006139358505606651,
-0.01343260146677494,
0.1125621423125267,
0.043563976883888245,
0.17571404576301575,
-0.06900050491094589,
0.0410098098218441,
0.030397240072488785,
-0.09372604638338089,
-0.11529459059238434,
-0.03764539211988449,
0.007523290812969208,
0.02554769441485405,
0.023335551843047142,
0.07879548519849777,
-0.08727483451366425,
-0.025905346497893333,
0.06566808372735977,
-0.13941846787929535,
-0.14415471255779266,
-0.044537127017974854,
0.0015866647008806467,
0.014032955281436443,
0.08997421711683273,
-0.039208490401506424,
0.006662530824542046,
0.09613091498613358,
-0.035732559859752655,
-0.07875901460647583,
-0.032662078738212585,
0.03844873979687691,
-0.0929093137383461,
0.03491045534610748,
-0.0032187877222895622,
0.12462837994098663,
0.1117820218205452,
-0.024457275867462158,
-0.10227666795253754,
-0.005623272620141506,
0.05709613859653473,
-0.14113855361938477,
0.049922071397304535,
0.11948060244321823,
-0.04156526178121567,
0.09640330821275711,
0.021942807361483574,
-0.22761493921279907,
0.017482919618487358,
0.09671416133642197,
-0.01917942985892296,
-0.03316165506839752,
0.053110115230083466,
-0.05227018892765045,
0.16082334518432617,
0.23843872547149658,
-0.022116124629974365,
-0.02258775755763054,
0.012635587714612484,
0.05032724142074585,
0.07335339486598969,
0.08656872063875198,
-0.056378770619630814,
-0.23977182805538177,
0.007951049134135246,
-0.0189533568918705,
0.04192838817834854,
-0.22394686937332153,
-0.11464553326368332,
-0.11958808451890945,
0.004086203873157501,
-0.03614838421344757,
0.030656062066555023,
0.1662142276763916,
0.016582807525992393,
-0.009652362205088139,
-0.18525491654872894,
-0.006098733749240637,
0.08093950152397156,
-0.07105667144060135,
-0.08697294443845749
] |
null | null | transformers | This repository contains weighted quants of https://huggingface.co/tiiuae/falcon-180B,
using an experimental (read: crappy) method based on 65k semi-random english-only tokens,
and requantized from TheBlokes Q8 quant rather than the original because my llama couldn't
read the f16 model.
It would be nice to see some real-world comparison between this Q2_K and the static Q2_K by TheBloke
for example.
The algorithm used is iterative, so if this works, there will be an i2 variant that might or might not be better.
<!-- provided-files -->
## Provided Quants
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/falcon-180B-i1-GGUF/resolve/main/falcon-180B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 47.8 | |
| [PART 1](https://huggingface.co/mradermacher/falcon-180B-i1-GGUF/resolve/main/falcon-180B.i1-IQ2_XS.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/falcon-180B-i1-GGUF/resolve/main/falcon-180B.i1-IQ2_XS.gguf.split-ab) | i1-IQ2_XS | 53.0 | |
| [PART 1](https://huggingface.co/mradermacher/falcon-180B-i1-GGUF/resolve/main/falcon-180B.i1-Q2_K_S.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/falcon-180B-i1-GGUF/resolve/main/falcon-180B.i1-Q2_K_S.gguf.split-ab) | i1-Q2_K_S | 61.7 | |
| [PART 1](https://huggingface.co/mradermacher/falcon-180B-i1-GGUF/resolve/main/falcon-180B.i1-Q2_K.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/falcon-180B-i1-GGUF/resolve/main/falcon-180B.i1-Q2_K.gguf.split-ab) | i1-Q2_K | 66.8 | |
| [PART 1](https://huggingface.co/mradermacher/falcon-180B-i1-GGUF/resolve/main/falcon-180B.i1-IQ3_XXS.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/falcon-180B-i1-GGUF/resolve/main/falcon-180B.i1-IQ3_XXS.gguf.split-ab) | i1-IQ3_XXS | 69.6 | fast, lower quality |
| [PART 1](https://huggingface.co/mradermacher/falcon-180B-i1-GGUF/resolve/main/falcon-180B.i1-Q3_K_XS.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/falcon-180B-i1-GGUF/resolve/main/falcon-180B.i1-Q3_K_XS.gguf.split-ab) | i1-Q3_K_XS | 75.4 | |
| [PART 1](https://huggingface.co/mradermacher/falcon-180B-i1-GGUF/resolve/main/falcon-180B.i1-Q3_K_S.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/falcon-180B-i1-GGUF/resolve/main/falcon-180B.i1-Q3_K_S.gguf.split-ab) | i1-Q3_K_S | 77.8 | |
| [PART 1](https://huggingface.co/mradermacher/falcon-180B-i1-GGUF/resolve/main/falcon-180B.i1-Q3_K_M.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/falcon-180B-i1-GGUF/resolve/main/falcon-180B.i1-Q3_K_M.gguf.split-ab) | i1-Q3_K_M | 85.5 | lower quality |
| [PART 1](https://huggingface.co/mradermacher/falcon-180B-i1-GGUF/resolve/main/falcon-180B.i1-Q3_K_L.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/falcon-180B-i1-GGUF/resolve/main/falcon-180B.i1-Q3_K_L.gguf.split-ab) | i1-Q3_K_L | 92.0 | |
| [PART 1](https://huggingface.co/mradermacher/falcon-180B-i1-GGUF/resolve/main/falcon-180B.i1-Q4_K_S.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/falcon-180B-i1-GGUF/resolve/main/falcon-180B.i1-Q4_K_S.gguf.split-ab) [PART 3](https://huggingface.co/mradermacher/falcon-180B-i1-GGUF/resolve/main/falcon-180B.i1-Q4_K_S.gguf.split-ac) | i1-Q4_K_S | 101.5 | fast, medium quality |
| [PART 1](https://huggingface.co/mradermacher/falcon-180B-i1-GGUF/resolve/main/falcon-180B.i1-Q4_K_M.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/falcon-180B-i1-GGUF/resolve/main/falcon-180B.i1-Q4_K_M.gguf.split-ab) [PART 3](https://huggingface.co/mradermacher/falcon-180B-i1-GGUF/resolve/main/falcon-180B.i1-Q4_K_M.gguf.split-ac) | i1-Q4_K_M | 108.8 | fast, medium quality |
<!-- end -->
| {"library_name": "transformers", "pipeline_tag": "text-generation"} | text-generation | mradermacher/falcon-180B-i1-GGUF | [
"transformers",
"gguf",
"text-generation",
"endpoints_compatible",
"region:us"
] | 2024-02-09T12:07:14+00:00 | [] | [] | TAGS
#transformers #gguf #text-generation #endpoints_compatible #region-us
| This repository contains weighted quants of URL
using an experimental (read: crappy) method based on 65k semi-random english-only tokens,
and requantized from TheBlokes Q8 quant rather than the original because my llama couldn't
read the f16 model.
It would be nice to see some real-world comparison between this Q2\_K and the static Q2\_K by TheBloke
for example.
The algorithm used is iterative, so if this works, there will be an i2 variant that might or might not be better.
Provided Quants
---------------
| [] | [
"TAGS\n#transformers #gguf #text-generation #endpoints_compatible #region-us \n"
] | [
25
] | [
"passage: TAGS\n#transformers #gguf #text-generation #endpoints_compatible #region-us \n"
] | [
-0.009579749777913094,
0.016051435843110085,
-0.00778944743797183,
-0.0606827586889267,
0.17959557473659515,
0.04390456900000572,
0.05772693082690239,
0.08898092061281204,
0.0652620941400528,
0.0004920579376630485,
0.13663704693317413,
0.12208554148674011,
0.004242816939949989,
0.07835976779460907,
-0.07947206497192383,
-0.2472376823425293,
0.10715211927890778,
0.056565575301647186,
-0.08158249408006668,
0.053426869213581085,
0.046937670558691025,
-0.01570023223757744,
0.09193092584609985,
-0.045532554388046265,
-0.20299391448497772,
0.046174556016922,
0.03110901266336441,
-0.06774543225765228,
0.07453011721372604,
0.10531035810709,
0.09625300019979477,
0.0060175945982337,
-0.15509317815303802,
-0.23109441995620728,
0.03242243826389313,
0.017691269516944885,
-0.10005950927734375,
0.001726817456074059,
0.032374605536460876,
-0.10376358032226562,
0.0906355157494545,
0.04741545394062996,
-0.09837349504232407,
0.0898582860827446,
-0.1877211183309555,
-0.08756867796182632,
-0.04876207560300827,
-0.03491636738181114,
0.01505382638424635,
0.06442180275917053,
-0.013353051617741585,
-0.04188106954097748,
-0.0708744004368782,
0.08128168433904648,
0.15614952147006989,
-0.3134511411190033,
-0.0046891276724636555,
0.18256108462810516,
0.0725054144859314,
0.0586109533905983,
-0.061780765652656555,
0.12141754478216171,
0.019321439787745476,
-0.012183133512735367,
-0.06757590919733047,
-0.08256939053535461,
-0.018916055560112,
0.1169932410120964,
-0.06847090274095535,
-0.06659001857042313,
0.19365370273590088,
-0.03753431886434555,
0.03441791608929634,
-0.019127504900097847,
-0.07204557210206985,
-0.045780349522829056,
-0.04394662380218506,
0.049071304500103,
-0.03188550844788551,
0.14522109925746918,
0.050162553787231445,
-0.09602239727973938,
-0.10060478001832962,
-0.03710633143782616,
-0.20041054487228394,
0.26695820689201355,
-0.02112429216504097,
0.08602497726678848,
-0.22190633416175842,
0.02547726221382618,
-0.15691377222537994,
-0.07777521759271622,
-0.03994326665997505,
-0.08868958801031113,
0.0007165444549173117,
0.03496411815285683,
-0.1054995059967041,
-0.05400620773434639,
0.1524859070777893,
0.10100986063480377,
-0.039991434663534164,
0.06680738180875778,
-0.05496573448181152,
0.09457684308290482,
0.007357186172157526,
0.08487115800380707,
0.004251695703715086,
-0.02679322101175785,
0.0028159006033092737,
-0.21648989617824554,
-0.02913138084113598,
-0.07440675050020218,
-0.13762322068214417,
-0.05771903321146965,
-0.06727461516857147,
0.08988132327795029,
-0.011513961479067802,
0.05322519689798355,
-0.014487922191619873,
0.029230542480945587,
0.01647862233221531,
-0.04698542132973671,
-0.016856428235769272,
-0.0006139421020634472,
0.042123325169086456,
0.13298702239990234,
-0.05349437892436981,
0.002476717112585902,
-0.04667213186621666,
0.045286741107702255,
-0.07442079484462738,
-0.010620219632983208,
-0.03220843896269798,
-0.017421729862689972,
0.01238013245165348,
-0.14715172350406647,
0.04519771412014961,
-0.1117033064365387,
-0.17961354553699493,
0.012365331873297691,
0.02541954070329666,
-0.027730686590075493,
0.05330321565270424,
-0.043738897889852524,
-0.03978561982512474,
0.05650516226887703,
-0.06196942925453186,
-0.05383311212062836,
-0.07760827243328094,
0.06954456865787506,
0.006530481390655041,
0.07355255633592606,
-0.19058609008789062,
0.06655817478895187,
-0.02438404969871044,
0.021579977124929428,
-0.137967050075531,
0.10443931818008423,
-0.06279003620147705,
0.17324736714363098,
0.0018789154710248113,
0.022720806300640106,
-0.14477799832820892,
0.07889829576015472,
-0.08049003779888153,
0.18953093886375427,
-0.06065136939287186,
-0.12099350243806839,
0.33334603905677795,
-0.07543940842151642,
-0.13493743538856506,
0.0689801499247551,
0.02829323336482048,
-0.025411535054445267,
0.09032180160284042,
0.2727244794368744,
0.037348054349422455,
0.007425226736813784,
0.08740752190351486,
0.17363785207271576,
-0.11715899407863617,
-0.08976823836565018,
0.02972390130162239,
-0.07228533923625946,
-0.13154412806034088,
0.038542065769433975,
0.017456594854593277,
0.13168790936470032,
-0.033883508294820786,
-0.006262729875743389,
-0.034181054681539536,
-0.007485904730856419,
0.02858523279428482,
-0.005158690735697746,
0.10653847455978394,
-0.06756997108459473,
0.00851160567253828,
-0.0791502594947815,
-0.04450387507677078,
-0.02837834320962429,
0.03613412007689476,
-0.02884381264448166,
0.10680101811885834,
-0.04231800138950348,
0.08531270921230316,
-0.1458498239517212,
-0.1421574354171753,
-0.025719186291098595,
0.10338063538074493,
-0.032638587057590485,
0.11590561270713806,
0.07598087936639786,
-0.07653150707483292,
-0.010251731611788273,
0.036094751209020615,
0.14052505791187286,
-0.01232639979571104,
-0.005007986444979906,
-0.02063787169754505,
0.10085758566856384,
-0.0727856233716011,
-0.07437864691019058,
-0.08648914098739624,
0.02121381089091301,
0.17789724469184875,
0.07818994671106339,
0.020845280960202217,
-0.013459769077599049,
-0.0017346810782328248,
-0.0031804873142391443,
-0.030983738601207733,
-0.022077839821577072,
0.07634211331605911,
-0.006328213028609753,
-0.12348480522632599,
0.22044001519680023,
-0.12334394454956055,
0.27089813351631165,
0.1863541156053543,
-0.21157202124595642,
0.040252525359392166,
-0.07312045991420746,
-0.003348080674186349,
0.0510278157889843,
0.08169369399547577,
-0.038423482328653336,
0.1006215363740921,
-0.011515987105667591,
0.14560577273368835,
-0.02439459040760994,
-0.015194443054497242,
-0.014426624402403831,
-0.024794336408376694,
-0.04696030542254448,
0.040061965584754944,
0.0040283603593707085,
-0.1499948799610138,
0.21265046298503876,
0.16437600553035736,
0.10120455920696259,
0.2301403433084488,
-0.052775606513023376,
-0.006179410964250565,
0.06962192803621292,
0.04979134351015091,
-0.02862548828125,
-0.023439612239599228,
-0.27340593934059143,
-0.04803221672773361,
0.058091115206480026,
0.08838862180709839,
0.1594451367855072,
-0.11775633692741394,
-0.07357568293809891,
0.011395692825317383,
-0.07802775502204895,
-0.032421816140413284,
0.10294470191001892,
-0.0038269758224487305,
0.07158119976520538,
0.01997542567551136,
0.05874188244342804,
0.11971315741539001,
-0.013143796473741531,
-0.07740460336208344,
0.19619908928871155,
-0.14169634878635406,
-0.28958284854888916,
-0.1811203956604004,
-0.19683189690113068,
-0.04825779050588608,
0.08782356977462769,
0.1169954314827919,
-0.1564473658800125,
-0.03824961185455322,
0.04453830048441887,
0.12497194111347198,
-0.13104239106178284,
0.03814001381397247,
0.021633630618453026,
0.0357159860432148,
-0.10764139890670776,
-0.08001222461462021,
-0.05617373436689377,
-0.010578549467027187,
-0.03604588285088539,
0.08127055317163467,
-0.1475372612476349,
0.11227642744779587,
0.13462066650390625,
0.07316968590021133,
0.10140632838010788,
-0.012971841730177402,
0.20399945974349976,
-0.13861972093582153,
-0.08125321567058563,
0.17675228416919708,
-0.0056007071398198605,
0.059339489787817,
0.08977903425693512,
0.0006586898816749454,
-0.13959509134292603,
-0.011487981304526329,
-0.027896802872419357,
-0.13169744610786438,
-0.1854529231786728,
-0.0752529427409172,
-0.1555994153022766,
0.0391971692442894,
-0.035490378737449646,
0.0891667827963829,
0.11595811694860458,
0.07381458580493927,
0.023958778008818626,
0.009837509132921696,
0.03174016997218132,
0.05706017464399338,
0.16474942862987518,
-0.012225826270878315,
0.06657849252223969,
-0.08221769332885742,
-0.06015617400407791,
0.09020262211561203,
0.08751114457845688,
0.17842808365821838,
0.1306467205286026,
0.14687080681324005,
0.040677037090063095,
-0.025401374325156212,
0.1577799767255783,
0.0967152863740921,
0.0021733229514211416,
-0.06181129068136215,
-0.014838159084320068,
-0.0008649908122606575,
-0.03697579726576805,
0.0287077184766531,
0.01853453554213047,
-0.2132999449968338,
-0.030649419873952866,
-0.18822017312049866,
0.13289867341518402,
0.022944875061511993,
0.04152572527527809,
-0.13737933337688446,
-0.012605909258127213,
0.11767126619815826,
-0.018848104402422905,
-0.11539360880851746,
0.08806689828634262,
0.07253143936395645,
-0.09908895939588547,
0.10482010990381241,
-0.04038228839635849,
0.12548591196537018,
-0.012067651376128197,
0.09052639454603195,
-0.054524339735507965,
-0.09063196182250977,
0.02122526243329048,
0.09754706919193268,
-0.2797391414642334,
0.20971086621284485,
0.02209780551493168,
-0.037529509514570236,
-0.05445846915245056,
0.003275763476267457,
0.02146379090845585,
0.17078383266925812,
0.13981258869171143,
0.024530909955501556,
-0.11239399015903473,
-0.07275530695915222,
0.0757327750325203,
0.050253208726644516,
0.1992543339729309,
-0.08020731806755066,
-0.029789097607135773,
-0.010982216335833073,
0.0018502890598028898,
-0.03299505263566971,
0.0070411707274615765,
0.0035219250712543726,
-0.22908763587474823,
0.06321971863508224,
0.06728363037109375,
0.1331171989440918,
-0.007174460217356682,
0.0938580259680748,
-0.0987543985247612,
0.20914298295974731,
-0.10293351858854294,
-0.0611550472676754,
-0.12668845057487488,
-0.07016585767269135,
0.055333029478788376,
-0.0459693968296051,
0.06438574194908142,
-0.09787745028734207,
0.004825790412724018,
-0.09091890603303909,
-0.2175038456916809,
0.11621169745922089,
-0.08599694073200226,
0.05296287685632706,
-0.025739246979355812,
0.14477863907814026,
-0.06959626078605652,
-0.03430679440498352,
0.02216348610818386,
0.01117691956460476,
-0.07169883698225021,
-0.14873681962490082,
0.036700405180454254,
0.0059575242921710014,
0.012029055505990982,
0.11613360792398453,
-0.012293081730604172,
0.07422421872615814,
0.019829515367746353,
-0.03598923981189728,
0.2794061303138733,
0.14528462290763855,
-0.023540012538433075,
0.16387425363063812,
0.1182202398777008,
-0.06954378634691238,
-0.3015587329864502,
-0.05622600018978119,
-0.17103037238121033,
-0.009528614580631256,
-0.13196390867233276,
-0.22375774383544922,
0.08889705687761307,
0.03337733820080757,
-0.002300830790773034,
0.23360541462898254,
-0.22890694439411163,
-0.03639558330178261,
0.09377763420343399,
-0.008005589246749878,
0.4988834857940674,
-0.18573682010173798,
-0.15002183616161346,
-0.0720629021525383,
-0.2446787804365158,
0.14136669039726257,
-0.07708404213190079,
0.11433251202106476,
0.0017991859931498766,
0.04815256968140602,
0.02332199737429619,
-0.06559228152036667,
0.17698130011558533,
0.04580013081431389,
0.03142178803682327,
-0.08555825054645538,
-0.008231335319578648,
0.07476440072059631,
0.008679834194481373,
-0.010487770661711693,
-0.06746388971805573,
-0.007445100229233503,
-0.1339777708053589,
-0.055862344801425934,
-0.07582037150859833,
0.03865489736199379,
0.0799262747168541,
-0.020358197391033173,
-0.05047819763422012,
-0.05339556559920311,
-0.016170836985111237,
0.04169759154319763,
0.25741541385650635,
-0.1039213016629219,
0.1687774360179901,
0.025945115834474564,
0.04493308067321777,
-0.20477426052093506,
-0.004618740640580654,
-0.046843063086271286,
-0.01980600692331791,
0.0935465469956398,
-0.16110028326511383,
0.05819813162088394,
0.07944425195455551,
-0.05106005072593689,
0.06884531676769257,
0.1359410583972931,
0.014532159082591534,
0.021173717454075813,
0.1467812955379486,
-0.18967050313949585,
-0.12311629951000214,
-0.07608576118946075,
-0.1257346123456955,
0.13505223393440247,
0.10438317060470581,
0.13074788451194763,
0.10108643770217896,
0.01909380964934826,
-0.04022539034485817,
-0.0070203510113060474,
-0.0709037110209465,
0.01386108249425888,
0.00817179773002863,
0.02584969438612461,
-0.15160691738128662,
0.0819007083773613,
-0.03959338366985321,
-0.18340301513671875,
-0.008320484310388565,
0.128557488322258,
-0.15399695932865143,
-0.09042810648679733,
-0.11891915649175644,
0.12760032713413239,
-0.14738141000270844,
-0.033070772886276245,
-0.010086797177791595,
-0.1264544129371643,
0.06237873435020447,
0.23380206525325775,
0.04952940717339516,
0.1272558718919754,
-0.0008513382636010647,
0.004208550788462162,
0.027461236342787743,
-0.10672212392091751,
-0.05929316207766533,
0.018589265644550323,
-0.09067489206790924,
-0.040657006204128265,
-0.06846732646226883,
0.1384158432483673,
-0.08021344989538193,
-0.05024665221571922,
-0.1991536170244217,
0.023239627480506897,
-0.12186726182699203,
-0.05760553851723671,
-0.12232958525419235,
-0.05573355779051781,
0.017527537420392036,
-0.032693274319171906,
-0.03974439203739166,
-0.039665255695581436,
-0.13093158602714539,
0.009885036386549473,
-0.03841420263051987,
0.00771184591576457,
-0.05131201073527336,
0.012187164276838303,
0.09882780909538269,
-0.03772640600800514,
0.12072275578975677,
0.17711962759494781,
-0.06689894944429398,
0.1500464826822281,
-0.26132556796073914,
-0.11999466270208359,
0.10191045701503754,
-0.045100219547748566,
0.03038318082690239,
0.1263023316860199,
0.03314247727394104,
0.04125086963176727,
0.01655580848455429,
0.0664728581905365,
-0.021218232810497284,
-0.11617148667573929,
-0.023018088191747665,
-0.08339186012744904,
-0.08072522282600403,
-0.05001387000083923,
-0.05469566211104393,
0.1577877551317215,
0.04257164150476456,
0.05085008218884468,
-0.021031958982348442,
0.08851058781147003,
0.029373785480856895,
0.019602583721280098,
0.031050896272063255,
-0.19086246192455292,
0.05620424449443817,
-0.07514713704586029,
0.003966974094510078,
0.022788207978010178,
0.34499022364616394,
-0.05453638732433319,
0.01073573436588049,
0.02188924327492714,
0.022196220234036446,
0.09242333471775055,
0.02054937183856964,
0.30792322754859924,
0.15773355960845947,
-0.047039639204740524,
-0.11123811453580856,
0.0990198403596878,
0.02290377765893936,
-0.04754915088415146,
0.12262656539678574,
0.050504595041275024,
-0.07515083253383636,
0.1626535952091217,
-0.051976338028907776,
-0.014229681342840195,
-0.016774345189332962,
-0.061775192618370056,
-0.03942084684967995,
0.030239198356866837,
-0.003761051222681999,
0.02561202459037304,
0.17811238765716553,
-0.06015368551015854,
0.09523767977952957,
0.023459063842892647,
-0.047965023666620255,
-0.1466878056526184,
-0.1085495874285698,
-0.04624064639210701,
-0.1821233481168747,
0.05373648926615715,
-0.11874468624591827,
0.07605502009391785,
0.1288948655128479,
0.05164184421300888,
0.002530656987801194,
0.1609726846218109,
0.04395661503076553,
-0.10577259957790375,
0.10061278194189072,
-0.06672453135251999,
0.047171708196401596,
0.07377781718969345,
-0.03795863687992096,
-0.06771766394376755,
-0.08864762634038925,
-0.00720007810741663,
0.08406072109937668,
-0.015572836622595787,
0.02659783698618412,
-0.16683253645896912,
-0.07534679025411606,
-0.05712404102087021,
0.11183422058820724,
-0.07889340072870255,
0.11370791494846344,
0.0017686465289443731,
-0.05400395765900612,
0.04991501197218895,
0.2105201780796051,
-0.059385403990745544,
0.025879031047225,
-0.04132382199168205,
0.07631102204322815,
0.05708041042089462,
0.1591852456331253,
-0.09176953881978989,
-0.017336152493953705,
-0.08061710000038147,
0.3502628803253174,
0.23014387488365173,
-0.052850354462862015,
0.02067919820547104,
0.05188370496034622,
0.05179297551512718,
0.2027982473373413,
0.10301763564348221,
0.06460312008857727,
0.27206170558929443,
-0.04580971226096153,
-0.079527847468853,
0.035237859934568405,
-0.04374440386891365,
-0.12283738702535629,
0.12384460121393204,
0.04579421505331993,
-0.06362751126289368,
-0.053369324654340744,
0.13975189626216888,
-0.2425307184457779,
0.11192592978477478,
0.019153717905282974,
-0.15003347396850586,
-0.00391812901943922,
-0.0630134865641594,
0.08068794012069702,
-0.0178346186876297,
0.10188694298267365,
-0.0034701921977102757,
-0.15066954493522644,
0.009997948072850704,
0.05140922963619232,
-0.28469008207321167,
-0.026178516447544098,
0.01790185272693634,
-0.009450799785554409,
-0.03424009680747986,
-0.03393350541591644,
-0.046783559024333954,
0.07180610299110413,
0.035729311406612396,
-0.007510825525969267,
0.05646010860800743,
-0.03149822726845741,
-0.042454853653907776,
-0.05317913368344307,
0.08055443316698074,
-0.013036716729402542,
-0.12445653975009918,
0.05525088682770729,
-0.15915890038013458,
0.056089311838150024,
0.0342070534825325,
-0.047501031309366226,
0.014744436368346214,
-0.05187036097049713,
-0.11269349604845047,
0.03578566014766693,
0.06540670990943909,
0.016533343121409416,
0.015689637511968613,
-0.024289604276418686,
0.013355252332985401,
0.013228170573711395,
-0.014545965008437634,
-0.12462245672941208,
-0.045247167348861694,
-0.11810245364904404,
0.19476470351219177,
-0.026068395003676414,
-0.15855787694454193,
0.03035876713693142,
-0.05968214571475983,
0.13963234424591064,
-0.0934998020529747,
0.08311305940151215,
0.13333196938037872,
0.03356841579079628,
-0.028563642874360085,
-0.1888405978679657,
0.09133175015449524,
0.07190821319818497,
-0.05834801867604256,
-0.10380897670984268
] |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Large V2
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2995
- Wer: 10.8891
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.5753 | 0.13 | 30 | 0.3392 | 14.8670 |
| 0.2975 | 0.25 | 60 | 0.3042 | 19.4403 |
| 0.2893 | 0.38 | 90 | 0.3097 | 38.1677 |
| 0.2771 | 0.51 | 120 | 0.2772 | 13.8256 |
| 0.2656 | 0.63 | 150 | 0.2709 | 15.7969 |
| 0.2518 | 0.76 | 180 | 0.2602 | 14.6440 |
| 0.2419 | 0.89 | 210 | 0.2567 | 16.0404 |
| 0.2391 | 1.01 | 240 | 0.2599 | 13.9781 |
| 0.1319 | 1.14 | 270 | 0.2546 | 13.2594 |
| 0.128 | 1.27 | 300 | 0.2591 | 18.8448 |
| 0.1299 | 1.39 | 330 | 0.2599 | 11.7135 |
| 0.1261 | 1.52 | 360 | 0.2587 | 13.1303 |
| 0.1329 | 1.65 | 390 | 0.2541 | 12.4850 |
| 0.1303 | 1.77 | 420 | 0.2501 | 11.9980 |
| 0.115 | 1.9 | 450 | 0.2506 | 13.2799 |
| 0.1189 | 2.03 | 480 | 0.2486 | 11.0270 |
| 0.0577 | 2.15 | 510 | 0.2554 | 12.2532 |
| 0.0566 | 2.28 | 540 | 0.2587 | 11.3702 |
| 0.0573 | 2.41 | 570 | 0.2651 | 10.9390 |
| 0.0533 | 2.53 | 600 | 0.2590 | 11.1473 |
| 0.0519 | 2.66 | 630 | 0.2636 | 10.8363 |
| 0.056 | 2.78 | 660 | 0.2577 | 11.3732 |
| 0.062 | 2.91 | 690 | 0.2537 | 11.9833 |
| 0.0447 | 3.04 | 720 | 0.2675 | 11.7017 |
| 0.0232 | 3.16 | 750 | 0.2757 | 11.7927 |
| 0.0237 | 3.29 | 780 | 0.2751 | 12.5378 |
| 0.0216 | 3.42 | 810 | 0.2791 | 12.0244 |
| 0.0195 | 3.54 | 840 | 0.2787 | 10.9390 |
| 0.022 | 3.67 | 870 | 0.2749 | 12.4967 |
| 0.0213 | 3.8 | 900 | 0.2757 | 11.9012 |
| 0.0201 | 3.92 | 930 | 0.2735 | 11.5052 |
| 0.0146 | 4.05 | 960 | 0.2854 | 11.3673 |
| 0.0096 | 4.18 | 990 | 0.2956 | 11.7399 |
| 0.008 | 4.3 | 1020 | 0.2955 | 11.8073 |
| 0.008 | 4.43 | 1050 | 0.2963 | 11.8044 |
| 0.0087 | 4.56 | 1080 | 0.2971 | 11.6519 |
| 0.0076 | 4.68 | 1110 | 0.2971 | 11.7017 |
| 0.0073 | 4.81 | 1140 | 0.2982 | 10.8246 |
| 0.0068 | 4.94 | 1170 | 0.2995 | 10.8891 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0
| {"language": ["nl"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "openai/whisper-large-v2", "model-index": [{"name": "Whisper Large V2", "results": []}]} | automatic-speech-recognition | golesheed/whisper-1-dutch | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"nl",
"base_model:openai/whisper-large-v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-09T12:16:13+00:00 | [] | [
"nl"
] | TAGS
#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #nl #base_model-openai/whisper-large-v2 #license-apache-2.0 #endpoints_compatible #region-us
| Whisper Large V2
================
This model is a fine-tuned version of openai/whisper-large-v2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2995
* Wer: 10.8891
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 3e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 20
* num\_epochs: 5
### Training results
### Framework versions
* Transformers 4.38.0.dev0
* Pytorch 2.1.0+cu121
* Datasets 2.14.6
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 20\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #nl #base_model-openai/whisper-large-v2 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 20\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
] | [
74,
116,
4,
38
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #nl #base_model-openai/whisper-large-v2 #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 20\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0"
] | [
-0.13621632754802704,
0.15968677401542664,
-0.001858389237895608,
0.08833666890859604,
0.08782155066728592,
-0.012314315885305405,
0.1681707203388214,
0.12549269199371338,
-0.0416557751595974,
0.10454203933477402,
0.11453307420015335,
0.07736121118068695,
0.04561321437358856,
0.19964902102947235,
-0.06639396399259567,
-0.18399325013160706,
0.06508026272058487,
-0.01929723657667637,
-0.012141751125454903,
0.10182169824838638,
0.06421684473752975,
-0.12256090342998505,
0.06952235847711563,
-0.005073512438684702,
-0.12787765264511108,
-0.04992425814270973,
-0.003603593911975622,
-0.08829036355018616,
0.10478577762842178,
0.011877073906362057,
0.07441696524620056,
0.055234555155038834,
0.0800032690167427,
-0.20251408219337463,
0.008391182869672775,
0.03783120587468147,
0.020442096516489983,
0.06881789863109589,
0.023107007145881653,
0.002746920334175229,
0.017535895109176636,
-0.09417406469583511,
0.06630997359752655,
0.024000227451324463,
-0.09732259809970856,
-0.2184467762708664,
-0.10026310384273529,
0.03434481471776962,
0.08626585453748703,
0.07408887147903442,
-0.013106338679790497,
0.1581668108701706,
-0.012119011022150517,
0.09104708582162857,
0.23101380467414856,
-0.3176605999469757,
-0.04742036387324333,
-0.014271175488829613,
0.026294108480215073,
0.06450144946575165,
-0.07825247943401337,
-0.004888627678155899,
0.050102706998586655,
0.031746502965688705,
0.10774286836385727,
-0.004931247793138027,
-0.06233746558427811,
-0.02886657603085041,
-0.14197440445423126,
-0.05681544914841652,
0.15547265112400055,
0.045779261738061905,
-0.06491699069738388,
-0.09191487729549408,
-0.07131195068359375,
-0.1496184766292572,
-0.04956618696451187,
-0.0026261198800057173,
0.040708139538764954,
-0.04003528878092766,
-0.10001271963119507,
-0.006219570990651846,
-0.06729330122470856,
-0.08073122799396515,
-0.03193339332938194,
0.14468765258789062,
0.03321274742484093,
0.009420378133654594,
-0.014335672371089458,
0.05693294107913971,
-0.0019842665642499924,
-0.16536004841327667,
-0.03401283174753189,
0.033182013779878616,
-0.021878091618418694,
-0.020712248980998993,
-0.03135780245065689,
-0.03883081674575806,
0.06537950783967972,
0.15842470526695251,
-0.04659705609083176,
0.06207548454403877,
-0.0385638028383255,
0.023183785378932953,
-0.09648232907056808,
0.18515583872795105,
-0.044731661677360535,
-0.04666309803724289,
0.029621554538607597,
0.1070999950170517,
0.09432975202798843,
-0.03299562633037567,
-0.09444667398929596,
0.03328503668308258,
0.11510292440652847,
0.05439945310354233,
-0.02153661474585533,
0.05379653349518776,
-0.037912797182798386,
0.004595438949763775,
0.04724494367837906,
-0.1189795732498169,
0.013170532882213593,
0.012636043131351471,
-0.043195948004722595,
-0.055782828480005264,
0.029871907085180283,
0.015721013769507408,
-0.01764751970767975,
0.04835459217429161,
-0.06849675625562668,
-0.011128749698400497,
-0.03830641880631447,
-0.10412869602441788,
0.03652309626340866,
-0.08999405056238174,
-0.004184857476502657,
-0.1197713240981102,
-0.14654004573822021,
-0.010356196202337742,
0.02779223397374153,
-0.03712131455540657,
-0.03228628262877464,
-0.09426835924386978,
-0.11704937368631363,
0.032112982124090195,
-0.02710411697626114,
0.014512862078845501,
-0.07628854364156723,
0.08355537056922913,
0.04701763018965721,
0.09049330651760101,
-0.03954431042075157,
0.01878131553530693,
-0.08150075376033783,
0.04918791353702545,
-0.17111089825630188,
0.07168842107057571,
-0.08916273713111877,
0.07433856278657913,
-0.11224762350320816,
-0.07275255769491196,
0.043903231620788574,
-0.023215988650918007,
0.102077417075634,
0.11524751782417297,
-0.19717739522457123,
-0.04972013831138611,
0.21730080246925354,
-0.1178780272603035,
-0.15726397931575775,
0.15235035121440887,
-0.01904238387942314,
-0.018581777811050415,
0.06867604702711105,
0.24456007778644562,
0.06123080849647522,
-0.1375725120306015,
-0.056367188692092896,
-0.0261027030646801,
0.07121604681015015,
-0.05355974659323692,
0.07951906323432922,
-0.004325232934206724,
0.04291364923119545,
0.010138435289263725,
-0.01653929427266121,
0.04194147512316704,
-0.07492784410715103,
-0.09569421410560608,
-0.04744570702314377,
-0.11836248636245728,
0.01733187958598137,
0.002287462819367647,
0.02962525747716427,
-0.12062839418649673,
-0.08092740178108215,
0.0054471115581691265,
0.11621628701686859,
-0.09865685552358627,
0.026550618931651115,
-0.1331365555524826,
0.11432939022779465,
-0.07463306933641434,
-0.010850919410586357,
-0.1483161598443985,
-0.018835563212633133,
0.044661860913038254,
-0.03598916903138161,
0.02147132158279419,
-0.0885128602385521,
0.07264025509357452,
0.07467518001794815,
-0.02658570371568203,
-0.04248100146651268,
-0.0021844636648893356,
0.011659912765026093,
-0.08558013290166855,
-0.20805923640727997,
-0.02916303463280201,
-0.05072628706693649,
0.13512323796749115,
-0.16510847210884094,
0.026289111003279686,
0.036941152065992355,
0.10755885392427444,
0.050888173282146454,
-0.028413690626621246,
0.012378765270113945,
0.0576338954269886,
-0.021883849054574966,
-0.07558631896972656,
0.028089145198464394,
0.03416816145181656,
-0.09576820582151413,
0.017973311245441437,
-0.1792900711297989,
0.13350984454154968,
0.13961872458457947,
0.05354544147849083,
-0.03920949250459671,
0.026662219315767288,
-0.03780747950077057,
-0.03187672048807144,
-0.026316704228520393,
0.004363284446299076,
0.1465822458267212,
0.0021496822591871023,
0.12176033109426498,
-0.09753876179456711,
-0.02674207277595997,
0.045568548142910004,
-0.03195665404200554,
-0.012703437358140945,
0.09416963160037994,
0.021390480920672417,
-0.07712744921445847,
0.11574635654687881,
0.1106874868273735,
-0.07978503406047821,
0.11232184618711472,
-0.07221370190382004,
-0.05422946438193321,
-0.02878420613706112,
0.027434226125478745,
0.043337490409612656,
0.12183227390050888,
-0.09068036079406738,
-0.01438656821846962,
0.02515447698533535,
0.012049616314470768,
0.006123725324869156,
-0.1886918991804123,
0.005767221562564373,
0.016797926276922226,
-0.09008780866861343,
-0.025112684816122055,
-0.006492929067462683,
-0.011711121536791325,
0.09337366372346878,
-0.00300964480265975,
-0.10617176443338394,
0.014498971402645111,
-0.017466768622398376,
-0.07522863149642944,
0.17250363528728485,
-0.09990600496530533,
-0.15192309021949768,
-0.11562493443489075,
-0.048458099365234375,
-0.04277130961418152,
0.020420284941792488,
0.06840915232896805,
-0.06886673718690872,
-0.04794713854789734,
-0.12919248640537262,
-0.04088391363620758,
0.06478952616453171,
0.04791481792926788,
0.08473362773656845,
-0.0035977375227957964,
0.0837816372513771,
-0.10559368878602982,
-0.008524681441485882,
-0.028159063309431076,
-0.01048053614795208,
0.014065103605389595,
0.03254552185535431,
0.11960489302873611,
0.1376330703496933,
-0.000840022403281182,
0.019899478182196617,
-0.028172969818115234,
0.2301720380783081,
-0.07224448770284653,
-0.031108040362596512,
0.12259376049041748,
-0.02640325389802456,
0.051638029515743256,
0.17324084043502808,
0.030002977699041367,
-0.11868065595626831,
-0.00007048338738968596,
-0.023971926420927048,
-0.04381057620048523,
-0.21067218482494354,
-0.06343332678079605,
-0.038883596658706665,
0.04803768917918205,
0.07741225510835648,
0.03194250538945198,
0.01576736941933632,
0.0287628173828125,
0.010801567696034908,
0.03336828202009201,
-0.000778556102886796,
0.07745110988616943,
0.10073007643222809,
0.053871072828769684,
0.11427044123411179,
-0.04864861071109772,
-0.03424227237701416,
0.0309415552765131,
0.008827924728393555,
0.21411235630512238,
-0.006806745659559965,
0.1936895102262497,
0.031086552888154984,
0.14399775862693787,
0.03124992921948433,
0.05868235230445862,
-0.004321998450905085,
-0.0014536561211571097,
0.00011419778456911445,
-0.06801947951316833,
-0.049581386148929596,
0.026712197810411453,
-0.027490390464663506,
0.04718725383281708,
-0.09096401929855347,
0.06585752964019775,
0.06531890481710434,
0.291382759809494,
0.08233320713043213,
-0.35608431696891785,
-0.10410318523645401,
0.013939191587269306,
-0.04568859934806824,
-0.010707268491387367,
0.04109747335314751,
0.1676306277513504,
-0.035800375044345856,
0.05957348644733429,
-0.04908604547381401,
0.07021871209144592,
-0.07418491691350937,
0.031817398965358734,
0.02570035308599472,
0.0855240598320961,
0.0018178020836785436,
0.023594986647367477,
-0.2272135466337204,
0.28047269582748413,
0.012436628341674805,
0.10276336967945099,
-0.0487116314470768,
0.0020353891886770725,
0.02182648703455925,
0.016720861196517944,
0.10008277744054794,
-0.015368645079433918,
-0.12311194837093353,
-0.1574331372976303,
-0.1477552056312561,
0.045148614794015884,
0.09810465574264526,
0.0223187617957592,
0.11414927989244461,
-0.014110207557678223,
-0.032044459134340286,
0.04537680372595787,
-0.06469541043043137,
-0.053272537887096405,
-0.07595355063676834,
0.006568394135683775,
0.11694630980491638,
0.004027381539344788,
-0.07790198922157288,
-0.09135306626558304,
-0.09162882715463638,
0.11549058556556702,
-0.04482784494757652,
-0.03849310800433159,
-0.09641735255718231,
0.003951740451157093,
0.11544102430343628,
-0.08045060932636261,
0.044089674949645996,
0.018454937264323235,
0.10842259973287582,
0.01175297424197197,
-0.05085345357656479,
0.10011888295412064,
-0.08017635345458984,
-0.1976318061351776,
-0.04278148338198662,
0.14413614571094513,
0.007582133635878563,
0.0570187009871006,
0.016380207613110542,
0.03411366418004036,
0.002453245222568512,
-0.0755670964717865,
0.03509826213121414,
0.05489535257220268,
0.017490698024630547,
0.005914448760449886,
0.014068059623241425,
-0.05549638345837593,
-0.07103073596954346,
-0.026736579835414886,
0.16581539809703827,
0.29358914494514465,
-0.07713106274604797,
0.07019561529159546,
0.10554484277963638,
-0.033897191286087036,
-0.20164474844932556,
-0.01919488050043583,
0.044733766466379166,
0.014007529243826866,
-0.03216458484530449,
-0.13453763723373413,
0.07363015413284302,
0.06231628358364105,
-0.048025429248809814,
0.07798503339290619,
-0.29027286171913147,
-0.14117367565631866,
0.11903156340122223,
0.10698550939559937,
0.09159891307353973,
-0.13823311030864716,
-0.06350408494472504,
-0.03951125591993332,
-0.10792451351881027,
0.0806211605668068,
-0.13480418920516968,
0.11504227668046951,
0.011054912582039833,
0.054707806557416916,
0.009646838530898094,
-0.06326767057180405,
0.1307542473077774,
0.011582973413169384,
0.07547330111265182,
-0.04929868131875992,
0.01807730831205845,
0.026336537674069405,
-0.07765660434961319,
0.06403639912605286,
-0.1037367582321167,
0.06644802540540695,
-0.01571423001587391,
-0.02818179689347744,
-0.05265707150101662,
0.0024104963522404432,
-0.001534575829282403,
-0.030629007145762444,
-0.024864450097084045,
0.017817918211221695,
0.07605646550655365,
0.003878478892147541,
0.12219811975955963,
0.003997919615358114,
0.10227310657501221,
0.14183922111988068,
0.11386533826589584,
-0.08582790940999985,
-0.0023103870917111635,
0.017696576192975044,
-0.05048684403300285,
0.056129369884729385,
-0.12267884612083435,
0.05080711469054222,
0.1187099888920784,
0.02622661180794239,
0.11883791536092758,
0.05286730080842972,
-0.05940454453229904,
0.034165505319833755,
0.05844949930906296,
-0.14980162680149078,
-0.1553071290254593,
0.023392148315906525,
0.03544645383954048,
-0.1201973706483841,
0.08259966224431992,
0.14752541482448578,
-0.0755700096487999,
0.0036739788483828306,
-0.01621028408408165,
0.031722839921712875,
-0.02674170583486557,
0.2017463594675064,
0.04023810103535652,
0.0588664673268795,
-0.11580227315425873,
0.0918835923075676,
0.04340014234185219,
-0.09641899168491364,
0.07375139743089676,
0.05963040515780449,
-0.11412306874990463,
-0.029042372480034828,
0.012517434544861317,
0.1298513263463974,
0.023425843566656113,
-0.0739997997879982,
-0.13419552147388458,
-0.11811395734548569,
0.07012086361646652,
0.2163677215576172,
0.06164073571562767,
0.03168521821498871,
-0.017447616904973984,
0.008418331854045391,
-0.11339852213859558,
0.11061214655637741,
0.04519093409180641,
0.06823432445526123,
-0.14646613597869873,
0.11587050557136536,
-0.015727465972304344,
0.02495487593114376,
-0.02533199079334736,
0.026403261348605156,
-0.11744826287031174,
0.01147575955837965,
-0.14627821743488312,
0.05394823104143143,
-0.05587800592184067,
0.004396585747599602,
0.00706110754981637,
-0.056114312261343,
-0.0706772431731224,
0.034763094037771225,
-0.09286361187696457,
-0.030004320666193962,
0.0049022468738257885,
0.03647990897297859,
-0.13249318301677704,
-0.03307902812957764,
0.018925711512565613,
-0.09960555285215378,
0.1019238755106926,
0.05885067582130432,
-0.018056461587548256,
0.04574736952781677,
-0.1150609701871872,
-0.03010399267077446,
0.07337243109941483,
0.014473870396614075,
0.05624442547559738,
-0.12246954441070557,
-0.03631245344877243,
0.01771981082856655,
0.022298045456409454,
0.01760168746113777,
0.11799582093954086,
-0.09618406742811203,
0.01204153336584568,
-0.022656630724668503,
-0.01208792719990015,
-0.05695350468158722,
0.01780855469405651,
0.11104132980108261,
0.022894855588674545,
0.15558363497257233,
-0.09993931651115417,
0.008565147407352924,
-0.1832948923110962,
-0.0005412423051893711,
-0.019095033407211304,
-0.11106836795806885,
-0.11574822664260864,
-0.0049544572830200195,
0.08544056117534637,
-0.07203175127506256,
0.11254741251468658,
-0.05579017847776413,
0.01858634501695633,
0.02793101966381073,
-0.052619196474552155,
-0.03907065838575363,
0.05091674625873566,
0.20321890711784363,
0.04325638338923454,
-0.03485757112503052,
0.06155044957995415,
-0.014558817259967327,
0.09744757413864136,
0.09995421022176743,
0.15737716853618622,
0.15704390406608582,
0.07030757516622543,
0.12281396985054016,
0.08372491598129272,
-0.05185065045952797,
-0.15671880543231964,
0.05434349551796913,
-0.07844970375299454,
0.11937134712934494,
-0.003698879387229681,
0.19599823653697968,
0.10510700941085815,
-0.12325511127710342,
0.017096078023314476,
-0.04304680973291397,
-0.08304844051599503,
-0.10995044559240341,
-0.06291265785694122,
-0.10993708670139313,
-0.13319607079029083,
0.0006502840551547706,
-0.11679433286190033,
0.02228342927992344,
0.08178167790174484,
0.02289886213839054,
0.019076641649007797,
0.1472093164920807,
-0.008880493231117725,
0.05105264112353325,
0.07077731937170029,
-0.009628964588046074,
-0.05563129484653473,
-0.016096483916044235,
-0.10109423100948334,
0.04725433886051178,
0.025623423978686333,
0.05894210562109947,
-0.009952942840754986,
-0.035064611583948135,
0.07017426937818527,
-0.02025395818054676,
-0.11677761375904083,
0.014983261935412884,
0.00980759970843792,
0.056707728654146194,
0.029363341629505157,
0.05678620934486389,
-0.013942847959697247,
0.01449260301887989,
0.20588436722755432,
-0.09717857837677002,
-0.11521829664707184,
-0.14177344739437103,
0.18006600439548492,
-0.013315352611243725,
-0.014675081707537174,
0.014569193124771118,
-0.08950605243444443,
-0.02605634182691574,
0.17809627950191498,
0.19374603033065796,
-0.05199835076928139,
0.003235545242205262,
-0.03814956545829773,
-0.002785347169265151,
-0.07368181645870209,
0.0733838677406311,
0.12397715449333191,
0.07091996818780899,
-0.05419512465596199,
-0.05520976334810257,
-0.03642336651682854,
-0.030847154557704926,
-0.0404658168554306,
0.03606013208627701,
-0.028954196721315384,
-0.006494378205388784,
-0.04469893127679825,
0.057992979884147644,
-0.09258225560188293,
-0.10527916252613068,
-0.00040223964606411755,
-0.20833276212215424,
-0.1687675267457962,
0.00417300732806325,
0.07911945879459381,
0.03212394937872887,
0.028318211436271667,
-0.012253234162926674,
0.00021580942848231643,
0.08203965425491333,
-0.029652435332536697,
-0.06635201722383499,
-0.04245767742395401,
0.0534469336271286,
-0.09109541773796082,
0.21040211617946625,
-0.019157133996486664,
0.06370812654495239,
0.12168209254741669,
0.06113966926932335,
-0.11188666522502899,
0.08761792629957199,
0.06347261369228363,
-0.10575605183839798,
0.029891571030020714,
0.14618797600269318,
-0.06277395039796829,
0.13370433449745178,
0.05734676495194435,
-0.1043623760342598,
-0.03006412647664547,
-0.02347303181886673,
-0.03269651532173157,
-0.061209358274936676,
-0.05205981805920601,
-0.053864605724811554,
0.1346656084060669,
0.1382024586200714,
-0.07198037952184677,
0.003933044616132975,
-0.009475797414779663,
0.024455105885863304,
0.041742440313100815,
0.0062119378708302975,
-0.03529017046093941,
-0.2672055959701538,
0.009498773142695427,
0.004874990321695805,
0.017476601526141167,
-0.2497686743736267,
-0.07322251796722412,
-0.007318982854485512,
-0.031901322305202484,
-0.07763360440731049,
0.09062253683805466,
0.11694224178791046,
0.03633561730384827,
-0.06715060025453568,
-0.022874874994158745,
-0.031744956970214844,
0.1601707637310028,
-0.15446433424949646,
-0.10832486301660538
] |
null | null | clinicadl | # Model Card for maps_bis
This model was trained with ClinicaDL. You can find here all the information.
## General information
This model was trained for **classification** and the architecture chosen is **Conv4_FC3**.
### Model
**architecture**: Conv4_FC3
**multi_network**: False
**ssda_network**: False
### Architecture
**dropout**: 0.0
**latent_space_size**: 2
**feature_size**: 1024
**n_conv**: 4
**io_layer_channels**: 8
**recons_weight**: 1
**kl_weight**: 1
**normalization**: batch
### Classification
**selection_metrics**: ['loss']
**label**: diagnosis
**label_code**: {'AD': 0, 'CN': 1}
**selection_threshold**: 0.0
**loss**: None
### Computational
**gpu**: True
**n_proc**: 32
**batch_size**: 32
**evaluation_steps**: 20
**fully_sharded_data_parallel**: False
**amp**: False
### Reproducibility
**seed**: 0
**deterministic**: False
**compensation**: memory
**track_exp**:
### Transfer_learning
**transfer_path**: ../../autoencoders/exp3/maps
**transfer_selection_metric**: loss
**nb_unfrozen_layer**: 0
### Mode
**use_extracted_features**: False
### Data
**multi_cohort**: False
**diagnoses**: ['AD', 'CN']
**baseline**: True
**normalize**: True
**data_augmentation**: False
**sampler**: random
**size_reduction**: False
**size_reduction_factor**: 2
**caps_target**:
**tsv_target_lab**:
**tsv_target_unlab**:
**preprocessing_dict_target**:
### Cross_validation
**n_splits**: 5
**split**: []
### Optimization
**optimizer**: Adam
**epochs**: 200
**learning_rate**: 1e-05
**adaptive_learning_rate**: False
**weight_decay**: 0.0001
**patience**: 10
**tolerance**: 0.0
**accumulation_steps**: 1
**profiler**: False
**save_all_models**: False
### Informations
**emissions_calculator**: False
### Other information
**latent_space_dimension**: 64
**preprocessing_dict**: {'preprocessing': 't1-linear', 'mode': 'roi', 'use_uncropped_image': False, 'roi_list': ['leftHippocampusBox', 'rightHippocampusBox'], 'uncropped_roi': False, 'prepare_dl': False, 'file_type': {'pattern': '*space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.nii.gz', 'description': 'T1W Image registered using t1-linear and cropped (matrix size 169×208×179, 1 mm isotropic voxels)', 'needed_pipeline': 't1-linear'}}
**mode**: roi
**network_task**: classification
**caps_directory**: $WORK/../commun/datasets/adni/caps/caps_v2021
**tsv_path**: $WORK/Aramis_tools/ClinicaDL_tools/experiments_ADDL/data/ADNI/train
**validation**: KFoldSplit
**num_networks**: 2
**output_size**: 2
**input_size**: [1, 50, 50, 50]
| {"language": "en", "license": "mit", "library_name": "clinicadl", "tags": ["clinicadl"]} | null | camillebri/maps_bis | [
"clinicadl",
"en",
"license:mit",
"region:us"
] | 2024-02-09T12:16:56+00:00 | [] | [
"en"
] | TAGS
#clinicadl #en #license-mit #region-us
| # Model Card for maps_bis
This model was trained with ClinicaDL. You can find here all the information.
## General information
This model was trained for classification and the architecture chosen is Conv4_FC3.
### Model
architecture: Conv4_FC3
multi_network: False
ssda_network: False
### Architecture
dropout: 0.0
latent_space_size: 2
feature_size: 1024
n_conv: 4
io_layer_channels: 8
recons_weight: 1
kl_weight: 1
normalization: batch
### Classification
selection_metrics: ['loss']
label: diagnosis
label_code: {'AD': 0, 'CN': 1}
selection_threshold: 0.0
loss: None
### Computational
gpu: True
n_proc: 32
batch_size: 32
evaluation_steps: 20
fully_sharded_data_parallel: False
amp: False
### Reproducibility
seed: 0
deterministic: False
compensation: memory
track_exp:
### Transfer_learning
transfer_path: ../../autoencoders/exp3/maps
transfer_selection_metric: loss
nb_unfrozen_layer: 0
### Mode
use_extracted_features: False
### Data
multi_cohort: False
diagnoses: ['AD', 'CN']
baseline: True
normalize: True
data_augmentation: False
sampler: random
size_reduction: False
size_reduction_factor: 2
caps_target:
tsv_target_lab:
tsv_target_unlab:
preprocessing_dict_target:
### Cross_validation
n_splits: 5
split: []
### Optimization
optimizer: Adam
epochs: 200
learning_rate: 1e-05
adaptive_learning_rate: False
weight_decay: 0.0001
patience: 10
tolerance: 0.0
accumulation_steps: 1
profiler: False
save_all_models: False
### Informations
emissions_calculator: False
### Other information
latent_space_dimension: 64
preprocessing_dict: {'preprocessing': 't1-linear', 'mode': 'roi', 'use_uncropped_image': False, 'roi_list': ['leftHippocampusBox', 'rightHippocampusBox'], 'uncropped_roi': False, 'prepare_dl': False, 'file_type': {'pattern': '*space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.URL', 'description': 'T1W Image registered using t1-linear and cropped (matrix size 169×208×179, 1 mm isotropic voxels)', 'needed_pipeline': 't1-linear'}}
mode: roi
network_task: classification
caps_directory: $WORK/../commun/datasets/adni/caps/caps_v2021
tsv_path: $WORK/Aramis_tools/ClinicaDL_tools/experiments_ADDL/data/ADNI/train
validation: KFoldSplit
num_networks: 2
output_size: 2
input_size: [1, 50, 50, 50]
| [
"# Model Card for maps_bis \nThis model was trained with ClinicaDL. You can find here all the information.",
"## General information \nThis model was trained for classification and the architecture chosen is Conv4_FC3.",
"### Model \narchitecture: Conv4_FC3 \nmulti_network: False \nssda_network: False",
"### Architecture \ndropout: 0.0 \nlatent_space_size: 2 \nfeature_size: 1024 \nn_conv: 4 \nio_layer_channels: 8 \nrecons_weight: 1 \nkl_weight: 1 \nnormalization: batch",
"### Classification \nselection_metrics: ['loss'] \nlabel: diagnosis \nlabel_code: {'AD': 0, 'CN': 1} \nselection_threshold: 0.0 \nloss: None",
"### Computational \ngpu: True \nn_proc: 32 \nbatch_size: 32 \nevaluation_steps: 20 \nfully_sharded_data_parallel: False \namp: False",
"### Reproducibility \nseed: 0 \ndeterministic: False \ncompensation: memory \ntrack_exp:",
"### Transfer_learning \ntransfer_path: ../../autoencoders/exp3/maps \ntransfer_selection_metric: loss \nnb_unfrozen_layer: 0",
"### Mode \nuse_extracted_features: False",
"### Data \nmulti_cohort: False \ndiagnoses: ['AD', 'CN'] \nbaseline: True \nnormalize: True \ndata_augmentation: False \nsampler: random \nsize_reduction: False \nsize_reduction_factor: 2 \ncaps_target: \ntsv_target_lab: \ntsv_target_unlab: \npreprocessing_dict_target:",
"### Cross_validation \nn_splits: 5 \nsplit: []",
"### Optimization \noptimizer: Adam \nepochs: 200 \nlearning_rate: 1e-05 \nadaptive_learning_rate: False \nweight_decay: 0.0001 \npatience: 10 \ntolerance: 0.0 \naccumulation_steps: 1 \nprofiler: False \nsave_all_models: False",
"### Informations \nemissions_calculator: False",
"### Other information \nlatent_space_dimension: 64 \npreprocessing_dict: {'preprocessing': 't1-linear', 'mode': 'roi', 'use_uncropped_image': False, 'roi_list': ['leftHippocampusBox', 'rightHippocampusBox'], 'uncropped_roi': False, 'prepare_dl': False, 'file_type': {'pattern': '*space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.URL', 'description': 'T1W Image registered using t1-linear and cropped (matrix size 169×208×179, 1 mm isotropic voxels)', 'needed_pipeline': 't1-linear'}} \nmode: roi \nnetwork_task: classification \ncaps_directory: $WORK/../commun/datasets/adni/caps/caps_v2021 \ntsv_path: $WORK/Aramis_tools/ClinicaDL_tools/experiments_ADDL/data/ADNI/train \nvalidation: KFoldSplit \nnum_networks: 2 \noutput_size: 2 \ninput_size: [1, 50, 50, 50]"
] | [
"TAGS\n#clinicadl #en #license-mit #region-us \n",
"# Model Card for maps_bis \nThis model was trained with ClinicaDL. You can find here all the information.",
"## General information \nThis model was trained for classification and the architecture chosen is Conv4_FC3.",
"### Model \narchitecture: Conv4_FC3 \nmulti_network: False \nssda_network: False",
"### Architecture \ndropout: 0.0 \nlatent_space_size: 2 \nfeature_size: 1024 \nn_conv: 4 \nio_layer_channels: 8 \nrecons_weight: 1 \nkl_weight: 1 \nnormalization: batch",
"### Classification \nselection_metrics: ['loss'] \nlabel: diagnosis \nlabel_code: {'AD': 0, 'CN': 1} \nselection_threshold: 0.0 \nloss: None",
"### Computational \ngpu: True \nn_proc: 32 \nbatch_size: 32 \nevaluation_steps: 20 \nfully_sharded_data_parallel: False \namp: False",
"### Reproducibility \nseed: 0 \ndeterministic: False \ncompensation: memory \ntrack_exp:",
"### Transfer_learning \ntransfer_path: ../../autoencoders/exp3/maps \ntransfer_selection_metric: loss \nnb_unfrozen_layer: 0",
"### Mode \nuse_extracted_features: False",
"### Data \nmulti_cohort: False \ndiagnoses: ['AD', 'CN'] \nbaseline: True \nnormalize: True \ndata_augmentation: False \nsampler: random \nsize_reduction: False \nsize_reduction_factor: 2 \ncaps_target: \ntsv_target_lab: \ntsv_target_unlab: \npreprocessing_dict_target:",
"### Cross_validation \nn_splits: 5 \nsplit: []",
"### Optimization \noptimizer: Adam \nepochs: 200 \nlearning_rate: 1e-05 \nadaptive_learning_rate: False \nweight_decay: 0.0001 \npatience: 10 \ntolerance: 0.0 \naccumulation_steps: 1 \nprofiler: False \nsave_all_models: False",
"### Informations \nemissions_calculator: False",
"### Other information \nlatent_space_dimension: 64 \npreprocessing_dict: {'preprocessing': 't1-linear', 'mode': 'roi', 'use_uncropped_image': False, 'roi_list': ['leftHippocampusBox', 'rightHippocampusBox'], 'uncropped_roi': False, 'prepare_dl': False, 'file_type': {'pattern': '*space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.URL', 'description': 'T1W Image registered using t1-linear and cropped (matrix size 169×208×179, 1 mm isotropic voxels)', 'needed_pipeline': 't1-linear'}} \nmode: roi \nnetwork_task: classification \ncaps_directory: $WORK/../commun/datasets/adni/caps/caps_v2021 \ntsv_path: $WORK/Aramis_tools/ClinicaDL_tools/experiments_ADDL/data/ADNI/train \nvalidation: KFoldSplit \nnum_networks: 2 \noutput_size: 2 \ninput_size: [1, 50, 50, 50]"
] | [
17,
26,
25,
28,
53,
48,
45,
22,
41,
13,
88,
18,
65,
12,
313
] | [
"passage: TAGS\n#clinicadl #en #license-mit #region-us \n# Model Card for maps_bis \nThis model was trained with ClinicaDL. You can find here all the information.## General information \nThis model was trained for classification and the architecture chosen is Conv4_FC3.### Model \narchitecture: Conv4_FC3 \nmulti_network: False \nssda_network: False### Architecture \ndropout: 0.0 \nlatent_space_size: 2 \nfeature_size: 1024 \nn_conv: 4 \nio_layer_channels: 8 \nrecons_weight: 1 \nkl_weight: 1 \nnormalization: batch### Classification \nselection_metrics: ['loss'] \nlabel: diagnosis \nlabel_code: {'AD': 0, 'CN': 1} \nselection_threshold: 0.0 \nloss: None### Computational \ngpu: True \nn_proc: 32 \nbatch_size: 32 \nevaluation_steps: 20 \nfully_sharded_data_parallel: False \namp: False### Reproducibility \nseed: 0 \ndeterministic: False \ncompensation: memory \ntrack_exp:### Transfer_learning \ntransfer_path: ../../autoencoders/exp3/maps \ntransfer_selection_metric: loss \nnb_unfrozen_layer: 0### Mode \nuse_extracted_features: False### Data \nmulti_cohort: False \ndiagnoses: ['AD', 'CN'] \nbaseline: True \nnormalize: True \ndata_augmentation: False \nsampler: random \nsize_reduction: False \nsize_reduction_factor: 2 \ncaps_target: \ntsv_target_lab: \ntsv_target_unlab: \npreprocessing_dict_target:### Cross_validation \nn_splits: 5 \nsplit: []### Optimization \noptimizer: Adam \nepochs: 200 \nlearning_rate: 1e-05 \nadaptive_learning_rate: False \nweight_decay: 0.0001 \npatience: 10 \ntolerance: 0.0 \naccumulation_steps: 1 \nprofiler: False \nsave_all_models: False### Informations \nemissions_calculator: False"
] | [
-0.0825246050953865,
0.17681154608726501,
-0.00621649669483304,
0.04385893791913986,
0.07733955979347229,
0.05292659252882004,
0.04911874234676361,
0.14860808849334717,
0.004744921810925007,
0.17544716596603394,
0.01728087104856968,
0.09117188304662704,
0.09148379415273666,
0.2565262019634247,
-0.04660407826304436,
-0.14712394773960114,
0.045874688774347305,
-0.08363613486289978,
0.011466983705759048,
0.06061504781246185,
0.055885616689920425,
-0.10657690465450287,
0.009904766455292702,
-0.010216469876468182,
-0.033783599734306335,
-0.012687411159276962,
-0.02705911174416542,
-0.0170400682836771,
0.023048825562000275,
0.001855011680163443,
0.03164304792881012,
0.058024220168590546,
0.06013962998986244,
-0.2568333148956299,
0.008635682053864002,
0.07335792481899261,
0.036020923405885696,
0.06702904403209686,
0.09568459540605545,
-0.0018592277774587274,
0.0434434749186039,
-0.14779336750507355,
0.08945789188146591,
0.035637516528367996,
-0.07020869106054306,
-0.2677817642688751,
-0.09911757707595825,
0.10931628197431564,
0.13815806806087494,
0.019633783027529716,
-0.023100828751921654,
0.10427539050579071,
-0.06607655435800552,
0.05705224350094795,
0.0818755105137825,
-0.23448553681373596,
-0.024935344234108925,
0.031214481219649315,
0.0011550354538485408,
-0.01927865855395794,
-0.080832839012146,
-0.024600272998213768,
0.011025314219295979,
-0.006664132233709097,
0.014172888360917568,
-0.019893603399395943,
0.01742234267294407,
-0.04352906346321106,
-0.09759766608476639,
-0.037191905081272125,
0.1384623795747757,
0.032429419457912445,
-0.016054488718509674,
-0.18710941076278687,
-0.015756474807858467,
-0.12390532344579697,
-0.04353523626923561,
-0.015250157564878464,
0.0013039108598604798,
-0.04914764314889908,
-0.021824441850185394,
0.0742134228348732,
-0.0012985244393348694,
-0.049848075956106186,
0.03146890923380852,
0.06548717617988586,
0.08633708208799362,
-0.025529958307743073,
0.09204713255167007,
0.0846462994813919,
0.016854966059327126,
-0.14065468311309814,
-0.0557238906621933,
-0.036069612950086594,
-0.2030205875635147,
-0.054747387766838074,
0.005588081665337086,
0.07857432961463928,
0.12121915072202682,
0.21955257654190063,
-0.010506377555429935,
0.08001003414392471,
-0.0077575636096298695,
-0.010946502909064293,
0.024863431230187416,
0.04157599061727524,
-0.09516245871782303,
-0.061198052018880844,
-0.04696539044380188,
0.07614161819219589,
0.00866957288235426,
0.012828787788748741,
0.00084266735939309,
0.03443506360054016,
0.09761984646320343,
0.09758099168539047,
0.0073762801475822926,
0.04899967089295387,
-0.11021819710731506,
-0.059522874653339386,
0.10057784616947174,
-0.13015532493591309,
0.01764739863574505,
0.07425373792648315,
-0.07496771216392517,
0.00718380743637681,
0.01088680699467659,
-0.02551230974495411,
-0.06318367272615433,
0.07157697528600693,
-0.046231720596551895,
-0.03922878950834274,
-0.0227251797914505,
-0.05919107049703598,
0.04014602303504944,
-0.0193489920347929,
-0.02999674715101719,
-0.060323070734739304,
-0.13967964053153992,
-0.07083195447921753,
0.010006314143538475,
-0.08941719681024551,
-0.029048142954707146,
-0.037393998354673386,
-0.04702991992235184,
0.03576342761516571,
-0.01711992360651493,
0.05974685028195381,
-0.07278873771429062,
0.019768379628658295,
-0.07205662876367569,
0.04094192013144493,
0.13482964038848877,
0.026723433285951614,
-0.045093242079019547,
0.076958566904068,
-0.12202738970518112,
0.1400914043188095,
-0.12595820426940918,
0.010094713419675827,
-0.1723785698413849,
-0.0621417872607708,
0.031440045684576035,
-0.044734809547662735,
0.07083043456077576,
0.12164972722530365,
-0.18278303742408752,
-0.03393033519387245,
0.134856179356575,
-0.0455385185778141,
-0.08183097094297409,
0.06424339860677719,
-0.02555377408862114,
-0.04416009783744812,
0.03925750404596329,
0.06769300997257233,
0.14695680141448975,
-0.029095329344272614,
-0.05462458357214928,
-0.0576007179915905,
0.03247181698679924,
0.09485763311386108,
0.05808517336845398,
-0.0451849065721035,
0.027607601135969162,
0.022067276760935783,
-0.029014114290475845,
-0.0048095425590872765,
-0.05688077583909035,
-0.03747082129120827,
-0.0025497942697256804,
-0.08484694361686707,
0.034894909709692,
-0.00120094558224082,
-0.012467586435377598,
-0.024759793654084206,
-0.11286848783493042,
-0.03231806680560112,
0.1507691591978073,
-0.04388180375099182,
-0.009044695645570755,
-0.10091979801654816,
0.04519728198647499,
-0.006780760828405619,
0.02172801084816456,
-0.17584362626075745,
-0.12306305021047592,
0.08540231734514236,
-0.12512364983558655,
-0.04402397200465202,
0.011363917961716652,
0.014368823729455471,
0.014530670829117298,
-0.027128973975777626,
-0.06003650650382042,
0.056383438408374786,
0.019879432395100594,
-0.05573826655745506,
-0.1816471815109253,
-0.027265382930636406,
-0.04807135835289955,
0.1210651844739914,
-0.1834717094898224,
-0.016530590131878853,
0.025020282715559006,
0.1657090187072754,
-0.005786092020571232,
-0.07112810760736465,
0.01708100363612175,
-0.0184274073690176,
-0.01393306627869606,
-0.09008011966943741,
0.0052120136097073555,
-0.031170323491096497,
-0.11625917255878448,
0.030999308452010155,
-0.16041386127471924,
-0.051597241312265396,
0.04830879718065262,
0.09654832631349564,
-0.14426547288894653,
-0.033351436257362366,
-0.04484209045767784,
-0.047289978712797165,
-0.04383031651377678,
-0.012487252242863178,
0.1509975790977478,
0.061015211045742035,
0.08794635534286499,
-0.04322526976466179,
-0.07126183062791824,
-0.0073108309879899025,
0.05696325749158859,
0.00829967949539423,
0.18004776537418365,
-0.03117404691874981,
-0.14432795345783234,
0.03272778540849686,
0.053911834955215454,
0.006504715420305729,
0.03580288961529732,
-0.05910681560635567,
-0.06902283430099487,
-0.054019466042518616,
0.05754347890615463,
0.056940119713544846,
0.07360798120498657,
0.003989995922893286,
0.05242696404457092,
0.06320253014564514,
-0.015941515564918518,
-0.022206943482160568,
-0.03826919570565224,
0.04134748876094818,
0.0012747637229040265,
-0.03012946806848049,
0.05773577466607094,
0.03641487658023834,
0.04884523153305054,
0.11737409234046936,
-0.006191494408994913,
-0.034147635102272034,
-0.012605689465999603,
-0.06539413332939148,
-0.06437075138092041,
0.19137828052043915,
-0.10622962564229965,
-0.1417992115020752,
-0.04228382185101509,
-0.012008009478449821,
-0.031645920127630234,
-0.05036889389157295,
-0.041499070823192596,
-0.04305124655365944,
-0.13063450157642365,
-0.09514473378658295,
-0.08236874639987946,
0.05686546862125397,
-0.021780885756015778,
0.05388350784778595,
-0.01835966855287552,
0.11373551189899445,
-0.07027272880077362,
0.013188906013965607,
-0.0038429058622568846,
-0.053159985691308975,
-0.005996315740048885,
0.043718405067920685,
0.027437441051006317,
0.15844330191612244,
0.06444591283798218,
0.021511135622859,
0.0037503549829125404,
0.11186035722494125,
-0.06090528517961502,
0.0448579341173172,
0.10598388314247131,
0.010193496942520142,
0.049795154482126236,
0.17745241522789001,
0.02566629648208618,
-0.06316345930099487,
0.03789248317480087,
0.07027630507946014,
-0.008438730612397194,
-0.2328692525625229,
-0.10441779345273972,
-0.047528285533189774,
-0.04528025537729263,
0.13106578588485718,
0.07207249850034714,
0.009213211014866829,
0.028954776003956795,
-0.03856134042143822,
-0.007003986742347479,
0.08761999756097794,
0.09097945690155029,
0.05309808999300003,
0.04779916629195213,
0.1136569231748581,
-0.00011260286555625498,
0.013986720703542233,
0.06342749297618866,
-0.020322171971201897,
0.22516337037086487,
-0.05108163505792618,
0.13890612125396729,
0.06971950083971024,
0.058243513107299805,
-0.025845734402537346,
0.00854337215423584,
0.03169984370470047,
0.05161891132593155,
0.04289918392896652,
-0.04575466737151146,
0.01169423945248127,
0.035257112234830856,
0.09361826628446579,
0.01697513833642006,
-0.04218767583370209,
-0.017727894708514214,
0.07593627274036407,
0.1081453412771225,
0.16621004045009613,
-0.23805369436740875,
0.00013132119784131646,
0.051539335399866104,
-0.041382625699043274,
-0.0670531615614891,
-0.0026217703707516193,
0.019970078021287918,
-0.11937757581472397,
0.11163399368524551,
-0.06893196702003479,
0.08512257039546967,
-0.12996943295001984,
-0.01800648868083954,
0.06972102075815201,
0.09142874926328659,
0.0004006591625511646,
0.048841048032045364,
-0.1952337771654129,
0.12219705432653427,
-0.006120036356151104,
0.029325829818844795,
-0.05864274874329567,
0.052348289638757706,
0.054733049124479294,
-0.01669149659574032,
0.16041556000709534,
-0.005491920281201601,
-0.09671302884817123,
-0.1952058970928192,
-0.12436602264642715,
-0.02506333962082863,
0.0838427022099495,
-0.08190750330686569,
0.07074689865112305,
-0.000783582916483283,
-0.038598764687776566,
-0.02524995431303978,
-0.0741763487458229,
-0.16119591891765594,
-0.11102326214313507,
0.08189994841814041,
-0.011270835064351559,
-0.016400452703237534,
-0.06367827951908112,
-0.06364516913890839,
-0.15489579737186432,
0.2142326831817627,
-0.11958693712949753,
-0.09097292274236679,
-0.1598375141620636,
0.05671127513051033,
0.21356870234012604,
-0.0785129964351654,
0.04310998693108559,
-0.005366637371480465,
0.09768596291542053,
0.0168246291577816,
-0.08922377973794937,
0.05714002624154091,
-0.07154755294322968,
-0.234638050198555,
-0.05428466945886612,
0.12278826534748077,
0.07258428633213043,
0.016022052615880966,
0.013604825362563133,
0.018217936158180237,
0.032241202890872955,
-0.09856504946947098,
0.01858249306678772,
0.07959865778684616,
0.0716392919421196,
0.14692705869674683,
-0.039671752601861954,
-0.060791537165641785,
-0.0883820578455925,
0.04114400967955589,
0.006886766292154789,
0.2641367018222809,
-0.08126691728830338,
0.07498783618211746,
-0.016273848712444305,
-0.07569293677806854,
-0.18015754222869873,
-0.04889557883143425,
0.07726535946130753,
-0.01800313964486122,
0.043291348963975906,
-0.10809597373008728,
0.06884735077619553,
0.09847462922334671,
-0.02233758382499218,
0.07488410919904709,
-0.25831660628318787,
-0.15272951126098633,
0.11147382110357285,
0.03053179755806923,
-0.06911277025938034,
-0.13487935066223145,
-0.07906768471002579,
-0.04144326597452164,
-0.023687871173024178,
0.06145889684557915,
0.0445481613278389,
0.07569029182195663,
-0.026752950623631477,
-0.07937449961900711,
0.04402797296643257,
-0.0467403270304203,
0.15636876225471497,
0.07871866226196289,
0.05377162620425224,
-0.03897653892636299,
0.04976397380232811,
0.002390306442975998,
-0.09498394280672073,
0.03070681355893612,
-0.02518378384411335,
0.060306236147880554,
-0.15333476662635803,
-0.007047364488244057,
-0.057202037423849106,
0.037935275584459305,
-0.03725031018257141,
-0.008743642829358578,
-0.07809078693389893,
0.12982310354709625,
0.1260244995355606,
-0.00174190872348845,
0.04224185273051262,
-0.004503814037889242,
0.07308370620012283,
0.12741568684577942,
0.07654229551553726,
0.1317458301782608,
-0.07459484040737152,
0.012191452085971832,
-0.021412605419754982,
0.0033017341047525406,
-0.09282522648572922,
0.09478875249624252,
0.14989076554775238,
0.01009075716137886,
0.1088469848036766,
0.025634776800870895,
-0.11101988703012466,
-0.036842331290245056,
0.05297316238284111,
-0.04780504107475281,
-0.1203690841794014,
0.02809245139360428,
0.012809976935386658,
-0.08914690464735031,
-0.001691213226877153,
0.09931425005197525,
0.0025401434395462275,
-0.01342490129172802,
0.025441348552703857,
0.0800689086318016,
0.02821013703942299,
0.22317007184028625,
-0.0013459601905196905,
0.07814493030309677,
-0.11271832138299942,
0.12281839549541473,
0.1008751168847084,
-0.01614692248404026,
0.032951004803180695,
0.023009521886706352,
-0.08986280858516693,
-0.037963755428791046,
0.06756371259689331,
0.06315949559211731,
0.06760644912719727,
-0.03997626155614853,
-0.028899481520056725,
-0.14508117735385895,
0.08003322780132294,
0.0851670652627945,
0.026170942932367325,
0.04855711758136749,
0.007822823710739613,
-0.05143912881612778,
-0.051682982593774796,
0.15870115160942078,
0.13711996376514435,
0.07250501960515976,
-0.060133062303066254,
0.06612218916416168,
-0.0075839813798666,
0.02961253933608532,
-0.015439122915267944,
0.003928657155483961,
-0.12307179719209671,
-0.021534809842705727,
-0.09565724432468414,
0.047900937497615814,
-0.07824800908565521,
-0.023857906460762024,
-0.010244063101708889,
0.021317463368177414,
-0.02740408293902874,
0.022578569129109383,
-0.06378301978111267,
-0.08014041185379028,
0.028589950874447823,
0.09149612486362457,
-0.1262378990650177,
-0.06203083693981171,
0.024458257481455803,
-0.12538069486618042,
0.11140651255846024,
-0.029928943142294884,
0.026691436767578125,
0.0058398921974003315,
-0.04977365583181381,
-0.015058794058859348,
0.008256985805928707,
0.07393579930067062,
0.021054450422525406,
-0.2060895562171936,
0.021871231496334076,
-0.040182117372751236,
-0.07248213142156601,
0.017320802435278893,
-0.06148981302976608,
-0.08652345091104507,
-0.023124247789382935,
-0.09106975793838501,
0.006907853297889233,
-0.03972063213586807,
0.03239201009273529,
0.07688742130994797,
0.04661042243242264,
0.13795588910579681,
-0.046374328434467316,
0.04831085726618767,
-0.16784049570560455,
-0.04372946172952652,
0.025801299139857292,
-0.03738895803689957,
0.019073640927672386,
0.031591564416885376,
0.11898791044950485,
-0.03905034810304642,
0.13947734236717224,
-0.1273086965084076,
0.008935458026826382,
0.022924300283193588,
0.022660477086901665,
-0.037855397909879684,
0.06413584202528,
0.10071352124214172,
0.04487767443060875,
-0.019008729606866837,
0.025284456089138985,
-0.02940058894455433,
0.014310454949736595,
-0.02196229062974453,
0.14247514307498932,
0.13356155157089233,
0.056867633014917374,
-0.009753207676112652,
0.02420843578875065,
-0.15333051979541779,
-0.00663283234462142,
0.13493938744068146,
-0.1383560299873352,
0.09569068998098373,
-0.01128480490297079,
0.04347645863890648,
0.0003798186080530286,
-0.22223743796348572,
0.07264970242977142,
-0.017908168956637383,
-0.09741893410682678,
-0.09153854101896286,
-0.16886505484580994,
-0.07143155485391617,
-0.06546653062105179,
0.01137519534677267,
-0.08252734690904617,
0.024803850799798965,
0.13783711194992065,
0.046676795929670334,
0.020739877596497536,
0.04473201557993889,
-0.10706351697444916,
0.0038578512612730265,
0.10670919716358185,
0.02544875256717205,
-0.013072988949716091,
0.03426726534962654,
-0.03630440682172775,
0.02033652924001217,
0.05330818146467209,
0.0666658952832222,
0.00395051296800375,
0.018975380808115005,
0.04776871204376221,
0.015671202912926674,
-0.06908292323350906,
0.03588472679257393,
-0.04491802304983139,
-0.03017795830965042,
0.08867669850587845,
0.08547491580247879,
0.027554940432310104,
-0.05001867190003395,
0.1456773728132248,
-0.040017593652009964,
-0.016414310783147812,
-0.17019224166870117,
0.1449562907218933,
0.015117005445063114,
-0.009861433878540993,
0.051459554582834244,
-0.08056914806365967,
-0.025401901453733444,
0.09412810951471329,
0.0824526697397232,
-0.04982465133070946,
-0.003055526642128825,
0.0299244187772274,
-0.015049156732857227,
0.0003281269164290279,
0.08020245283842087,
0.045827869325876236,
0.008138995617628098,
-0.00520955678075552,
0.022625841200351715,
0.020030735060572624,
-0.037729427218437195,
-0.06922843307256699,
0.07182414084672928,
0.020654423162341118,
-0.0009213879820890725,
-0.05378555878996849,
0.0850118026137352,
-0.10494069010019302,
-0.12994436919689178,
0.10174276679754257,
-0.06694095581769943,
-0.18008477985858917,
-0.06296399980783463,
-0.011716630309820175,
-0.0023134900256991386,
-0.027318427339196205,
0.0020744826178997755,
0.007057847920805216,
0.12414237856864929,
-0.015453832224011421,
0.005803399253636599,
-0.10225191712379456,
0.023156849667429924,
-0.019107652828097343,
0.19977426528930664,
0.021676458418369293,
0.06180696561932564,
0.11420517414808273,
-0.037866897881031036,
-0.1333758383989334,
0.051125120371580124,
0.06944242864847183,
-0.10009269416332245,
0.09118007868528366,
0.15846163034439087,
0.010205235332250595,
0.12148609757423401,
0.08338769525289536,
-0.049317046999931335,
0.003834507893770933,
-0.008089515380561352,
0.00882456824183464,
-0.1185758039355278,
0.0611201748251915,
-0.04274114593863487,
0.1405072659254074,
0.1731545776128769,
-0.044250279664993286,
0.03045368194580078,
0.0024378634989261627,
0.008317165076732635,
-0.033345382660627365,
0.157579705119133,
-0.0016135378973558545,
-0.16182422637939453,
0.09792286157608032,
-0.0818270742893219,
0.11242542415857315,
-0.1110781729221344,
-0.06502428650856018,
-0.03811117261648178,
-0.070094533264637,
-0.06123920902609825,
0.18781833350658417,
0.01125268917530775,
0.0209756288677454,
-0.052338894456624985,
-0.08923733234405518,
0.009021054022014141,
0.11746364831924438,
-0.12355807423591614,
-0.02056244947016239
] |
null | null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | null | yeniceriSGK/mistral_7b-instruct-pi-brain | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-09T12:21:34+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
31,
6,
3,
82,
28,
3,
4,
9,
9,
10,
42,
20,
3,
4,
5,
9,
11,
13,
3,
12,
5,
4,
5,
3,
4,
9,
53,
9,
8,
6,
3,
14,
8,
7,
9,
4
] | [
"passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
] | [
-0.06646376848220825,
0.2168014943599701,
-0.00225935154594481,
0.023818302899599075,
0.1271018385887146,
-0.001635765191167593,
0.04218708351254463,
0.13324736058712006,
-0.020175931975245476,
0.11144465953111649,
0.046588581055402756,
0.09377603232860565,
0.09928803145885468,
0.18404334783554077,
0.04859916493296623,
-0.2059975117444992,
0.007056170143187046,
-0.09090408682823181,
0.014076028019189835,
0.1116579994559288,
0.13719257712364197,
-0.10291384905576706,
0.08272874355316162,
-0.04045208916068077,
-0.02019004337489605,
0.00012576708104461432,
-0.09259183704853058,
-0.07032395154237747,
0.06885425746440887,
0.06264153122901917,
0.051234472543001175,
0.001456156256608665,
0.09140396863222122,
-0.2864592671394348,
0.017265573143959045,
0.08406311273574829,
0.0027674848679453135,
0.06290827691555023,
0.07236549258232117,
-0.07389893382787704,
0.11328595131635666,
-0.08021481335163116,
0.13019037246704102,
0.08625296503305435,
-0.062064990401268005,
-0.23071379959583282,
-0.07525765895843506,
0.0963398814201355,
0.12251301854848862,
0.06215599179267883,
-0.022921854630112648,
0.15455181896686554,
-0.06248689442873001,
0.012971068732440472,
0.1294165402650833,
-0.11526761949062347,
-0.05572471022605896,
0.061741601675748825,
0.11775490641593933,
0.10740239918231964,
-0.14110268652439117,
-0.0017287094378843904,
0.04900608956813812,
0.029121357947587967,
0.08589313924312592,
0.022661056369543076,
0.12003941088914871,
0.04652795568108559,
-0.13695219159126282,
-0.04037507623434067,
0.12011898308992386,
0.038862764835357666,
-0.06446044892072678,
-0.2168138176202774,
-0.006778308190405369,
-0.0601806715130806,
-0.014732478186488152,
-0.07019448280334473,
0.039128515869379044,
-0.02470310963690281,
0.07317749410867691,
-0.04465159401297569,
-0.1063927412033081,
-0.0421026237308979,
0.0892222449183464,
0.07748593389987946,
0.011527054943144321,
-0.02519804798066616,
0.04627908393740654,
0.13455867767333984,
0.05402068421244621,
-0.10399353504180908,
-0.07017925381660461,
-0.06942764669656754,
-0.09420394152402878,
-0.04035796597599983,
0.056760527193546295,
0.031942449510097504,
0.02665667235851288,
0.22703726589679718,
0.016653569415211678,
0.04155244305729866,
0.0224777739495039,
0.01032855175435543,
0.043662428855895996,
0.0955500528216362,
-0.05303520709276199,
-0.15660029649734497,
-0.04072032496333122,
0.09077946096658707,
-0.0027527001220732927,
-0.036689214408397675,
-0.03966725245118141,
0.03849169611930847,
0.06843466311693192,
0.13122352957725525,
0.07552056759595871,
-0.017929591238498688,
-0.04813180863857269,
-0.030096933245658875,
0.23523783683776855,
-0.1493375599384308,
0.04426715523004532,
-0.02271856553852558,
-0.01804111897945404,
-0.03908449783921242,
0.03597262129187584,
0.022118929773569107,
-0.000004518366949923802,
0.09706240892410278,
-0.058981191366910934,
-0.05378659814596176,
-0.10168042778968811,
-0.03272576630115509,
0.04088849574327469,
-0.013975566253066063,
-0.010589460842311382,
-0.09025166928768158,
-0.09490354359149933,
-0.04766594246029854,
0.05537205561995506,
-0.05123869329690933,
-0.03770573064684868,
0.009465423412621021,
-0.08151785284280777,
-0.005444355774670839,
-0.005417742300778627,
0.10699385404586792,
-0.03222226724028587,
0.04445803165435791,
-0.027600755915045738,
0.05225523188710213,
0.09919606149196625,
0.031576547771692276,
-0.0773419588804245,
0.0561848059296608,
-0.22559374570846558,
0.07503069192171097,
-0.11481974273920059,
0.04335082694888115,
-0.1704932004213333,
-0.042439818382263184,
0.005444696638733149,
0.0139949731528759,
0.013206101022660732,
0.12720820307731628,
-0.19255615770816803,
-0.01654396951198578,
0.13260798156261444,
-0.09212633967399597,
-0.118110790848732,
0.07884611934423447,
-0.029701577499508858,
0.1624738723039627,
0.04682036489248276,
-0.027025915682315826,
0.09224298596382141,
-0.16434773802757263,
-0.07092688232660294,
-0.00949116237461567,
-0.01727987825870514,
0.12109188735485077,
0.07512219995260239,
-0.05991523340344429,
0.046571120619773865,
0.02832140028476715,
-0.038078423589468,
-0.04424772411584854,
-0.050857074558734894,
-0.10884185880422592,
-0.01070026308298111,
-0.08987759798765182,
0.04065500199794769,
-0.01250192429870367,
-0.07916021347045898,
-0.029885273426771164,
-0.18612512946128845,
-0.0030564051121473312,
0.10038342326879501,
0.0035033065360039473,
-0.005652366206049919,
-0.08666291832923889,
0.026358824223279953,
-0.03112892620265484,
-0.008404186926782131,
-0.16764774918556213,
-0.04399421438574791,
0.046902090311050415,
-0.16094985604286194,
0.020117372274398804,
-0.06413903087377548,
0.06334125250577927,
0.03641495108604431,
-0.05590536445379257,
-0.0248766727745533,
-0.01730942726135254,
0.011945613659918308,
-0.05083848536014557,
-0.18994836509227753,
-0.056277405470609665,
-0.037882111966609955,
0.149809330701828,
-0.25956398248672485,
0.032966937869787216,
0.051140617579221725,
0.14649195969104767,
0.00406361510977149,
-0.05115427449345589,
0.01429014839231968,
-0.05360214412212372,
-0.054652128368616104,
-0.06746816635131836,
-0.006135428790003061,
-0.027576493099331856,
-0.05147203803062439,
0.019243421033024788,
-0.1755700707435608,
-0.021410830318927765,
0.09424154460430145,
0.12876708805561066,
-0.1486445665359497,
-0.018640631809830666,
-0.048725154250860214,
-0.06339836865663528,
-0.0715010017156601,
-0.07038594037294388,
0.10712739825248718,
0.0513901449739933,
0.04796046018600464,
-0.07435787469148636,
-0.07092321664094925,
0.02726263552904129,
0.006906150374561548,
-0.03382374346256256,
0.08727246522903442,
0.05199531093239784,
-0.09209315478801727,
0.0756213590502739,
0.1092359870672226,
0.07177663594484329,
0.09363535046577454,
0.01574566215276718,
-0.11756632477045059,
-0.028492970392107964,
0.036266472190618515,
0.02740776725113392,
0.1465986967086792,
-0.05952361226081848,
0.04016614332795143,
0.04494241625070572,
-0.04170418903231621,
0.022319864481687546,
-0.08787637203931808,
0.024075502529740334,
0.025203049182891846,
-0.0034381982404738665,
0.06284574419260025,
-0.02525499276816845,
-0.0050758360885083675,
0.07016654312610626,
0.047779910266399384,
0.04621000960469246,
0.009655474685132504,
-0.01720241829752922,
-0.1047825813293457,
0.16950392723083496,
-0.0951867327094078,
-0.269941508769989,
-0.17632324993610382,
0.026197833940386772,
0.04035249724984169,
-0.022378476336598396,
0.031619444489479065,
-0.07056326419115067,
-0.10630585998296738,
-0.1060405746102333,
-0.002429972169920802,
0.01714223250746727,
-0.06364088505506516,
-0.0741225928068161,
0.07348573952913284,
0.04382912442088127,
-0.14902326464653015,
0.038552410900592804,
0.055694397538900375,
-0.057955220341682434,
-0.0233661737293005,
0.09118817001581192,
0.12397737801074982,
0.14583967626094818,
-0.021366750821471214,
-0.028626007959246635,
0.029004426673054695,
0.19620531797409058,
-0.13469526171684265,
0.10371150821447372,
0.13814030587673187,
-0.04545360431075096,
0.08360563963651657,
0.1560150384902954,
0.029186224564909935,
-0.08317049592733383,
0.05044832453131676,
0.04082648828625679,
-0.043159641325473785,
-0.2666129767894745,
-0.0534592866897583,
0.012832709588110447,
-0.06255637854337692,
0.09786593168973923,
0.10183793306350708,
0.11542957276105881,
0.034910861402750015,
-0.07166364789009094,
-0.043925940990448,
-0.0058974819257855415,
0.11737963557243347,
-0.05490213260054588,
-0.012639665976166725,
0.07686592638492584,
-0.05086168646812439,
0.005355054512619972,
0.10266812145709991,
0.02973790094256401,
0.17442677915096283,
0.020399179309606552,
0.11231429129838943,
0.06195578724145889,
0.08633565157651901,
0.0007386076031252742,
0.02951662428677082,
0.05147615820169449,
0.017203815281391144,
-0.002300140680745244,
-0.10421168059110641,
-0.006156572140753269,
0.1449710875749588,
0.028103826567530632,
0.029669636860489845,
-0.0018948549404740334,
-0.005003341939300299,
0.05121048167347908,
0.1746254414319992,
-0.011592294089496136,
-0.22072425484657288,
-0.0845772922039032,
0.06936841458082199,
-0.06218599155545235,
-0.12968985736370087,
-0.026130788028240204,
0.045467354357242584,
-0.17519839107990265,
0.026703642681241035,
-0.027433741837739944,
0.0919293761253357,
-0.09345759451389313,
-0.02221956104040146,
0.03687324374914169,
0.084866963326931,
-0.014529162086546421,
0.08703910559415817,
-0.14498743414878845,
0.11886418610811234,
0.02978132851421833,
0.09024628251791,
-0.11081171780824661,
0.07909037172794342,
-0.007550720125436783,
0.009180475026369095,
0.19379350543022156,
-0.011335089802742004,
-0.03514958545565605,
-0.08774717897176743,
-0.11210042238235474,
-0.013537433929741383,
0.12687496840953827,
-0.1243172138929367,
0.08773399889469147,
-0.015198243781924248,
-0.044079482555389404,
0.00937260314822197,
-0.12100647389888763,
-0.17273177206516266,
-0.19628387689590454,
0.05585884302854538,
-0.09575839340686798,
0.025643249973654747,
-0.11914430558681488,
-0.07089093327522278,
-0.02952558360993862,
0.241120383143425,
-0.1745356321334839,
-0.06510113179683685,
-0.1468164622783661,
-0.046294767409563065,
0.1662203073501587,
-0.04437198117375374,
0.0718095526099205,
-0.0208172257989645,
0.20345525443553925,
0.005988610442727804,
-0.004939318168908358,
0.06724198162555695,
-0.08892562240362167,
-0.16873881220817566,
-0.06771010160446167,
0.1510489284992218,
0.11680185794830322,
0.04907919466495514,
-0.002248800592496991,
0.0011772146681323647,
-0.016943959519267082,
-0.1137804463505745,
-0.0033210667315870523,
0.16037839651107788,
0.03878779336810112,
0.025986969470977783,
-0.05243593826889992,
-0.08797456324100494,
-0.06899320334196091,
-0.06853509694337845,
0.06221301481127739,
0.19590823352336884,
-0.10376439243555069,
0.1700313836336136,
0.147536963224411,
-0.07305635511875153,
-0.23175598680973053,
0.035342130810022354,
0.04983805492520332,
0.0014306638622656465,
0.04886869341135025,
-0.18252557516098022,
0.10521943867206573,
0.019543392583727837,
-0.05505957826972008,
0.13485197722911835,
-0.1557481735944748,
-0.1552847921848297,
0.0722852572798729,
0.03904085233807564,
-0.22423844039440155,
-0.1354004591703415,
-0.09622503817081451,
-0.05825018882751465,
-0.14065024256706238,
0.06054598465561867,
-0.002136280992999673,
0.015948504209518433,
0.03500790148973465,
-0.0015643214574083686,
0.027123261243104935,
-0.058935679495334625,
0.18609118461608887,
-0.004065449349582195,
0.020676052197813988,
-0.060264769941568375,
-0.0478842556476593,
0.09839435666799545,
-0.06130504235625267,
0.12208222597837448,
0.004057085141539574,
0.01594383642077446,
-0.10362856835126877,
-0.048314861953258514,
-0.04328322783112526,
0.05154227837920189,
-0.07548051327466965,
-0.10070807486772537,
-0.043625857681035995,
0.08841723203659058,
0.07005169242620468,
-0.03383097052574158,
0.00549331633374095,
-0.07189501076936722,
0.10019614547491074,
0.17795267701148987,
0.17573626339435577,
0.009926567785441875,
-0.07241068035364151,
0.01677953451871872,
-0.04142116755247116,
0.044231921434402466,
-0.2513144314289093,
0.03756171092391014,
0.06098250672221184,
0.029438555240631104,
0.09217222779989243,
-0.020435843616724014,
-0.1820858269929886,
-0.04050002992153168,
0.08094815909862518,
-0.05452597141265869,
-0.22617179155349731,
-0.019085140898823738,
0.0954197570681572,
-0.2020406424999237,
-0.007372708059847355,
0.03995226323604584,
-0.048725228756666183,
-0.023169852793216705,
0.00010950004070764408,
0.06317184865474701,
0.002471912419423461,
0.09773622453212738,
0.0735151618719101,
0.09715340286493301,
-0.08337292820215225,
0.10562895983457565,
0.10150538384914398,
-0.09572599828243256,
0.03605884686112404,
0.06754924356937408,
-0.05300498008728027,
-0.043293699622154236,
0.03665391728281975,
0.033023297786712646,
0.005234600510448217,
-0.060321882367134094,
0.013913018628954887,
-0.036497246474027634,
0.044923391193151474,
0.08326134830713272,
0.03754979372024536,
-0.013354414142668247,
0.06462216377258301,
0.03401726484298706,
-0.10898099094629288,
0.10366570204496384,
0.01731540448963642,
0.04105307161808014,
-0.08384523540735245,
-0.019968897104263306,
0.035425446927547455,
0.030576206743717194,
-0.01765924133360386,
-0.02306121215224266,
-0.02860277332365513,
-0.01614218018949032,
-0.14299540221691132,
-0.023106401786208153,
-0.07243485748767853,
0.006181265693157911,
0.014656842686235905,
-0.031884219497442245,
-0.011233693920075893,
0.02475680410861969,
-0.06979699432849884,
-0.07426341623067856,
-0.006949664559215307,
0.09833318740129471,
-0.15115703642368317,
0.008848577737808228,
0.06907843053340912,
-0.11088496446609497,
0.08190931379795074,
-0.008411259390413761,
0.016245156526565552,
0.022527478635311127,
-0.15448406338691711,
0.05601610988378525,
0.0008648968650959432,
0.01916889287531376,
0.025886621326208115,
-0.16471809148788452,
0.004104440100491047,
-0.04661374166607857,
-0.02149827405810356,
-0.00004464812809601426,
-0.02647159807384014,
-0.12325995415449142,
0.06858719140291214,
-0.015622655861079693,
-0.035931166261434555,
-0.02701525390148163,
0.0539589487016201,
0.07888586074113846,
-0.027474910020828247,
0.10445091128349304,
-0.008690856397151947,
0.04941811040043831,
-0.16801609098911285,
-0.02470702864229679,
-0.04982255399227142,
0.019377702847123146,
0.009884213097393513,
-0.007693959400057793,
0.04183054715394974,
-0.00976533442735672,
0.21883612871170044,
-0.05075952783226967,
0.1607085019350052,
0.05847611650824547,
-0.017352959141135216,
-0.0007513365126214921,
0.06180921941995621,
0.05997028574347496,
0.04658793285489082,
0.009480604901909828,
0.023740366101264954,
-0.022450892254710197,
-0.006695089396089315,
-0.15932634472846985,
0.01890849508345127,
0.14999441802501678,
0.06301083415746689,
0.024745315313339233,
0.05866100639104843,
-0.12775006890296936,
-0.12135478109121323,
0.09311001747846603,
-0.026755332946777344,
0.00928465835750103,
-0.08245618641376495,
0.1358020007610321,
0.14980104565620422,
-0.14000412821769714,
0.05256148427724838,
-0.06134212389588356,
-0.05217423290014267,
-0.10388828068971634,
-0.12032219022512436,
-0.05887215584516525,
-0.053666237741708755,
0.002330566756427288,
-0.03760887682437897,
0.054546963423490524,
0.03344334661960602,
-0.009351172484457493,
-0.00022941511997487396,
0.13597318530082703,
-0.019751882180571556,
-0.0028988157864660025,
0.048313532024621964,
0.03693558648228645,
0.02373051457107067,
-0.05275435373187065,
0.02940409444272518,
0.02539868652820587,
0.032232340425252914,
0.06546790152788162,
0.033412106335163116,
-0.047448933124542236,
0.03804153576493263,
-0.0025254099164158106,
-0.11207924783229828,
0.019641218706965446,
-0.00460948096588254,
-0.0742158442735672,
0.1268945336341858,
0.0407399944961071,
0.010224059224128723,
-0.03741471841931343,
0.24361543357372284,
-0.06653323769569397,
-0.06378097087144852,
-0.13251738250255585,
0.10491154342889786,
-0.0027236645109951496,
0.06476365029811859,
0.023412218317389488,
-0.1284150779247284,
0.005243356805294752,
0.13858191668987274,
0.12181595712900162,
0.0045748427510261536,
0.009228081442415714,
0.0518609918653965,
0.0025186820421367884,
-0.06998204439878464,
0.054019294679164886,
0.06992026418447495,
0.12919506430625916,
-0.07847554981708527,
0.07680778950452805,
0.0006860480643808842,
-0.08370215445756912,
-0.02947772853076458,
0.11312682181596756,
-0.0409729965031147,
0.03491825982928276,
-0.047444481402635574,
0.10916327685117722,
-0.05787910893559456,
-0.29412412643432617,
0.02350960113108158,
-0.09588567912578583,
-0.15202060341835022,
-0.018367812037467957,
0.05944539234042168,
-0.02624768204987049,
0.018029648810625076,
0.06971040368080139,
-0.06011629104614258,
0.20098382234573364,
0.0335683599114418,
-0.07864278554916382,
-0.0664360448718071,
0.04837050288915634,
-0.06564252078533173,
0.2949807047843933,
0.008418165147304535,
0.02863333560526371,
0.10770907253026962,
-0.03253700211644173,
-0.18271861970424652,
0.010723991319537163,
0.1133992001414299,
-0.08056149631738663,
0.08200647681951523,
0.19000613689422607,
-0.012578671798110008,
0.1209007054567337,
0.05294662341475487,
-0.047376248985528946,
0.04217283055186272,
-0.03389401361346245,
-0.051268599927425385,
-0.10752558708190918,
0.058453381061553955,
-0.05909625440835953,
0.15447644889354706,
0.10152646154165268,
-0.05671518296003342,
-0.004550917539745569,
-0.05555408447980881,
0.04875178262591362,
0.01804669201374054,
0.12263146042823792,
0.02951994352042675,
-0.1865430772304535,
0.032826557755470276,
-0.01144319772720337,
0.10186848044395447,
-0.25588861107826233,
-0.08421015739440918,
0.08833149075508118,
-0.011924264021217823,
-0.05105875805020332,
0.10560628771781921,
0.057650718837976456,
0.04243382066488266,
-0.043439045548439026,
-0.10480839014053345,
-0.02186836116015911,
0.14663739502429962,
-0.1469624787569046,
-0.025013303384184837
] |
null | null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | text-classification | technocrat3128/sentiment_analysis_Twitter_roberta_fine_tune_hashtag_removed | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-09T12:24:58+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #roberta #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #roberta #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
47,
6,
3,
82,
28,
3,
4,
9,
9,
10,
42,
20,
3,
4,
5,
9,
11,
13,
3,
12,
5,
4,
5,
3,
4,
9,
53,
9,
8,
6,
3,
14,
8,
7,
9,
4
] | [
"passage: TAGS\n#transformers #safetensors #roberta #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
] | [
-0.07097413390874863,
0.1667894870042801,
-0.003666079370304942,
0.021719951182603836,
0.1169515997171402,
0.008284984156489372,
0.07978618890047073,
0.10868363827466965,
-0.0287666916847229,
0.12472489476203918,
0.03958536684513092,
0.100002720952034,
0.11150532960891724,
0.19375544786453247,
0.0031385887414216995,
-0.2044016718864441,
0.0626462921500206,
-0.11493270099163055,
0.012976650148630142,
0.12250365316867828,
0.1423846185207367,
-0.10432620346546173,
0.07270357757806778,
-0.038304854184389114,
-0.02555735409259796,
-0.02992432750761509,
-0.06288715451955795,
-0.06023360788822174,
0.06700628250837326,
0.060681991279125214,
0.0691668838262558,
0.022982222959399223,
0.08022861182689667,
-0.2922787070274353,
0.0204449649900198,
0.07901842147111893,
0.0025627717841416597,
0.06292086839675903,
0.07761354744434357,
-0.07406430691480637,
0.11906187236309052,
-0.055618006736040115,
0.15586547553539276,
0.07508774101734161,
-0.09551873803138733,
-0.1806546300649643,
-0.08346612006425858,
0.09468797594308853,
0.16155311465263367,
0.05840203911066055,
-0.03498850762844086,
0.14096277952194214,
-0.08077409118413925,
0.016175461933016777,
0.06836850196123123,
-0.07082965970039368,
-0.05214150249958038,
0.05027921497821808,
0.07374554872512817,
0.09283687174320221,
-0.1307315081357956,
-0.010299519635736942,
0.04289179667830467,
0.018450511619448662,
0.10514726489782333,
0.023654451593756676,
0.11117657274007797,
0.028080156072974205,
-0.13854995369911194,
-0.0629403293132782,
0.12449787557125092,
0.032600224018096924,
-0.060118548572063446,
-0.23809053003787994,
-0.004739876836538315,
-0.03291091322898865,
-0.021559255197644234,
-0.04033812880516052,
0.04089386388659477,
-0.029875123873353004,
0.08085108548402786,
0.006228691898286343,
-0.07215629518032074,
-0.052459850907325745,
0.08968427777290344,
0.05656275153160095,
0.02484809421002865,
-0.02587738446891308,
0.02272983267903328,
0.1178721934556961,
0.09954223781824112,
-0.11430655419826508,
-0.06540504842996597,
-0.06454093754291534,
-0.08725714683532715,
-0.04798560217022896,
0.03668604791164398,
0.07030875980854034,
0.046011876314878464,
0.19716040790081024,
0.00269759027287364,
0.05310693010687828,
0.02948983944952488,
0.016264021396636963,
0.06394703686237335,
0.06814983487129211,
-0.05028722807765007,
-0.12879575788974762,
-0.03764699772000313,
0.11916788667440414,
0.006199078168720007,
-0.032489337027072906,
-0.0359247624874115,
0.05915093049407005,
0.05401566997170448,
0.11897829174995422,
0.06329395622015,
0.01798311620950699,
-0.06940232217311859,
-0.041600339114665985,
0.1802349090576172,
-0.15566512942314148,
0.020810957998037338,
0.015188573859632015,
-0.055285364389419556,
-0.042803313583135605,
0.023135531693696976,
0.009154262952506542,
-0.02835596352815628,
0.10190211236476898,
-0.06733403354883194,
-0.03976709768176079,
-0.10797561705112457,
-0.05405012145638466,
0.03397495672106743,
-0.023470325395464897,
-0.0295037180185318,
-0.04243861138820648,
-0.12201286107301712,
-0.07699050009250641,
0.06754051148891449,
-0.06153370812535286,
-0.06873670220375061,
-0.039611347019672394,
-0.0595494844019413,
0.012631349265575409,
0.002569467294961214,
0.12651456892490387,
-0.029271753504872322,
0.04821896180510521,
-0.05001894757151604,
0.06899536401033401,
0.13243289291858673,
0.03377855569124222,
-0.07108951359987259,
0.06568732112646103,
-0.21172066032886505,
0.10609432309865952,
-0.09376481175422668,
0.03139003366231918,
-0.16234688460826874,
-0.024603933095932007,
0.027305902913212776,
0.03785030171275139,
-0.01080681849271059,
0.14199338853359222,
-0.18069814145565033,
-0.0368097685277462,
0.18344907462596893,
-0.13205336034297943,
-0.09475254267454147,
0.06138899549841881,
-0.06121760979294777,
0.13521988689899445,
0.05350736901164055,
-0.023734936490654945,
0.05919842794537544,
-0.1313716471195221,
-0.025711048394441605,
-0.05855761095881462,
-0.008730711415410042,
0.14457696676254272,
0.06305380910634995,
-0.05395244061946869,
0.026081737130880356,
0.018570488318800926,
-0.019653121009469032,
-0.04788879305124283,
-0.03383433073759079,
-0.09769339114427567,
0.005564571358263493,
-0.08092508465051651,
0.01647305116057396,
-0.018450245261192322,
-0.08526520431041718,
-0.040688324719667435,
-0.1556665003299713,
0.002278752624988556,
0.09955150634050369,
0.00428511667996645,
-0.028858626261353493,
-0.09412826597690582,
0.0010344249894842505,
0.014487308450043201,
-0.011919399723410606,
-0.15230298042297363,
-0.04881799593567848,
0.026287198066711426,
-0.1660703718662262,
0.03276912495493889,
-0.05137382447719574,
0.03549332916736603,
0.044475916773080826,
-0.0461643822491169,
-0.024930957704782486,
0.014140721410512924,
0.017139434814453125,
-0.02374175749719143,
-0.24581307172775269,
-0.013825789093971252,
-0.04980151727795601,
0.18321004509925842,
-0.24926310777664185,
0.04708116501569748,
0.061669353395700455,
0.11851225793361664,
0.005235658492892981,
-0.047542329877614975,
0.04137692227959633,
-0.049956902861595154,
-0.04146651551127434,
-0.06634348630905151,
-0.003162689507007599,
-0.03229794278740883,
-0.04517984762787819,
0.041715674102306366,
-0.190790057182312,
-0.026901599019765854,
0.11227498203516006,
0.0757983848452568,
-0.172615647315979,
-0.07716702669858932,
-0.032237354665994644,
-0.06075936183333397,
-0.08794162422418594,
-0.04783210903406143,
0.1008552610874176,
0.040512196719646454,
0.05559884011745453,
-0.07266750186681747,
-0.05319931358098984,
0.01357761025428772,
-0.011024849489331245,
-0.03397562727332115,
0.09108693152666092,
0.08268309384584427,
-0.1214602142572403,
0.1049051284790039,
0.06639663875102997,
0.06267683953046799,
0.10698967427015305,
0.00657825730741024,
-0.09603405743837357,
-0.011494014412164688,
0.02610582485795021,
0.01399005576968193,
0.1426926553249359,
-0.07498639076948166,
0.030146000906825066,
0.042116980999708176,
-0.030949845910072327,
0.012327825650572777,
-0.1036502942442894,
0.0180202666670084,
0.03208072856068611,
-0.00865737535059452,
0.015741337090730667,
-0.056021902710199356,
0.013855318538844585,
0.10478801280260086,
0.03378775715827942,
0.028230587020516396,
0.01726776920258999,
-0.039543844759464264,
-0.12787796556949615,
0.1788790076971054,
-0.09654324501752853,
-0.25226548314094543,
-0.13508103787899017,
0.00398462638258934,
0.046175748109817505,
-0.011871901340782642,
0.01716979779303074,
-0.05890684202313423,
-0.10515041649341583,
-0.10667064040899277,
0.0201003048568964,
0.05383666977286339,
-0.0892900750041008,
-0.06228140741586685,
0.054920271039009094,
0.037761468440294266,
-0.12314439564943314,
0.023868469521403313,
0.045075058937072754,
-0.07041552662849426,
0.006107859779149294,
0.0533861480653286,
0.08385360985994339,
0.17951355874538422,
0.008954732678830624,
-0.01666683331131935,
0.009695366024971008,
0.2122192084789276,
-0.14731436967849731,
0.09077969938516617,
0.13881319761276245,
-0.05979595333337784,
0.08313421159982681,
0.2037704885005951,
0.028123976662755013,
-0.09402397274971008,
0.04063381254673004,
0.03390387445688248,
-0.04006393998861313,
-0.24267235398292542,
-0.07322345674037933,
0.003262192476540804,
-0.06732312589883804,
0.10439975559711456,
0.09025674313306808,
0.11683525890111923,
0.05397806316614151,
-0.10713037848472595,
-0.06759513914585114,
0.048913486301898956,
0.11977367848157883,
-0.031416673213243484,
0.004043765366077423,
0.0945456475019455,
-0.03565856069326401,
0.023265741765499115,
0.09114573150873184,
0.016503766179084778,
0.18390727043151855,
0.03800623491406441,
0.1316893994808197,
0.08565124869346619,
0.06437213718891144,
0.019635362550616264,
0.020543145015835762,
0.02446223795413971,
0.029573822394013405,
-0.020725078880786896,
-0.08386499434709549,
-0.01673775352537632,
0.14245417714118958,
0.024977976456284523,
0.0360303595662117,
0.0035881029907613993,
-0.03668458014726639,
0.06648463755846024,
0.16771891713142395,
0.012426458299160004,
-0.23092281818389893,
-0.06617839634418488,
0.0744035392999649,
-0.06997847557067871,
-0.11378128826618195,
-0.011021395213901997,
0.03138574957847595,
-0.1820969581604004,
0.04344824329018593,
-0.02262875810265541,
0.10113118588924408,
-0.11110582947731018,
-0.025930341333150864,
0.038102298974990845,
0.06534357368946075,
-0.03762030974030495,
0.07928402721881866,
-0.20931516587734222,
0.14360107481479645,
0.006824537646025419,
0.0662476122379303,
-0.1070002019405365,
0.08016478270292282,
0.02166800945997238,
0.003962947987020016,
0.1639549285173416,
-0.006579355336725712,
-0.08173416554927826,
-0.08530484884977341,
-0.07792343199253082,
-0.01293096225708723,
0.09758622944355011,
-0.1083054319024086,
0.08659143000841141,
-0.007826665416359901,
-0.031724635511636734,
-0.002251977799460292,
-0.11942502856254578,
-0.13174815475940704,
-0.1834975630044937,
0.04998116195201874,
-0.11704818159341812,
0.03951563686132431,
-0.11107321828603745,
-0.061925847083330154,
-0.03264854475855827,
0.19538627564907074,
-0.1968487948179245,
-0.08019812405109406,
-0.14597393572330475,
-0.0734013020992279,
0.11603168398141861,
-0.04134324938058853,
0.07953549921512604,
0.003630653489381075,
0.2027774453163147,
-0.0049596261233091354,
0.0008760678465478122,
0.0867764800786972,
-0.09453176707029343,
-0.2061263471841812,
-0.095054991543293,
0.13693954050540924,
0.12683691084384918,
0.045141350477933884,
-0.001808035303838551,
0.022449098527431488,
-0.003543051891028881,
-0.10818912833929062,
0.030619105324149132,
0.1492711752653122,
0.09657935798168182,
0.0439409464597702,
-0.025595664978027344,
-0.14485116302967072,
-0.10286740958690643,
-0.05655559152364731,
0.017665131017565727,
0.183364138007164,
-0.06921800225973129,
0.16631482541561127,
0.15526814758777618,
-0.06590348482131958,
-0.20979635417461395,
0.03614744171500206,
0.03232418745756149,
-0.008261965587735176,
0.03450188785791397,
-0.20602507889270782,
0.07341582328081131,
0.01584669202566147,
-0.057912733405828476,
0.13327911496162415,
-0.17143049836158752,
-0.1488124579191208,
0.08935882896184921,
0.0777517780661583,
-0.211406409740448,
-0.13250647485256195,
-0.09561139345169067,
-0.05425233766436577,
-0.1049741804599762,
0.08951318264007568,
-0.00034212550963275135,
0.00718674948439002,
0.035794805735349655,
0.02204621024429798,
0.017388639971613884,
-0.05294845998287201,
0.19216622412204742,
-0.0027261620853096247,
0.04696741700172424,
-0.08073770254850388,
-0.08108212053775787,
0.03557400405406952,
-0.06683739274740219,
0.0830080509185791,
-0.023084791377186775,
0.004679036792367697,
-0.11573878675699234,
-0.06346318125724792,
-0.04972510412335396,
0.03276039659976959,
-0.08775602281093597,
-0.09446880221366882,
-0.05614441633224487,
0.10359876602888107,
0.08980697393417358,
-0.03496086969971657,
-0.061577584594488144,
-0.09710686653852463,
0.07230715453624725,
0.22115971148014069,
0.18863879144191742,
0.07647473365068436,
-0.0675395280122757,
-0.0029117604717612267,
-0.023906316608190536,
0.054080698639154434,
-0.2153206318616867,
0.044365670531988144,
0.03903084248304367,
0.03029310703277588,
0.13431638479232788,
-0.023942356929183006,
-0.16170847415924072,
-0.044563427567481995,
0.06042470782995224,
-0.0675726905465126,
-0.15454024076461792,
0.003008861094713211,
0.08846548199653625,
-0.16023053228855133,
-0.053557612001895905,
0.02649034932255745,
-0.0350460447371006,
-0.027545088902115822,
0.0010348923970013857,
0.08224344998598099,
0.02413026988506317,
0.11039666831493378,
0.07115603238344193,
0.11174404621124268,
-0.10106326639652252,
0.08730421215295792,
0.0898137167096138,
-0.10840801149606705,
0.03910919278860092,
0.06986082345247269,
-0.06326653808355331,
-0.0329873263835907,
0.031108932569622993,
0.08609528094530106,
0.02910410612821579,
-0.07589155435562134,
-0.0003110404359176755,
-0.10957848280668259,
0.06651712954044342,
0.14283788204193115,
0.03319181129336357,
0.007173929363489151,
0.04601660370826721,
0.029003625735640526,
-0.10390203446149826,
0.11265801638364792,
0.041551440954208374,
0.0359308086335659,
-0.04995269328355789,
0.003519897349178791,
0.04222669079899788,
-0.011379980482161045,
-0.015903322026133537,
-0.039394035935401917,
-0.06654489040374756,
-0.009303842671215534,
-0.1557016372680664,
0.02798696979880333,
-0.0701727494597435,
0.008773589506745338,
0.016523631289601326,
-0.02984452061355114,
0.00421373825520277,
0.010279021225869656,
-0.07592474669218063,
-0.03921252489089966,
-0.00597892701625824,
0.10799336433410645,
-0.16164712607860565,
0.008396364748477936,
0.0864807590842247,
-0.12359115481376648,
0.0785754844546318,
-0.004872123710811138,
-0.008228191174566746,
0.016140099614858627,
-0.14468397200107574,
0.06475566327571869,
-0.009952590800821781,
0.002042346866801381,
0.02179797925055027,
-0.20463575422763824,
0.004303002264350653,
-0.04947707802057266,
-0.05808398127555847,
-0.0054858666844666,
-0.0371098592877388,
-0.11110534518957138,
0.10392162203788757,
0.016078852117061615,
-0.08070345222949982,
-0.01963779143989086,
0.054263342171907425,
0.11082571744918823,
-0.05670001730322838,
0.1372474879026413,
-0.0212235189974308,
0.05716216191649437,
-0.17756865918636322,
-0.01764446683228016,
-0.017806867137551308,
0.013603313826024532,
-0.03553657978773117,
-0.008313823491334915,
0.05301200598478317,
-0.016943611204624176,
0.2223619669675827,
-0.021118339151144028,
0.024224206805229187,
0.06494368612766266,
0.003768373280763626,
-0.014263768680393696,
0.09235488623380661,
0.04585632309317589,
0.016036443412303925,
0.01857670024037361,
0.017341768369078636,
-0.043770886957645416,
-0.012837734073400497,
-0.12971961498260498,
0.0822075679898262,
0.16555102169513702,
0.08159321546554565,
-0.0025153302121907473,
0.04910283908247948,
-0.11745497584342957,
-0.08390354365110397,
0.09864985197782516,
-0.03327968344092369,
-0.010939104482531548,
-0.05545666068792343,
0.1393168866634369,
0.15790824592113495,
-0.18001005053520203,
0.06630969792604446,
-0.06947531551122665,
-0.05813371390104294,
-0.10700751096010208,
-0.17336682975292206,
-0.06371892243623734,
-0.032686252146959305,
-0.007239010650664568,
-0.06101612001657486,
0.06587019562721252,
0.10927599668502808,
0.011966768652200699,
0.0038074126932770014,
0.08745987713336945,
-0.03549729287624359,
0.0032165138982236385,
0.04436643421649933,
0.053415216505527496,
0.018384460359811783,
-0.06577059626579285,
0.0062953270971775055,
0.005856741219758987,
0.03797626122832298,
0.05783457309007645,
0.030920613557100296,
-0.010121258907020092,
0.010502477176487446,
-0.017619112506508827,
-0.09953264892101288,
0.040120288729667664,
-0.02619595266878605,
-0.04532664641737938,
0.14967231452465057,
0.019792752340435982,
-0.0015246463008224964,
-0.020656753331422806,
0.23401693999767303,
-0.06644817441701889,
-0.07621476799249649,
-0.139224112033844,
0.14507612586021423,
-0.04275493323802948,
0.049066413193941116,
0.04825753718614578,
-0.10474846512079239,
0.03731349855661392,
0.1451096534729004,
0.14759132266044617,
-0.028853775933384895,
0.008417275734245777,
0.010314252227544785,
0.004880689084529877,
-0.023987766355276108,
0.05473437160253525,
0.04762427508831024,
0.1143009290099144,
-0.06655429303646088,
0.0966365858912468,
-0.004376910626888275,
-0.08667857199907303,
-0.01979442685842514,
0.1345251053571701,
0.00039378736983053386,
0.02452908828854561,
-0.07970208674669266,
0.11903424561023712,
-0.06584613770246506,
-0.254661500453949,
0.06372799724340439,
-0.06844745576381683,
-0.15212500095367432,
-0.020589474588632584,
0.0192123856395483,
-0.00045748791308142245,
0.021751869469881058,
0.06367123872041702,
-0.060426320880651474,
0.1489165723323822,
0.03729896992444992,
-0.07695354521274567,
-0.07863835990428925,
0.07556982338428497,
-0.08442215621471405,
0.3028976023197174,
0.005024306941777468,
0.04913705214858055,
0.09561952203512192,
-0.03656364977359772,
-0.13419130444526672,
0.03908977657556534,
0.0922386571764946,
-0.052813757210969925,
0.06462136656045914,
0.20481634140014648,
-0.011546511203050613,
0.11757413297891617,
0.07377773523330688,
-0.08157313615083694,
0.05285948887467384,
-0.08638615161180496,
-0.0914662778377533,
-0.09036537259817123,
0.09207529574632645,
-0.060405224561691284,
0.1555742472410202,
0.13106994330883026,
-0.046590786427259445,
0.0021229966077953577,
-0.02774938941001892,
0.05171699821949005,
-0.0023280514869838953,
0.1081797182559967,
0.027423536404967308,
-0.1910448670387268,
0.03156386315822601,
-0.01286277174949646,
0.09995201975107193,
-0.2582627832889557,
-0.0796453133225441,
0.039762452244758606,
-0.01255226694047451,
-0.05679939687252045,
0.12189354747533798,
0.05551491677761078,
0.05127199366688728,
-0.055598754435777664,
-0.0537382997572422,
-0.00422793859615922,
0.16415901482105255,
-0.1055506095290184,
-0.00439038872718811
] |
null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.8.2 | {"library_name": "peft", "base_model": "meta-llama/Llama-2-7b-chat-hf"} | null | AyushRaj01/new_llama2_qna_tuned | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | 2024-02-09T12:26:42+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-chat-hf #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
### Framework versions
- PEFT 0.8.2 | [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.8.2"
] | [
"TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-chat-hf #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.8.2"
] | [
43,
6,
3,
54,
28,
3,
4,
9,
9,
10,
42,
20,
3,
4,
5,
9,
11,
13,
3,
12,
5,
4,
5,
3,
4,
9,
53,
9,
8,
6,
3,
14,
8,
7,
9,
4,
11
] | [
"passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-chat-hf #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2"
] | [
-0.11841969192028046,
0.2022479772567749,
-0.002833316335454583,
0.025214798748493195,
0.07878436893224716,
0.014637600630521774,
0.054947324097156525,
0.13261918723583221,
0.031206980347633362,
0.11594788730144501,
0.0704292580485344,
0.12123072147369385,
0.1148030087351799,
0.22123698890209198,
0.0024283265229314566,
-0.16631874442100525,
0.018979905173182487,
-0.07302169501781464,
0.016381023451685905,
0.11812689900398254,
0.14273041486740112,
-0.10027965903282166,
0.07790949940681458,
-0.020062439143657684,
0.00297375675290823,
-0.025623472407460213,
-0.06733770668506622,
-0.011316695250570774,
0.055085279047489166,
0.032493267208337784,
0.05675147473812103,
-0.01242026686668396,
0.08613253384828568,
-0.27130234241485596,
0.019111471250653267,
0.04248370602726936,
0.000569017487578094,
0.08215060085058212,
0.09670201689004898,
-0.0444314070045948,
0.12485171854496002,
-0.02200436405837536,
0.13342905044555664,
0.09037809073925018,
-0.0938122570514679,
-0.2350092977285385,
-0.06285054981708527,
0.07791074365377426,
0.18942241370677948,
0.08486548066139221,
-0.04237056523561478,
0.12417622655630112,
-0.06252552568912506,
0.022963838651776314,
0.0681467354297638,
-0.10646878182888031,
-0.06395009160041809,
0.06358126550912857,
0.13075068593025208,
0.0779242292046547,
-0.12267430126667023,
-0.03599616885185242,
0.037518780678510666,
0.047688230872154236,
0.05932023748755455,
0.005480028688907623,
0.14942720532417297,
0.02960800565779209,
-0.14777646958827972,
-0.049745529890060425,
0.1382848173379898,
0.008481799624860287,
-0.03733452036976814,
-0.2166338562965393,
-0.004278909415006638,
-0.09387403726577759,
-0.038559917360544205,
-0.047141555696725845,
0.03695870563387871,
0.009430876933038235,
0.135284885764122,
-0.04993730038404465,
-0.09213044494390488,
-0.015873964875936508,
0.11037076264619827,
0.06301694363355637,
0.021671734750270844,
-0.019621863961219788,
0.007796366699039936,
0.12289830297231674,
0.06711064279079437,
-0.13340502977371216,
-0.06347095221281052,
-0.06772393733263016,
-0.03309933468699455,
-0.025378989055752754,
0.03916075453162193,
0.013862552121281624,
0.06162525713443756,
0.2704956829547882,
-0.0361158587038517,
0.06424067169427872,
0.04128924012184143,
0.023274436593055725,
0.03106401301920414,
0.10570167005062103,
-0.03335681930184364,
-0.16363930702209473,
-0.006066539790481329,
0.1021636500954628,
0.005269186571240425,
-0.03356136754155159,
-0.0575731135904789,
0.033218465745449066,
0.03494277223944664,
0.11904200911521912,
0.10777641087770462,
-0.026936259120702744,
-0.07471957057714462,
-0.056122612208127975,
0.18977482616901398,
-0.1583964079618454,
0.04259619489312172,
0.028574561700224876,
-0.0007661975105293095,
-0.06331504136323929,
0.008500078693032265,
0.017876628786325455,
-0.0326581709086895,
0.07348033785820007,
-0.06629349291324615,
-0.03976455330848694,
-0.1225791648030281,
-0.030754001811146736,
0.036102790385484695,
0.011039101518690586,
-0.045644305646419525,
-0.04470548778772354,
-0.07084322720766068,
-0.10975594073534012,
0.1086718961596489,
-0.0533391498029232,
-0.0584905780851841,
-0.027842281386256218,
-0.08158765733242035,
0.018186388537287712,
0.03455948084592819,
0.07204455882310867,
-0.026769137009978294,
0.04611458256840706,
-0.00838975328952074,
0.06878869235515594,
0.0681796669960022,
0.03200815990567207,
-0.08002108335494995,
0.06604992598295212,
-0.20021094381809235,
0.07224752008914948,
-0.07970471680164337,
0.04483740031719208,
-0.1596209704875946,
-0.0029718363657593727,
-0.0011344619560986757,
0.02985399216413498,
0.0434272401034832,
0.15935692191123962,
-0.2135726511478424,
-0.029793042689561844,
0.16791950166225433,
-0.10727810859680176,
-0.13444580137729645,
0.0401771180331707,
-0.0368795283138752,
0.1831674724817276,
0.02828303724527359,
0.03127031400799751,
0.08623716980218887,
-0.16210220754146576,
-0.01951930858194828,
-0.018076803535223007,
0.011043324135243893,
0.06555145233869553,
0.08102724701166153,
-0.09610390663146973,
-0.0023298393934965134,
0.01012781634926796,
-0.06316709518432617,
-0.01702534593641758,
-0.039961762726306915,
-0.10497736185789108,
0.004573068581521511,
-0.0882081687450409,
0.007134882267564535,
0.0047986991703510284,
-0.09463135153055191,
-0.007963144220411777,
-0.1485801339149475,
-0.056260500103235245,
0.08556819707155228,
0.00011770156561397016,
-0.013645577244460583,
-0.09492235630750656,
0.06436865031719208,
-0.034437887370586395,
-0.0207976084202528,
-0.14474037289619446,
-0.015378634445369244,
0.017427733168005943,
-0.1374157816171646,
0.0014722946798428893,
-0.1234297901391983,
0.06658602505922318,
0.005409769713878632,
-0.04861108958721161,
-0.04309869185090065,
-0.0011642315657809377,
-0.005226568318903446,
-0.06228909641504288,
-0.23726429045200348,
-0.026040317490696907,
-0.05230928212404251,
0.17159488797187805,
-0.23139667510986328,
0.04260158911347389,
0.0014305426739156246,
0.11765606701374054,
0.003682577284052968,
-0.057311564683914185,
0.02398480474948883,
-0.061556376516819,
-0.024972781538963318,
-0.06899271160364151,
-0.004306004848331213,
0.003678214270621538,
-0.028467074036598206,
0.0172374676913023,
-0.12182189524173737,
-0.06390262395143509,
0.09575977921485901,
0.058616749942302704,
-0.144285187125206,
0.007470404263585806,
-0.039536915719509125,
-0.05693695321679115,
-0.0692095160484314,
-0.07306204736232758,
0.08581232279539108,
0.052091702818870544,
0.04898935928940773,
-0.08366259187459946,
-0.06795194000005722,
0.0017730706604197621,
-0.02408025600016117,
-0.014203298836946487,
0.12551769614219666,
0.09366302192211151,
-0.09788685292005539,
0.09077483415603638,
0.07236184924840927,
0.020635321736335754,
0.08561757951974869,
-0.02191724255681038,
-0.10640561580657959,
-0.02413744106888771,
0.05799376592040062,
0.0104227215051651,
0.17153726518154144,
-0.07242308557033539,
0.05372016876935959,
0.04657841473817825,
-0.05825139209628105,
0.04805567488074303,
-0.0942406952381134,
0.006741635501384735,
-0.0020910254679620266,
-0.01715562306344509,
0.03819238021969795,
-0.016451144590973854,
0.0045061614364385605,
0.08970677107572556,
0.06358800083398819,
0.020104393362998962,
0.012234842404723167,
-0.03635336831212044,
-0.14209528267383575,
0.18017657101154327,
-0.09265387058258057,
-0.2387935370206833,
-0.1502833515405655,
0.05508073419332504,
0.057465776801109314,
-0.013616018928587437,
0.030873771756887436,
-0.053414277732372284,
-0.09585152566432953,
-0.08886886388063431,
0.005775135476142168,
0.03257879614830017,
-0.0598512627184391,
-0.06361827254295349,
0.0356811098754406,
0.038803283125162125,
-0.12098096311092377,
0.023206323385238647,
0.05626961961388588,
-0.00030593329574912786,
-0.004632396623492241,
0.04566420987248421,
0.09388307482004166,
0.20650815963745117,
-0.0026797864120453596,
0.006903663277626038,
0.05885152146220207,
0.27509474754333496,
-0.15893805027008057,
0.1122642308473587,
0.14039568603038788,
-0.06539268046617508,
0.07685618102550507,
0.19088444113731384,
0.02999345026910305,
-0.09469958394765854,
0.020210471004247665,
0.03164779022336006,
-0.024126915261149406,
-0.2705962657928467,
-0.05174269154667854,
-0.02329191379249096,
-0.0759558379650116,
0.0810973271727562,
0.08916730433702469,
0.08851192146539688,
0.0283801332116127,
-0.06416374444961548,
-0.09953121095895767,
0.026287248358130455,
0.11184750497341156,
-0.017220601439476013,
0.0005588262574747205,
0.08089913427829742,
-0.04922328516840935,
0.004495428875088692,
0.0850226879119873,
-0.01943057030439377,
0.12471865862607956,
0.05583042651414871,
0.10548539459705353,
0.08322282880544662,
0.08421782404184341,
-0.011091911233961582,
0.030204126611351967,
0.0021399131510406733,
0.02026619017124176,
0.020473042502999306,
-0.0920662209391594,
0.017105726525187492,
0.11616898328065872,
0.01301006693392992,
0.021310830488801003,
0.012936944141983986,
-0.05871041119098663,
0.03751898184418678,
0.19599121809005737,
0.030408132821321487,
-0.20756502449512482,
-0.07820630818605423,
0.05454209819436073,
-0.07449423521757126,
-0.15448258817195892,
-0.007993509992957115,
0.015589401125907898,
-0.1572435200214386,
0.01965176686644554,
-0.040165774524211884,
0.1071954146027565,
-0.06553623825311661,
-0.039081230759620667,
0.10466840863227844,
0.04897420108318329,
-0.028243253007531166,
0.04939184710383415,
-0.19378678500652313,
0.10783959180116653,
0.029651999473571777,
0.06652279198169708,
-0.08952849358320236,
0.08815857768058777,
-0.0009539489983581007,
-0.008400502614676952,
0.1639070361852646,
-0.0028770852368324995,
-0.05996514484286308,
-0.07725836336612701,
-0.07801879197359085,
-0.004398467484861612,
0.08028727024793625,
-0.13435475528240204,
0.07514414191246033,
-0.03372026979923248,
-0.031052809208631516,
-0.00705273263156414,
-0.08697285503149033,
-0.11882320046424866,
-0.16237111389636993,
0.05998644605278969,
-0.08237051963806152,
0.023532802239060402,
-0.0811910331249237,
-0.05269665643572807,
0.03230386972427368,
0.1784893274307251,
-0.20052258670330048,
-0.10986663401126862,
-0.14313088357448578,
-0.10344167053699493,
0.1505298912525177,
-0.04688514024019241,
0.0869874581694603,
-0.00678601348772645,
0.16243359446525574,
-0.0017706137150526047,
-0.01965048909187317,
0.08490510284900665,
-0.09530394524335861,
-0.18210020661354065,
-0.045954860746860504,
0.18391013145446777,
0.13061505556106567,
0.027842462062835693,
-0.01077960804104805,
0.023732082918286324,
-0.06628851592540741,
-0.10851065814495087,
0.02867046743631363,
0.14973591268062592,
0.0679076761007309,
-0.019772430881857872,
-0.04459957778453827,
-0.09549282491207123,
-0.06528210639953613,
-0.04367255046963692,
-0.0026712443213909864,
0.20309782028198242,
-0.070224329829216,
0.15427879989147186,
0.1093449667096138,
-0.05977393314242363,
-0.21325206756591797,
0.03404560685157776,
0.039459507912397385,
0.018009165301918983,
0.03304220736026764,
-0.1925593912601471,
0.08790822327136993,
-0.026455776765942574,
-0.08254260569810867,
0.18139700591564178,
-0.19805435836315155,
-0.12958575785160065,
0.10780748724937439,
0.023734191432595253,
-0.20183995366096497,
-0.15102897584438324,
-0.10348422080278397,
-0.019556045532226562,
-0.11666704714298248,
0.04461781680583954,
0.005849367473274469,
0.012282634153962135,
0.012120379135012627,
0.023080049082636833,
0.04095204547047615,
-0.048215702176094055,
0.20284287631511688,
-0.04465726763010025,
-0.004615394398570061,
-0.054399244487285614,
-0.07717253267765045,
0.01144018117338419,
-0.05585108697414398,
0.12605004012584686,
-0.0183758232742548,
0.032445382326841354,
-0.16380934417247772,
-0.04305853694677353,
-0.06154195964336395,
0.036692824214696884,
-0.09523627907037735,
-0.0796978622674942,
-0.04406590387225151,
0.08158984035253525,
0.09010761231184006,
-0.012375871650874615,
0.01274719089269638,
-0.09797146171331406,
0.09352044761180878,
0.20045848190784454,
0.19386810064315796,
0.05849941819906235,
-0.05112674459815025,
0.031096486374735832,
-0.037340350449085236,
0.044957272708415985,
-0.22006148099899292,
0.04224920645356178,
0.06433545053005219,
0.026117078959941864,
0.06896114349365234,
-0.005938500631600618,
-0.16264402866363525,
-0.09170280396938324,
0.08979775011539459,
-0.06330763548612595,
-0.17210939526557922,
-0.03336741030216217,
0.043475694954395294,
-0.2086360901594162,
-0.045446790754795074,
0.03812176734209061,
-0.018296774476766586,
-0.041149482131004333,
0.025328725576400757,
0.07988516241312027,
-0.022403165698051453,
0.08674918860197067,
0.09573429077863693,
0.08903922140598297,
-0.09498248994350433,
0.05226321518421173,
0.07856904715299606,
-0.018535977229475975,
0.0304799135774374,
0.13945432007312775,
-0.03685386851429939,
-0.04631688445806503,
0.07959377020597458,
0.12010376155376434,
-0.0033436238300055265,
-0.05605100095272064,
0.0032215185929089785,
-0.04974310100078583,
0.06081010028719902,
0.12305159866809845,
0.023317599669098854,
-0.012600637041032314,
0.0795939639210701,
0.02478138729929924,
-0.09210304915904999,
0.12318375706672668,
0.04055234044790268,
0.021684853360056877,
-0.0366547554731369,
-0.026249924674630165,
-0.013689846731722355,
0.0002803995448630303,
-0.014865689910948277,
-0.00007673895743209869,
-0.08958888798952103,
0.0035043652169406414,
-0.11512363702058792,
0.016982130706310272,
-0.06837422400712967,
-0.0008048090385273099,
0.02900013141334057,
-0.04715961590409279,
-0.002973934169858694,
-0.003941493108868599,
-0.0781969502568245,
-0.05296732112765312,
-0.02327139489352703,
0.07703189551830292,
-0.1404045671224594,
0.03319313004612923,
0.0726260244846344,
-0.10301343351602554,
0.06786274164915085,
-0.008978006429970264,
0.01246207021176815,
0.006548548117280006,
-0.14330315589904785,
0.05524631589651108,
-0.02716415748000145,
-0.005913495551794767,
0.0017825834220275283,
-0.18028929829597473,
-0.011561863124370575,
-0.04203815013170242,
-0.07048872113227844,
0.013538499362766743,
-0.010618218220770359,
-0.12361013144254684,
0.11164049804210663,
0.008142155595123768,
-0.06555002182722092,
-0.014209393411874771,
0.045770782977342606,
0.06950034201145172,
-0.012030795216560364,
0.10721991956233978,
-0.028388338163495064,
0.08126239478588104,
-0.17948873341083527,
-0.005567618645727634,
-0.01767108030617237,
0.05406760051846504,
-0.019447123631834984,
-0.04624241217970848,
0.05607226863503456,
-0.021038172766566277,
0.16597358882427216,
0.001201031031087041,
0.07291246205568314,
0.052258096635341644,
0.009863500483334064,
0.049231916666030884,
0.07235457003116608,
0.06393732875585556,
-0.017952879890799522,
-0.0034213082399219275,
0.035087961703538895,
-0.0003371878410689533,
-0.0429510734975338,
-0.13689856231212616,
0.07309827208518982,
0.1780329793691635,
0.06970378756523132,
0.023680763319134712,
0.009693092666566372,
-0.13437838852405548,
-0.07288879156112671,
0.10340617597103119,
-0.016469500958919525,
-0.030209295451641083,
-0.06658680737018585,
0.2279769629240036,
0.149847149848938,
-0.19086331129074097,
0.07381682097911835,
-0.053968559950590134,
-0.03833002969622612,
-0.1446501910686493,
-0.16726554930210114,
-0.05763225257396698,
-0.04865129664540291,
-0.03282582759857178,
-0.05854584649205208,
0.05107874795794487,
0.03925701230764389,
-0.005034600850194693,
-0.02154175192117691,
0.11090117692947388,
0.030328158289194107,
-0.04055379703640938,
0.04574092850089073,
0.06117111071944237,
0.0430884025990963,
-0.10009471327066422,
0.010834514163434505,
0.0015906146727502346,
0.005567676853388548,
0.060623519122600555,
0.02280796505510807,
-0.06977298110723495,
0.030523555353283882,
-0.017839113250374794,
-0.11917506903409958,
0.04812857136130333,
-0.007085130084306002,
-0.0192719716578722,
0.14955510199069977,
0.03638613596558571,
0.0067066615447402,
-0.010921784676611423,
0.23901939392089844,
-0.07274337857961655,
-0.08275589346885681,
-0.13010941445827484,
0.08575557917356491,
-0.06338372081518173,
0.02393944002687931,
0.01457033772021532,
-0.12370127439498901,
0.012576477602124214,
0.1811080425977707,
0.11841431260108948,
-0.02014615572988987,
0.012890190817415714,
0.043329838663339615,
0.009545013308525085,
-0.0356808602809906,
0.013286566361784935,
0.058273132890462875,
0.20682431757450104,
-0.07682578265666962,
0.05843240022659302,
-0.018598806113004684,
-0.06908367574214935,
-0.033512406051158905,
0.10653847455978394,
-0.010618019849061966,
-0.011107569560408592,
-0.056724995374679565,
0.14132548868656158,
-0.07343736290931702,
-0.2114740014076233,
0.04993782192468643,
-0.08149006962776184,
-0.13802959024906158,
-0.04958459362387657,
0.027714397758245468,
-0.026771001517772675,
0.006788461469113827,
0.05862199887633324,
-0.05243329703807831,
0.1808834671974182,
0.030405297875404358,
-0.045123547315597534,
-0.0976032093167305,
0.057225968688726425,
-0.1650933474302292,
0.28233802318573,
0.021309996023774147,
0.048848800361156464,
0.1090799942612648,
-0.022136030718684196,
-0.13216084241867065,
0.01612909696996212,
0.11375436931848526,
-0.06287107616662979,
0.0637020692229271,
0.15915238857269287,
0.002595594385638833,
0.12412583827972412,
0.06419782340526581,
-0.05646252632141113,
0.03692901134490967,
-0.07038163393735886,
-0.053154993802309036,
-0.11799506098031998,
0.07709497958421707,
-0.09816699475049973,
0.15287183225154877,
0.12202243506908417,
-0.07251662760972977,
-0.002066534711048007,
-0.02180672436952591,
0.0802772268652916,
0.018743474036455154,
0.10922583192586899,
0.00815130677074194,
-0.18655212223529816,
0.04600553214550018,
0.0053938524797558784,
0.09805204719305038,
-0.2142297923564911,
-0.048961423337459564,
0.0420696847140789,
-0.017897838726639748,
-0.08475267142057419,
0.11375176906585693,
0.03989323973655701,
0.01743496209383011,
-0.035473063588142395,
-0.04967032000422478,
0.01726243831217289,
0.15297353267669678,
-0.10595374554395676,
-0.014555229805409908
] |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# LernnaviBERT
This model is a fine-tuned version of [dbmdz/bert-base-german-uncased](https://huggingface.co/dbmdz/bert-base-german-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0060
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0385 | 1.0 | 2405 | 0.0137 |
| 0.0142 | 2.0 | 4810 | 0.0084 |
| 0.0096 | 3.0 | 7215 | 0.0072 |
### Framework versions
- Transformers 4.37.1
- Pytorch 2.2.0
- Datasets 2.2.1
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "dbmdz/bert-base-german-uncased", "model-index": [{"name": "LernnaviBERT", "results": []}]} | fill-mask | tommymarto/LernnaviBERT | [
"transformers",
"safetensors",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:dbmdz/bert-base-german-uncased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-09T12:27:56+00:00 | [] | [] | TAGS
#transformers #safetensors #bert #fill-mask #generated_from_trainer #base_model-dbmdz/bert-base-german-uncased #license-mit #autotrain_compatible #endpoints_compatible #region-us
| LernnaviBERT
============
This model is a fine-tuned version of dbmdz/bert-base-german-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0060
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.37.1
* Pytorch 2.2.0
* Datasets 2.2.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.1\n* Pytorch 2.2.0\n* Datasets 2.2.1\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #safetensors #bert #fill-mask #generated_from_trainer #base_model-dbmdz/bert-base-german-uncased #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.1\n* Pytorch 2.2.0\n* Datasets 2.2.1\n* Tokenizers 0.15.1"
] | [
68,
113,
4,
30
] | [
"passage: TAGS\n#transformers #safetensors #bert #fill-mask #generated_from_trainer #base_model-dbmdz/bert-base-german-uncased #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.1\n* Pytorch 2.2.0\n* Datasets 2.2.1\n* Tokenizers 0.15.1"
] | [
-0.10622338950634003,
0.041322365403175354,
-0.0024110181257128716,
0.10554464906454086,
0.12842710316181183,
0.01622597500681877,
0.1577998697757721,
0.08531695604324341,
-0.03967679664492607,
0.04383723437786102,
0.12310557067394257,
0.08715838193893433,
0.012540366500616074,
0.16925817728042603,
-0.045790236443281174,
-0.2525727450847626,
0.034339357167482376,
0.00624509435147047,
-0.0794447585940361,
0.09670598059892654,
0.10759691894054413,
-0.12527060508728027,
0.0760650485754013,
0.010595511645078659,
-0.14365819096565247,
0.03752351179718971,
0.037553489208221436,
-0.049043506383895874,
0.1268637478351593,
0.028770407661795616,
0.1433284729719162,
0.011796610429883003,
0.10428232699632645,
-0.18416866660118103,
0.004931192845106125,
0.041861094534397125,
-0.012097548693418503,
0.055047087371349335,
0.037673696875572205,
0.0015877691330388188,
0.0697837695479393,
-0.08748769015073776,
0.05354645848274231,
0.02609020471572876,
-0.13173912465572357,
-0.26064151525497437,
-0.08412058651447296,
0.0528830960392952,
0.09084095805883408,
0.07791754603385925,
-0.007970179431140423,
0.11154519021511078,
-0.07103212922811508,
0.07816847413778305,
0.21798941493034363,
-0.3081590235233307,
-0.06453625112771988,
0.016887208446860313,
0.03339070826768875,
0.032272160053253174,
-0.10631833970546722,
-0.018841389566659927,
0.05477939173579216,
0.024061603471636772,
0.12130902707576752,
-0.023999804630875587,
-0.021770069375634193,
-0.01562933251261711,
-0.136830136179924,
-0.02483527921140194,
0.1305878460407257,
0.06042015925049782,
-0.07503464818000793,
-0.04136103391647339,
-0.054235950112342834,
-0.12232264876365662,
-0.05051378905773163,
-0.02304808795452118,
0.04219994321465492,
-0.031029121950268745,
-0.06947474926710129,
-0.009398328140377998,
-0.09444738924503326,
-0.09086202085018158,
-0.047198474407196045,
0.25826847553253174,
0.04368632286787033,
-0.0018881739815697074,
0.0026081863325089216,
0.08941630274057388,
-0.10841917991638184,
-0.15239761769771576,
-0.0017282390035688877,
0.019746428355574608,
-0.010896589607000351,
-0.04726962000131607,
-0.05938843637704849,
-0.0637621283531189,
0.01715700328350067,
0.1999596804380417,
-0.07410293072462082,
0.03323513641953468,
0.025735782459378242,
0.03573840111494064,
-0.11118803918361664,
0.16116909682750702,
-0.02748498134315014,
-0.028506701812148094,
0.002963484264910221,
0.04590709134936333,
0.04743495211005211,
0.007662509102374315,
-0.10910195112228394,
0.0006713271141052246,
0.08810193091630936,
0.014835719019174576,
-0.0836394801735878,
0.06639337539672852,
-0.058257780969142914,
0.006054169498383999,
0.030205368995666504,
-0.09346399456262589,
0.027581574395298958,
-0.00016469621914438903,
-0.057933591306209564,
-0.06393136829137802,
0.026858892291784286,
0.017084462568163872,
0.024828394874930382,
0.133271262049675,
-0.0876423791050911,
0.008695907890796661,
-0.10475967079401016,
-0.14792126417160034,
0.00783055555075407,
-0.046336352825164795,
0.026616843417286873,
-0.11121626198291779,
-0.18645203113555908,
0.009843767620623112,
0.07463765144348145,
-0.023711886256933212,
0.005354571621865034,
-0.033399734646081924,
-0.06790637224912643,
0.033148132264614105,
-0.014121254906058311,
0.046206552535295486,
-0.0636310949921608,
0.08669226616621017,
0.053425200283527374,
0.07406989485025406,
-0.0633947104215622,
0.03867984935641289,
-0.12289608269929886,
0.032397352159023285,
-0.21805454790592194,
-0.0030634275171905756,
-0.09024619311094284,
0.037800103425979614,
-0.07325717061758041,
-0.08974325656890869,
-0.003534207586199045,
0.019109275192022324,
0.09425650537014008,
0.11995141208171844,
-0.14384062588214874,
-0.0773601084947586,
0.19924664497375488,
-0.11733054369688034,
-0.12031654268503189,
0.13461074233055115,
-0.056672342121601105,
0.03637642040848732,
0.04431639611721039,
0.1472255140542984,
0.07640256732702255,
-0.1577659398317337,
0.0004989883746020496,
-0.02393009327352047,
0.054889947175979614,
0.007586742285639048,
0.08784838765859604,
-0.014946936629712582,
-0.03782935440540314,
0.018432607874274254,
-0.06269223242998123,
0.054925937205553055,
-0.08728234469890594,
-0.08166187256574631,
-0.028974508866667747,
-0.08776868134737015,
0.1109144315123558,
0.03363681584596634,
0.0550137497484684,
-0.137446328997612,
-0.11011461168527603,
0.07878807187080383,
0.07724719494581223,
-0.053536176681518555,
0.005714260041713715,
-0.060809917747974396,
0.056441061198711395,
-0.06287183612585068,
-0.02873067557811737,
-0.1221136525273323,
-0.09067858755588531,
0.02129879593849182,
-0.04543568566441536,
0.03387388214468956,
-0.013911120593547821,
0.09612339735031128,
0.11173878610134125,
-0.08525069802999496,
-0.054650839418172836,
-0.08407421410083771,
0.015392502769827843,
-0.09078656882047653,
-0.20025721192359924,
-0.04169674590229988,
-0.03274805471301079,
0.11043349653482437,
-0.19279810786247253,
0.03663032874464989,
-0.04323901608586311,
0.10870036482810974,
0.04435689374804497,
-0.002027336973696947,
-0.06726811081171036,
0.07242866605520248,
-0.03376874700188637,
-0.04720441251993179,
0.031583961099386215,
-0.0015645595267415047,
-0.08632449060678482,
-0.02627398446202278,
-0.12597258388996124,
0.20618152618408203,
0.11632438004016876,
-0.08153299242258072,
-0.1026897057890892,
-0.01473808940500021,
-0.05405863747000694,
-0.02429947443306446,
-0.061232659965753555,
-0.014048691838979721,
0.1159830316901207,
-0.0238407664000988,
0.13191848993301392,
-0.0748145580291748,
-0.015396575443446636,
0.033254578709602356,
-0.05928296968340874,
-0.007501513697206974,
0.08675026148557663,
0.07651368528604507,
-0.10040885210037231,
0.16072970628738403,
0.20124267041683197,
-0.10325894504785538,
0.17251060903072357,
-0.02384953573346138,
-0.06616808474063873,
-0.03916499763727188,
0.0043076807633042336,
0.011593245901167393,
0.15816789865493774,
-0.10683643072843552,
0.0111131202429533,
0.008513877168297768,
0.026799311861395836,
0.0033426296431571245,
-0.19754496216773987,
-0.0328495167195797,
0.043058592826128006,
-0.05331679806113243,
-0.030502866953611374,
-0.0039037265814840794,
-0.00796016026288271,
0.09712899476289749,
0.005539130885154009,
-0.078118234872818,
0.04687467962503433,
0.006432502996176481,
-0.07466766238212585,
0.2075103223323822,
-0.09334761649370193,
-0.12021705508232117,
-0.15232056379318237,
-0.057913362979888916,
-0.07208788394927979,
0.04405897483229637,
0.0671190395951271,
-0.07317297160625458,
-0.052444782108068466,
-0.07273941487073898,
0.0444670207798481,
0.021988963708281517,
0.036205459386110306,
-0.020756471902132034,
-0.013946201652288437,
0.1013084128499031,
-0.10333395004272461,
-0.026720266789197922,
-0.026413926854729652,
-0.05032633990049362,
0.031873513013124466,
0.04520716890692711,
0.12377849966287613,
0.09714870154857635,
-0.0419209860265255,
-0.006318565458059311,
-0.019314749166369438,
0.22714920341968536,
-0.06744910031557083,
-0.021519983187317848,
0.1297750473022461,
-0.017838187515735626,
0.04368946701288223,
0.1573469042778015,
0.0694209560751915,
-0.09159070253372192,
0.023095404729247093,
0.039259038865566254,
-0.023524878546595573,
-0.18615734577178955,
-0.055632494390010834,
-0.04799701273441315,
-0.021184811368584633,
0.10418444871902466,
0.01889912597835064,
0.028880830854177475,
0.06886892765760422,
-0.005540118087083101,
0.048667654395103455,
-0.022331541404128075,
0.09686010330915451,
0.09146341681480408,
0.04924977570772171,
0.12236817181110382,
-0.025281516835093498,
-0.07065989822149277,
0.03401876613497734,
-0.01216820627450943,
0.19176249206066132,
0.041357770562171936,
0.09636873751878738,
0.06275840848684311,
0.15780124068260193,
-0.006940911523997784,
0.09286059439182281,
0.023796016350388527,
-0.06277833878993988,
-0.00180395960342139,
-0.06801040470600128,
-0.01615552045404911,
0.060712866485118866,
-0.07368668168783188,
0.07298599928617477,
-0.14323590695858002,
-0.013428295962512493,
0.05961659923195839,
0.2298603653907776,
0.06655226647853851,
-0.31377896666526794,
-0.12385684996843338,
0.011912316083908081,
-0.028137633576989174,
-0.026589330285787582,
0.012002949602901936,
0.10929062962532043,
-0.05706866830587387,
0.05820561200380325,
-0.0656149610877037,
0.07448634505271912,
0.03635416552424431,
0.03834574669599533,
0.04909629374742508,
0.12142384797334671,
-0.013275288045406342,
0.06893587857484818,
-0.28261101245880127,
0.3174456059932709,
0.016527801752090454,
0.09027460962533951,
-0.051683325320482254,
0.011560292914509773,
0.037871040403842926,
0.05545063316822052,
0.09355446696281433,
-0.012103170156478882,
-0.09762141108512878,
-0.20852218568325043,
-0.029472213238477707,
0.0174376480281353,
0.11826049536466599,
-0.03498075157403946,
0.11932475119829178,
-0.029562192037701607,
-0.008417924866080284,
0.08428370207548141,
0.03732280433177948,
-0.151667982339859,
-0.06161164492368698,
-0.011405248194932938,
0.032707542181015015,
-0.0009945842903107405,
-0.09239254146814346,
-0.10041944682598114,
-0.10874529927968979,
0.17040039598941803,
-0.03544122353196144,
-0.013869049027562141,
-0.12035278230905533,
0.055883586406707764,
0.07063194364309311,
-0.09187926352024078,
0.06966471672058105,
0.005205969326198101,
0.11419802904129028,
-0.008067270740866661,
-0.04769265651702881,
0.14142273366451263,
-0.10846447944641113,
-0.16483677923679352,
-0.08036790043115616,
0.10124853998422623,
0.03978951275348663,
0.051973920315504074,
0.005759653635323048,
0.021164672449231148,
0.004396932665258646,
-0.08586679399013519,
0.041827838867902756,
-0.043025512248277664,
0.042082056403160095,
0.05082674324512482,
-0.08892498165369034,
-0.0276498943567276,
-0.03195466101169586,
-0.017211394384503365,
0.12338986992835999,
0.32580098509788513,
-0.0998339056968689,
-0.000049599253543419763,
0.07039885967969894,
-0.0456542894244194,
-0.22196942567825317,
0.04133044555783272,
0.053124286234378815,
0.024490641430020332,
0.05092104896903038,
-0.15181758999824524,
0.09858768433332443,
0.10284364968538284,
-0.03069666214287281,
0.0850454643368721,
-0.2333480417728424,
-0.1441311091184616,
0.14818885922431946,
0.1455899029970169,
0.12536339461803436,
-0.13079951703548431,
-0.024008991196751595,
-0.047420136630535126,
-0.1196112111210823,
0.08292189240455627,
-0.09893142431974411,
0.10699044167995453,
-0.010910818353295326,
0.06118781492114067,
0.009144230745732784,
-0.06512745469808578,
0.14204438030719757,
-0.008987904526293278,
0.1204850971698761,
-0.05677887797355652,
-0.0008026532013900578,
0.04848671704530716,
-0.04695752635598183,
0.04912276193499565,
-0.08513828366994858,
0.027870461344718933,
-0.026664365082979202,
-0.03121633268892765,
-0.06069684028625488,
0.08462651818990707,
-0.04287901148200035,
-0.0857994332909584,
-0.027637993916869164,
0.048072271049022675,
0.03291110694408417,
-0.019004657864570618,
0.1354576200246811,
0.00686440197750926,
0.16808730363845825,
0.10929884016513824,
0.06136214733123779,
-0.031150363385677338,
-0.024945612996816635,
0.011911941692233086,
-0.041774947196245193,
0.07961229979991913,
-0.09404939413070679,
0.046901945024728775,
0.11739838868379593,
0.019076690077781677,
0.1564035266637802,
0.06730086356401443,
-0.040731433779001236,
0.018671289086341858,
0.08106750249862671,
-0.15797346830368042,
-0.08622728288173676,
-0.01865796186029911,
-0.026705559343099594,
-0.10566577315330505,
0.06697007268667221,
0.1211283877491951,
-0.09330213069915771,
0.0180826298892498,
-0.021721305325627327,
-0.0009520805906504393,
-0.06845186650753021,
0.1883620321750641,
0.050373878329992294,
0.04944879189133644,
-0.0644439235329628,
0.07644455879926682,
0.018497545272111893,
-0.0754794105887413,
0.009748507291078568,
0.02675274945795536,
-0.061485085636377335,
-0.034653980284929276,
0.0400436669588089,
0.17567548155784607,
-0.07262901216745377,
-0.05600821599364281,
-0.1467404067516327,
-0.11709976196289062,
0.04424398019909859,
0.17339825630187988,
0.08529628068208694,
-0.013130451552569866,
-0.0055753737688064575,
0.04326409846544266,
-0.0940808653831482,
0.1026962623000145,
0.05586596950888634,
0.08680775761604309,
-0.13657669723033905,
0.14839185774326324,
-0.01786142960190773,
0.01038371678441763,
-0.028669286519289017,
0.048735711723566055,
-0.1301470398902893,
-0.014224696904420853,
-0.14634279906749725,
-0.036375366151332855,
-0.0314202755689621,
0.003408430377021432,
0.0015655665192753077,
-0.0797126367688179,
-0.0625084713101387,
0.024261243641376495,
-0.11233088374137878,
-0.025631671771407127,
0.041943784803152084,
0.05377243086695671,
-0.12203257530927658,
-0.04456119239330292,
0.0423014760017395,
-0.06929352134466171,
0.05338529869914055,
0.05101646110415459,
0.04029400646686554,
0.06067197397351265,
-0.1880641132593155,
0.015674740076065063,
0.04647287726402283,
-0.002615852514281869,
0.050999533385038376,
-0.09610049426555634,
-0.01236762385815382,
-0.0076556880958378315,
0.04605730623006821,
0.008189206011593342,
0.06286695599555969,
-0.13195322453975677,
0.0005102827562950552,
0.0013465603115037084,
-0.08126754313707352,
-0.04956480860710144,
0.02142484486103058,
0.08432432264089584,
-0.015367644838988781,
0.19623102247714996,
-0.12594513595104218,
0.032334718853235245,
-0.19276681542396545,
0.0064933327957987785,
-0.005434532184153795,
-0.10363973677158356,
-0.10707322508096695,
-0.045158613473176956,
0.04358915612101555,
-0.04664705693721771,
0.1467345803976059,
0.006828754208981991,
0.05739888921380043,
0.03893550485372543,
-0.07682882994413376,
0.05455419793725014,
0.021812804043293,
0.2372649759054184,
0.019096825271844864,
-0.041087690740823746,
-0.007317105773836374,
0.04809999838471413,
0.09979979693889618,
0.07558032125234604,
0.19275541603565216,
0.1824849396944046,
-0.03812607377767563,
0.10272684693336487,
0.06302765756845474,
-0.06934411823749542,
-0.1640254408121109,
-0.006982260383665562,
0.0015692131128162146,
0.09086187183856964,
-0.0076352511532604694,
0.1832272857427597,
0.11232034862041473,
-0.17937515676021576,
0.0365341417491436,
-0.027385307475924492,
-0.06349560618400574,
-0.11306660622358322,
-0.046743448823690414,
-0.09328743070363998,
-0.15963171422481537,
0.018154460936784744,
-0.1052113026380539,
0.02073126658797264,
0.06807205080986023,
0.011005127802491188,
-0.00831027701497078,
0.2177453339099884,
-0.03611413761973381,
0.03504875674843788,
0.047490909695625305,
-0.004265546333044767,
-0.028158102184534073,
-0.051306795328855515,
-0.10637164115905762,
-0.0032864150125533342,
-0.04039577767252922,
0.012185781262814999,
-0.05689511448144913,
-0.04884865880012512,
0.026164496317505836,
0.016251230612397194,
-0.10326183587312698,
0.0047922139056026936,
0.02055421657860279,
0.05666239559650421,
0.051327530294656754,
0.004098840523511171,
0.01728743501007557,
-0.012363433837890625,
0.18434013426303864,
-0.07996438443660736,
-0.08260171115398407,
-0.10646327584981918,
0.2543429136276245,
0.03853479400277138,
0.04164983332157135,
0.0038746302016079426,
-0.08882252126932144,
0.0258261077105999,
0.17891357839107513,
0.18159860372543335,
-0.04635944589972496,
0.01893574558198452,
-0.00739187840372324,
-0.018603548407554626,
-0.03710758686065674,
0.08827227354049683,
0.0727728009223938,
0.05423692986369133,
-0.06581933796405792,
-0.03261023014783859,
-0.052796076983213425,
-0.006502232979983091,
-0.04738201946020126,
0.06335935741662979,
0.023259330540895462,
-0.005859741009771824,
-0.059824757277965546,
0.05455983802676201,
-0.011723914183676243,
-0.10200313478708267,
0.06729317456483841,
-0.16286475956439972,
-0.14929166436195374,
-0.019979024305939674,
0.08443578332662582,
0.025969062000513077,
0.06886238604784012,
-0.042731232941150665,
-0.002993040718138218,
0.07320833206176758,
-0.012894360348582268,
-0.03928770497441292,
-0.1279255449771881,
0.07572876662015915,
-0.03760556876659393,
0.2680842876434326,
-0.023634828627109528,
0.06302211433649063,
0.12670978903770447,
0.03545437753200531,
-0.060123980045318604,
0.0928167775273323,
0.06604721397161484,
-0.11005040258169174,
0.02478155680000782,
0.11267544329166412,
-0.06312251836061478,
0.12996171414852142,
0.03860216587781906,
-0.18338237702846527,
0.02971876598894596,
-0.03144191578030586,
-0.11131079494953156,
-0.05245443806052208,
0.008997032418847084,
-0.05786151438951492,
0.13252194225788116,
0.206959068775177,
-0.030028456822037697,
-0.011666879057884216,
-0.05514446645975113,
0.07047636806964874,
0.09693176299333572,
0.05671189725399017,
-0.03814971074461937,
-0.24031157791614532,
0.040710579603910446,
0.08418301492929459,
-0.020044084638357162,
-0.2773713767528534,
-0.09830871969461441,
0.004850097466260195,
-0.03607622906565666,
-0.07934792339801788,
0.07734080404043198,
0.11527982354164124,
0.05429432541131973,
-0.05195009335875511,
-0.1502181738615036,
-0.0704079419374466,
0.14803002774715424,
-0.115811288356781,
-0.09230566024780273
] |
null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
| {"library_name": "peft", "base_model": "meta-llama/Llama-2-13b-chat-hf"} | null | bmehrba/Llama-2-13b-chat-hf-fine-tuned-adapters_Gpt4_t1_Llama13b_Seed104 | [
"peft",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-13b-chat-hf",
"region:us"
] | 2024-02-09T12:31:28+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #arxiv-1910.09700 #base_model-meta-llama/Llama-2-13b-chat-hf #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16",
"### Framework versions\n\n\n- PEFT 0.7.0.dev0",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16",
"### Framework versions\n\n\n- PEFT 0.7.0.dev0"
] | [
"TAGS\n#peft #arxiv-1910.09700 #base_model-meta-llama/Llama-2-13b-chat-hf #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16",
"### Framework versions\n\n\n- PEFT 0.7.0.dev0",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16",
"### Framework versions\n\n\n- PEFT 0.7.0.dev0"
] | [
38,
6,
3,
45,
28,
3,
4,
9,
9,
10,
42,
20,
3,
4,
5,
9,
11,
13,
3,
12,
5,
4,
5,
3,
4,
9,
53,
9,
8,
6,
3,
14,
8,
7,
9,
4,
154,
14,
154,
14
] | [
"passage: TAGS\n#peft #arxiv-1910.09700 #base_model-meta-llama/Llama-2-13b-chat-hf #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
] | [
-0.08950838446617126,
0.17622625827789307,
-0.003707088530063629,
0.032576385885477066,
0.08380123972892761,
0.019701125100255013,
0.05203324928879738,
0.11702486872673035,
-0.05330678075551987,
0.09448089450597763,
0.048484884202480316,
0.10060896724462509,
0.09846198558807373,
0.18868719041347504,
-0.0011855853954330087,
-0.2060726284980774,
0.015578063204884529,
-0.10931064933538437,
0.005876870360225439,
0.12358442693948746,
0.15569306910037994,
-0.09741293638944626,
0.08712729811668396,
-0.01551457867026329,
-0.010067826136946678,
-0.025396287441253662,
-0.07361544668674469,
-0.05290524289011955,
0.04710441827774048,
0.07490185648202896,
0.047730859369039536,
0.003742797765880823,
0.08045824617147446,
-0.2711505889892578,
0.01725192740559578,
0.03912210091948509,
-0.010164672508835793,
0.08416316658258438,
0.08157632499933243,
-0.061213672161102295,
0.10719792544841766,
-0.04486960545182228,
0.12389195710420609,
0.06922121345996857,
-0.06562015414237976,
-0.1487942785024643,
-0.0805540531873703,
0.06815578043460846,
0.16221418976783752,
0.07476766407489777,
-0.04304589703679085,
0.16949640214443207,
-0.13273242115974426,
0.007597264833748341,
0.046794891357421875,
-0.035554688423871994,
-0.08115267008543015,
0.060742560774087906,
0.09725039452314377,
0.07205293327569962,
-0.13358467817306519,
-0.029269445687532425,
0.031876083463430405,
0.026171350851655006,
0.07599646598100662,
0.02472980134189129,
0.14272165298461914,
0.05110684782266617,
-0.13597595691680908,
-0.032095685601234436,
0.1667022556066513,
0.05657454952597618,
-0.05146843194961548,
-0.20977118611335754,
0.010412882082164288,
-0.06257046014070511,
-0.019110077992081642,
-0.0394989438354969,
0.04172099754214287,
-0.026554755866527557,
0.06876977533102036,
0.0052980040200054646,
-0.0955195426940918,
-0.042122215032577515,
0.08467143774032593,
0.03501870483160019,
0.025577984750270844,
-0.03146751970052719,
-0.005369491875171661,
0.13237224519252777,
0.05266503989696503,
-0.11971335113048553,
-0.06415551900863647,
-0.06459555774927139,
-0.05922604724764824,
-0.05847278982400894,
0.025247467681765556,
0.031127413734793663,
0.0707581415772438,
0.20909400284290314,
0.02113768272101879,
0.04728280380368233,
0.06350736320018768,
0.01767423190176487,
0.07364732772111893,
0.08452971279621124,
-0.08042320609092712,
-0.13752959668636322,
-0.026864496991038322,
0.09401044249534607,
-0.004670456051826477,
-0.015377101488411427,
-0.04042273387312889,
0.04590466991066933,
0.03928038105368614,
0.09635873883962631,
0.08342839032411575,
-0.006302335299551487,
-0.08958663791418076,
-0.05172271281480789,
0.21430253982543945,
-0.1486416757106781,
0.022579502314329147,
0.00532573601230979,
-0.046220771968364716,
-0.050389427691698074,
0.013791119679808617,
0.021902183070778847,
-0.01725425384938717,
0.09078584611415863,
-0.07412354648113251,
-0.030390940606594086,
-0.11564502120018005,
-0.00758272223174572,
0.035115793347358704,
0.05083532631397247,
-0.0026497903745621443,
-0.019051065668463707,
-0.06038069352507591,
-0.07015779614448547,
0.08611448109149933,
-0.08802679926156998,
-0.06949871778488159,
-0.022058209404349327,
-0.08482711762189865,
0.008333494886755943,
0.004399609286338091,
0.13455772399902344,
-0.032166268676519394,
0.04013873636722565,
-0.009890900924801826,
0.05181796848773956,
0.06774567812681198,
0.03500198572874069,
-0.053186893463134766,
0.056685443967580795,
-0.19885419309139252,
0.10022944211959839,
-0.09629994630813599,
0.028232630342245102,
-0.15368616580963135,
-0.016224225983023643,
0.024259883910417557,
0.00603050272911787,
0.023533180356025696,
0.13508757948875427,
-0.2269131988286972,
-0.009413540363311768,
0.1492016613483429,
-0.08191759884357452,
-0.11286741495132446,
0.05882270261645317,
-0.06703686714172363,
0.13632111251354218,
0.024114999920129776,
-0.03846221789717674,
0.05126623064279556,
-0.1477012187242508,
-0.034279413521289825,
-0.027603546157479286,
-0.011836200952529907,
0.11866577714681625,
0.09630073606967926,
-0.0608704648911953,
0.048884205520153046,
0.020479585975408554,
-0.032701265066862106,
-0.042141854763031006,
-0.050704531371593475,
-0.12829554080963135,
0.0009587573586031795,
-0.07328714430332184,
0.04790837690234184,
-0.02088468335568905,
-0.06889110058546066,
-0.018932033330202103,
-0.16518932580947876,
0.002006813418120146,
0.09172286838293076,
0.02033841609954834,
-0.03539799153804779,
-0.10069174319505692,
0.0036235731095075607,
-0.011536587961018085,
-0.035604726523160934,
-0.13578550517559052,
-0.02210777997970581,
0.019318837672472,
-0.13882264494895935,
0.030753053724765778,
-0.07345959544181824,
0.051180385053157806,
0.016524922102689743,
-0.05861951783299446,
-0.010977345518767834,
-0.023012345656752586,
0.024373451247811317,
-0.0456857830286026,
-0.24518829584121704,
-0.01426833588629961,
-0.032443173229694366,
0.1618536114692688,
-0.23377619683742523,
0.038241252303123474,
0.06515999883413315,
0.11937034130096436,
-0.02269211784005165,
-0.050194818526506424,
0.02402755618095398,
-0.0810660794377327,
-0.03478178381919861,
-0.05240238085389137,
-0.0170640479773283,
-0.02249637059867382,
-0.06970936059951782,
0.013335862196981907,
-0.10944215208292007,
-0.04154296964406967,
0.10713886469602585,
0.08292265236377716,
-0.15724287927150726,
-0.043278347700834274,
-0.03408950939774513,
-0.08576270937919617,
-0.08529800176620483,
-0.0566803403198719,
0.13487502932548523,
0.05090935528278351,
0.02855822816491127,
-0.08846847712993622,
-0.07940267771482468,
0.00988192018121481,
-0.03207101300358772,
-0.028083765879273415,
0.10094649344682693,
0.07611845433712006,
-0.10813652724027634,
0.08834784477949142,
0.07578150928020477,
0.012136061675846577,
0.11384404450654984,
-0.011400082148611546,
-0.11351825296878815,
-0.04137531667947769,
0.03633233532309532,
0.002555434126406908,
0.1695048063993454,
-0.09464383870363235,
0.06803114712238312,
0.03927377983927727,
-0.022211823612451553,
0.05476415529847145,
-0.10076725482940674,
0.01427049096673727,
0.006726768799126148,
-0.012228100560605526,
-0.011376895941793919,
-0.036163002252578735,
0.020614514127373695,
0.07891662418842316,
0.03816615790128708,
0.036182720214128494,
0.03572281077504158,
-0.04122483730316162,
-0.1245279312133789,
0.19345727562904358,
-0.10554436594247818,
-0.2273423671722412,
-0.1516016721725464,
0.05401213839650154,
0.03572985157370567,
-0.030572842806577682,
0.008941974490880966,
-0.05140937119722366,
-0.0966159775853157,
-0.08070044219493866,
0.005514310672879219,
0.03883929178118706,
-0.07613059133291245,
-0.07262902706861496,
0.05921752378344536,
0.05427297204732895,
-0.13442036509513855,
0.0406947135925293,
0.054035235196352005,
-0.04148136079311371,
0.008404599502682686,
0.06944910436868668,
0.07862463593482971,
0.15086530148983002,
-0.020428497344255447,
-0.020412612706422806,
0.05437345430254936,
0.2643863558769226,
-0.15086820721626282,
0.09670513868331909,
0.09954504668712616,
-0.06504277884960175,
0.07992210984230042,
0.18344183266162872,
0.033216435462236404,
-0.10660552978515625,
0.045308101922273636,
0.031075740233063698,
-0.0188649483025074,
-0.2811678647994995,
-0.06357815116643906,
0.0033266504760831594,
-0.10220301896333694,
0.062428005039691925,
0.0793466567993164,
0.09731262922286987,
0.04918764531612396,
-0.06440604478120804,
-0.07534892857074738,
0.02199655771255493,
0.07507231831550598,
-0.04625728353857994,
0.0006049389485269785,
0.08203481882810593,
-0.0200007613748312,
0.008962401188910007,
0.11015255749225616,
0.013906295411288738,
0.1873634159564972,
0.04269689694046974,
0.11463924497365952,
0.10168035328388214,
0.10507753491401672,
0.000024342234610230662,
0.015555954538285732,
0.02079109288752079,
0.012282595038414001,
-0.002983907237648964,
-0.08613301068544388,
0.02277722768485546,
0.12184786051511765,
0.06945348531007767,
0.04476168751716614,
0.024970298632979393,
-0.050061535090208054,
0.05980529636144638,
0.1768452227115631,
-0.01209972519427538,
-0.1998264193534851,
-0.062326882034540176,
0.06751304864883423,
-0.082801952958107,
-0.11640139669179916,
-0.02261449582874775,
0.050769247114658356,
-0.17440687119960785,
0.015001747757196426,
-0.04254560545086861,
0.09033802151679993,
-0.09127394109964371,
-0.037229955196380615,
0.05321357026696205,
0.07545126974582672,
-0.023492055013775826,
0.09048163145780563,
-0.17921186983585358,
0.13352392613887787,
0.01737614907324314,
0.06370522826910019,
-0.09815072268247604,
0.10393797606229782,
0.015243546105921268,
-0.0071698566898703575,
0.14627893269062042,
0.008973979391157627,
-0.019879506900906563,
-0.058314017951488495,
-0.10938628017902374,
-0.0015536772552877665,
0.08220188319683075,
-0.11720426380634308,
0.06481732428073883,
0.00044200546108186245,
-0.019408708438277245,
0.010529479943215847,
-0.0697939544916153,
-0.14233455061912537,
-0.1691078543663025,
0.06332679092884064,
-0.12960782647132874,
0.05657918378710747,
-0.10196143388748169,
-0.07344398647546768,
-0.006228356156498194,
0.1857890486717224,
-0.19167372584342957,
-0.0651763305068016,
-0.13295814394950867,
-0.08307469636201859,
0.17686748504638672,
-0.038926977664232254,
0.07132517546415329,
0.017756011337041855,
0.17197521030902863,
0.030676020309329033,
0.013996497727930546,
0.10165295004844666,
-0.0863775908946991,
-0.18250107765197754,
-0.06872538477182388,
0.145328551530838,
0.15727265179157257,
0.04947395995259285,
-0.01222315151244402,
0.0006382534629665315,
-0.05825969576835632,
-0.12492486834526062,
0.00552456034347415,
0.14077237248420715,
0.09738009423017502,
0.015011516399681568,
-0.02072962000966072,
-0.12298290431499481,
-0.06933344155550003,
-0.07234511524438858,
0.010791660286486149,
0.1811780333518982,
-0.06657543778419495,
0.1483541578054428,
0.12124106287956238,
-0.0507206916809082,
-0.18955619633197784,
0.04781363531947136,
0.0678601861000061,
0.021055543795228004,
0.06329847872257233,
-0.1708568036556244,
0.10241113603115082,
0.03779063746333122,
-0.056044332683086395,
0.12532320618629456,
-0.13762390613555908,
-0.15448996424674988,
0.08908607810735703,
0.059379611164331436,
-0.23717626929283142,
-0.10756765305995941,
-0.09208329766988754,
-0.04467558488249779,
-0.11974717676639557,
0.07756773382425308,
-0.008080631494522095,
0.01312070433050394,
0.038425788283348083,
0.04747161641716957,
0.010422809049487114,
-0.04883774369955063,
0.2077513337135315,
0.00663892924785614,
0.03319171071052551,
-0.04891526326537132,
-0.10318257659673691,
0.04049978777766228,
-0.04806138575077057,
0.09715691953897476,
-0.014642413705587387,
0.021955221891403198,
-0.1253223717212677,
-0.0439610481262207,
-0.06654173135757446,
0.030696231871843338,
-0.09619533270597458,
-0.09483709931373596,
-0.05548068508505821,
0.10141977667808533,
0.07960876822471619,
-0.03827962279319763,
-0.018101584166288376,
-0.08076406270265579,
0.028281690552830696,
0.192597895860672,
0.20835207402706146,
0.049149978905916214,
-0.06995424628257751,
0.007349140010774136,
-0.012700160034000874,
0.04521884396672249,
-0.2468501627445221,
0.056316666305065155,
0.04637942090630531,
0.019014067947864532,
0.11265500634908676,
-0.035475291311740875,
-0.16250301897525787,
-0.05557123199105263,
0.07098683714866638,
-0.039137084037065506,
-0.15694621205329895,
-0.024994002655148506,
0.05066932737827301,
-0.20187702775001526,
-0.029669208452105522,
0.010474429465830326,
-0.02148980274796486,
-0.04393318295478821,
0.011044103652238846,
0.08090483397245407,
-0.018578581511974335,
0.1367349922657013,
0.07980240881443024,
0.09522033482789993,
-0.10692083835601807,
0.07168128341436386,
0.06122429668903351,
-0.051465462893247604,
0.021644625812768936,
0.06818753480911255,
-0.04446205869317055,
-0.032580625265836716,
0.07838873565196991,
0.058368146419525146,
0.04023381322622299,
-0.0497741736471653,
-0.009552556090056896,
-0.05499427020549774,
0.049196142703294754,
0.10447074472904205,
0.05076836422085762,
0.0006935194251127541,
0.047793444246053696,
0.018387768417596817,
-0.08049451559782028,
0.10598240047693253,
0.05339374020695686,
0.02360537275671959,
-0.0398079976439476,
-0.03602069616317749,
0.018247995525598526,
-0.010786417871713638,
-0.0149832833558321,
-0.016455529257655144,
-0.07099823653697968,
-0.013593231327831745,
-0.13733075559139252,
0.04016523063182831,
-0.08189219981431961,
0.01841694675385952,
0.022008292376995087,
-0.05440347641706467,
-0.007398437242954969,
0.015957478433847427,
-0.07759089022874832,
-0.04222242161631584,
-0.0045568388886749744,
0.12033451348543167,
-0.11743347346782684,
0.041315708309412,
0.0889706164598465,
-0.10073781758546829,
0.08179357647895813,
0.005519764963537455,
0.006593905854970217,
0.027770070359110832,
-0.18307223916053772,
0.07270024716854095,
-0.02148648537695408,
0.003687589429318905,
0.03217103332281113,
-0.22772879898548126,
-0.010953521355986595,
-0.03648538142442703,
-0.016809485852718353,
0.0019160229712724686,
-0.03937701880931854,
-0.13335061073303223,
0.07287079840898514,
-0.01058956515043974,
-0.08660455048084259,
-0.032185930758714676,
0.03226194903254509,
0.1112515926361084,
-0.03534836322069168,
0.15059389173984528,
-0.005941883195191622,
0.05801843851804733,
-0.17130136489868164,
-0.011426819488406181,
-0.019129110500216484,
0.03652174770832062,
-0.018265437334775925,
-0.014729461632668972,
0.053084973245859146,
-0.03412574157118797,
0.2234855443239212,
-0.03480256348848343,
0.06502514332532883,
0.05183198302984238,
0.02280556410551071,
-0.006614799611270428,
0.08636770397424698,
0.06560425460338593,
-0.01096076425164938,
0.02718065120279789,
0.028059065341949463,
-0.012954981066286564,
-0.037562232464551926,
-0.1630524843931198,
0.05572279915213585,
0.1581650972366333,
0.04094236344099045,
0.011616811156272888,
0.06928509473800659,
-0.10752071440219879,
-0.07898375391960144,
0.1387312412261963,
-0.01259393710643053,
-0.032576363533735275,
-0.07013807445764542,
0.13943122327327728,
0.124080128967762,
-0.19758351147174835,
0.07208021730184555,
-0.0731193795800209,
-0.07801702618598938,
-0.10079838335514069,
-0.14738084375858307,
-0.061444323509931564,
-0.052179500460624695,
-0.011450962163507938,
-0.06768535077571869,
0.05396997556090355,
0.10480605065822601,
0.0069710006937384605,
-0.026146549731492996,
0.10475686937570572,
0.0007574855699203908,
-0.027480410411953926,
0.0275881364941597,
0.06416697055101395,
0.01868068240582943,
-0.10241235792636871,
0.016462087631225586,
0.0009010558133013546,
0.028261849656701088,
0.058421481400728226,
0.0037333546206355095,
-0.035359520465135574,
-0.012541528791189194,
-0.022329136729240417,
-0.11025683581829071,
0.038418930023908615,
-0.031967371702194214,
-0.03549599647521973,
0.11972174793481827,
0.021107889711856842,
0.0024782961700111628,
-0.022964047268033028,
0.22632580995559692,
-0.07606904208660126,
-0.0824858620762825,
-0.1684485524892807,
0.048732075840234756,
-0.06246444582939148,
0.03944636881351471,
0.04816613346338272,
-0.1110905185341835,
0.02492443658411503,
0.13681943714618683,
0.13383808732032776,
-0.017702074721455574,
0.0072706313803792,
0.041554342955350876,
-0.001966990763321519,
-0.051138825714588165,
0.022816691547632217,
0.04751669988036156,
0.09492984414100647,
-0.05958498641848564,
0.09289880096912384,
-0.006714127957820892,
-0.08313115686178207,
0.011414550244808197,
0.11385775357484818,
-0.004354037344455719,
0.008586743846535683,
-0.06612556427717209,
0.14033369719982147,
-0.05520116165280342,
-0.2502851188182831,
0.03959165886044502,
-0.0734434500336647,
-0.16861815750598907,
-0.03511347249150276,
0.018955450505018234,
-0.019131824374198914,
0.017461534589529037,
0.07813186943531036,
-0.05068197101354599,
0.17512299120426178,
0.04293905943632126,
-0.08064883947372437,
-0.06616055220365524,
0.07387921214103699,
-0.11062787473201752,
0.28079262375831604,
0.012751048430800438,
0.06857820600271225,
0.10455191880464554,
-0.016430502757430077,
-0.11872978508472443,
0.042664192616939545,
0.10075171291828156,
-0.07164205610752106,
0.08039859682321548,
0.18360178172588348,
0.0013276869431138039,
0.15462037920951843,
0.06878916919231415,
-0.0453730933368206,
0.03654608130455017,
-0.12163300812244415,
-0.05294680967926979,
-0.10768717527389526,
0.08729486167430878,
-0.07798956334590912,
0.15596513450145721,
0.13275524973869324,
-0.07110930234193802,
-0.006204865872859955,
-0.025767024606466293,
0.08593760430812836,
-0.009336618706583977,
0.1176052987575531,
0.00486786337569356,
-0.20527753233909607,
0.022964732721447945,
0.006658138707280159,
0.10234756767749786,
-0.21353045105934143,
-0.06055140495300293,
0.06063069403171539,
-0.027994666248559952,
-0.050338197499513626,
0.11621229350566864,
0.05960828810930252,
0.04527933895587921,
-0.034697841852903366,
-0.03217756003141403,
-0.02518811635673046,
0.13280846178531647,
-0.11107352375984192,
-0.014744595624506474
] |
null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
| {"library_name": "peft", "base_model": "meta-llama/Llama-2-13b-chat-hf"} | null | bmehrba/Llama-2-13b-chat-hf-fine-tuned_Gpt4_t1_Llama13b_Seed104 | [
"peft",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-13b-chat-hf",
"region:us"
] | 2024-02-09T12:31:47+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #arxiv-1910.09700 #base_model-meta-llama/Llama-2-13b-chat-hf #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16",
"### Framework versions\n\n\n- PEFT 0.7.0.dev0"
] | [
"TAGS\n#peft #arxiv-1910.09700 #base_model-meta-llama/Llama-2-13b-chat-hf #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16",
"### Framework versions\n\n\n- PEFT 0.7.0.dev0"
] | [
38,
6,
3,
45,
28,
3,
4,
9,
9,
10,
42,
20,
3,
4,
5,
9,
11,
13,
3,
12,
5,
4,
5,
3,
4,
9,
53,
9,
8,
6,
3,
14,
8,
7,
9,
4,
154,
14
] | [
"passage: TAGS\n#peft #arxiv-1910.09700 #base_model-meta-llama/Llama-2-13b-chat-hf #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
] | [
-0.08950838446617126,
0.17622625827789307,
-0.003707088530063629,
0.032576385885477066,
0.08380123972892761,
0.019701125100255013,
0.05203324928879738,
0.11702486872673035,
-0.05330678075551987,
0.09448089450597763,
0.048484884202480316,
0.10060896724462509,
0.09846198558807373,
0.18868719041347504,
-0.0011855853954330087,
-0.2060726284980774,
0.015578063204884529,
-0.10931064933538437,
0.005876870360225439,
0.12358442693948746,
0.15569306910037994,
-0.09741293638944626,
0.08712729811668396,
-0.01551457867026329,
-0.010067826136946678,
-0.025396287441253662,
-0.07361544668674469,
-0.05290524289011955,
0.04710441827774048,
0.07490185648202896,
0.047730859369039536,
0.003742797765880823,
0.08045824617147446,
-0.2711505889892578,
0.01725192740559578,
0.03912210091948509,
-0.010164672508835793,
0.08416316658258438,
0.08157632499933243,
-0.061213672161102295,
0.10719792544841766,
-0.04486960545182228,
0.12389195710420609,
0.06922121345996857,
-0.06562015414237976,
-0.1487942785024643,
-0.0805540531873703,
0.06815578043460846,
0.16221418976783752,
0.07476766407489777,
-0.04304589703679085,
0.16949640214443207,
-0.13273242115974426,
0.007597264833748341,
0.046794891357421875,
-0.035554688423871994,
-0.08115267008543015,
0.060742560774087906,
0.09725039452314377,
0.07205293327569962,
-0.13358467817306519,
-0.029269445687532425,
0.031876083463430405,
0.026171350851655006,
0.07599646598100662,
0.02472980134189129,
0.14272165298461914,
0.05110684782266617,
-0.13597595691680908,
-0.032095685601234436,
0.1667022556066513,
0.05657454952597618,
-0.05146843194961548,
-0.20977118611335754,
0.010412882082164288,
-0.06257046014070511,
-0.019110077992081642,
-0.0394989438354969,
0.04172099754214287,
-0.026554755866527557,
0.06876977533102036,
0.0052980040200054646,
-0.0955195426940918,
-0.042122215032577515,
0.08467143774032593,
0.03501870483160019,
0.025577984750270844,
-0.03146751970052719,
-0.005369491875171661,
0.13237224519252777,
0.05266503989696503,
-0.11971335113048553,
-0.06415551900863647,
-0.06459555774927139,
-0.05922604724764824,
-0.05847278982400894,
0.025247467681765556,
0.031127413734793663,
0.0707581415772438,
0.20909400284290314,
0.02113768272101879,
0.04728280380368233,
0.06350736320018768,
0.01767423190176487,
0.07364732772111893,
0.08452971279621124,
-0.08042320609092712,
-0.13752959668636322,
-0.026864496991038322,
0.09401044249534607,
-0.004670456051826477,
-0.015377101488411427,
-0.04042273387312889,
0.04590466991066933,
0.03928038105368614,
0.09635873883962631,
0.08342839032411575,
-0.006302335299551487,
-0.08958663791418076,
-0.05172271281480789,
0.21430253982543945,
-0.1486416757106781,
0.022579502314329147,
0.00532573601230979,
-0.046220771968364716,
-0.050389427691698074,
0.013791119679808617,
0.021902183070778847,
-0.01725425384938717,
0.09078584611415863,
-0.07412354648113251,
-0.030390940606594086,
-0.11564502120018005,
-0.00758272223174572,
0.035115793347358704,
0.05083532631397247,
-0.0026497903745621443,
-0.019051065668463707,
-0.06038069352507591,
-0.07015779614448547,
0.08611448109149933,
-0.08802679926156998,
-0.06949871778488159,
-0.022058209404349327,
-0.08482711762189865,
0.008333494886755943,
0.004399609286338091,
0.13455772399902344,
-0.032166268676519394,
0.04013873636722565,
-0.009890900924801826,
0.05181796848773956,
0.06774567812681198,
0.03500198572874069,
-0.053186893463134766,
0.056685443967580795,
-0.19885419309139252,
0.10022944211959839,
-0.09629994630813599,
0.028232630342245102,
-0.15368616580963135,
-0.016224225983023643,
0.024259883910417557,
0.00603050272911787,
0.023533180356025696,
0.13508757948875427,
-0.2269131988286972,
-0.009413540363311768,
0.1492016613483429,
-0.08191759884357452,
-0.11286741495132446,
0.05882270261645317,
-0.06703686714172363,
0.13632111251354218,
0.024114999920129776,
-0.03846221789717674,
0.05126623064279556,
-0.1477012187242508,
-0.034279413521289825,
-0.027603546157479286,
-0.011836200952529907,
0.11866577714681625,
0.09630073606967926,
-0.0608704648911953,
0.048884205520153046,
0.020479585975408554,
-0.032701265066862106,
-0.042141854763031006,
-0.050704531371593475,
-0.12829554080963135,
0.0009587573586031795,
-0.07328714430332184,
0.04790837690234184,
-0.02088468335568905,
-0.06889110058546066,
-0.018932033330202103,
-0.16518932580947876,
0.002006813418120146,
0.09172286838293076,
0.02033841609954834,
-0.03539799153804779,
-0.10069174319505692,
0.0036235731095075607,
-0.011536587961018085,
-0.035604726523160934,
-0.13578550517559052,
-0.02210777997970581,
0.019318837672472,
-0.13882264494895935,
0.030753053724765778,
-0.07345959544181824,
0.051180385053157806,
0.016524922102689743,
-0.05861951783299446,
-0.010977345518767834,
-0.023012345656752586,
0.024373451247811317,
-0.0456857830286026,
-0.24518829584121704,
-0.01426833588629961,
-0.032443173229694366,
0.1618536114692688,
-0.23377619683742523,
0.038241252303123474,
0.06515999883413315,
0.11937034130096436,
-0.02269211784005165,
-0.050194818526506424,
0.02402755618095398,
-0.0810660794377327,
-0.03478178381919861,
-0.05240238085389137,
-0.0170640479773283,
-0.02249637059867382,
-0.06970936059951782,
0.013335862196981907,
-0.10944215208292007,
-0.04154296964406967,
0.10713886469602585,
0.08292265236377716,
-0.15724287927150726,
-0.043278347700834274,
-0.03408950939774513,
-0.08576270937919617,
-0.08529800176620483,
-0.0566803403198719,
0.13487502932548523,
0.05090935528278351,
0.02855822816491127,
-0.08846847712993622,
-0.07940267771482468,
0.00988192018121481,
-0.03207101300358772,
-0.028083765879273415,
0.10094649344682693,
0.07611845433712006,
-0.10813652724027634,
0.08834784477949142,
0.07578150928020477,
0.012136061675846577,
0.11384404450654984,
-0.011400082148611546,
-0.11351825296878815,
-0.04137531667947769,
0.03633233532309532,
0.002555434126406908,
0.1695048063993454,
-0.09464383870363235,
0.06803114712238312,
0.03927377983927727,
-0.022211823612451553,
0.05476415529847145,
-0.10076725482940674,
0.01427049096673727,
0.006726768799126148,
-0.012228100560605526,
-0.011376895941793919,
-0.036163002252578735,
0.020614514127373695,
0.07891662418842316,
0.03816615790128708,
0.036182720214128494,
0.03572281077504158,
-0.04122483730316162,
-0.1245279312133789,
0.19345727562904358,
-0.10554436594247818,
-0.2273423671722412,
-0.1516016721725464,
0.05401213839650154,
0.03572985157370567,
-0.030572842806577682,
0.008941974490880966,
-0.05140937119722366,
-0.0966159775853157,
-0.08070044219493866,
0.005514310672879219,
0.03883929178118706,
-0.07613059133291245,
-0.07262902706861496,
0.05921752378344536,
0.05427297204732895,
-0.13442036509513855,
0.0406947135925293,
0.054035235196352005,
-0.04148136079311371,
0.008404599502682686,
0.06944910436868668,
0.07862463593482971,
0.15086530148983002,
-0.020428497344255447,
-0.020412612706422806,
0.05437345430254936,
0.2643863558769226,
-0.15086820721626282,
0.09670513868331909,
0.09954504668712616,
-0.06504277884960175,
0.07992210984230042,
0.18344183266162872,
0.033216435462236404,
-0.10660552978515625,
0.045308101922273636,
0.031075740233063698,
-0.0188649483025074,
-0.2811678647994995,
-0.06357815116643906,
0.0033266504760831594,
-0.10220301896333694,
0.062428005039691925,
0.0793466567993164,
0.09731262922286987,
0.04918764531612396,
-0.06440604478120804,
-0.07534892857074738,
0.02199655771255493,
0.07507231831550598,
-0.04625728353857994,
0.0006049389485269785,
0.08203481882810593,
-0.0200007613748312,
0.008962401188910007,
0.11015255749225616,
0.013906295411288738,
0.1873634159564972,
0.04269689694046974,
0.11463924497365952,
0.10168035328388214,
0.10507753491401672,
0.000024342234610230662,
0.015555954538285732,
0.02079109288752079,
0.012282595038414001,
-0.002983907237648964,
-0.08613301068544388,
0.02277722768485546,
0.12184786051511765,
0.06945348531007767,
0.04476168751716614,
0.024970298632979393,
-0.050061535090208054,
0.05980529636144638,
0.1768452227115631,
-0.01209972519427538,
-0.1998264193534851,
-0.062326882034540176,
0.06751304864883423,
-0.082801952958107,
-0.11640139669179916,
-0.02261449582874775,
0.050769247114658356,
-0.17440687119960785,
0.015001747757196426,
-0.04254560545086861,
0.09033802151679993,
-0.09127394109964371,
-0.037229955196380615,
0.05321357026696205,
0.07545126974582672,
-0.023492055013775826,
0.09048163145780563,
-0.17921186983585358,
0.13352392613887787,
0.01737614907324314,
0.06370522826910019,
-0.09815072268247604,
0.10393797606229782,
0.015243546105921268,
-0.0071698566898703575,
0.14627893269062042,
0.008973979391157627,
-0.019879506900906563,
-0.058314017951488495,
-0.10938628017902374,
-0.0015536772552877665,
0.08220188319683075,
-0.11720426380634308,
0.06481732428073883,
0.00044200546108186245,
-0.019408708438277245,
0.010529479943215847,
-0.0697939544916153,
-0.14233455061912537,
-0.1691078543663025,
0.06332679092884064,
-0.12960782647132874,
0.05657918378710747,
-0.10196143388748169,
-0.07344398647546768,
-0.006228356156498194,
0.1857890486717224,
-0.19167372584342957,
-0.0651763305068016,
-0.13295814394950867,
-0.08307469636201859,
0.17686748504638672,
-0.038926977664232254,
0.07132517546415329,
0.017756011337041855,
0.17197521030902863,
0.030676020309329033,
0.013996497727930546,
0.10165295004844666,
-0.0863775908946991,
-0.18250107765197754,
-0.06872538477182388,
0.145328551530838,
0.15727265179157257,
0.04947395995259285,
-0.01222315151244402,
0.0006382534629665315,
-0.05825969576835632,
-0.12492486834526062,
0.00552456034347415,
0.14077237248420715,
0.09738009423017502,
0.015011516399681568,
-0.02072962000966072,
-0.12298290431499481,
-0.06933344155550003,
-0.07234511524438858,
0.010791660286486149,
0.1811780333518982,
-0.06657543778419495,
0.1483541578054428,
0.12124106287956238,
-0.0507206916809082,
-0.18955619633197784,
0.04781363531947136,
0.0678601861000061,
0.021055543795228004,
0.06329847872257233,
-0.1708568036556244,
0.10241113603115082,
0.03779063746333122,
-0.056044332683086395,
0.12532320618629456,
-0.13762390613555908,
-0.15448996424674988,
0.08908607810735703,
0.059379611164331436,
-0.23717626929283142,
-0.10756765305995941,
-0.09208329766988754,
-0.04467558488249779,
-0.11974717676639557,
0.07756773382425308,
-0.008080631494522095,
0.01312070433050394,
0.038425788283348083,
0.04747161641716957,
0.010422809049487114,
-0.04883774369955063,
0.2077513337135315,
0.00663892924785614,
0.03319171071052551,
-0.04891526326537132,
-0.10318257659673691,
0.04049978777766228,
-0.04806138575077057,
0.09715691953897476,
-0.014642413705587387,
0.021955221891403198,
-0.1253223717212677,
-0.0439610481262207,
-0.06654173135757446,
0.030696231871843338,
-0.09619533270597458,
-0.09483709931373596,
-0.05548068508505821,
0.10141977667808533,
0.07960876822471619,
-0.03827962279319763,
-0.018101584166288376,
-0.08076406270265579,
0.028281690552830696,
0.192597895860672,
0.20835207402706146,
0.049149978905916214,
-0.06995424628257751,
0.007349140010774136,
-0.012700160034000874,
0.04521884396672249,
-0.2468501627445221,
0.056316666305065155,
0.04637942090630531,
0.019014067947864532,
0.11265500634908676,
-0.035475291311740875,
-0.16250301897525787,
-0.05557123199105263,
0.07098683714866638,
-0.039137084037065506,
-0.15694621205329895,
-0.024994002655148506,
0.05066932737827301,
-0.20187702775001526,
-0.029669208452105522,
0.010474429465830326,
-0.02148980274796486,
-0.04393318295478821,
0.011044103652238846,
0.08090483397245407,
-0.018578581511974335,
0.1367349922657013,
0.07980240881443024,
0.09522033482789993,
-0.10692083835601807,
0.07168128341436386,
0.06122429668903351,
-0.051465462893247604,
0.021644625812768936,
0.06818753480911255,
-0.04446205869317055,
-0.032580625265836716,
0.07838873565196991,
0.058368146419525146,
0.04023381322622299,
-0.0497741736471653,
-0.009552556090056896,
-0.05499427020549774,
0.049196142703294754,
0.10447074472904205,
0.05076836422085762,
0.0006935194251127541,
0.047793444246053696,
0.018387768417596817,
-0.08049451559782028,
0.10598240047693253,
0.05339374020695686,
0.02360537275671959,
-0.0398079976439476,
-0.03602069616317749,
0.018247995525598526,
-0.010786417871713638,
-0.0149832833558321,
-0.016455529257655144,
-0.07099823653697968,
-0.013593231327831745,
-0.13733075559139252,
0.04016523063182831,
-0.08189219981431961,
0.01841694675385952,
0.022008292376995087,
-0.05440347641706467,
-0.007398437242954969,
0.015957478433847427,
-0.07759089022874832,
-0.04222242161631584,
-0.0045568388886749744,
0.12033451348543167,
-0.11743347346782684,
0.041315708309412,
0.0889706164598465,
-0.10073781758546829,
0.08179357647895813,
0.005519764963537455,
0.006593905854970217,
0.027770070359110832,
-0.18307223916053772,
0.07270024716854095,
-0.02148648537695408,
0.003687589429318905,
0.03217103332281113,
-0.22772879898548126,
-0.010953521355986595,
-0.03648538142442703,
-0.016809485852718353,
0.0019160229712724686,
-0.03937701880931854,
-0.13335061073303223,
0.07287079840898514,
-0.01058956515043974,
-0.08660455048084259,
-0.032185930758714676,
0.03226194903254509,
0.1112515926361084,
-0.03534836322069168,
0.15059389173984528,
-0.005941883195191622,
0.05801843851804733,
-0.17130136489868164,
-0.011426819488406181,
-0.019129110500216484,
0.03652174770832062,
-0.018265437334775925,
-0.014729461632668972,
0.053084973245859146,
-0.03412574157118797,
0.2234855443239212,
-0.03480256348848343,
0.06502514332532883,
0.05183198302984238,
0.02280556410551071,
-0.006614799611270428,
0.08636770397424698,
0.06560425460338593,
-0.01096076425164938,
0.02718065120279789,
0.028059065341949463,
-0.012954981066286564,
-0.037562232464551926,
-0.1630524843931198,
0.05572279915213585,
0.1581650972366333,
0.04094236344099045,
0.011616811156272888,
0.06928509473800659,
-0.10752071440219879,
-0.07898375391960144,
0.1387312412261963,
-0.01259393710643053,
-0.032576363533735275,
-0.07013807445764542,
0.13943122327327728,
0.124080128967762,
-0.19758351147174835,
0.07208021730184555,
-0.0731193795800209,
-0.07801702618598938,
-0.10079838335514069,
-0.14738084375858307,
-0.061444323509931564,
-0.052179500460624695,
-0.011450962163507938,
-0.06768535077571869,
0.05396997556090355,
0.10480605065822601,
0.0069710006937384605,
-0.026146549731492996,
0.10475686937570572,
0.0007574855699203908,
-0.027480410411953926,
0.0275881364941597,
0.06416697055101395,
0.01868068240582943,
-0.10241235792636871,
0.016462087631225586,
0.0009010558133013546,
0.028261849656701088,
0.058421481400728226,
0.0037333546206355095,
-0.035359520465135574,
-0.012541528791189194,
-0.022329136729240417,
-0.11025683581829071,
0.038418930023908615,
-0.031967371702194214,
-0.03549599647521973,
0.11972174793481827,
0.021107889711856842,
0.0024782961700111628,
-0.022964047268033028,
0.22632580995559692,
-0.07606904208660126,
-0.0824858620762825,
-0.1684485524892807,
0.048732075840234756,
-0.06246444582939148,
0.03944636881351471,
0.04816613346338272,
-0.1110905185341835,
0.02492443658411503,
0.13681943714618683,
0.13383808732032776,
-0.017702074721455574,
0.0072706313803792,
0.041554342955350876,
-0.001966990763321519,
-0.051138825714588165,
0.022816691547632217,
0.04751669988036156,
0.09492984414100647,
-0.05958498641848564,
0.09289880096912384,
-0.006714127957820892,
-0.08313115686178207,
0.011414550244808197,
0.11385775357484818,
-0.004354037344455719,
0.008586743846535683,
-0.06612556427717209,
0.14033369719982147,
-0.05520116165280342,
-0.2502851188182831,
0.03959165886044502,
-0.0734434500336647,
-0.16861815750598907,
-0.03511347249150276,
0.018955450505018234,
-0.019131824374198914,
0.017461534589529037,
0.07813186943531036,
-0.05068197101354599,
0.17512299120426178,
0.04293905943632126,
-0.08064883947372437,
-0.06616055220365524,
0.07387921214103699,
-0.11062787473201752,
0.28079262375831604,
0.012751048430800438,
0.06857820600271225,
0.10455191880464554,
-0.016430502757430077,
-0.11872978508472443,
0.042664192616939545,
0.10075171291828156,
-0.07164205610752106,
0.08039859682321548,
0.18360178172588348,
0.0013276869431138039,
0.15462037920951843,
0.06878916919231415,
-0.0453730933368206,
0.03654608130455017,
-0.12163300812244415,
-0.05294680967926979,
-0.10768717527389526,
0.08729486167430878,
-0.07798956334590912,
0.15596513450145721,
0.13275524973869324,
-0.07110930234193802,
-0.006204865872859955,
-0.025767024606466293,
0.08593760430812836,
-0.009336618706583977,
0.1176052987575531,
0.00486786337569356,
-0.20527753233909607,
0.022964732721447945,
0.006658138707280159,
0.10234756767749786,
-0.21353045105934143,
-0.06055140495300293,
0.06063069403171539,
-0.027994666248559952,
-0.050338197499513626,
0.11621229350566864,
0.05960828810930252,
0.04527933895587921,
-0.034697841852903366,
-0.03217756003141403,
-0.02518811635673046,
0.13280846178531647,
-0.11107352375984192,
-0.014744595624506474
] |
null | null | transformers |
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 1.2659317351795386e+24
f1_macro: 2.895499120347367e-06
f1_micro: 0.0012045290291496024
f1_weighted: 2.8982892905428356e-06
precision_macro: 1.4494934165458512e-06
precision_micro: 0.0012045290291496024
precision_weighted: 1.4508901820640839e-06
recall_macro: 0.0012033694344163659
recall_micro: 0.0012045290291496024
recall_weighted: 0.0012045290291496024
accuracy: 0.0012045290291496024
| {"tags": ["autotrain", "image-classification"], "datasets": ["footballer-recognition-5/autotrain-data"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg", "example_title": "Palace"}]} | image-classification | IsaacMwesigwa/footballer-recognition-5 | [
"transformers",
"safetensors",
"resnet",
"image-classification",
"autotrain",
"dataset:footballer-recognition-5/autotrain-data",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-09T12:34:59+00:00 | [] | [] | TAGS
#transformers #safetensors #resnet #image-classification #autotrain #dataset-footballer-recognition-5/autotrain-data #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 1.2659317351795386e+24
f1_macro: 2.895499120347367e-06
f1_micro: 0.0012045290291496024
f1_weighted: 2.8982892905428356e-06
precision_macro: 1.4494934165458512e-06
precision_micro: 0.0012045290291496024
precision_weighted: 1.4508901820640839e-06
recall_macro: 0.0012033694344163659
recall_micro: 0.0012045290291496024
recall_weighted: 0.0012045290291496024
accuracy: 0.0012045290291496024
| [
"# Model Trained Using AutoTrain\n\n- Problem type: Image Classification",
"## Validation Metricsg\nloss: 1.2659317351795386e+24\n\nf1_macro: 2.895499120347367e-06\n\nf1_micro: 0.0012045290291496024\n\nf1_weighted: 2.8982892905428356e-06\n\nprecision_macro: 1.4494934165458512e-06\n\nprecision_micro: 0.0012045290291496024\n\nprecision_weighted: 1.4508901820640839e-06\n\nrecall_macro: 0.0012033694344163659\n\nrecall_micro: 0.0012045290291496024\n\nrecall_weighted: 0.0012045290291496024\n\naccuracy: 0.0012045290291496024"
] | [
"TAGS\n#transformers #safetensors #resnet #image-classification #autotrain #dataset-footballer-recognition-5/autotrain-data #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Image Classification",
"## Validation Metricsg\nloss: 1.2659317351795386e+24\n\nf1_macro: 2.895499120347367e-06\n\nf1_micro: 0.0012045290291496024\n\nf1_weighted: 2.8982892905428356e-06\n\nprecision_macro: 1.4494934165458512e-06\n\nprecision_micro: 0.0012045290291496024\n\nprecision_weighted: 1.4508901820640839e-06\n\nrecall_macro: 0.0012033694344163659\n\nrecall_micro: 0.0012045290291496024\n\nrecall_weighted: 0.0012045290291496024\n\naccuracy: 0.0012045290291496024"
] | [
60,
16,
163
] | [
"passage: TAGS\n#transformers #safetensors #resnet #image-classification #autotrain #dataset-footballer-recognition-5/autotrain-data #autotrain_compatible #endpoints_compatible #region-us \n# Model Trained Using AutoTrain\n\n- Problem type: Image Classification## Validation Metricsg\nloss: 1.2659317351795386e+24\n\nf1_macro: 2.895499120347367e-06\n\nf1_micro: 0.0012045290291496024\n\nf1_weighted: 2.8982892905428356e-06\n\nprecision_macro: 1.4494934165458512e-06\n\nprecision_micro: 0.0012045290291496024\n\nprecision_weighted: 1.4508901820640839e-06\n\nrecall_macro: 0.0012033694344163659\n\nrecall_micro: 0.0012045290291496024\n\nrecall_weighted: 0.0012045290291496024\n\naccuracy: 0.0012045290291496024"
] | [
-0.12204644083976746,
0.13464505970478058,
-0.0008704765350557864,
0.10839223116636276,
0.14285330474376678,
0.0500168576836586,
0.05768490210175514,
0.13584904372692108,
-0.041940994560718536,
0.1281837522983551,
0.15860438346862793,
0.02758694253861904,
0.08831899613142014,
0.14661964774131775,
-0.0855376273393631,
-0.08029304444789886,
0.014182022772729397,
-0.01083102636039257,
0.07707659155130386,
0.06571509689092636,
0.03980923071503639,
-0.1293613612651825,
0.11460699886083603,
-0.04505748674273491,
-0.25451672077178955,
0.04668285697698593,
0.1344265341758728,
-0.07754869014024734,
0.055565740913152695,
0.10843086987733841,
0.096704863011837,
-0.037203215062618256,
0.07926464825868607,
-0.0994492620229721,
-0.013408882543444633,
0.020652493461966515,
-0.010065170004963875,
0.015338498167693615,
0.15822191536426544,
0.03804885596036911,
-0.045960936695337296,
-0.05521279573440552,
0.04749535396695137,
0.06796273589134216,
-0.10038433223962784,
-0.07486297190189362,
-0.12199979275465012,
0.048049863427877426,
0.06379027664661407,
0.10475299507379532,
-0.005428226664662361,
0.16610616445541382,
-0.00905605684965849,
0.05159899219870567,
0.12285894900560379,
-0.2250736653804779,
-0.08035307377576828,
0.1510307937860489,
-0.08922035247087479,
-0.06574396789073944,
-0.0687185674905777,
0.06741872429847717,
0.09995181113481522,
0.019640840590000153,
-0.005769266281276941,
0.015947535634040833,
0.006362218409776688,
-0.005178646184504032,
-0.0605069100856781,
-0.05433329939842224,
0.16825677454471588,
0.0325511135160923,
-0.07165758311748505,
-0.08803702890872955,
-0.027422457933425903,
-0.14258670806884766,
-0.09284092485904694,
0.003139892593026161,
0.03097224049270153,
-0.06784170120954514,
-0.1167316660284996,
0.10372128337621689,
-0.03882957622408867,
-0.0740671157836914,
-0.19990380108356476,
0.09408140927553177,
-0.038610707968473434,
0.02557981386780739,
0.017040427774190903,
0.037581637501716614,
0.005499954801052809,
-0.05928896367549896,
0.0033365292474627495,
0.034684792160987854,
-0.13941353559494019,
-0.0958460122346878,
-0.00028081200434826314,
-0.0313677154481411,
0.04878871515393257,
0.10342726111412048,
0.00798708014190197,
0.08375781029462814,
-0.021303679794073105,
0.024975107982754707,
-0.12187687307596207,
0.17213663458824158,
-0.1413245052099228,
-0.10792295634746552,
0.024171194061636925,
0.0408036932349205,
0.04596380889415741,
0.007327105384320021,
-0.07331740111112595,
-0.06945345550775528,
0.10931716114282608,
0.039561860263347626,
0.01400841400027275,
0.031821753829717636,
-0.08943845331668854,
-0.023316333070397377,
0.010048799216747284,
-0.06419529765844345,
0.031516607850790024,
-0.014890163205564022,
-0.1564524620771408,
-0.04205087572336197,
0.04439769685268402,
-0.004273711238056421,
-0.016677841544151306,
0.07072021812200546,
-0.1421831101179123,
0.006402258761227131,
-0.050629157572984695,
-0.10344008356332779,
0.07505246251821518,
-0.06588169932365417,
0.019208408892154694,
-0.1252811700105667,
-0.2066430002450943,
-0.042095448821783066,
-0.05557872727513313,
-0.11739523708820343,
0.014201996847987175,
-0.07615233957767487,
-0.1167646199464798,
0.02903730422258377,
0.05388384684920311,
0.08524766564369202,
-0.043539248406887054,
0.03936956822872162,
0.015377862378954887,
0.12312108278274536,
-0.06795242428779602,
0.01797679252922535,
-0.08528947830200195,
-0.002940481062978506,
-0.1936778873205185,
0.057039111852645874,
-0.01985514536499977,
0.04183294624090195,
-0.09396375715732574,
0.0066239493899047375,
0.005035412032157183,
-0.00048158332356251776,
0.04819469526410103,
0.1472650021314621,
-0.17254363000392914,
-0.06688112020492554,
0.0933581069111824,
-0.0896037369966507,
-0.11930567026138306,
0.13328835368156433,
0.03338555246591568,
-0.029270295053720474,
0.06017788499593735,
0.1087573692202568,
0.06007534638047218,
-0.11343434453010559,
-0.062311045825481415,
-0.06652466952800751,
-0.10427805036306381,
-0.05888233706355095,
0.052855852991342545,
-0.013406317681074142,
-0.04257965087890625,
-0.01836913265287876,
0.029010862112045288,
0.09143400937318802,
-0.09748901426792145,
-0.05536144971847534,
-0.01003979705274105,
-0.0987921878695488,
0.053983282297849655,
0.03239747881889343,
0.039718516170978546,
-0.1003653034567833,
-0.010825393721461296,
0.09376063197851181,
0.07985657453536987,
0.028454666957259178,
-0.07144153118133545,
-0.14620399475097656,
0.06042309105396271,
-0.1715768724679947,
-0.021107546985149384,
-0.1782926470041275,
-0.15269869565963745,
-0.0005284118815325201,
-0.0474008172750473,
-0.013143460266292095,
-0.09309281408786774,
0.12061502784490585,
0.054783981293439865,
-0.06306809931993484,
-0.045816659927368164,
0.04646099731326103,
-0.020083477720618248,
-0.07799861580133438,
-0.11494997888803482,
-0.010435894131660461,
0.025384027510881424,
0.20253868401050568,
-0.19375698268413544,
-0.016270218417048454,
0.03223951905965805,
0.12201223522424698,
0.06683263182640076,
-0.06174058839678764,
-0.02597610466182232,
0.04869452863931656,
0.02447069250047207,
-0.08593401312828064,
0.06298746913671494,
-0.04036868363618851,
-0.08316121250391006,
0.020252078771591187,
-0.23094458878040314,
0.303789883852005,
0.15246284008026123,
0.05130821838974953,
-0.10030336678028107,
-0.005438446998596191,
0.02522689290344715,
-0.0751001164317131,
-0.060672033578157425,
-0.016535863280296326,
0.04001927375793457,
0.017665395513176918,
0.12781739234924316,
-0.0690932646393776,
-0.02574584260582924,
0.058318473398685455,
0.01714019849896431,
-0.08272767812013626,
0.11216049641370773,
-0.05435046926140785,
-0.1899515837430954,
0.13337388634681702,
0.04514053836464882,
-0.054778631776571274,
0.16847439110279083,
0.013334819115698338,
-0.04058467224240303,
-0.06107591092586517,
-0.01123239565640688,
0.019669197499752045,
0.1112106442451477,
0.07797704637050629,
0.04480147361755371,
0.013821805827319622,
-0.02589641511440277,
-0.01909443736076355,
-0.07840563356876373,
-0.020615138113498688,
0.00525108864530921,
-0.022031215950846672,
-0.006578165106475353,
-0.006043639034032822,
0.09614814817905426,
0.16170106828212738,
0.028120247647166252,
-0.032994020730257034,
0.06104826182126999,
-0.013234289363026619,
-0.07880209386348724,
0.19489935040473938,
-0.09240070730447769,
-0.16458159685134888,
-0.21004998683929443,
-0.010435941629111767,
-0.1148214116692543,
-0.005583214573562145,
-0.026921622455120087,
-0.09298378229141235,
-0.06690709292888641,
-0.07450085133314133,
0.029177501797676086,
0.022629985585808754,
-0.031870730221271515,
0.0847751572728157,
-0.022430256009101868,
0.08971840888261795,
-0.0496477372944355,
-0.028910566121339798,
-0.008688372559845448,
-0.07216400653123856,
0.05561002343893051,
0.006043221801519394,
0.15121063590049744,
0.12163574248552322,
-0.09428521990776062,
0.03648212179541588,
-0.0614483579993248,
0.10781234502792358,
-0.031010402366518974,
0.0030018824618309736,
0.14558576047420502,
0.03799230605363846,
0.024607516825199127,
0.09375974535942078,
0.013477868400514126,
-0.07865551114082336,
0.034888796508312225,
0.08008863031864166,
-0.005067372694611549,
-0.10285443812608719,
-0.13840757310390472,
0.03428536280989647,
0.047466620802879333,
0.1540377140045166,
0.006713076028972864,
0.06182466447353363,
0.06441570073366165,
-0.0015527368523180485,
0.06422773003578186,
-0.02327629365026951,
0.0680231899023056,
0.11308550089597702,
0.043420981615781784,
0.12883786857128143,
-0.07721979171037674,
-0.003432691562920809,
0.08105424046516418,
-0.04580136761069298,
0.1244993656873703,
0.03405661880970001,
0.04498416185379028,
-0.025964492931962013,
0.053069818764925,
0.048057738691568375,
0.11733439564704895,
0.09402018785476685,
-0.08422409743070602,
0.028506802394986153,
-0.07713831216096878,
-0.05532870441675186,
0.008223488926887512,
0.02589285746216774,
0.1257958561182022,
-0.13763241469860077,
0.08679550886154175,
0.042934808880090714,
0.1627667099237442,
0.05556883290410042,
-0.4894818067550659,
-0.09874454885721207,
0.02155551128089428,
0.05331933870911598,
-0.11296480894088745,
-0.004313817713409662,
0.09729278832674026,
-0.10822635889053345,
0.04122361168265343,
-0.040146633982658386,
0.06746519356966019,
-0.040256861597299576,
-0.006292068399488926,
0.003998942207545042,
0.09233370423316956,
-0.03607967495918274,
-0.012954563833773136,
-0.1342344582080841,
0.18279895186424255,
0.06807124614715576,
0.06856874376535416,
-0.07475755363702774,
-0.02116396836936474,
0.08721685409545898,
-0.019880468025803566,
0.1540556401014328,
0.004385014995932579,
-0.15062713623046875,
-0.40184876322746277,
-0.04243748262524605,
-0.043540067970752716,
0.03661488741636276,
0.05489776283502579,
0.1031842902302742,
-0.0526614785194397,
-0.0038137140218168497,
0.016323907300829887,
-0.05436565354466438,
-0.08627262711524963,
-0.007891119457781315,
-0.03453445807099342,
0.14613063633441925,
0.04342135787010193,
-0.06506317108869553,
-0.07834038883447647,
-0.05751998350024223,
0.03402825817465782,
-0.10450009256601334,
-0.050704795867204666,
-0.1348767876625061,
0.1491890400648117,
0.12373815476894379,
-0.07752520591020584,
0.08549773693084717,
0.0029692337848246098,
0.13162146508693695,
-0.0023345365189015865,
-0.10023278743028641,
0.08969731628894806,
-0.10318675637245178,
-0.06927046179771423,
-0.014127291738986969,
0.03448563441634178,
0.012954382225871086,
0.03563959151506424,
0.04372892901301384,
0.061520930379629135,
0.03347621113061905,
-0.06477311253547668,
0.12055875360965729,
-0.0007744184113107622,
0.13244956731796265,
0.11567976325750351,
-0.005365683697164059,
-0.18746830523014069,
-0.016453415155410767,
0.049534671008586884,
0.06322919577360153,
0.30274489521980286,
-0.0974578782916069,
-0.03681279718875885,
-0.005569176282733679,
-0.03262465447187424,
-0.27860182523727417,
0.0807458832859993,
-0.0017603152664378285,
0.034838687628507614,
0.02816878817975521,
-0.013516408391296864,
0.1301816999912262,
0.18333247303962708,
0.001425434253178537,
0.08229116350412369,
-0.24132280051708221,
-0.09450879693031311,
0.19710712134838104,
0.13376684486865997,
0.1384006291627884,
-0.12438566982746124,
-0.06846947968006134,
-0.13113337755203247,
-0.16694484651088715,
-0.01216014102101326,
0.010173097252845764,
0.06103695183992386,
-0.06541908532381058,
0.0168963223695755,
0.0485764779150486,
-0.07526881992816925,
0.12243781983852386,
-0.02082744985818863,
0.1304071694612503,
-0.08251751959323883,
-0.0773649513721466,
0.02795177884399891,
-0.06320614367723465,
0.09916119277477264,
0.10752471536397934,
0.019425390288233757,
-0.09348145127296448,
0.020345209166407585,
-0.04253189265727997,
0.05033336579799652,
-0.02867501601576805,
0.01489584892988205,
-0.017440885305404663,
0.07305265218019485,
-0.07362731546163559,
-0.030280519276857376,
-0.05016893520951271,
-0.026054125279188156,
0.09521348774433136,
0.17287009954452515,
0.07277797162532806,
-0.018556565046310425,
-0.04023647680878639,
0.04387068375945091,
-0.08275899291038513,
0.06805787980556488,
0.007552513387054205,
0.05971775949001312,
0.14128661155700684,
0.028317712247371674,
0.11039163172245026,
0.055696189403533936,
-0.03253056854009628,
0.03541998565196991,
0.06569468230009079,
-0.04952169582247734,
0.009595923125743866,
-0.023087529465556145,
-0.027285249903798103,
-0.03435952588915825,
-0.03332049772143364,
0.10115770995616913,
-0.044479843229055405,
0.02883068099617958,
-0.0030343588441610336,
-0.006677214987576008,
0.034948572516441345,
0.2354585826396942,
-0.027873283252120018,
0.05813917890191078,
-0.11057288944721222,
0.18729469180107117,
0.004762786906212568,
-0.17210958898067474,
0.009020134806632996,
-0.0489688478410244,
-0.09391164034605026,
-0.01956167444586754,
0.014331720769405365,
0.18032249808311462,
-0.16309645771980286,
-0.062312547117471695,
-0.10983733087778091,
-0.17205928266048431,
0.09481882303953171,
0.2809133231639862,
0.05391122028231621,
0.019898397848010063,
0.029265737161040306,
-0.04124493524432182,
-0.08227550238370895,
0.05774924159049988,
0.120024174451828,
0.034174397587776184,
-0.11512311547994614,
0.12336237728595734,
-0.049890946596860886,
-0.04020176827907562,
-0.027929024770855904,
0.05040935054421425,
-0.15880697965621948,
-0.004354843404144049,
-0.08742745965719223,
0.0605698898434639,
-0.034983813762664795,
-0.008266381919384003,
-0.02132425457239151,
0.01834375411272049,
-0.050936926156282425,
0.02126113511621952,
-0.05635823681950569,
-0.04307262599468231,
0.05867939069867134,
0.08031801879405975,
-0.11443911492824554,
-0.08622785657644272,
0.02048461325466633,
-0.011349993757903576,
0.037358358502388,
0.07732422649860382,
0.10169439017772675,
-0.010445784777402878,
-0.04168116673827171,
-0.0592963881790638,
0.05967298895120621,
-0.08936085551977158,
0.027806919068098068,
-0.15032364428043365,
0.033189207315444946,
0.01658286340534687,
-0.014565808698534966,
0.08015310019254684,
0.038999222218990326,
-0.09279374033212662,
0.012140574865043163,
-0.06759409606456757,
-0.016371767967939377,
-0.139028862118721,
0.026988830417394638,
0.22305928170681,
-0.01753934472799301,
0.11729800701141357,
-0.1123000755906105,
-0.01574801094830036,
-0.22487081587314606,
0.004087700042873621,
-0.04636551812291145,
-0.0986982136964798,
-0.1262582689523697,
0.01666801981627941,
0.0825178399682045,
-0.003660809248685837,
0.04263712465763092,
-0.04212368279695511,
0.026282427832484245,
0.05366525799036026,
0.16537272930145264,
-0.09195630252361298,
0.036199864000082016,
0.1867237091064453,
0.06148266792297363,
-0.057562217116355896,
0.10111791640520096,
0.10254285484552383,
0.102113738656044,
-0.004040653817355633,
0.06369847059249878,
0.1033545508980751,
-0.09617524594068527,
0.15117664635181427,
0.026375535875558853,
-0.03544038161635399,
-0.016368575394153595,
0.1911294162273407,
-0.11044053733348846,
0.016812797635793686,
-0.03903941810131073,
0.0024745359551161528,
0.18932154774665833,
-0.12624314427375793,
0.012454628013074398,
-0.08129120618104935,
-0.022989438846707344,
-0.08396996557712555,
-0.0436348021030426,
-0.1300019919872284,
-0.08424124866724014,
0.008316543884575367,
-0.09234698116779327,
0.056126438081264496,
0.17531423270702362,
0.011637834832072258,
-0.003439278807491064,
0.204551100730896,
-0.2023923546075821,
-0.04049435257911682,
0.04023183882236481,
-0.028458578512072563,
-0.05189826339483261,
-0.06046319380402565,
-0.07462053745985031,
0.08927175402641296,
0.005520353093743324,
0.019715338945388794,
0.005334739573299885,
0.027020273730158806,
0.028138132765889168,
0.000704611127730459,
-0.10897716879844666,
-0.015957524999976158,
0.0418512336909771,
0.01711328513920307,
0.007625247351825237,
-0.017687179148197174,
0.05878950282931328,
-0.03265201672911644,
0.17543385922908783,
-0.09843594580888748,
-0.02606060914695263,
-0.0617087185382843,
0.19519634544849396,
0.005992625840008259,
0.07738367468118668,
0.000988755957223475,
-0.051468946039676666,
0.09999310225248337,
0.11901447921991348,
0.06124565750360489,
-0.03567202016711235,
-0.06547760218381882,
-0.040196795016527176,
-0.018672842532396317,
-0.05737190321087837,
0.06856861710548401,
0.026818430051207542,
-0.05798288434743881,
-0.07951182872056961,
0.07223648577928543,
-0.01242141518741846,
-0.022807074710726738,
0.034856513142585754,
0.04224385321140289,
0.02766704559326172,
0.067657470703125,
-0.05944090336561203,
0.07269608974456787,
-0.015025767497718334,
0.02675030380487442,
0.18935737013816833,
-0.14802902936935425,
-0.1165509968996048,
0.03260499984025955,
0.025930743664503098,
-0.018059758469462395,
0.11037089675664902,
-0.06784288585186005,
0.023365642875432968,
-0.033461183309555054,
-0.04710999131202698,
-0.2036433070898056,
-0.16828519105911255,
-0.06743871420621872,
0.13438603281974792,
0.2681719660758972,
-0.012008207850158215,
0.05789419636130333,
0.10724581032991409,
-0.03283398225903511,
-0.16694684326648712,
0.08528368175029755,
-0.057085324078798294,
-0.12446848303079605,
0.0861685499548912,
0.11372508108615875,
-0.013672669418156147,
0.20225733518600464,
0.01706210896372795,
-0.19655637443065643,
0.006623768247663975,
-0.018804823979735374,
-0.07127219438552856,
-0.05360570549964905,
0.03233792260289192,
-0.04388567805290222,
0.1506006419658661,
0.11511072516441345,
-0.01384671125560999,
0.0014144928427413106,
-0.024082999676465988,
0.03844226524233818,
0.06333538144826889,
0.10233745723962784,
0.0017900470411404967,
-0.08299913257360458,
0.06533248722553253,
-0.13546457886695862,
-0.045912280678749084,
-0.34930676221847534,
-0.04626556858420372,
-0.031459931284189224,
-0.07197408378124237,
-0.0388542115688324,
0.09565406292676926,
0.09485964477062225,
0.04166901856660843,
-0.03757845610380173,
-0.2508569061756134,
0.10360778868198395,
0.17520111799240112,
-0.04837225377559662,
-0.11277542263269424
] |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MeMo Model (Word Sense Disambiguation)
This model is a fine-tuned version of [MiMe-MeMo/MeMo-BERT-01](https://huggingface.co/MiMe-MeMo/MeMo-BERT-01) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7214
- F1-score: 0.6667
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1-score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 11 | 0.7214 | 0.6667 |
| No log | 2.0 | 22 | 1.2543 | 0.5429 |
| No log | 3.0 | 33 | 1.0829 | 0.6837 |
| No log | 4.0 | 44 | 1.3815 | 0.7552 |
| No log | 5.0 | 55 | 1.4733 | 0.7005 |
| No log | 6.0 | 66 | 2.3876 | 0.5513 |
| No log | 7.0 | 77 | 1.3215 | 0.8004 |
| No log | 8.0 | 88 | 1.4006 | 0.7608 |
| No log | 9.0 | 99 | 1.4862 | 0.7608 |
| No log | 10.0 | 110 | 1.4974 | 0.7608 |
| No log | 11.0 | 121 | 1.4966 | 0.7608 |
| No log | 12.0 | 132 | 1.5040 | 0.7608 |
| No log | 13.0 | 143 | 1.5010 | 0.7608 |
| No log | 14.0 | 154 | 1.4741 | 0.7608 |
| No log | 15.0 | 165 | 1.4507 | 0.7608 |
| No log | 16.0 | 176 | 1.4420 | 0.7608 |
| No log | 17.0 | 187 | 1.4398 | 0.7608 |
| No log | 18.0 | 198 | 1.4426 | 0.7608 |
| No log | 19.0 | 209 | 1.4438 | 0.7608 |
| No log | 20.0 | 220 | 1.4439 | 0.7608 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"language": "da", "tags": ["generated_from_trainer"], "base_model": "MiMe-MeMo/MeMo-BERT-01", "widget": [{"text": "Men havde Gud vendt sig fra ham , saa kunde han ogsaa vende sig fra Gud . Havde Gud ingen \u00d8ren , saa havde han heller ingen L\u00e6ber , havde Gud ingen Naade , saa havde han heller ingen Tilbedelse , og han trodsede og viste Gud ud af sit Hj\u00e6rte ."}], "model-index": [{"name": "new_memo_model", "results": []}]} | text-classification | MiMe-MeMo/MeMo-BERT-WSD | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"da",
"base_model:MiMe-MeMo/MeMo-BERT-01",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-09T12:37:02+00:00 | [] | [
"da"
] | TAGS
#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #da #base_model-MiMe-MeMo/MeMo-BERT-01 #autotrain_compatible #endpoints_compatible #region-us
| MeMo Model (Word Sense Disambiguation)
======================================
This model is a fine-tuned version of MiMe-MeMo/MeMo-BERT-01 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7214
* F1-score: 0.6667
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #da #base_model-MiMe-MeMo/MeMo-BERT-01 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
67,
98,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #da #base_model-MiMe-MeMo/MeMo-BERT-01 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
-0.10871054232120514,
0.04625952988862991,
-0.00184342497959733,
0.08255433291196823,
0.16141830384731293,
0.03179406002163887,
0.15382592380046844,
0.0903131514787674,
-0.06268982589244843,
0.05723752826452255,
0.14200091361999512,
0.11840182542800903,
-0.001991894096136093,
0.1501048356294632,
-0.06009180471301079,
-0.2651718854904175,
0.012688002549111843,
0.021211575716733932,
-0.0959053486585617,
0.09813472628593445,
0.0879654586315155,
-0.13561829924583435,
0.08877108246088028,
-0.024956626817584038,
-0.198927640914917,
0.021777596324682236,
0.025462832301855087,
-0.06705256551504135,
0.12823691964149475,
0.029220644384622574,
0.13559091091156006,
0.016190528869628906,
0.07516946643590927,
-0.2057511955499649,
0.020290229469537735,
0.05000130459666252,
-0.007250161375850439,
0.06415199488401413,
0.05605488643050194,
-0.016024386510252953,
0.11303708702325821,
-0.08932341635227203,
0.06901049613952637,
0.017002487555146217,
-0.1323048174381256,
-0.18122021853923798,
-0.05545146390795708,
-0.011523357592523098,
0.09043274074792862,
0.08216989040374756,
-0.02092692255973816,
0.1247979924082756,
-0.04391517862677574,
0.09392856061458588,
0.22981128096580505,
-0.29812249541282654,
-0.07713430374860764,
0.06449821591377258,
0.035122841596603394,
0.08439391106367111,
-0.12566809356212616,
-0.002938167192041874,
0.08657003194093704,
0.03389108180999756,
0.11683771014213562,
-0.03555455431342125,
0.004446480888873339,
0.0049448078498244286,
-0.13685698807239532,
0.010291713289916515,
0.15924639999866486,
0.04289251193404198,
-0.030567828565835953,
-0.05294979736208916,
-0.05397024750709534,
-0.15023653209209442,
-0.053813476115465164,
-0.02961510233581066,
0.05328753963112831,
-0.030942199751734734,
-0.07797923684120178,
-0.012209400534629822,
-0.10379087924957275,
-0.09649061411619186,
-0.05424768477678299,
0.17710040509700775,
0.023572765290737152,
-0.01458309218287468,
-0.03375508636236191,
0.0930626317858696,
-0.04598720371723175,
-0.1214812770485878,
0.021490763872861862,
0.02216162346303463,
-0.008781098760664463,
-0.07497163861989975,
-0.0520772747695446,
-0.13213390111923218,
0.016445497050881386,
0.08450821042060852,
-0.059579819440841675,
0.07267492264509201,
-0.004958692938089371,
0.02766077220439911,
-0.07192394882440567,
0.17065833508968353,
-0.056603118777275085,
-0.02356738969683647,
-0.001652359962463379,
0.0652189701795578,
0.01894446834921837,
-0.001782796229235828,
-0.09693033248186111,
0.055605318397283554,
0.12444940209388733,
0.014997421763837337,
-0.09868928790092468,
0.08653303235769272,
-0.0668606087565422,
-0.01578971929848194,
-0.01141221821308136,
-0.09234362840652466,
0.054200876504182816,
0.016604410484433174,
-0.062233153730630875,
-0.049398574978113174,
0.01465526595711708,
0.022272786125540733,
0.00836293213069439,
0.10927420109510422,
-0.09326604008674622,
0.025508452206850052,
-0.09791234135627747,
-0.1336730718612671,
0.026956548914313316,
-0.05932025983929634,
0.020476605743169785,
-0.10836616903543472,
-0.13319790363311768,
-0.03174706920981407,
0.06020275130867958,
-0.036093588918447495,
0.00812524277716875,
-0.08447349816560745,
-0.07043967396020889,
0.03093878924846649,
0.009845496155321598,
0.10075214505195618,
-0.05557856336236,
0.08028243482112885,
0.030219072476029396,
0.08692114055156708,
-0.018954934552311897,
0.05219974368810654,
-0.08497435599565506,
0.044618040323257446,
-0.23501327633857727,
0.014608162455260754,
-0.07810395210981369,
0.0755455419421196,
-0.06365247070789337,
-0.06735831499099731,
-0.032957691699266434,
0.013308885507285595,
0.07263272255659103,
0.10137077420949936,
-0.15866170823574066,
-0.07624442875385284,
0.1713174730539322,
-0.09064790606498718,
-0.12658560276031494,
0.11563839018344879,
-0.06984131783246994,
0.088640496134758,
0.06811799108982086,
0.18209175765514374,
0.060230132192373276,
-0.10730161517858505,
0.005546175874769688,
-0.030037468299269676,
0.048396795988082886,
-0.03066731058061123,
0.0465536005795002,
0.0034454341512173414,
-0.02067258395254612,
0.006803486961871386,
-0.0019265093142166734,
0.04566618800163269,
-0.08366728574037552,
-0.06817778944969177,
-0.0162416510283947,
-0.107154481112957,
0.06357260048389435,
0.050844576209783554,
0.09303735196590424,
-0.1485690176486969,
-0.05879686027765274,
0.08926322311162949,
0.05369899794459343,
-0.08698184788227081,
0.016788577660918236,
-0.06049316003918648,
0.03320367634296417,
-0.06663838773965836,
-0.012197013944387436,
-0.16262203454971313,
-0.05030966177582741,
0.027368685230612755,
0.020504778251051903,
-0.006009040400385857,
-0.010093041695654392,
0.10474278777837753,
0.08695942163467407,
-0.06447865813970566,
-0.027003316208720207,
-0.03489328548312187,
0.026682378724217415,
-0.14064283668994904,
-0.194877490401268,
0.010154513642191887,
-0.05764372646808624,
0.1411820352077484,
-0.25498834252357483,
0.046927787363529205,
0.02266576513648033,
0.09511992335319519,
0.056486938148736954,
-0.01452404260635376,
-0.02445130981504917,
0.06878988444805145,
-0.025976793840527534,
-0.06345260143280029,
0.06074582040309906,
0.008581900037825108,
-0.07336562126874924,
-0.028095848858356476,
-0.1823849081993103,
0.17580284178256989,
0.14500883221626282,
-0.12342981994152069,
-0.09048227220773697,
-0.0030694229062646627,
-0.03654561936855316,
-0.024784669280052185,
-0.03852866590023041,
-0.005260375328361988,
0.1426483541727066,
-0.022032205015420914,
0.15497787296772003,
-0.07518160343170166,
-0.032648954540491104,
0.039537061005830765,
-0.0564553402364254,
-0.03730454295873642,
0.11884891241788864,
0.10047408938407898,
-0.11379066109657288,
0.14182810485363007,
0.17083145678043365,
-0.08063377439975739,
0.18275171518325806,
-0.02241070568561554,
-0.050314679741859436,
-0.024346783757209778,
0.00882345624268055,
0.018172014504671097,
0.11605064570903778,
-0.153800830245018,
-0.02981281839311123,
-0.007142720278352499,
0.028276100754737854,
0.036389026790857315,
-0.22607503831386566,
-0.028703942894935608,
0.041605301201343536,
-0.02912622131407261,
0.038829505443573,
-0.009791877120733261,
-0.005844466388225555,
0.10601633787155151,
0.014234394766390324,
-0.06942889094352722,
0.030302921310067177,
0.0020684609189629555,
-0.11088293790817261,
0.22151248157024384,
-0.10603838413953781,
-0.12191056460142136,
-0.12060540914535522,
-0.07645191252231598,
-0.02904673106968403,
0.03839075192809105,
0.0506751574575901,
-0.1049148365855217,
-0.015497622080147266,
-0.1006508469581604,
0.029226599261164665,
0.0025746694300323725,
0.037121087312698364,
0.05605170130729675,
0.0055142855271697044,
0.06142621859908104,
-0.08803682029247284,
-0.0272518377751112,
-0.05915232375264168,
-0.03401727229356766,
0.04201598837971687,
-0.0063434774056077,
0.129435196518898,
0.14495624601840973,
-0.03439844772219658,
0.005776034668087959,
-0.04013921692967415,
0.22546440362930298,
-0.060943715274333954,
-0.01021723635494709,
0.10617747902870178,
-0.022367367520928383,
0.032971661537885666,
0.12213627249002457,
0.05711479112505913,
-0.11228226870298386,
0.04028851538896561,
0.04288526996970177,
-0.04151245951652527,
-0.18838176131248474,
-0.03316762298345566,
-0.04114341363310814,
0.03732890635728836,
0.10475849360227585,
0.021643737331032753,
0.029213175177574158,
0.06347930431365967,
0.05246564373373985,
0.08059336990118027,
-0.009489385411143303,
0.06667284667491913,
0.0688670426607132,
0.03588232025504112,
0.10507571697235107,
-0.049899399280548096,
-0.06578235328197479,
0.026746420189738274,
-0.01735418476164341,
0.2050880491733551,
0.047537051141262054,
0.11078137159347534,
0.0609767809510231,
0.08975952863693237,
0.012525506317615509,
0.07342597842216492,
-0.006405351217836142,
-0.06802837550640106,
-0.004456648137420416,
-0.05386961251497269,
-0.01354909222573042,
0.04391730949282646,
-0.07630671560764313,
0.06262241303920746,
-0.15519241988658905,
0.02288743108510971,
0.0650663822889328,
0.21437573432922363,
0.053102973848581314,
-0.32061490416526794,
-0.08473972231149673,
0.03605687990784645,
-0.018809229135513306,
-0.014352311380207539,
0.021983103826642036,
0.1472208946943283,
-0.0684383362531662,
0.029313012957572937,
-0.08187070488929749,
0.06331327557563782,
-0.034594859927892685,
0.044872891157865524,
0.06248745694756508,
0.08666665852069855,
-0.036618590354919434,
0.06001566722989082,
-0.2763054072856903,
0.2860133647918701,
0.008101013489067554,
0.0913151353597641,
-0.05571616441011429,
-0.012971263378858566,
0.03236575797200203,
0.077732153236866,
0.04910296946763992,
-0.019961047917604446,
-0.08603966236114502,
-0.19781355559825897,
-0.03893999382853508,
0.02742883190512657,
0.12988555431365967,
-0.037615321576595306,
0.11357920616865158,
-0.009604298509657383,
-0.0020683950278908014,
0.08167768269777298,
-0.02733042649924755,
-0.09385434538125992,
-0.09786400198936462,
-0.02134445123374462,
0.04445386305451393,
-0.018533864989876747,
-0.08541686832904816,
-0.09967917948961258,
-0.13674628734588623,
0.16572155058383942,
-0.0345330536365509,
-0.0018163052154704928,
-0.11738420277833939,
0.07180499285459518,
0.027931369841098785,
-0.07900784909725189,
0.030984366312623024,
0.014484506100416183,
0.08498473465442657,
0.0027434546500444412,
-0.057920921593904495,
0.1470370888710022,
-0.08899930864572525,
-0.16048578917980194,
-0.07462459802627563,
0.11431704461574554,
0.03411024808883667,
0.052869271486997604,
-0.009070535190403461,
0.007656867150217295,
0.01137198694050312,
-0.07448838651180267,
0.05326437950134277,
0.0038787468802183867,
0.06774551421403885,
0.004775410518050194,
-0.0904288962483406,
0.011177128180861473,
-0.05205586552619934,
-0.018568284809589386,
0.1777171492576599,
0.281279981136322,
-0.07299864292144775,
-0.008689007721841335,
0.06095751002430916,
-0.07504665106534958,
-0.21544697880744934,
0.09277364611625671,
0.036720987409353256,
0.01840250939130783,
0.014856348745524883,
-0.17062686383724213,
0.1164776086807251,
0.10287375748157501,
-0.00825762189924717,
0.10441194474697113,
-0.2791527211666107,
-0.1447962075471878,
0.11262857168912888,
0.1767899990081787,
0.14615526795387268,
-0.1437382847070694,
-0.023705508559942245,
-0.03865060582756996,
-0.09004386514425278,
0.08769670128822327,
-0.13337361812591553,
0.11538971960544586,
0.010055161081254482,
0.03973159193992615,
0.004932671785354614,
-0.06989443302154541,
0.1330268383026123,
-0.01760701648890972,
0.12263444066047668,
-0.05289943516254425,
-0.06519991904497147,
0.020114652812480927,
-0.06273820996284485,
0.018345395103096962,
-0.07678568363189697,
0.003994886297732592,
-0.05179847404360771,
-0.03668750822544098,
-0.04274022951722145,
0.026895681396126747,
-0.03466637432575226,
-0.06492514163255692,
-0.029862212017178535,
0.03644881397485733,
0.03714079409837723,
-0.01738392747938633,
0.1304796040058136,
-0.00035369794932194054,
0.17430193722248077,
0.11957895010709763,
0.09672266989946365,
-0.0668426975607872,
0.004406186286360025,
0.0037268137093633413,
-0.03607097640633583,
0.0680370181798935,
-0.14349831640720367,
0.04540048912167549,
0.11686570197343826,
0.0011577215045690536,
0.16095520555973053,
0.07958067953586578,
-0.0421505942940712,
0.016767941415309906,
0.07630180567502975,
-0.14752598106861115,
-0.10440576821565628,
-0.010588869452476501,
-0.04363788291811943,
-0.1111004501581192,
0.07293616235256195,
0.12670549750328064,
-0.06962128728628159,
0.01913987658917904,
-0.027095064520835876,
0.0027404758147895336,
-0.04035215452313423,
0.21342629194259644,
0.06989748030900955,
0.03885699063539505,
-0.07051960378885269,
0.07962889969348907,
0.014824073761701584,
-0.09074771404266357,
0.03279990702867508,
0.0524696446955204,
-0.08614307641983032,
-0.03393397107720375,
0.05560453236103058,
0.22935838997364044,
0.015738975256681442,
-0.0496077686548233,
-0.16952234506607056,
-0.1098642349243164,
0.06616712361574173,
0.20977234840393066,
0.1051347404718399,
0.002335923258215189,
-0.043256111443042755,
0.023267142474651337,
-0.1294386386871338,
0.11672838032245636,
0.03974170237779617,
0.09323140978813171,
-0.1592121720314026,
0.15360979735851288,
-0.01037677749991417,
0.0205555260181427,
-0.030411625280976295,
0.021873675286769867,
-0.1455920934677124,
-0.0043716528452932835,
-0.1008988469839096,
-0.03566242381930351,
-0.041706763207912445,
-0.009224963374435902,
0.013233610428869724,
-0.036595359444618225,
-0.07499668747186661,
0.00652109831571579,
-0.10889673233032227,
-0.018357371911406517,
0.02193489670753479,
0.049223389476537704,
-0.0899452343583107,
-0.044967297464609146,
0.03356953710317612,
-0.07292322814464569,
0.045106191188097,
0.033104412257671356,
0.03258354216814041,
0.03823116794228554,
-0.1895069181919098,
0.023253941908478737,
0.08476298302412033,
-0.010767272673547268,
0.06130898743867874,
-0.08831708878278732,
-0.016862403601408005,
-0.013576852157711983,
0.06961685419082642,
0.03557094186544418,
0.08729348331689835,
-0.12018883228302002,
0.04827586188912392,
-0.05807700753211975,
-0.07658780366182327,
-0.05647262930870056,
0.02588144689798355,
0.0831637755036354,
-0.02471848390996456,
0.18277865648269653,
-0.1131126657128334,
0.0246944148093462,
-0.181145578622818,
0.0038524153642356396,
-0.004648176021873951,
-0.10275455564260483,
-0.06916280090808868,
-0.051053762435913086,
0.06533091515302658,
-0.058148983865976334,
0.12516804039478302,
0.024136461317539215,
0.007589371409267187,
0.045210063457489014,
-0.07757244259119034,
0.030514590442180634,
0.057850904762744904,
0.2056431919336319,
0.03200472518801689,
-0.05203293263912201,
0.02364228665828705,
0.07799288630485535,
0.14601220190525055,
0.11946216970682144,
0.18633313477039337,
0.17981961369514465,
-0.07776398956775665,
0.12117832899093628,
0.03615284338593483,
-0.05429596081376076,
-0.12376587837934494,
0.06223101168870926,
-0.06304517388343811,
0.08536195009946823,
-0.025998994708061218,
0.18038010597229004,
0.09432345628738403,
-0.16196048259735107,
0.0048265340737998486,
-0.03898965194821358,
-0.09067883342504501,
-0.11458221822977066,
-0.027762046083807945,
-0.10629309713840485,
-0.15025463700294495,
-0.0007888072868809104,
-0.10890459269285202,
-0.011273292824625969,
0.07542864233255386,
0.01830778643488884,
0.001367322402074933,
0.22123731672763824,
-0.034810591489076614,
0.041237425059080124,
0.07847759127616882,
-0.0037608707789331675,
-0.025809066370129585,
-0.09021749347448349,
-0.08656492084264755,
-0.003385932417586446,
-0.04639676213264465,
0.01690315082669258,
-0.05631604790687561,
-0.04614643380045891,
0.034327760338783264,
-0.007461467292159796,
-0.11187244951725006,
0.01855560578405857,
0.02803877554833889,
0.039357803761959076,
0.023018276318907738,
0.021663088351488113,
-0.016543252393603325,
-0.002032086020335555,
0.19920021295547485,
-0.08198792487382889,
-0.03857944905757904,
-0.12308752536773682,
0.20536503195762634,
0.041678350418806076,
0.05793290212750435,
-0.008159304037690163,
-0.1017710343003273,
0.025625623762607574,
0.2321857064962387,
0.20352372527122498,
-0.08305880427360535,
0.010069449432194233,
-0.006776278372853994,
-0.00764503376558423,
-0.02628326416015625,
0.10030079632997513,
0.1103782057762146,
0.016431357711553574,
-0.0640491470694542,
-0.04664910212159157,
-0.044761352241039276,
-0.032773181796073914,
-0.0587572380900383,
0.05442921444773674,
0.07124720513820648,
0.03070889785885811,
-0.049959585070610046,
0.0735689178109169,
0.00199873186647892,
-0.11728637665510178,
0.05833922699093819,
-0.2027001827955246,
-0.13950598239898682,
-0.006186744663864374,
0.08753179013729095,
-0.017065199092030525,
0.08347580581903458,
-0.02744438871741295,
-0.029299939051270485,
0.03790896013379097,
0.002176993526518345,
-0.08076787739992142,
-0.06814064085483551,
0.04850492626428604,
-0.11332634091377258,
0.2218756228685379,
-0.05947042629122734,
0.023053482174873352,
0.13824351131916046,
0.019978364929556847,
-0.058444879949092865,
0.09308598935604095,
0.05702129378914833,
-0.08281910419464111,
0.037183694541454315,
0.10146604478359222,
-0.05281224474310875,
0.11182481050491333,
0.05456208810210228,
-0.15557816624641418,
0.02654525637626648,
-0.10501142591238022,
-0.10223834961652756,
-0.05708831548690796,
-0.026416705921292305,
-0.0711347684264183,
0.12928466498851776,
0.20291028916835785,
-0.020128430798649788,
0.015529093332588673,
-0.026745440438389778,
0.029027409851551056,
0.06741523742675781,
0.04635005071759224,
-0.04087827354669571,
-0.27226242423057556,
0.009718790650367737,
0.05358749255537987,
-0.001487341825850308,
-0.30439960956573486,
-0.09327951818704605,
0.0002717542229220271,
-0.025977903977036476,
-0.07334300875663757,
0.06984580308198929,
0.12038332968950272,
0.05104811489582062,
-0.07753637433052063,
-0.11074528098106384,
-0.0713394358754158,
0.16997966170310974,
-0.1095055416226387,
-0.10865262895822525
] |
null | null | null |
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 1500000
'learning_rate': 0.001
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.999
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'IrinaArcadievna/ppo_scratch-LunarLander-v2'
'batch_size': 512
'minibatch_size': 128}
```
| {"tags": ["LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "160.00 +/- 96.58", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | IrinaArcadievna/ppo_scratch-LunarLander-v2 | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] | 2024-02-09T12:37:16+00:00 | [] | [] | TAGS
#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us
|
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
| [
"# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n\n # Hyperparameters"
] | [
"TAGS\n#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us \n",
"# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n\n # Hyperparameters"
] | [
51,
37
] | [
"passage: TAGS\n#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us \n# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n\n # Hyperparameters"
] | [
0.07948226481676102,
-0.021824665367603302,
-0.005334289278835058,
0.07425090670585632,
0.11451162397861481,
-0.051334477961063385,
0.11827225238084793,
0.05111894756555557,
0.0632978081703186,
0.08233953267335892,
0.09910695254802704,
0.11526558548212051,
0.02103434130549431,
0.12346389144659042,
0.10133372992277145,
-0.26653239130973816,
0.0048308540135622025,
-0.042133692651987076,
0.020121442154049873,
0.07062754780054092,
-0.028985055163502693,
-0.12164036184549332,
0.02042403817176819,
-0.008055811747908592,
0.04164125770330429,
0.03685355558991432,
-0.020250989124178886,
-0.07061084359884262,
0.1035412922501564,
-0.04342407360672951,
0.07646117359399796,
0.04053044691681862,
0.12915800511837006,
-0.11266650259494781,
0.03731851652264595,
0.047094929963350296,
-0.058420803397893906,
0.040810972452163696,
0.023221731185913086,
0.07433853298425674,
0.15582501888275146,
0.0008022422553040087,
0.10807766020298004,
-0.019928930327296257,
-0.15859591960906982,
-0.0564296655356884,
0.04013175517320633,
0.10688508301973343,
0.041339244693517685,
0.05763867497444153,
0.01518392562866211,
0.24210692942142487,
-0.07300914824008942,
0.0014766358071938157,
0.1963091939687729,
-0.2750851511955261,
-0.056198850274086,
0.2650637924671173,
0.08425293117761612,
0.09438422322273254,
-0.09869689494371414,
-0.0236953292042017,
0.007850034162402153,
0.013983802869915962,
-0.038732558488845825,
-0.07621388882398605,
0.1343805193901062,
0.06358266621828079,
-0.07906194031238556,
-0.05448254942893982,
0.09211132675409317,
0.015635671094059944,
0.03398676961660385,
0.0008897133520804346,
-0.015260354615747929,
0.03964465111494064,
-0.008004734292626381,
-0.08323223143815994,
0.067534439265728,
0.017411211505532265,
-0.059903185814619064,
-0.11101946979761124,
-0.11182308942079544,
-0.028280947357416153,
-0.08438915759325027,
0.16840966045856476,
-0.023494480177760124,
0.07285201549530029,
-0.06215810775756836,
0.06860414892435074,
-0.037912189960479736,
0.004227026831358671,
0.006380763836205006,
-0.049948662519454956,
-0.04539962485432625,
-0.025878654792904854,
0.006328459829092026,
0.011017742566764355,
0.11213880032300949,
-0.002449487103149295,
0.0508684441447258,
0.04856472462415695,
0.014653711579740047,
0.0942535474896431,
0.04126615449786186,
0.18958540260791779,
-0.006363034248352051,
0.0650586485862732,
0.062062907963991165,
0.017491057515144348,
0.022076671943068504,
-0.05142693966627121,
-0.1658715307712555,
0.0807771384716034,
-0.08260773122310638,
-0.028765955939888954,
0.09323479980230331,
-0.044928085058927536,
-0.1112084910273552,
-0.01773354969918728,
-0.07590804249048233,
-0.025731517001986504,
-0.01252016518265009,
0.01790926419198513,
-0.035574477165937424,
0.005672375671565533,
0.03449513763189316,
0.08204318583011627,
0.033907562494277954,
-0.08674118667840958,
0.00984077900648117,
0.012360874563455582,
-0.122767873108387,
-0.004771664272993803,
0.010288639925420284,
0.04804306477308273,
0.04491464048624039,
-0.1116413027048111,
-0.2020648866891861,
-0.08828215301036835,
0.053431469947099686,
-0.07537820190191269,
-0.15614600479602814,
-0.11512033641338348,
0.02302604168653488,
-0.10217837989330292,
-0.046169016510248184,
-0.0017400066135451198,
-0.019300667569041252,
0.05366985872387886,
-0.06531468033790588,
0.1828034669160843,
0.0271916463971138,
-0.00020129751646891236,
-0.14947181940078735,
0.019320663064718246,
-0.2362208217382431,
0.07685942947864532,
-0.04987453296780586,
0.07074880599975586,
-0.04584719240665436,
-0.09154892712831497,
-0.01864667609333992,
0.054014526307582855,
0.013841784559190273,
0.10950348526239395,
-0.1638582944869995,
-0.05129624530673027,
0.024843567982316017,
-0.08068934828042984,
-0.0030390452593564987,
-0.04837793856859207,
-0.04604795575141907,
0.1606992781162262,
0.018704978749155998,
0.14688511192798615,
-0.12919624149799347,
-0.09930720180273056,
0.19129104912281036,
0.03531093895435333,
-0.16984215378761292,
-0.036521974951028824,
0.09952033311128616,
0.019277004525065422,
-0.01849931664764881,
-0.05688142776489258,
-0.07599073648452759,
0.015944182872772217,
-0.08702079951763153,
-0.04182637855410576,
0.04013517126441002,
-0.042824242264032364,
0.14606650173664093,
0.10223949700593948,
0.07952884584665298,
-0.07538176327943802,
-0.007020880468189716,
0.08674140274524689,
0.06271850317716599,
0.045035574585199356,
0.03672485426068306,
-0.05614851415157318,
0.03206208720803261,
-0.025039123371243477,
-0.01738123595714569,
-0.13521039485931396,
0.0019960827194154263,
-0.06055765971541405,
0.1118607297539711,
0.13101612031459808,
0.28467631340026855,
0.10075046867132187,
0.02464960888028145,
0.07675616443157196,
-0.07042508572340012,
-0.10758408159017563,
0.002032244112342596,
0.0235405582934618,
-0.1785016655921936,
0.026378504931926727,
-0.07599464803934097,
-0.14044412970542908,
-0.1351996809244156,
-0.025685761123895645,
-0.17195537686347961,
0.02159930020570755,
0.054728612303733826,
-0.018639836460351944,
0.0013907389948144555,
0.12220112234354019,
0.013543038628995419,
-0.053733617067337036,
0.10188740491867065,
0.009542218409478664,
-0.05206648260354996,
-0.045367226004600525,
0.1050298660993576,
0.13431710004806519,
0.1365344226360321,
-0.2098493129014969,
0.008600602857768536,
0.1119711846113205,
-0.04708562791347504,
0.03519878163933754,
0.026510966941714287,
0.21071651577949524,
0.2740876078605652,
0.0374440960586071,
0.008118349127471447,
-0.05789022892713547,
0.0453064851462841,
-0.05260699614882469,
-0.11800429224967957,
-0.05410657823085785,
0.17159637808799744,
0.07862472534179688,
-0.006237224210053682,
0.09871696680784225,
0.07909595966339111,
0.037818074226379395,
0.16045765578746796,
0.03334520757198334,
-0.09544764459133148,
-0.03232238441705704,
-0.026171676814556122,
-0.0047440179623663425,
0.06791821867227554,
-0.0798373743891716,
-0.032012078911066055,
0.021649274975061417,
-0.13788609206676483,
0.018513672053813934,
-0.18612799048423767,
-0.1437452882528305,
0.03805195167660713,
0.043561313301324844,
-0.008401780389249325,
0.04065251722931862,
-0.0160639937967062,
0.05676067993044853,
0.03282754495739937,
-0.08861549198627472,
0.04405612871050835,
-0.005384152289479971,
0.009959283284842968,
0.03441033884882927,
-0.01767686940729618,
-0.21204280853271484,
-0.15340813994407654,
0.013550614938139915,
-0.05142427980899811,
0.05592547729611397,
-0.008550947532057762,
-0.19242143630981445,
0.025911282747983932,
-0.014332908205688,
0.02364996261894703,
-0.03164665028452873,
-0.03833974152803421,
0.1345074623823166,
0.14185978472232819,
-0.026165392249822617,
0.00023905932903289795,
-0.03341824188828468,
-0.14318081736564636,
-0.180479034781456,
0.06557876616716385,
0.0740460753440857,
0.006866236217319965,
0.1220167726278305,
0.004434254486113787,
0.026604121550917625,
-0.00636066310107708,
0.007762894034385681,
-0.07827747613191605,
-0.10268643498420715,
0.2943233549594879,
0.02490289881825447,
-0.022609207779169083,
-0.023361563682556152,
0.022680940106511116,
-0.005913543980568647,
0.020695405080914497,
-0.06731052696704865,
-0.11051533371210098,
-0.10214895755052567,
-0.018064133822917938,
-0.05326148122549057,
0.08696132898330688,
0.05207669362425804,
-0.0023201601579785347,
-0.058658841997385025,
0.0491698756814003,
0.15816207230091095,
0.0022554483730345964,
-0.07889559864997864,
0.00756099633872509,
0.06827649474143982,
-0.10357149690389633,
0.019141824916005135,
-0.011750275269150734,
-0.06115471199154854,
0.01578802429139614,
0.021844392642378807,
0.02698187716305256,
0.10298074781894684,
-0.21004606783390045,
0.04396829754114151,
0.06455216556787491,
0.025463011115789413,
0.08768844604492188,
0.05016043782234192,
-0.11047832667827606,
-0.016628960147500038,
-0.0343489907681942,
-0.16258354485034943,
0.1297316700220108,
0.14130131900310516,
0.06893892586231232,
0.039022352546453476,
0.04288983345031738,
-0.07514789700508118,
0.058336563408374786,
-0.03656633570790291,
-0.1470387876033783,
-0.018523573875427246,
0.03902188688516617,
0.03257647529244423,
0.038807060569524765,
0.10827972739934921,
0.10223158448934555,
-0.14332416653633118,
-0.03201044723391533,
0.06512229144573212,
-0.008886558935046196,
-0.04119880497455597,
0.004403908737003803,
-0.09832779318094254,
0.07498125731945038,
-0.0024919756688177586,
0.04813602566719055,
-0.20199769735336304,
0.16434083878993988,
-0.09330786764621735,
0.034300561994314194,
-0.04896155744791031,
-0.044333528727293015,
0.03555295243859291,
-0.09057865291833878,
0.20472288131713867,
0.0057462104596197605,
0.008313721977174282,
-0.12209630757570267,
-0.17661772668361664,
-0.034985676407814026,
-0.09205599129199982,
-0.07460658252239227,
0.02909865602850914,
0.0682184249162674,
0.029013507068157196,
-0.044006895273923874,
0.1327963024377823,
-0.007539169397205114,
0.08532623946666718,
-0.09495806694030762,
-0.09892267733812332,
-0.06850815564393997,
-0.09003753960132599,
-0.13165755569934845,
-0.069197878241539,
0.05082700401544571,
0.12665395438671112,
0.02109835296869278,
-0.02864154241979122,
0.016000375151634216,
-0.01131656114012003,
0.0060316757299005985,
-0.006539386231452227,
0.0482512004673481,
0.015850301831960678,
-0.05547862499952316,
-0.13189296424388885,
0.08252222090959549,
-0.06544385105371475,
-0.06556238979101181,
-0.023766927421092987,
0.09430349618196487,
0.09706855565309525,
0.1314772367477417,
-0.052682001143693924,
0.028886299580335617,
-0.03723334148526192,
-0.04484548792243004,
0.18565788865089417,
0.0040725888684391975,
-0.07140722125768661,
0.04510314390063286,
0.08041586726903915,
0.05989309027791023,
0.0390491709113121,
-0.031676698476076126,
0.20406655967235565,
0.15550298988819122,
-0.018378838896751404,
0.19636642932891846,
-0.017176153138279915,
-0.0269333329051733,
-0.20952188968658447,
0.006836839485913515,
-0.019357649609446526,
0.029477683827280998,
0.1340312361717224,
-0.1391998678445816,
0.02293945848941803,
-0.004865060094743967,
-0.02284914068877697,
-0.07053285837173462,
-0.3114997148513794,
-0.06468415260314941,
0.20102077722549438,
0.17379379272460938,
0.30399972200393677,
-0.10662104934453964,
0.05403600633144379,
0.02176249772310257,
0.035715505480766296,
0.03934846818447113,
-0.07645441591739655,
0.1000572219491005,
-0.11122481524944305,
0.16528162360191345,
0.08111181855201721,
-0.020749825984239578,
-0.02004031278192997,
-0.13701297342777252,
0.018633954226970673,
-0.12466508150100708,
-0.017992790788412094,
0.08779406547546387,
-0.003319771494716406,
-0.09328535199165344,
0.23242005705833435,
-0.06734555959701538,
-0.127778559923172,
-0.028943995013833046,
-0.057271506637334824,
-0.030531147494912148,
0.012628542259335518,
-0.09404513984918594,
0.005903336685150862,
0.1308545619249344,
-0.011834635399281979,
0.11608193069696426,
0.16071371734142303,
-0.035819161683321,
0.07980551570653915,
0.11671095341444016,
0.041628848761320114,
0.06653126329183578,
-0.16247588396072388,
-0.008802353404462337,
-0.0202709399163723,
0.029673689976334572,
-0.1328430324792862,
-0.08996491879224777,
0.037999510765075684,
0.055287107825279236,
-0.016219541430473328,
0.11157703399658203,
-0.02790040522813797,
0.0671137273311615,
0.05197756364941597,
-0.14911557734012604,
-0.21309031546115875,
0.043088413774967194,
-0.03457297012209892,
0.16741053760051727,
0.032527483999729156,
0.07026690244674683,
-0.1318490356206894,
0.005996404681354761,
-0.008010598830878735,
-0.02555401436984539,
-0.113502137362957,
-0.04016893729567528,
0.10736791044473648,
0.01890859194099903,
-0.05588224157691002,
0.11932288110256195,
0.053731534630060196,
0.07207717001438141,
0.022103527560830116,
0.036430660635232925,
0.10638459026813507,
-0.05759545415639877,
0.08525355905294418,
0.19163745641708374,
0.022084489464759827,
-0.050156377255916595,
-0.1069810688495636,
-0.142279252409935,
0.1059383824467659,
-0.029212607070803642,
0.06867408007383347,
-0.16743674874305725,
-0.09695854038000107,
0.03239866718649864,
-0.006085241679102182,
-0.045712824910879135,
-0.04037291929125786,
-0.029692232608795166,
-0.1638854742050171,
0.07177262753248215,
-0.026750473305583,
0.09733851999044418,
-0.07764898240566254,
-0.08057862520217896,
-0.1878826767206192,
0.0927230566740036,
0.11600489169359207,
-0.09250454604625702,
-0.07816965878009796,
0.0006463889149017632,
0.007188722491264343,
-0.05905555561184883,
-0.05547625944018364,
0.05128099024295807,
-0.1268264353275299,
0.03925716504454613,
0.02211940288543701,
0.07955963909626007,
-0.013168327510356903,
-0.022237133234739304,
0.053730763494968414,
-0.05526714771986008,
-0.004513209220021963,
-0.0007778665167279541,
-0.010598957538604736,
-0.04734821990132332,
-0.2539333701133728,
0.026826584711670876,
0.015074611641466618,
0.023000292479991913,
0.11450504511594772,
0.052672553807497025,
0.002142281737178564,
-0.022901082411408424,
-0.09921795129776001,
0.004082086030393839,
0.0676940307021141,
-0.0444176085293293,
0.02973432093858719,
0.04361078143119812,
-0.10892095416784286,
-0.011856138706207275,
-0.024206269532442093,
0.07134921103715897,
0.010941405780613422,
0.06965811550617218,
-0.07052738219499588,
0.09066002070903778,
-0.1813029795885086,
-0.042003389447927475,
0.02394963428378105,
0.0719861164689064,
0.12007027864456177,
-0.10232933610677719,
0.05554276332259178,
0.007666701916605234,
0.16984406113624573,
0.10653958469629288,
-0.002575549529865384,
-0.03601353242993355,
0.06471540033817291,
0.09858960658311844,
0.034707363694906235,
0.04066390544176102,
0.06345933675765991,
-0.010203788988292217,
0.10382732003927231,
0.10297582298517227,
0.14551296830177307,
0.050692107528448105,
0.15706492960453033,
0.03763074800372124,
0.008729667402803898,
0.07412492483854294,
0.0944521427154541,
0.08652419596910477,
-0.006242257542908192,
0.1731923371553421,
-0.007543493993580341,
-0.01751723699271679,
-0.03595760464668274,
0.16348356008529663,
0.06810002774000168,
-0.10502735525369644,
0.032236937433481216,
-0.05084357038140297,
0.025795334950089455,
-0.021152885630726814,
-0.15513712167739868,
-0.03436838835477829,
-0.2639841139316559,
0.12161721289157867,
-0.04934193193912506,
-0.00526955584064126,
0.0620683990418911,
-0.019800636917352676,
-0.053851764649152756,
-0.00036916558747179806,
0.0654521957039833,
0.026729213073849678,
0.01114212442189455,
-0.028801998123526573,
-0.021474527195096016,
-0.19075548648834229,
-0.11265835911035538,
-0.04041624069213867,
-0.13205185532569885,
-0.026539895683526993,
0.02738100476562977,
-0.05638997629284859,
0.00884995236992836,
-0.0025031883269548416,
-0.01385815255343914,
0.04824291169643402,
-0.052424367517232895,
0.045965224504470825,
0.051154542714357376,
0.06721315532922745,
-0.07684784382581711,
0.00411610584706068,
0.11700203269720078,
0.03185063600540161,
-0.09347992390394211,
0.055158115923404694,
0.12995439767837524,
-0.058530066162347794,
0.026019345968961716,
-0.007744444999843836,
-0.032847896218299866,
-0.09708602726459503,
0.19312189519405365,
0.11783043295145035,
-0.16847896575927734,
0.0006766151054762304,
-0.036616407334804535,
-0.01160040870308876,
-0.09233774989843369,
0.12344596534967422,
0.1592838317155838,
0.055998723953962326,
-0.15062640607357025,
-0.11043619364500046,
-0.10300665348768234,
0.06709197163581848,
-0.07569106668233871,
-0.07460284233093262,
0.15964122116565704,
-0.02457398921251297,
-0.10188330709934235,
0.03819292411208153,
-0.21867942810058594,
-0.01995755359530449,
0.19039398431777954,
-0.29568302631378174,
-0.11494400352239609,
-0.07910088449716568,
0.18586759269237518,
0.025469033047556877,
0.11436232179403305,
-0.023825788870453835,
-0.02012297883629799,
-0.221383735537529,
0.0029703411273658276,
-0.08713068813085556,
0.034245800226926804,
0.0651308074593544,
-0.09516268968582153,
0.24007263779640198,
-0.09044498205184937,
0.05269941687583923,
0.033750344067811966,
0.07691317796707153,
0.01018204540014267,
0.05163824185729027,
-0.048588331788778305,
-0.16688252985477448,
-0.09095858782529831,
0.014404932036995888,
0.03795035555958748,
0.0503084696829319,
0.09903772920370102,
-0.04082057997584343,
0.04713768512010574,
0.0953395888209343,
0.030845828354358673,
-0.004454230889678001,
0.052237071096897125,
-0.15630710124969482,
0.05534590780735016,
0.018921079114079475,
-0.025683825835585594,
0.02539582923054695,
-0.08227502554655075,
0.10333657264709473,
0.03491305932402611,
0.0618959404528141,
-0.0665573701262474,
0.03160114586353302,
-0.009742318652570248,
-0.12334126234054565,
-0.04329211637377739,
-0.18513770401477814,
-0.0893927589058876,
-0.1391412913799286,
-0.03897256776690483,
-0.04044290632009506,
-0.025919048115611076,
0.01644543558359146,
0.00776201207190752,
-0.0044921645894646645,
-0.11029971390962601,
0.07136444747447968,
0.11884529888629913,
-0.030008424073457718,
0.0031494214199483395
] |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# discourage
This model is a fine-tuned version of [markussagen/xlm-roberta-longformer-base-4096](https://huggingface.co/markussagen/xlm-roberta-longformer-base-4096) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0143
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 88 | 0.1187 |
| No log | 2.0 | 176 | 0.2467 |
| No log | 3.0 | 264 | 0.0560 |
| No log | 4.0 | 352 | 0.0252 |
| No log | 5.0 | 440 | 0.0143 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "markussagen/xlm-roberta-longformer-base-4096", "model-index": [{"name": "discourage", "results": []}]} | text-classification | Sydelabs/all_discourage_included | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:markussagen/xlm-roberta-longformer-base-4096",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-09T12:38:38+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-markussagen/xlm-roberta-longformer-base-4096 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| discourage
==========
This model is a fine-tuned version of markussagen/xlm-roberta-longformer-base-4096 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0143
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.37.0
* Pytorch 2.1.2
* Datasets 2.1.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-markussagen/xlm-roberta-longformer-base-4096 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1"
] | [
81,
141,
4,
30
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-markussagen/xlm-roberta-longformer-base-4096 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1"
] | [
-0.13306421041488647,
0.10598044842481613,
-0.0022279939148575068,
0.05563877895474434,
0.12847670912742615,
0.00089550536358729,
0.1090536043047905,
0.15384747087955475,
-0.08698450773954391,
0.08248511701822281,
0.10813693702220917,
0.08531813323497772,
0.06662680953741074,
0.13671299815177917,
-0.04516526311635971,
-0.30887898802757263,
0.022024041041731834,
0.025785459205508232,
-0.14984014630317688,
0.11387765407562256,
0.11845207959413528,
-0.10828373581171036,
0.04288756474852562,
0.03113878145813942,
-0.11843975633382797,
0.01757902279496193,
0.0008533902000635862,
-0.0702773705124855,
0.10192830115556717,
0.044337593019008636,
0.1196044385433197,
0.026954593136906624,
0.08035308867692947,
-0.23393970727920532,
0.019679676741361618,
0.0774846225976944,
0.03307881951332092,
0.08679329603910446,
0.11118122190237045,
-0.029876571148633957,
0.11863574385643005,
-0.0777110680937767,
0.08220324665307999,
0.0493943877518177,
-0.10262931138277054,
-0.327496737241745,
-0.09983121603727341,
0.051905110478401184,
0.1308816820383072,
0.07226475328207016,
-0.01827825792133808,
0.07928577065467834,
-0.060544032603502274,
0.06660308688879013,
0.22196786105632782,
-0.27577394247055054,
-0.08988364785909653,
0.014290179125964642,
0.06451775133609772,
0.05019858106970787,
-0.13205429911613464,
-0.031741343438625336,
0.039564769715070724,
0.01658327877521515,
0.124583899974823,
0.008994034491479397,
0.036899831146001816,
0.021522274240851402,
-0.1417475938796997,
-0.037400685250759125,
0.15779969096183777,
0.09077220410108566,
-0.04699481651186943,
-0.07933741062879562,
-0.006336609832942486,
-0.17314618825912476,
-0.047742851078510284,
0.0017441206146031618,
0.024410828948020935,
-0.029830483719706535,
-0.09994864463806152,
-0.0104013467207551,
-0.09810719639062881,
-0.09542892128229141,
0.01695350743830204,
0.1484547108411789,
0.052049148827791214,
-0.023243742063641548,
0.004561061039566994,
0.11445167660713196,
0.02722805365920067,
-0.1308000236749649,
-0.016276907175779343,
0.017205463722348213,
-0.08583216369152069,
-0.03386859968304634,
-0.030753280967473984,
-0.07020076364278793,
0.00569302961230278,
0.13542073965072632,
-0.015030517242848873,
0.07350966334342957,
0.008086808025836945,
0.04585403576493263,
-0.10561709105968475,
0.17321783304214478,
-0.06543225795030594,
-0.01928749494254589,
-0.021800167858600616,
0.10584967583417892,
-0.010949427261948586,
-0.009062690660357475,
-0.06875385344028473,
0.027760092169046402,
0.11644193530082703,
0.04703974723815918,
-0.016784625127911568,
0.027287252247333527,
-0.0722220167517662,
-0.03104788064956665,
-0.0012987685622647405,
-0.099753737449646,
0.04562533274292946,
0.009935674257576466,
-0.0880671814084053,
-0.019392430782318115,
0.01586449146270752,
0.02348892204463482,
-0.004823938012123108,
0.10848609358072281,
-0.07438098639249802,
-0.0026901233941316605,
-0.11503764241933823,
-0.09888385981321335,
0.025033487007021904,
-0.025214526802301407,
0.004198045935481787,
-0.0850619450211525,
-0.1450808048248291,
-0.05057037994265556,
0.07282149791717529,
-0.040362026542425156,
-0.07176455110311508,
-0.04679106920957565,
-0.07047716528177261,
0.0557573139667511,
-0.019948210567235947,
0.1497737467288971,
-0.05205373093485832,
0.11016357690095901,
0.015293323434889317,
0.03978091478347778,
0.02870989590883255,
0.0552399717271328,
-0.061564475297927856,
0.063052237033844,
-0.15612411499023438,
0.04185544326901436,
-0.09693977236747742,
0.08549663424491882,
-0.13819152116775513,
-0.10594149678945541,
-0.032046955078840256,
-0.0024163781199604273,
0.09053041785955429,
0.07698354870080948,
-0.1534096896648407,
-0.07107377052307129,
0.17495913803577423,
-0.088774673640728,
-0.14280393719673157,
0.11157822608947754,
-0.03303968161344528,
0.030405258759856224,
0.02896123006939888,
0.1487080156803131,
0.11000601947307587,
-0.08479750901460648,
0.015831204131245613,
-0.054055105894804,
0.10670118033885956,
-0.011009681038558483,
0.12044470757246017,
-0.03491253778338432,
-0.021642323583364487,
0.0011896734358742833,
-0.06182333081960678,
0.06314308941364288,
-0.08010675013065338,
-0.08381730318069458,
-0.03201312944293022,
-0.08056335896253586,
0.020249946042895317,
0.05823986232280731,
0.052321288734674454,
-0.10225424915552139,
-0.13294030725955963,
0.03974613919854164,
0.10898323357105255,
-0.08639844506978989,
0.01774010993540287,
-0.07774008810520172,
0.0571654736995697,
-0.06640271842479706,
-0.009262876585125923,
-0.1474245935678482,
-0.06768347322940826,
0.019821204245090485,
-0.03183167427778244,
-0.0002828048018272966,
-0.010877014137804508,
0.0828954428434372,
0.04328854754567146,
-0.057017505168914795,
-0.08954382687807083,
-0.07447353005409241,
-0.006844259332865477,
-0.08056089282035828,
-0.2160571813583374,
-0.07436904311180115,
-0.03656326234340668,
0.14471572637557983,
-0.2686833143234253,
0.0342758484184742,
0.011779330670833588,
0.10792498290538788,
0.059537749737501144,
-0.03448645398020744,
-0.013751373626291752,
0.055714529007673264,
-0.036132778972387314,
-0.07957203686237335,
0.035495053976774216,
0.023963216692209244,
-0.12711794674396515,
0.03369223698973656,
-0.11900194734334946,
0.15446636080741882,
0.09449461102485657,
-0.006556025706231594,
-0.07912716269493103,
-0.08754061907529831,
-0.06902910768985748,
-0.05964385345578194,
-0.0379081629216671,
0.0006248397403396666,
0.13515949249267578,
0.02294069714844227,
0.13276781141757965,
-0.09218025207519531,
-0.04558590054512024,
0.017689647153019905,
-0.022660832852125168,
-0.01772778108716011,
0.121234230697155,
0.060306303203105927,
-0.058976221829652786,
0.1102878525853157,
0.12798598408699036,
-0.08557990938425064,
0.14552700519561768,
-0.06867093592882156,
-0.11994165182113647,
-0.01806613989174366,
0.04599813371896744,
0.05727062374353409,
0.13506175577640533,
-0.10476843267679214,
0.012332816608250141,
0.02113720029592514,
0.033978186547756195,
0.030397813767194748,
-0.2059863805770874,
-0.022765422239899635,
0.04424480348825455,
-0.05377783998847008,
-0.003633001819252968,
-0.03135918825864792,
0.0026701767928898335,
0.0944964662194252,
0.010259930044412613,
-0.04048094525933266,
0.012330726720392704,
-0.011116293258965015,
-0.09302471578121185,
0.20549064874649048,
-0.08936417102813721,
-0.14017120003700256,
-0.16117499768733978,
-0.010972152464091778,
-0.017712512984871864,
-0.011489092372357845,
0.03393739089369774,
-0.08597776293754578,
-0.040101464837789536,
-0.08370917290449142,
0.041084110736846924,
-0.05566864460706711,
0.028725726529955864,
-0.012358319945633411,
0.03284357860684395,
0.10218837112188339,
-0.09689931571483612,
0.023512469604611397,
-0.004332288634032011,
-0.06123306602239609,
0.03163366764783859,
0.024271801114082336,
0.1072319820523262,
0.159359410405159,
0.01582219824194908,
0.01865946687757969,
-0.04412226006388664,
0.10865514725446701,
-0.08793419599533081,
-0.015597078949213028,
0.11596912890672684,
0.012289495207369328,
0.052491407841444016,
0.121039479970932,
0.047677189111709595,
-0.07887145131826401,
0.04348000884056091,
0.057413145899772644,
-0.013211271725594997,
-0.2456810623407364,
-0.005591703578829765,
-0.051502615213394165,
-0.017749620601534843,
0.13635015487670898,
0.0485227145254612,
0.008746578358113766,
0.07013602554798126,
-0.01478917058557272,
0.012759387493133545,
0.0011571608483791351,
0.09466847032308578,
0.031338222324848175,
0.05210819095373154,
0.12695743143558502,
-0.036210086196660995,
-0.03291377052664757,
0.032739847898483276,
0.024000776931643486,
0.26272591948509216,
-0.01395550835877657,
0.14897781610488892,
0.059095192700624466,
0.16228461265563965,
0.015623985789716244,
0.06853538006544113,
0.012410426512360573,
-0.03830569237470627,
0.019277557730674744,
-0.053279075771570206,
-0.015666520223021507,
0.05949786305427551,
0.012660401873290539,
0.07040735334157944,
-0.1388259083032608,
-0.009806054644286633,
0.024677321314811707,
0.3298868238925934,
0.05670393630862236,
-0.31497424840927124,
-0.09507177025079727,
0.02174268290400505,
-0.06215518340468407,
-0.06499336659908295,
0.022186091169714928,
0.09895628690719604,
-0.10124213248491287,
0.0441211573779583,
-0.10519911348819733,
0.10491138696670532,
-0.04454928636550903,
-0.029727501794695854,
0.07559027522802353,
0.09363100677728653,
-0.014515696093440056,
0.08712863177061081,
-0.2681795656681061,
0.29141899943351746,
-0.011926659382879734,
0.08119599521160126,
-0.033651333302259445,
0.038189105689525604,
0.047514162957668304,
-0.0034711158368736506,
0.06668742746114731,
-0.017303338274359703,
-0.13789977133274078,
-0.18737471103668213,
-0.08819615840911865,
0.025317685678601265,
0.11182626336812973,
-0.0725744292140007,
0.13060355186462402,
-0.04352307692170143,
0.0046850829385221004,
0.058523908257484436,
-0.08291144669055939,
-0.115992471575737,
-0.11291880160570145,
0.013337901793420315,
0.00010007365926867351,
0.07287295162677765,
-0.14069975912570953,
-0.1056937649846077,
-0.0527849905192852,
0.19825509190559387,
-0.06698906421661377,
-0.010433921590447426,
-0.1444564312696457,
0.09893561154603958,
0.12568050622940063,
-0.07075372338294983,
0.04724239557981491,
0.0053368257358670235,
0.15066958963871002,
0.03376724198460579,
-0.01655338518321514,
0.11096172034740448,
-0.0828658789396286,
-0.1863107532262802,
-0.06355605274438858,
0.14045841991901398,
0.025172865018248558,
0.04549701511859894,
-0.00767270615324378,
0.009726570919156075,
-0.015121303498744965,
-0.08489800989627838,
0.044128645211458206,
0.010501840151846409,
0.02711225487291813,
0.053334370255470276,
-0.054349664598703384,
0.020803391933441162,
-0.05207189545035362,
-0.06234249472618103,
0.13862000405788422,
0.2736138701438904,
-0.08235758543014526,
-0.00024498600396327674,
0.00849596131592989,
-0.0584222711622715,
-0.15883587300777435,
0.06377537548542023,
0.11354941874742508,
0.02832033671438694,
0.016127808019518852,
-0.20343950390815735,
0.07780881971120834,
0.10840898007154465,
-0.03624006733298302,
0.09891446679830551,
-0.29859140515327454,
-0.12346598505973816,
0.10911135375499725,
0.142504021525383,
-0.019891301169991493,
-0.18225891888141632,
-0.06953975558280945,
-0.013392356224358082,
-0.07957318425178528,
0.07651625573635101,
-0.05208013579249382,
0.10016054660081863,
-0.021419264376163483,
0.03135630115866661,
0.021120896562933922,
-0.061992347240448,
0.16640621423721313,
-0.04675284028053284,
0.08913163095712662,
-0.021520452573895454,
0.08168355375528336,
0.06311789900064468,
-0.08174841850996017,
0.03135021775960922,
-0.08084742724895477,
0.02312338910996914,
-0.15501104295253754,
-0.0325048454105854,
-0.07157678157091141,
0.04101449251174927,
-0.04177657514810562,
-0.0430811308324337,
-0.0278170183300972,
0.06170599162578583,
0.04528951272368431,
0.000012956372302141972,
0.15024694800376892,
-0.036107201129198074,
0.1680540293455124,
0.06764136999845505,
0.09281858801841736,
-0.01845329813659191,
-0.07980529963970184,
-0.010827556252479553,
-0.015676574781537056,
0.058796513825654984,
-0.15007297694683075,
0.03925630822777748,
0.13511501252651215,
0.018517665565013885,
0.15781697630882263,
0.06677187234163284,
-0.07537122815847397,
0.030516749247908592,
0.09659188240766525,
-0.07284757494926453,
-0.11750996112823486,
-0.028879215940833092,
0.10570140182971954,
-0.1695939600467682,
0.0260472372174263,
0.09658436477184296,
-0.058402881026268005,
-0.010946765542030334,
0.008744141086935997,
0.01994253136217594,
-0.03108777478337288,
0.18122923374176025,
0.06178775057196617,
0.0790938287973404,
-0.060646165162324905,
0.09628438949584961,
0.06500820070505142,
-0.15308482944965363,
0.0049054683186113834,
0.06529934704303741,
-0.0374029278755188,
-0.02055235393345356,
0.04005306586623192,
0.11838943511247635,
-0.012529385276138783,
-0.07451988011598587,
-0.13398905098438263,
-0.1354500949382782,
0.06378520280122757,
0.08409317582845688,
0.042262788861989975,
0.02035757713019848,
-0.010533955879509449,
0.02857368066906929,
-0.11323626339435577,
0.11042438447475433,
0.09450565278530121,
0.10290732234716415,
-0.16590771079063416,
0.1251623034477234,
0.0038711554370820522,
0.005141140893101692,
0.004279371351003647,
0.007396301254630089,
-0.10222157835960388,
0.004488620441406965,
-0.11374836415052414,
-0.014735476113855839,
-0.06312943994998932,
-0.005319399293512106,
0.01317913830280304,
-0.05314015597105026,
-0.061037369072437286,
0.016217967495322227,
-0.11387680470943451,
-0.052116647362709045,
0.0019027885282412171,
0.06692595034837723,
-0.10426221042871475,
-0.029968544840812683,
0.051178377121686935,
-0.10838950425386429,
0.07174772024154663,
0.023257920518517494,
0.05545484647154808,
0.029612192884087563,
-0.12544193863868713,
0.058067772537469864,
0.033428262919187546,
-0.014424951747059822,
0.021262872964143753,
-0.15751652419567108,
0.0033079187851399183,
-0.01196280587464571,
0.02304196171462536,
-0.005516878329217434,
0.011624058708548546,
-0.1499217301607132,
-0.0524633526802063,
-0.02627444826066494,
-0.05288395285606384,
-0.05917413905262947,
0.05941179767251015,
0.051077987998723984,
0.042357832193374634,
0.17495526373386383,
-0.08798490464687347,
0.027116568759083748,
-0.2282446026802063,
0.01600026711821556,
-0.033273935317993164,
-0.0638202652335167,
-0.040310829877853394,
-0.02499706856906414,
0.061100322753190994,
-0.06752531230449677,
0.08866365253925323,
-0.032834090292453766,
0.03719931095838547,
0.038311149924993515,
-0.11618717759847641,
0.09575385600328445,
0.04933817312121391,
0.23744244873523712,
0.033907048404216766,
-0.017379671335220337,
0.07233522832393646,
0.01961355283856392,
0.061821188777685165,
0.12180939316749573,
0.1568680852651596,
0.17778107523918152,
0.0009180268971249461,
0.09974931180477142,
0.03545219451189041,
-0.0896545946598053,
-0.11289329081773758,
0.12255623191595078,
-0.01682106778025627,
0.10438887774944305,
0.00021466171892825514,
0.21870407462120056,
0.16192367672920227,
-0.2026865929365158,
0.029855184257030487,
-0.021672323346138,
-0.08107643574476242,
-0.0923963412642479,
-0.08589832484722137,
-0.08957504481077194,
-0.1893749088048935,
0.002938266610726714,
-0.11592885851860046,
0.02079305797815323,
0.05585998669266701,
0.021637462079524994,
0.017951494082808495,
0.13730977475643158,
0.0590573325753212,
0.012403979897499084,
0.10246624797582626,
0.002569357631728053,
-0.007342181634157896,
-0.02839335799217224,
-0.09758936613798141,
0.024025429040193558,
-0.04685809835791588,
0.040561627596616745,
-0.054387543350458145,
-0.0678338035941124,
0.0621560700237751,
0.016543539240956306,
-0.10544592142105103,
0.01598135009407997,
-0.008666926994919777,
0.05497221276164055,
0.07489506155252457,
0.03313704952597618,
0.0016229077009484172,
-0.029102101922035217,
0.2871396243572235,
-0.09000937640666962,
-0.025378787890076637,
-0.14630669355392456,
0.20852944254875183,
0.018703147768974304,
-0.025609184056520462,
0.01457236334681511,
-0.08905229717493057,
0.032365307211875916,
0.1392330676317215,
0.11437255889177322,
-0.01583925448358059,
-0.014328490942716599,
-0.00942193903028965,
-0.02578699216246605,
-0.05748696252703667,
0.08998259156942368,
0.11111582070589066,
0.02385312132537365,
-0.07738211005926132,
-0.05862320587038994,
-0.04998033866286278,
-0.027442464604973793,
-0.04159430414438248,
0.08287565410137177,
0.03141971305012703,
-0.00039003125857561827,
-0.02724393643438816,
0.11006228625774384,
-0.016358768567442894,
-0.07508442550897598,
0.03317840397357941,
-0.1468975692987442,
-0.18645650148391724,
-0.05221487954258919,
0.05621008947491646,
-0.012634544633328915,
0.04968639835715294,
-0.01627233810722828,
-0.016081728041172028,
0.07700630277395248,
-0.0014146536123007536,
-0.04083316773176193,
-0.1269409954547882,
0.08696940541267395,
-0.06450143456459045,
0.23702405393123627,
-0.04137818515300751,
-0.024042943492531776,
0.13728481531143188,
0.03731068968772888,
-0.10548074543476105,
0.05083358287811279,
0.06391480565071106,
-0.08363668620586395,
0.06339425593614578,
0.1741754561662674,
-0.0290694423019886,
0.1464516520500183,
0.04634895175695419,
-0.11653777211904526,
0.018449140712618828,
-0.13230034708976746,
-0.06343729794025421,
-0.07183463126420975,
-0.0022268034517765045,
-0.05047937482595444,
0.12689320743083954,
0.21359047293663025,
-0.06842144578695297,
-0.012218997813761234,
-0.06423889845609665,
0.02995813637971878,
0.04391191527247429,
0.11722763627767563,
-0.016582181677222252,
-0.24672742187976837,
0.017359642311930656,
0.048876769840717316,
0.007982922717928886,
-0.28973832726478577,
-0.0894814282655716,
0.026506610214710236,
-0.06259521096944809,
-0.06147143244743347,
0.12023202329874039,
0.09492634981870651,
0.055212799459695816,
-0.06688057631254196,
-0.09507131576538086,
-0.05898350477218628,
0.17991046607494354,
-0.1499050408601761,
-0.06234881654381752
] |
null | null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bn-adapter-895K-squad-model3
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 4
- seed: 9
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["varun-v-rao/squad"], "base_model": "roberta-base", "model-index": [{"name": "roberta-base-bn-adapter-895K-squad-model3", "results": []}]} | null | varun-v-rao/roberta-base-bn-adapter-895K-squad-model3 | [
"tensorboard",
"generated_from_trainer",
"dataset:varun-v-rao/squad",
"base_model:roberta-base",
"license:mit",
"region:us"
] | 2024-02-09T12:44:51+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #dataset-varun-v-rao/squad #base_model-roberta-base #license-mit #region-us
|
# roberta-base-bn-adapter-895K-squad-model3
This model is a fine-tuned version of roberta-base on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 4
- seed: 9
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| [
"# roberta-base-bn-adapter-895K-squad-model3\n\nThis model is a fine-tuned version of roberta-base on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 4\n- seed: 9\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
"TAGS\n#tensorboard #generated_from_trainer #dataset-varun-v-rao/squad #base_model-roberta-base #license-mit #region-us \n",
"# roberta-base-bn-adapter-895K-squad-model3\n\nThis model is a fine-tuned version of roberta-base on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 4\n- seed: 9\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
44,
39,
6,
12,
8,
3,
90,
4,
33
] | [
"passage: TAGS\n#tensorboard #generated_from_trainer #dataset-varun-v-rao/squad #base_model-roberta-base #license-mit #region-us \n# roberta-base-bn-adapter-895K-squad-model3\n\nThis model is a fine-tuned version of roberta-base on the squad dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 4\n- seed: 9\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0"
] | [
-0.10846013575792313,
0.10270270705223083,
-0.0021371962502598763,
0.08170457184314728,
0.1663026064634323,
0.026862064376473427,
0.13337302207946777,
0.10579714924097061,
-0.12530499696731567,
0.04702150449156761,
0.09610100090503693,
0.03063659742474556,
0.04135065898299217,
0.12705877423286438,
-0.0038720369338989258,
-0.2336302399635315,
0.011466742493212223,
0.013073084875941277,
-0.10659381002187729,
0.11119503527879715,
0.11749115586280823,
-0.08891646564006805,
0.06342417001724243,
0.013970010913908482,
-0.22331802546977997,
0.024945612996816635,
-0.0016673520440235734,
-0.06045961380004883,
0.10400111228227615,
0.004628606140613556,
0.13185419142246246,
0.010589594952762127,
0.1276800036430359,
-0.13056336343288422,
0.012730579823255539,
0.05222300440073013,
0.02480323612689972,
0.11045490205287933,
0.004163515288382769,
0.011982923373579979,
0.13086093962192535,
-0.08106882125139236,
0.07366824895143509,
0.02899438887834549,
-0.09518466889858246,
-0.18837876617908478,
-0.09839826822280884,
0.09760289639234543,
0.054560013115406036,
0.08290281891822815,
0.0010784459300339222,
0.15175706148147583,
-0.09626036137342453,
0.05912626162171364,
0.21659067273139954,
-0.3038804531097412,
-0.08098336309194565,
0.057515714317560196,
0.06086689233779907,
0.07063823193311691,
-0.09760476648807526,
-0.031511224806308746,
0.04219585657119751,
0.03779969364404678,
0.08381642401218414,
-0.006020144559442997,
-0.0627250149846077,
-0.007004964631050825,
-0.13126695156097412,
-0.04147755727171898,
0.20376275479793549,
0.03020702861249447,
-0.06112920120358467,
-0.009326925501227379,
-0.05036719888448715,
-0.09976551681756973,
-0.017643077298998833,
-0.017397256568074226,
0.008630255237221718,
-0.05240889638662338,
-0.12480127066373825,
-0.06966692209243774,
-0.08906074613332748,
-0.08935655653476715,
-0.005483807530254126,
0.14586161077022552,
0.05864224582910538,
0.041679222136735916,
-0.047182727605104446,
0.1362602263689041,
-0.005606557708233595,
-0.11005683988332748,
0.004510513041168451,
-0.013774114660918713,
-0.06204177439212799,
-0.032229408621788025,
-0.046413909643888474,
-0.006591466721147299,
0.013766269199550152,
0.19226494431495667,
-0.03342268615961075,
0.01588648371398449,
0.02520139515399933,
0.032425906509160995,
-0.040169306099414825,
0.14763298630714417,
-0.09115813672542572,
-0.024537337943911552,
0.04231777787208557,
0.09667906165122986,
0.01896144635975361,
0.00245350762270391,
-0.10172240436077118,
-0.05669304355978966,
0.1070573702454567,
0.04951747879385948,
-0.03770192712545395,
0.01776675321161747,
-0.0020056001376360655,
-0.035793572664260864,
0.04633123427629471,
-0.10132892429828644,
-0.0020031353924423456,
-0.004171096254140139,
-0.09663345664739609,
-0.03916654735803604,
0.03637510538101196,
-0.007849432528018951,
-0.014119279570877552,
0.03169213980436325,
-0.10511331260204315,
-0.018202533945441246,
-0.08752134442329407,
-0.08704359829425812,
-0.001540647353976965,
-0.13028006255626678,
0.010861632414162159,
-0.0988619476556778,
-0.14327126741409302,
-0.005610746331512928,
0.018653323873877525,
-0.06847066432237625,
-0.06544693559408188,
-0.020544545724987984,
-0.0766550600528717,
0.005012530833482742,
-0.006908124778419733,
0.15108339488506317,
-0.02166551910340786,
0.11048591136932373,
0.05872613191604614,
0.027891427278518677,
-0.03973625600337982,
0.03991931304335594,
-0.08055408298969269,
0.03674729913473129,
-0.15364930033683777,
0.043716080486774445,
-0.07697490602731705,
0.03178350627422333,
-0.11829627305269241,
-0.10068961977958679,
0.010670073330402374,
-0.033296436071395874,
0.11182714253664017,
0.09125325828790665,
-0.11086787283420563,
-0.03635438159108162,
0.1409534364938736,
-0.08670318126678467,
-0.06781110912561417,
0.10092044621706009,
-0.04895796254277229,
0.055078938603401184,
0.04223403334617615,
0.14023035764694214,
0.09834884107112885,
-0.08627496659755707,
-0.02925792522728443,
0.008244833908975124,
0.045236434787511826,
-0.07797517627477646,
0.10364563763141632,
0.02035752311348915,
-0.01009440328925848,
0.03332151472568512,
-0.0756918266415596,
0.047634415328502655,
-0.1116124764084816,
-0.07922545075416565,
-0.051677606999874115,
-0.09290614724159241,
0.04429467022418976,
0.03117957152426243,
0.05215516686439514,
-0.056010451167821884,
-0.10417603701353073,
0.08359984308481216,
0.16458667814731598,
-0.04188156872987747,
0.013984136283397675,
-0.09546616673469543,
0.11661344021558762,
-0.12009317427873611,
-0.021859217435121536,
-0.20654438436031342,
-0.07236722111701965,
0.024480702355504036,
0.025930648669600487,
0.06112643703818321,
0.013475163839757442,
0.0556836761534214,
0.06775524467229843,
-0.03187418729066849,
-0.01978382095694542,
-0.12105625122785568,
-0.02880864217877388,
-0.09857694804668427,
-0.17626667022705078,
-0.09368517249822617,
-0.03470692038536072,
0.17964114248752594,
-0.19935108721256256,
0.02736908383667469,
-0.018145641312003136,
0.13236959278583527,
0.02163679525256157,
-0.03576963022351265,
-0.014248177409172058,
0.04822428524494171,
-0.01481043640524149,
-0.08267686516046524,
0.043312933295965195,
0.018076781183481216,
-0.08274286240339279,
-0.05283921957015991,
-0.09064997732639313,
0.055848829448223114,
0.09565852582454681,
0.05736739560961723,
-0.08444815874099731,
0.019701819866895676,
-0.08352776616811752,
-0.03807235509157181,
-0.017626596614718437,
-0.002805355004966259,
0.13722145557403564,
0.0059535913169384,
0.14187245070934296,
-0.07582565397024155,
-0.03895684331655502,
0.03278213366866112,
-0.01322074793279171,
0.02418108657002449,
0.08430346846580505,
0.09934323281049728,
-0.12357912957668304,
0.08131818473339081,
0.08593744039535522,
-0.09053456783294678,
0.12760263681411743,
-0.04483133926987648,
-0.09705732762813568,
-0.04714938998222351,
-0.004371561110019684,
0.008347386494278908,
0.16851826012134552,
-0.08039726316928864,
-0.012722824700176716,
0.037342894822359085,
-0.0028291388880461454,
0.04081842675805092,
-0.17943240702152252,
-0.02039504610002041,
0.003517820267006755,
-0.030516115948557854,
-0.01485423929989338,
0.005169360898435116,
0.007559482008218765,
0.10214056819677353,
0.032720182090997696,
-0.04822618514299393,
0.02277546562254429,
0.010671577416360378,
-0.056150443851947784,
0.18896746635437012,
-0.0681099146604538,
-0.0970638319849968,
-0.1508011668920517,
0.02588195912539959,
-0.04965971037745476,
-0.021882200613617897,
0.010515031404793262,
-0.07792884856462479,
-0.015295125544071198,
-0.0622071735560894,
0.016814157366752625,
-0.05112374201416969,
0.01079191267490387,
0.006741019897162914,
0.03644050657749176,
0.10030843317508698,
-0.13589325547218323,
0.03067934326827526,
-0.05836695805191994,
-0.12908963859081268,
0.0012403714936226606,
0.05034436658024788,
0.11259531229734421,
0.093338243663311,
-0.03750086948275566,
0.013276760466396809,
-0.03118591196835041,
0.22902174293994904,
-0.056674934923648834,
-0.027884358540177345,
0.14474819600582123,
0.007437112741172314,
0.038836896419525146,
0.1024814248085022,
0.03652386739850044,
-0.06000113859772682,
0.01175597868859768,
0.03864496573805809,
-0.045716024935245514,
-0.26105624437332153,
-0.018169796094298363,
-0.04105391353368759,
-0.05445832759141922,
0.09026502817869186,
0.048440832644701004,
-0.01610376127064228,
0.08642826229333878,
-0.018257388845086098,
0.08828014135360718,
-0.06491200625896454,
0.08850104361772537,
0.07657856494188309,
0.0303566362708807,
0.10426760464906693,
-0.05320153385400772,
-0.04619815945625305,
0.04619744420051575,
0.041898272931575775,
0.30978602170944214,
-0.05400565639138222,
0.10242003202438354,
0.05476018413901329,
0.19407598674297333,
-0.00682693300768733,
0.05084015056490898,
-0.005286046769469976,
0.004837604705244303,
-0.008646981790661812,
-0.04117120802402496,
-0.027248341590166092,
-0.01065721083432436,
-0.029651187360286713,
0.07479092478752136,
-0.09183856844902039,
0.041101351380348206,
0.0008971659699454904,
0.2543950080871582,
0.03453866019845009,
-0.3026635944843292,
-0.11953293532133102,
-0.011904869228601456,
-0.008285243064165115,
-0.04583245888352394,
0.004557708278298378,
0.11087506264448166,
-0.11382882297039032,
-0.028391029685735703,
-0.0603804886341095,
0.08327856659889221,
-0.034353986382484436,
-0.01567898690700531,
0.027934985235333443,
0.1630910187959671,
0.006411596201360226,
0.0928959846496582,
-0.1846851110458374,
0.23806044459342957,
0.011108086444437504,
0.09928484261035919,
-0.023095445707440376,
0.013892317190766335,
0.009664679877460003,
0.05999757722020149,
0.06780681014060974,
-0.005477285012602806,
-0.026161212474107742,
-0.17700324952602386,
-0.1002279669046402,
0.04362314194440842,
0.08581697195768356,
-0.04702020436525345,
0.11256301403045654,
-0.04843878373503685,
0.02320685423910618,
0.033980339765548706,
-0.06018707528710365,
-0.1490258425474167,
-0.0975910946726799,
-0.004055645316839218,
-0.007611038163304329,
-0.06850002706050873,
-0.09341000020503998,
-0.0964544489979744,
0.016375910490751266,
0.15284159779548645,
-0.030441556125879288,
-0.03411173075437546,
-0.12145581096410751,
0.134883314371109,
0.14402762055397034,
-0.08276239037513733,
0.015359241515398026,
-0.011141272261738777,
0.1041109710931778,
0.014674938283860683,
-0.07802235335111618,
0.0395578034222126,
-0.054955095052719116,
-0.1675923615694046,
-0.040226276963949203,
0.12428846955299377,
0.0361342579126358,
0.05225428193807602,
-0.007710803300142288,
0.0075027188286185265,
-0.00536796310916543,
-0.0811513215303421,
0.012927650474011898,
0.01792457513511181,
0.029283247888088226,
0.03748675808310509,
-0.060645394027233124,
0.013472078368067741,
-0.05029290169477463,
-0.0033741844817996025,
0.13892009854316711,
0.2264685481786728,
-0.09388429671525955,
0.05090077221393585,
0.0886792242527008,
-0.06460164487361908,
-0.1698395311832428,
0.03261086344718933,
0.1029030829668045,
0.03901422396302223,
0.02051176130771637,
-0.20123723149299622,
0.08546479791402817,
0.09586804360151291,
-0.02863515540957451,
0.04921282082796097,
-0.29096555709838867,
-0.1128968894481659,
0.08511704206466675,
0.13408705592155457,
0.10930079966783524,
-0.12425155192613602,
-0.033815667033195496,
-0.010488303378224373,
-0.14864221215248108,
0.09965019673109055,
-0.1361907422542572,
0.12172986567020416,
-0.032312583178281784,
0.1026250571012497,
0.012806281447410583,
-0.04401526600122452,
0.13114723563194275,
0.01129374373704195,
0.06880169361829758,
-0.040218956768512726,
-0.0489853173494339,
0.1733577400445938,
-0.04614530876278877,
0.03644149750471115,
-0.018139608204364777,
0.07245545834302902,
-0.13888919353485107,
-0.011206652037799358,
-0.07740821689367294,
0.05393018200993538,
-0.0477629080414772,
-0.05086475610733032,
-0.052533045411109924,
0.06348181515932083,
-0.018428858369588852,
-0.020270921289920807,
0.06806741654872894,
0.03985583409667015,
0.09183959662914276,
0.07826603949069977,
0.051927756518125534,
0.05215568467974663,
-0.0554339624941349,
0.0006294376798905432,
-0.02006836235523224,
0.08275290578603745,
-0.15360203385353088,
-0.007755737751722336,
0.10453528165817261,
0.038433291018009186,
0.11026343703269958,
0.04845872148871422,
-0.09286941587924957,
0.053496092557907104,
0.05710022896528244,
-0.10686170309782028,
-0.12049881368875504,
-0.014044654555618763,
-0.030376853421330452,
-0.15977619588375092,
0.06169430539011955,
0.12619554996490479,
-0.08785392343997955,
-0.04317130520939827,
-0.017990902066230774,
-0.01623024418950081,
-0.0631445050239563,
0.17592760920524597,
0.09033619612455368,
0.0636725053191185,
-0.08692894130945206,
0.09790978580713272,
0.08520730584859848,
-0.008721434511244297,
0.03811124339699745,
0.057773981243371964,
-0.0948590338230133,
-0.016134116798639297,
0.0035669440403580666,
0.159073606133461,
-0.08287528157234192,
-0.0356624461710453,
-0.13673631846904755,
-0.07315432280302048,
0.04558306559920311,
0.09257245808839798,
0.06595702469348907,
0.0040615517646074295,
-0.04633191227912903,
0.03723860904574394,
-0.16255010664463043,
0.0866151973605156,
0.042070332914590836,
0.07200649380683899,
-0.14057707786560059,
0.0953550785779953,
0.012103014625608921,
0.06305298954248428,
-0.017996538430452347,
0.004411260597407818,
-0.1010495126247406,
-0.004969978239387274,
-0.12660740315914154,
-0.013686638325452805,
-0.0024522508028894663,
0.005186388269066811,
-0.01737285777926445,
-0.06632669270038605,
-0.06394043564796448,
0.05526071414351463,
-0.07853078842163086,
-0.0424642488360405,
0.019862286746501923,
0.034758310765028,
-0.12581801414489746,
0.010727409273386002,
0.020428050309419632,
-0.08348102122545242,
0.10061090439558029,
0.07748444378376007,
0.059703659266233444,
0.03987254947423935,
-0.07880701124668121,
-0.01593191549181938,
-0.012176773510873318,
0.00991295650601387,
0.0679842084646225,
-0.06905610859394073,
0.017290940508246422,
-0.03696058318018913,
0.05667810142040253,
0.0014422155218198895,
0.06160343065857887,
-0.13274525105953217,
-0.03126007318496704,
-0.061593759804964066,
-0.028211165219545364,
-0.07308240234851837,
0.05700939521193504,
0.0824022963643074,
0.08356621861457825,
0.13532643020153046,
-0.066214919090271,
0.02429088205099106,
-0.20829245448112488,
-0.01948612369596958,
-0.025635482743382454,
-0.04405920207500458,
-0.11645541340112686,
-0.04735679551959038,
0.05442463606595993,
-0.04770510271191597,
0.07784337550401688,
-0.012227094732224941,
0.08128650486469269,
0.023751109838485718,
-0.026730498299002647,
-0.004790072329342365,
0.010307998396456242,
0.1757374405860901,
0.06864067167043686,
-0.0017000080551952124,
0.06116875261068344,
0.0011981766438111663,
0.052699729800224304,
0.05759136751294136,
0.12921907007694244,
0.13688255846500397,
0.027784153819084167,
0.06729789078235626,
0.10654663294553757,
-0.05940734222531319,
-0.11046512424945831,
0.11021057516336441,
-0.009339543990790844,
0.0505046509206295,
-0.029900506138801575,
0.16141951084136963,
0.16772887110710144,
-0.1385086625814438,
0.021573856472969055,
-0.03779635205864906,
-0.08896134793758392,
-0.0845920667052269,
-0.04026113077998161,
-0.07251040637493134,
-0.111909419298172,
0.04034557566046715,
-0.11313623934984207,
-0.03239747881889343,
0.10612625628709793,
-0.0033600912429392338,
-0.005815508775413036,
0.12577536702156067,
0.051155511289834976,
0.019592082127928734,
0.035847507417201996,
0.0022716373205184937,
-0.019261879846453667,
-0.04083416238427162,
-0.05262095481157303,
0.07071249932050705,
0.0017109151231124997,
0.08394304662942886,
-0.04815854877233505,
-0.0042922478169202805,
0.051163122057914734,
-0.0035036744084209204,
-0.07704465836286545,
0.018393533304333687,
0.024645086377859116,
0.05989620089530945,
0.052816905081272125,
0.029122741892933846,
0.001078814617358148,
-0.04347744211554527,
0.258449912071228,
-0.048531562089920044,
-0.08311611413955688,
-0.13545969128608704,
0.19276179373264313,
0.017859546467661858,
-0.04540451616048813,
0.044320423156023026,
-0.10242315381765366,
-0.006109236273914576,
0.1647089719772339,
0.19347576797008514,
-0.051015399396419525,
-0.028738318011164665,
0.015476646833121777,
-0.030005021020770073,
-0.0696631371974945,
0.13672354817390442,
0.09236632287502289,
0.03769714757800102,
-0.07748380303382874,
-0.030440807342529297,
-0.045037638396024704,
-0.010655635967850685,
-0.03219977766275406,
0.04970439150929451,
0.024660538882017136,
0.009106849320232868,
-0.05730993300676346,
0.049821577966213226,
-0.020589740946888924,
-0.19829833507537842,
0.05706067383289337,
-0.13188989460468292,
-0.1894870549440384,
-0.038777194917201996,
0.06645169109106064,
-0.0015256092883646488,
0.07209678739309311,
-0.04896792769432068,
0.008844943717122078,
0.10840209573507309,
-0.019444240257143974,
-0.07078032940626144,
-0.09768329560756683,
0.1188560426235199,
-0.028361832723021507,
0.19323644042015076,
-0.043091848492622375,
0.0803663432598114,
0.12132550030946732,
0.022981787100434303,
-0.15417920053005219,
0.01159390527755022,
0.05164717510342598,
-0.034523990005254745,
0.012907956726849079,
0.15895278751850128,
-0.030461296439170837,
0.0871613621711731,
0.024843720719218254,
-0.09703744947910309,
-0.01646086387336254,
-0.06164633855223656,
0.0015305784763768315,
-0.07329637557268143,
0.0007289389031939209,
-0.06470731645822525,
0.1537894904613495,
0.19568508863449097,
-0.05630063638091087,
-0.016098402440547943,
-0.10370743274688721,
0.027332907542586327,
0.08385281264781952,
0.06909111142158508,
-0.014622842893004417,
-0.19154050946235657,
-0.0019406155915930867,
0.0508495531976223,
-0.00151126179844141,
-0.2941425144672394,
-0.06808284670114517,
0.03267626464366913,
-0.05817705765366554,
-0.06632880866527557,
0.1074105054140091,
0.07190144062042236,
0.06869903951883316,
-0.03667159005999565,
-0.07299158722162247,
-0.08722735941410065,
0.12336784601211548,
-0.17148813605308533,
-0.07529618591070175
] |
null | null | null |
## Exllama v2 Quantizations of Trinity-33B-v1.0
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.13">turboderp's ExLlamaV2 v0.0.13</a> for quantization.
<b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Original model: https://huggingface.co/WhiteRabbitNeo/Trinity-33B-v1.0
| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description |
| ------ | ---- | ------------ | ---- | ---- | ---- | ----------- |
| [6_5](https://huggingface.co/bartowski/Trinity-33B-v1.0-exl2/tree/6_5) | 6.5 | 8.0 | 28.9 GB | 31.6 GB | 35.6 GB | Near unquantized performance at vastly reduced size, **recommended**. |
| [4_25](https://huggingface.co/bartowski/Trinity-33B-v1.0-exl2/tree/4_25) | 4.25 | 6.0 | 19.5 GB | 22.2 GB | 26.2 GB | GPTQ equivalent bits per weight, slightly higher quality. |
| [3_5](https://huggingface.co/bartowski/Trinity-33B-v1.0-exl2/tree/3_5) | 3.5 | 6.0 | 16.5 GB | 19.2 GB | 23.2 GB | Lower quality, only use if you have to. |
| [3_0](https://huggingface.co/bartowski/Trinity-33B-v1.0-exl2/tree/3_0) | 3.0 | 6.0 | 14.3 GB | 17.0 GB | 21.0 GB | Very low quality, usable with 16gb of VRAM. |
| [2_4](https://huggingface.co/bartowski/Trinity-33B-v1.0-exl2/tree/3_0) | 2.4 | 6.0 | 12.0 GB | 14.7 GB | 18.7 GB | Extremely low quality, only recommended if trying to fit into 12GB with under 4k context. |
## Download instructions
With git:
```shell
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Trinity-33B-v1.0-exl2 Trinity-33B-v1.0-exl2-6_5
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `Trinity-33B-v1.0-exl2`:
```shell
mkdir Trinity-33B-v1.0-exl2
huggingface-cli download bartowski/Trinity-33B-v1.0-exl2 --local-dir Trinity-33B-v1.0-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
Linux:
```shell
mkdir Trinity-33B-v1.0-exl2-6_5
huggingface-cli download bartowski/Trinity-33B-v1.0-exl2 --revision 6_5 --local-dir Trinity-33B-v1.0-exl2-6_5 --local-dir-use-symlinks False
```
Windows (which apparently doesn't like _ in folders sometimes?):
```shell
mkdir Trinity-33B-v1.0-exl2-6.5
huggingface-cli download bartowski/Trinity-33B-v1.0-exl2 --revision 6_5 --local-dir Trinity-33B-v1.0-exl2-6.5 --local-dir-use-symlinks False
```
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
| {"license": "other", "license_name": "deepseek-coder-33b", "license_link": "https://huggingface.co/deepseek-ai/deepseek-coder-33b-base/blob/main/LICENSE", "quantized_by": "bartowski", "pipeline_tag": "text-generation"} | text-generation | bartowski/Trinity-33B-v1.0-exl2 | [
"text-generation",
"license:other",
"region:us"
] | 2024-02-09T12:47:28+00:00 | [] | [] | TAGS
#text-generation #license-other #region-us
| Exllama v2 Quantizations of Trinity-33B-v1.0
--------------------------------------------
Using <a href="URL ExLlamaV2 v0.0.13 for quantization.
**The "main" branch only contains the URL, download one of the other branches for the model (see below)**
Each branch contains an individual bits per weight, with the main one containing only the URL for further conversions.
Original model: URL
Download instructions
---------------------
With git:
With huggingface hub (credit to TheBloke for instructions):
To download the 'main' (only useful if you only care about URL) branch to a folder called 'Trinity-33B-v1.0-exl2':
To download from a different branch, add the '--revision' parameter:
Linux:
Windows (which apparently doesn't like \_ in folders sometimes?):
Want to support my work? Visit my ko-fi page here: URL
| [] | [
"TAGS\n#text-generation #license-other #region-us \n"
] | [
16
] | [
"passage: TAGS\n#text-generation #license-other #region-us \n"
] | [
0.051113277673721313,
0.07280271500349045,
-0.00824133213609457,
-0.029258456081151962,
0.05186934024095535,
0.05759657546877861,
0.26173263788223267,
0.052013128995895386,
0.16024252772331238,
-0.07162679731845856,
0.12387923151254654,
0.07674521952867508,
-0.010968969203531742,
-0.01746911182999611,
-0.008756989613175392,
-0.22287139296531677,
0.0332915298640728,
-0.041559215635061264,
0.08592100441455841,
0.031569451093673706,
0.015975257381796837,
-0.03128349781036377,
0.04749593883752823,
-0.019296273589134216,
-0.0547066330909729,
0.032732438296079636,
0.018436387181282043,
-0.0695817768573761,
0.11244364827871323,
0.025361454114317894,
0.08041546493768692,
0.04953852668404579,
-0.012904427945613861,
-0.23848390579223633,
0.01538369245827198,
-0.0567067451775074,
-0.14665944874286652,
0.012070606462657452,
0.11147847026586533,
-0.061152487993240356,
0.13792045414447784,
0.1737603396177292,
-0.0370553657412529,
0.08578582853078842,
-0.217189222574234,
-0.1445237100124359,
-0.053230296820402145,
-0.032334793359041214,
-0.0013099279021844268,
0.04982665181159973,
0.031117966398596764,
0.07717971503734589,
-0.10258638858795166,
0.002489553764462471,
0.07947617024183273,
-0.3823585510253906,
0.07169760763645172,
0.2283337116241455,
0.10137321054935455,
0.1176414042711258,
-0.08240504562854767,
0.1276022344827652,
0.0662834420800209,
-0.027175921946763992,
-0.11095832288265228,
-0.0720171257853508,
-0.005188196897506714,
0.13030612468719482,
-0.029301542788743973,
-0.09465056657791138,
0.3160237669944763,
-0.016224956139922142,
0.015271675772964954,
0.023694399744272232,
-0.010424119420349598,
0.006242743227630854,
0.03231469914317131,
0.09077271074056625,
0.009869622066617012,
0.18970972299575806,
0.15150140225887299,
-0.07814399898052216,
-0.1741591989994049,
-0.0247694943100214,
-0.22449561953544617,
0.09269101917743683,
-0.021027302369475365,
0.09018143266439438,
-0.1774020493030548,
0.018116425722837448,
-0.10580021142959595,
-0.02777145244181156,
-0.012132232077419758,
-0.10826212167739868,
0.07296755909919739,
0.004261552356183529,
-0.07343943417072296,
0.007886625826358795,
0.10201885551214218,
0.16097334027290344,
-0.018134545534849167,
-0.003449894953519106,
-0.14739274978637695,
0.18373046815395355,
0.07524828612804413,
-0.03453337401151657,
0.1257171332836151,
0.07018397003412247,
-0.024713413789868355,
-0.20516863465309143,
0.023036299273371696,
-0.054628968238830566,
-0.18145932257175446,
0.03261454403400421,
-0.17747576534748077,
0.12347130477428436,
-0.025986110791563988,
-0.016476087272167206,
-0.07626064121723175,
0.08179588615894318,
0.03667543828487396,
-0.009868769906461239,
-0.04568319022655487,
0.0036325708497315645,
0.03738816827535629,
-0.0043860869482159615,
-0.06194043904542923,
0.021748172119259834,
0.04213834926486015,
0.05603456497192383,
-0.09250175952911377,
-0.03715076297521591,
-0.02635471150279045,
0.02884920872747898,
0.10209540277719498,
-0.09726572036743164,
0.031015712767839432,
-0.05478508397936821,
-0.1821698546409607,
0.018972070887684822,
0.03591609001159668,
-0.01113453134894371,
0.052826762199401855,
-0.007784395013004541,
0.03258811682462692,
-0.019211703911423683,
-0.07846705615520477,
-0.07647065818309784,
-0.11812679469585419,
0.11317005008459091,
-0.10571415722370148,
-0.004494717810302973,
-0.2701650559902191,
-0.0008982017752714455,
-0.11863019317388535,
0.02906479313969612,
0.038022108376026154,
-0.01813790574669838,
-0.10403364896774292,
0.11481624841690063,
0.018568115308880806,
0.05824549123644829,
-0.13299670815467834,
0.08691953122615814,
-0.15734142065048218,
0.1283365786075592,
-0.16916656494140625,
-0.07631196081638336,
0.20524397492408752,
-0.10203299671411514,
-0.15625625848770142,
0.04170994088053703,
-0.027474243193864822,
0.10201277583837509,
0.06893005222082138,
0.41612130403518677,
-0.10395314544439316,
-0.08273299783468246,
0.11567328870296478,
0.20339176058769226,
-0.1102835163474083,
-0.13431215286254883,
0.09049348533153534,
-0.1184685081243515,
-0.10845805704593658,
0.018977632746100426,
0.0034886798821389675,
0.02323778346180916,
-0.02162548527121544,
-0.05909184738993645,
0.00801000650972128,
0.019373372197151184,
0.022377584129571915,
-0.0006920546293258667,
0.08470432460308075,
-0.05436784029006958,
0.044512789696455,
0.04089782387018204,
-0.008062923327088356,
0.08917564898729324,
0.014055371284484863,
0.0009400799754075706,
0.03900320082902908,
0.09713059663772583,
0.032443124800920486,
-0.10719595104455948,
-0.06988978385925293,
0.005831758491694927,
0.050291966646909714,
0.13584309816360474,
0.17612941563129425,
0.009959245100617409,
-0.04035770893096924,
0.011752489022910595,
0.0636662170290947,
0.050591349601745605,
0.013055491261184216,
0.032411716878414154,
-0.10684516280889511,
0.05765881389379501,
-0.03766191750764847,
-0.050920404493808746,
-0.11110757291316986,
-0.013807008974254131,
0.09427113085985184,
-0.044332779943943024,
-0.024098146706819534,
0.06753901392221451,
-0.026393771171569824,
0.009204035624861717,
-0.059557270258665085,
0.00891201850026846,
0.11837812513113022,
-0.042906470596790314,
-0.12022952735424042,
0.22972746193408966,
-0.11212354898452759,
0.13253340125083923,
0.17882251739501953,
-0.18217578530311584,
0.05405988544225693,
-0.19564756751060486,
-0.017932027578353882,
0.02679428458213806,
0.056160520762205124,
-0.022795427590608597,
0.017561059445142746,
-0.00632089888677001,
0.05738860368728638,
-0.08604484051465988,
-0.00505826948210597,
-0.06072509288787842,
-0.013321819715201855,
-0.10242893546819687,
0.07660426199436188,
0.22747068107128143,
-0.12425224483013153,
0.19238869845867157,
0.3892194330692291,
0.1676650047302246,
0.26362308859825134,
-0.12268207967281342,
-0.009216098114848137,
0.013546036556363106,
0.009355847723782063,
-0.036455295979976654,
0.05066202953457832,
-0.10135640949010849,
0.004963153041899204,
0.06392151862382889,
0.05706522613763809,
0.08463390171527863,
-0.16253194212913513,
-0.17021819949150085,
-0.03080006316304207,
-0.05405097454786301,
-0.15067742764949799,
0.060297269374132156,
-0.023324115201830864,
0.05560477823019028,
-0.03955763578414917,
-0.020785417407751083,
0.1483377367258072,
-0.026320308446884155,
-0.041783519089221954,
0.12454652786254883,
-0.17054054141044617,
-0.14009051024913788,
-0.21937227249145508,
-0.14806059002876282,
-0.02930319868028164,
0.07123185694217682,
0.10384712368249893,
-0.02189370058476925,
-0.026123011484742165,
0.00460547860711813,
-0.0008056126534938812,
-0.1523934304714203,
-0.06740467250347137,
-0.06429791450500488,
0.15101270377635956,
-0.1143912523984909,
-0.08738101273775101,
-0.04034104198217392,
-0.020819691941142082,
-0.03508289158344269,
0.09215255826711655,
-0.1155828982591629,
0.08634547889232635,
0.1235690712928772,
0.07498941570520401,
0.05444188043475151,
-0.09023185819387436,
0.13956019282341003,
-0.0792599692940712,
-0.10389330238103867,
0.12542828917503357,
-0.020058315247297287,
0.038570720702409744,
0.1677549183368683,
0.09100469201803207,
-0.13182814419269562,
-0.012684282846748829,
-0.05151563882827759,
-0.11626579612493515,
-0.2776685357093811,
-0.07961693406105042,
-0.0819220021367073,
0.15102580189704895,
0.013064580969512463,
0.10704316943883896,
0.11642955243587494,
-0.027703214436769485,
0.030562926083803177,
-0.021161040291190147,
0.08460357785224915,
0.05968623608350754,
0.21604979038238525,
-0.04026753827929497,
0.030423175543546677,
-0.11795999854803085,
-0.011704323813319206,
0.1330971121788025,
0.13934634625911713,
0.17068812251091003,
0.2776764929294586,
0.2027359902858734,
0.14586374163627625,
0.040925946086645126,
0.11567799001932144,
0.02521907165646553,
0.07925485074520111,
0.003445366397500038,
-0.07605946063995361,
-0.049234092235565186,
0.05948914214968681,
0.05621029809117317,
-0.021614382043480873,
-0.28480464220046997,
0.026623183861374855,
-0.2179100066423416,
0.008950499817728996,
-0.04815905541181564,
0.047702111303806305,
-0.007959261536598206,
0.15887567400932312,
0.09963452070951462,
0.05410312861204147,
-0.06358467787504196,
0.17280563712120056,
-0.00785810686647892,
-0.07394306361675262,
0.08640656620264053,
0.03742746636271477,
0.13141979277133942,
0.04538501799106598,
0.09633691608905792,
-0.1424817144870758,
-0.2101137638092041,
0.04085096716880798,
0.16390931606292725,
-0.17421174049377441,
0.32019275426864624,
0.016432035714387894,
-0.0805930569767952,
-0.06303185224533081,
-0.07582203298807144,
-0.02669195830821991,
0.1780855804681778,
0.13905420899391174,
0.0070540583692491055,
-0.19999825954437256,
-0.037003833800554276,
0.009432291612029076,
0.003801796119660139,
0.1480748951435089,
-0.0640290230512619,
-0.11209350824356079,
-0.04438619688153267,
0.052100688219070435,
-0.03326769173145294,
0.06123061850667,
-0.04626648500561714,
-0.17302148044109344,
-0.007822507992386818,
0.037032343447208405,
0.038436807692050934,
-0.029434943571686745,
0.06401388347148895,
-0.07786919176578522,
0.019633343443274498,
-0.12278435379266739,
0.021172670647501945,
-0.09150189161300659,
-0.1841532289981842,
0.03427326679229736,
0.007423440460115671,
-0.04157282039523125,
-0.0780501738190651,
-0.049627501517534256,
-0.18709217011928558,
-0.2156693935394287,
0.0762651190161705,
-0.015355338342487812,
0.013042271137237549,
-0.047672469168901443,
0.16922590136528015,
-0.09288360178470612,
0.023147594183683395,
0.014403603971004486,
0.01641439087688923,
-0.05183055251836777,
-0.12896092236042023,
0.05349568650126457,
-0.10350839793682098,
0.03209145739674568,
0.02126792073249817,
-0.13694500923156738,
0.014980402775108814,
0.04030625894665718,
-0.12208738178014755,
0.16527363657951355,
0.41341954469680786,
0.002004407113417983,
0.2869876027107239,
0.30313003063201904,
-0.18988239765167236,
-0.25911352038383484,
-0.1529323011636734,
-0.24904227256774902,
-0.09933856129646301,
0.2194807082414627,
-0.23590919375419617,
0.030198728665709496,
0.12825123965740204,
-0.07724212110042572,
0.19067294895648956,
-0.25205811858177185,
-0.05993475764989853,
0.08650796115398407,
-0.06583404541015625,
0.3914451003074646,
-0.14396697282791138,
-0.15762607753276825,
-0.07532897591590881,
-0.12155516445636749,
0.20231977105140686,
-0.06032327562570572,
0.10459005832672119,
0.017959753051400185,
0.006156530696898699,
-0.020413372665643692,
0.007687891833484173,
0.16557231545448303,
-0.02731385827064514,
0.12039194256067276,
-0.07805566489696503,
-0.06241440027952194,
0.23256750404834747,
0.025685781612992287,
-0.03536086529493332,
-0.15016105771064758,
-0.04722944647073746,
-0.0719594806432724,
-0.02089524269104004,
-0.04720233008265495,
0.08229599893093109,
-0.0012422397267073393,
-0.07447262108325958,
-0.06582468748092651,
-0.005101153161376715,
-0.1398579180240631,
-0.01918935775756836,
0.3671685457229614,
-0.04535582289099693,
0.1186198815703392,
0.07892081886529922,
0.04012256860733032,
-0.09970346838235855,
0.07172047346830368,
-0.11886956542730331,
-0.04559750109910965,
0.033694613724946976,
-0.16475287079811096,
-0.0399850532412529,
0.10029137134552002,
-0.0556812658905983,
0.08674351871013641,
0.09390465170145035,
-0.04871673509478569,
0.025482062250375748,
0.15383252501487732,
-0.121867835521698,
-0.1810571849346161,
-0.023636754602193832,
0.03012617491185665,
0.16588625311851501,
-0.015637414529919624,
0.06775834411382675,
0.07140350341796875,
0.00865251012146473,
0.026013558730483055,
-0.011932969093322754,
-0.06223458796739578,
-0.11102662235498428,
-0.033089667558670044,
-0.019617905840277672,
-0.11087963730096817,
0.1916974037885666,
0.09305498749017715,
-0.020921019837260246,
-0.044223252683877945,
0.14426079392433167,
-0.0707283541560173,
-0.08492685854434967,
-0.13230445981025696,
0.10024173557758331,
-0.12664638459682465,
-0.04875042289495468,
0.03254886344075203,
-0.0918443500995636,
0.002430184744298458,
0.13279929757118225,
0.027186531573534012,
0.1319728046655655,
0.021164346486330032,
-0.032574526965618134,
0.18355399370193481,
-0.04524102807044983,
-0.20850244164466858,
0.009407669305801392,
-0.058543480932712555,
-0.042749661952257156,
-0.012820111587643623,
0.07452286779880524,
-0.05846269801259041,
-0.11399035155773163,
-0.19080695509910583,
0.07458802312612534,
-0.1115003377199173,
-0.026408139616250992,
-0.07697312533855438,
-0.04022041708230972,
0.041520752012729645,
0.0052133481949567795,
-0.053644098341464996,
-0.06648391485214233,
-0.1793327033519745,
0.02686886489391327,
0.0265081524848938,
0.12986384332180023,
-0.09362731873989105,
-0.021305114030838013,
0.11811412870883942,
0.05614163726568222,
0.10702965408563614,
0.05382893234491348,
-0.008954410441219807,
0.1817544400691986,
-0.23414048552513123,
-0.02765231765806675,
0.10758350789546967,
0.01855950430035591,
-0.025194372981786728,
0.20125994086265564,
-0.06381778419017792,
0.05680418759584427,
-0.018838975578546524,
0.05359051376581192,
-0.214382141828537,
-0.151635080575943,
-0.06773028522729874,
0.02908042073249817,
-0.19476480782032013,
0.05022423341870308,
-0.12321760505437851,
0.16009506583213806,
-0.011485403403639793,
0.14703325927257538,
0.04024649038910866,
0.03752363473176956,
0.009975356049835682,
0.02670394815504551,
0.02340896800160408,
-0.12715643644332886,
-0.0871133878827095,
-0.1383061408996582,
-0.10871311277151108,
-0.012851173058152199,
0.40002745389938354,
0.05667657405138016,
-0.12921050190925598,
0.03463326022028923,
0.14608488976955414,
0.0049714138731360435,
0.0031385482288897038,
0.3185957968235016,
0.08978645503520966,
-0.022541925311088562,
-0.14219549298286438,
0.03576738387346268,
-0.06028951331973076,
-0.0982302576303482,
0.05978638678789139,
0.08235056698322296,
-0.0025104545056819916,
0.03977803885936737,
0.1539304405450821,
-0.0021260627545416355,
0.010878694243729115,
-0.09044335782527924,
0.15918925404548645,
0.03920920938253403,
-0.03468666598200798,
0.07447294890880585,
0.17062604427337646,
-0.015660172328352928,
0.07527946680784225,
-0.02319367229938507,
-0.023038258776068687,
-0.1602908819913864,
-0.20290572941303253,
0.022667700424790382,
-0.06812679767608643,
0.06805329024791718,
-0.019303012639284134,
0.10481838881969452,
0.1553175002336502,
0.0729072317481041,
-0.06109250336885452,
-0.030676955357193947,
-0.0642445906996727,
-0.12789960205554962,
-0.0173016544431448,
-0.06055523827672005,
0.05984283611178398,
-0.13693790137767792,
-0.09356412291526794,
-0.02185431867837906,
-0.13969303667545319,
-0.08052770793437958,
0.06057370826601982,
0.022471562027931213,
-0.05090975761413574,
-0.21456405520439148,
-0.0645330399274826,
-0.027264073491096497,
0.08318336308002472,
-0.05190209671854973,
0.21002164483070374,
0.0038530495949089527,
-0.014382446184754372,
0.08169036358594894,
0.1731843650341034,
0.02911439538002014,
-0.05880949646234512,
0.07685626298189163,
0.1232185810804367,
0.00005320906711858697,
0.1312958151102066,
-0.09769661724567413,
0.00903122965246439,
-0.024665724486112595,
0.21574661135673523,
0.3156037926673889,
-0.059593815356492996,
-0.02242070622742176,
-0.008918319828808308,
0.03892570734024048,
0.13255974650382996,
0.1688331663608551,
-0.029028672724962234,
0.20702791213989258,
-0.06112537533044815,
0.06509410589933395,
-0.018792826682329178,
0.06892839819192886,
-0.15150488913059235,
0.10976265370845795,
0.05003737285733223,
-0.12215645611286163,
-0.03778800368309021,
0.1806681901216507,
-0.237721249461174,
0.1446874886751175,
-0.0069007365964353085,
-0.0965697318315506,
0.007822769694030285,
-0.03824380785226822,
0.16155000030994415,
-0.02736365795135498,
0.043149031698703766,
-0.07432909309864044,
-0.11396221816539764,
-0.09085571765899658,
0.02265099249780178,
-0.3289490342140198,
-0.10560089349746704,
0.05958804488182068,
0.04829184338450432,
0.10282204300165176,
-0.006378531455993652,
0.03351665288209915,
-0.015334057621657848,
0.054687123745679855,
-0.012484274804592133,
0.08700809627771378,
0.04354175552725792,
0.050465572625398636,
-0.0944799855351448,
-0.1121637225151062,
-0.0027239799965173006,
-0.04963240027427673,
0.05358462408185005,
0.031767286360263824,
0.04253755882382393,
0.10081110149621964,
-0.10490889847278595,
0.00925770215690136,
0.017357412725687027,
-0.1410870999097824,
0.02549704536795616,
-0.027906913310289383,
0.034466128796339035,
-0.06366417557001114,
-0.04577085003256798,
-0.02078104391694069,
0.07299362123012543,
-0.19491559267044067,
-0.06872610747814178,
0.10733165591955185,
-0.09155190736055374,
0.22525925934314728,
0.008236642926931381,
-0.17803332209587097,
0.05482809618115425,
-0.06568421423435211,
0.12095781415700912,
-0.14252886176109314,
0.044272251427173615,
0.17977146804332733,
-0.015139179304242134,
0.019438134506344795,
-0.2554084360599518,
0.10831646621227264,
-0.05342736840248108,
-0.01328936219215393,
-0.04361620917916298
] |
null | null | transformers |
*I've copy-pased some information from TheBloke's model cards, hope it's ok*
## Prompt template: ChatML
```
<|im_start|>system
Jesteś pomocnym asystentem.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Example `llama.cpp` command
```shell
./main -m ./polka-1.1b-chat-gguf/polka-1.1b-chat-Q8_0.gguf --color -c 2048 --temp 0.2 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system\nJesteś pomocnym asystentem.<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 2048` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
## Notes on performance
For a model of this size, with stronger quantization, quality appears to decline much more than for larger models. Personally, I would advise to stick with `fp16` or `int8` for this model. | {"language": ["pl"], "license": "apache-2.0", "model_name": "Polka-1.1B-Chat", "base_model": "eryk-mazus/polka-1.1b-chat", "inference": false, "model_type": "tinyllama", "model_creator": "Eryk Mazu\u015b", "prompt_template": "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"} | text-generation | eryk-mazus/polka-1.1b-chat-gguf | [
"transformers",
"gguf",
"llama",
"text-generation",
"pl",
"base_model:eryk-mazus/polka-1.1b-chat",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T12:56:37+00:00 | [] | [
"pl"
] | TAGS
#transformers #gguf #llama #text-generation #pl #base_model-eryk-mazus/polka-1.1b-chat #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us
|
*I've copy-pased some information from TheBloke's model cards, hope it's ok*
## Prompt template: ChatML
## Example 'URL' command
Change '-ngl 32' to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change '-c 2048' to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by URL automatically.
If you want to have a chat-style conversation, replace the '-p <PROMPT>' argument with '-i -ins'
## Notes on performance
For a model of this size, with stronger quantization, quality appears to decline much more than for larger models. Personally, I would advise to stick with 'fp16' or 'int8' for this model. | [
"## Prompt template: ChatML",
"## Example 'URL' command\n\n\n\nChange '-ngl 32' to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.\n\nChange '-c 2048' to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by URL automatically.\n\nIf you want to have a chat-style conversation, replace the '-p <PROMPT>' argument with '-i -ins'",
"## Notes on performance\n\nFor a model of this size, with stronger quantization, quality appears to decline much more than for larger models. Personally, I would advise to stick with 'fp16' or 'int8' for this model."
] | [
"TAGS\n#transformers #gguf #llama #text-generation #pl #base_model-eryk-mazus/polka-1.1b-chat #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n",
"## Prompt template: ChatML",
"## Example 'URL' command\n\n\n\nChange '-ngl 32' to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.\n\nChange '-c 2048' to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by URL automatically.\n\nIf you want to have a chat-style conversation, replace the '-p <PROMPT>' argument with '-i -ins'",
"## Notes on performance\n\nFor a model of this size, with stronger quantization, quality appears to decline much more than for larger models. Personally, I would advise to stick with 'fp16' or 'int8' for this model."
] | [
65,
8,
124,
52
] | [
"passage: TAGS\n#transformers #gguf #llama #text-generation #pl #base_model-eryk-mazus/polka-1.1b-chat #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n## Prompt template: ChatML## Example 'URL' command\n\n\n\nChange '-ngl 32' to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.\n\nChange '-c 2048' to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by URL automatically.\n\nIf you want to have a chat-style conversation, replace the '-p <PROMPT>' argument with '-i -ins'## Notes on performance\n\nFor a model of this size, with stronger quantization, quality appears to decline much more than for larger models. Personally, I would advise to stick with 'fp16' or 'int8' for this model."
] | [
-0.0634324848651886,
0.067510686814785,
-0.002256295643746853,
0.09465429931879044,
0.09900744259357452,
0.0300405602902174,
0.06869721412658691,
0.12671104073524475,
0.03687606751918793,
0.07533689588308334,
0.11562849581241608,
0.04946030303835869,
0.10033492743968964,
0.17313818633556366,
-0.0011460421374067664,
-0.2846289575099945,
0.08575186878442764,
-0.020789263769984245,
0.08864415436983109,
0.03983032703399658,
0.09108688682317734,
-0.04115601256489754,
0.10473333299160004,
0.03037390485405922,
-0.10261698812246323,
-0.0011320413323119283,
0.05902303382754326,
-0.022704418748617172,
0.05371272936463356,
0.10299409925937653,
-0.07153523713350296,
0.06268809735774994,
-0.0015236408216878772,
-0.1657223403453827,
0.05308885499835014,
0.07239208370447159,
0.0035871067084372044,
0.09211906045675278,
0.010144522413611412,
0.10983800143003464,
0.1553490161895752,
-0.024055156856775284,
-0.06548888981342316,
0.09260740131139755,
-0.035825639963150024,
-0.13236644864082336,
-0.07514400035142899,
-0.02818349003791809,
0.0011009827721863985,
0.022385088726878166,
-0.009408999234437943,
0.13950124382972717,
-0.020969893783330917,
0.04416866600513458,
0.12265680730342865,
-0.2831932604312897,
-0.031897783279418945,
0.20473061501979828,
-0.05314559489488602,
0.08918114751577377,
-0.1399058997631073,
0.0649721622467041,
0.03398485854268074,
-0.023473214358091354,
0.035266805440187454,
0.037487730383872986,
-0.02312440611422062,
-0.040875714272260666,
-0.06110822409391403,
-0.018458018079400063,
0.07090333104133606,
0.09801709651947021,
-0.03829938918352127,
-0.12883856892585754,
-0.1524268090724945,
-0.115541972219944,
-0.056762903928756714,
-0.008531764149665833,
-0.008847064338624477,
0.027698984369635582,
-0.004135544877499342,
-0.11995504051446915,
-0.047544144093990326,
-0.05867330729961395,
-0.11658869683742523,
0.0014036011416465044,
0.0715014860033989,
0.10481967777013779,
0.03578900918364525,
0.050599370151758194,
-0.2700005769729614,
-0.06806135922670364,
-0.1024128645658493,
-0.13933002948760986,
-0.08822838217020035,
0.07342083007097244,
-0.00833936594426632,
0.019719623029232025,
0.06895054876804352,
0.12086248397827148,
-0.025970634073019028,
0.07160336524248123,
0.10943228006362915,
-0.02029222622513771,
0.0014197814743965864,
0.15987305343151093,
-0.03925248607993126,
-0.09290829300880432,
0.08664564788341522,
0.047829266637563705,
0.02804519049823284,
-0.04352964833378792,
-0.058167971670627594,
-0.07214714586734772,
-0.0011187891941517591,
-0.040000930428504944,
-0.0778413861989975,
0.07406708598136902,
0.02294931188225746,
-0.051837675273418427,
0.16710242629051208,
-0.032806720584630966,
-0.007518215570598841,
-0.006326381582766771,
-0.04602407291531563,
0.19386430084705353,
-0.01900951936841011,
-0.031315408647060394,
-0.02638966590166092,
-0.13108249008655548,
-0.08647972345352173,
-0.08872482925653458,
-0.08836308866739273,
-0.048427876085042953,
0.0026430676225572824,
-0.07629155367612839,
-0.024806758388876915,
-0.14857426285743713,
-0.12408467382192612,
0.040051110088825226,
0.05046294629573822,
-0.04822239279747009,
-0.06157982349395752,
0.03385467827320099,
-0.009851520881056786,
-0.02875135838985443,
-0.04365677759051323,
0.020475231111049652,
-0.04156186804175377,
0.031407248228788376,
0.08037673681974411,
0.11937081068754196,
-0.16614195704460144,
0.01825350522994995,
-0.006455752998590469,
-0.0251957755535841,
-0.20551566779613495,
0.10793785005807877,
-0.11174521595239639,
-0.041133418679237366,
-0.09308401495218277,
-0.06361225247383118,
-0.0419284850358963,
-0.012491771951317787,
0.01442547794431448,
0.06274841725826263,
-0.04097795858979225,
-0.024518214166164398,
0.15313668549060822,
-0.06483522057533264,
0.03102339617908001,
0.1959274709224701,
-0.01618087850511074,
-0.08096076548099518,
0.1594773232936859,
0.14288847148418427,
0.13634754717350006,
-0.09184759110212326,
-0.07277953624725342,
0.11797410249710083,
-0.03453585505485535,
-0.07173874974250793,
0.07714763283729553,
0.033449407666921616,
-0.009005705825984478,
0.09702170640230179,
0.06667539477348328,
0.0387730710208416,
-0.024524101987481117,
-0.03314674645662308,
-0.055033598095178604,
-0.017951423302292824,
0.015910455957055092,
-0.03856859356164932,
0.041983991861343384,
0.00511730695143342,
-0.1393490582704544,
-0.08399553596973419,
0.1634928286075592,
-0.041696079075336456,
-0.04953256621956825,
-0.08163239806890488,
0.05787193775177002,
-0.07097141444683075,
0.07267662137746811,
-0.05208726227283478,
-0.05924571678042412,
0.06548963487148285,
-0.11474274098873138,
0.050021231174468994,
0.09392920881509781,
0.03095756657421589,
0.03871987387537956,
-0.06056670472025871,
0.10395234078168869,
0.0339113213121891,
-0.08244074881076813,
-0.05570812150835991,
-0.038639891892671585,
-0.013929485343396664,
0.020485155284404755,
0.04699785262346268,
-0.13019229471683502,
0.044114187359809875,
0.07954070717096329,
0.04873951897025108,
0.014667406678199768,
-0.06431186199188232,
0.011607611551880836,
-0.13015444576740265,
-0.008997446857392788,
-0.05518876761198044,
0.005877717863768339,
0.02054004557430744,
0.031175646930933,
0.0657113790512085,
-0.07291539013385773,
0.035379745066165924,
0.1383533626794815,
0.19534651935100555,
0.060530632734298706,
-0.06418224424123764,
-0.055262260138988495,
0.036993980407714844,
-0.0583011768758297,
-0.06072164326906204,
0.1654435694217682,
0.020364873111248016,
0.12679412961006165,
-0.111040860414505,
-0.006969893351197243,
0.06417287141084671,
-0.001410514465533197,
-0.0055860066786408424,
-0.01588789001107216,
0.13692359626293182,
-0.06004377827048302,
0.02428467757999897,
-0.08838707953691483,
-0.024941081181168556,
0.18062308430671692,
-0.0042933993972837925,
-0.08138365298509598,
0.01573103666305542,
-0.013817720115184784,
-0.051918186247348785,
0.14454831182956696,
-0.08254575729370117,
0.05180956795811653,
0.08585232496261597,
0.018534883856773376,
0.08069825172424316,
-0.1097327247262001,
0.03326674923300743,
-0.029351504519581795,
-0.08843754976987839,
-0.046389855444431305,
0.06723615527153015,
-0.008608927950263023,
0.08414839953184128,
-0.05238048732280731,
0.011222399771213531,
-0.02811301313340664,
0.024678850546479225,
0.006087976973503828,
0.1251896321773529,
-0.04225381463766098,
-0.16366854310035706,
-0.14775674045085907,
-0.01250641979277134,
-0.20214831829071045,
-0.011551047675311565,
-0.0043634045869112015,
-0.11731825023889542,
-0.09514773637056351,
-0.05539756640791893,
0.1261036992073059,
-0.000977038056589663,
0.07723168283700943,
0.005437854211777449,
-0.011370033025741577,
0.013853302225470543,
-0.11169158667325974,
-0.03002665564417839,
-0.037103861570358276,
-0.04022748023271561,
0.06888952851295471,
-0.048895735293626785,
0.05237618833780289,
0.10304370522499084,
-0.048327427357435226,
0.016402345150709152,
-0.005622741300612688,
0.18566985428333282,
-0.058356743305921555,
0.09868300706148148,
0.2412734180688858,
0.16140849888324738,
0.05613802745938301,
0.09974949061870575,
-0.01130939181894064,
-0.08545651286840439,
0.014548824168741703,
0.05520116537809372,
-0.0695967748761177,
-0.1760554313659668,
0.006062207277864218,
-0.10138281434774399,
0.002621725667268038,
0.07528045028448105,
0.03077983669936657,
0.009480384178459644,
0.12747028470039368,
-0.0808010846376419,
0.0955652967095375,
0.033469103276729584,
0.030185602605342865,
0.2025323361158371,
-0.031213540583848953,
0.12784144282341003,
-0.047704875469207764,
-0.00798987690359354,
0.17721380293369293,
0.10854952782392502,
0.197239488363266,
-0.09590908139944077,
0.14967744052410126,
0.021360183134675026,
0.08821039646863937,
0.057091936469078064,
0.0201097559183836,
-0.01376523356884718,
-0.015615366399288177,
-0.07499583810567856,
-0.024127734825015068,
-0.04807888716459274,
0.019599558785557747,
0.01478995569050312,
-0.0477217435836792,
-0.04877830296754837,
0.12610706686973572,
0.0583331435918808,
0.09935670346021652,
0.054252199828624725,
-0.31841349601745605,
-0.12679272890090942,
-0.00008176413393812254,
-0.050747524946928024,
-0.12675635516643524,
0.02356841415166855,
0.13843946158885956,
-0.053193483501672745,
0.05058862641453743,
-0.07015242427587509,
0.06127049773931503,
-0.06412263214588165,
0.06001518294215202,
-0.012188234366476536,
0.3125632703304291,
-0.03476748242974281,
0.08566416054964066,
-0.07945575565099716,
0.09746502339839935,
0.016525564715266228,
0.023928185924887657,
-0.0771217942237854,
0.03131309151649475,
0.08527246862649918,
-0.04049013555049896,
0.02108149044215679,
-0.042463771998882294,
-0.002723634010180831,
-0.010978756472468376,
-0.19308795034885406,
0.12598861753940582,
0.022346192970871925,
0.04123200848698616,
0.0422261543571949,
-0.06887579709291458,
-0.008237814530730247,
-0.006969944108277559,
-0.05254354700446129,
-0.09343640506267548,
-0.1868189126253128,
0.013764522038400173,
0.15836560726165771,
-0.10413803160190582,
-0.015901273116469383,
-0.038235537707805634,
-0.011921504512429237,
0.19609637558460236,
0.13838346302509308,
-0.08539917320013046,
-0.1291709989309311,
0.11220012605190277,
0.1281617283821106,
-0.07065043598413467,
0.02041592262685299,
-0.10018695890903473,
0.20242005586624146,
-0.040994156152009964,
-0.11252553015947342,
0.04841930791735649,
-0.03794979676604271,
-0.05633683130145073,
0.010498578660190105,
0.08338305354118347,
-0.060143861919641495,
0.024790415540337563,
0.0229654423892498,
-0.06569980084896088,
-0.06790606677532196,
-0.17356927692890167,
-0.03479662165045738,
0.16010507941246033,
-0.08726059645414352,
-0.0049026403576135635,
-0.025016682222485542,
-0.05160776898264885,
-0.04529966041445732,
0.09177640825510025,
0.07735113799571991,
0.24742679297924042,
-0.08810735493898392,
0.0838795006275177,
0.1024184450507164,
0.0733332708477974,
-0.1774194985628128,
-0.013464509509503841,
0.07375887781381607,
0.009568305686116219,
-0.10187710076570511,
-0.12282121181488037,
0.08083198964595795,
0.08327895402908325,
-0.023753585293889046,
0.0637642964720726,
-0.20214061439037323,
-0.05610336735844612,
0.054986726492643356,
0.06254410743713379,
0.30166536569595337,
-0.05548699572682381,
-0.01268609520047903,
-0.09164899587631226,
-0.01465433370321989,
0.10160303115844727,
-0.09955570101737976,
0.16353802382946014,
-0.03631773591041565,
0.0916215032339096,
0.005863799247890711,
-0.011111550964415073,
0.17675895988941193,
0.006802662275731564,
0.034682225435972214,
0.0000459460643469356,
0.09602490067481995,
0.09143104404211044,
-0.06667816638946533,
0.16707299649715424,
-0.11374597251415253,
0.06784126162528992,
-0.11884079873561859,
-0.08835859596729279,
-0.04507911205291748,
0.019147338345646858,
0.0685824602842331,
-0.05046643689274788,
-0.06073446199297905,
0.0004283589369151741,
0.012801922857761383,
0.016103049740195274,
-0.03869981691241264,
-0.012840596958994865,
-0.08473654091358185,
0.10399635136127472,
-0.016987599432468414,
-0.17225098609924316,
-0.1189194768667221,
-0.009912295266985893,
0.006829136051237583,
0.08526177704334259,
-0.2248447835445404,
0.014567367732524872,
0.08001808822154999,
-0.045508161187171936,
0.012163939885795116,
-0.004110889043658972,
-0.0712485983967781,
0.020747972652316093,
0.052885595709085464,
-0.08257903158664703,
-0.15747511386871338,
-0.01913830265402794,
0.022154178470373154,
-0.11937179416418076,
0.05106791853904724,
0.1270953118801117,
-0.023717723786830902,
0.0012992012780159712,
0.03236650303006172,
0.016442162916064262,
0.03641717508435249,
0.12573140859603882,
0.0188786368817091,
0.03870733827352524,
-0.16632497310638428,
0.05626444146037102,
-0.0023751959670335054,
0.01082115899771452,
-0.01311816368252039,
0.025552256032824516,
-0.0965355783700943,
-0.05279582738876343,
-0.1191365048289299,
-0.13953007757663727,
-0.026996344327926636,
0.02736666053533554,
-0.06516814976930618,
-0.07959256321191788,
0.05939359962940216,
-0.09538313001394272,
0.032243967056274414,
-0.017471162602305412,
-0.06449335068464279,
0.02979712374508381,
-0.11328405886888504,
0.01839987002313137,
0.006137377582490444,
0.06161967292428017,
-0.16795587539672852,
-0.02748272195458412,
-0.012609682977199554,
0.03487849980592728,
-0.042943213135004044,
-0.005330914165824652,
-0.06969475001096725,
-0.08371134847402573,
-0.07481986284255981,
0.00824070442467928,
-0.02551180310547352,
0.014027347788214684,
-0.012020646594464779,
0.0005124857998453081,
-0.06026989221572876,
0.02909919247031212,
-0.08763539791107178,
-0.048393361270427704,
-0.014977607876062393,
0.02472813054919243,
-0.034952372312545776,
0.05642319470643997,
0.022475719451904297,
-0.08258050680160522,
0.1553088128566742,
0.01822330430150032,
0.007916949689388275,
0.02991155907511711,
-0.10908745974302292,
-0.07504662126302719,
0.0830279290676117,
0.12008169293403625,
0.0298062264919281,
0.07140067964792252,
0.12111791223287582,
-0.00045797941857017577,
0.08077063411474228,
-0.017325785011053085,
-0.016903391107916832,
-0.11139983683824539,
-0.019135255366563797,
-0.11344075202941895,
0.05251840129494667,
-0.025905029848217964,
-0.009751683101058006,
0.037007641047239304,
0.06089623644948006,
0.10857552289962769,
-0.010837416164577007,
-0.06291181594133377,
-0.22464847564697266,
0.046899814158678055,
-0.025448305532336235,
-0.12571647763252258,
-0.020469823852181435,
-0.043857403099536896,
0.07957729697227478,
0.04050910100340843,
0.24437840282917023,
0.05106455460190773,
-0.018312625586986542,
-0.01781279966235161,
0.05279636010527611,
0.10632915794849396,
-0.0561109222471714,
0.13855387270450592,
0.1443014293909073,
0.0715101808309555,
0.05511089414358139,
0.03889843821525574,
0.11781061440706253,
-0.037582069635391235,
0.14218372106552124,
0.06228842958807945,
-0.08946085721254349,
0.11590000987052917,
0.05896129831671715,
-0.0668797418475151,
-0.029344120994210243,
0.17467159032821655,
-0.16130295395851135,
0.060225363820791245,
-0.06384154409170151,
-0.004348081536591053,
0.15907633304595947,
-0.09202948957681656,
0.03865459933876991,
0.043742384761571884,
-0.07809486985206604,
-0.15603619813919067,
-0.12826935946941376,
-0.08940733969211578,
-0.21043623983860016,
0.03516468405723572,
-0.1151331290602684,
-0.03686264902353287,
0.059720925986766815,
0.014156528748571873,
-0.014346892945468426,
0.07923377305269241,
-0.0813809484243393,
-0.11036624759435654,
0.06521245092153549,
-0.003942421171814203,
-0.06374513357877731,
0.06917935609817505,
-0.06939323246479034,
0.08645910024642944,
0.018955951556563377,
0.11470040678977966,
-0.012243194505572319,
0.0786651149392128,
0.05146043002605438,
-0.030072098597884178,
-0.02145778387784958,
-0.082547128200531,
-0.009261402301490307,
0.011718830093741417,
0.10211613774299622,
0.01855948381125927,
-0.07872005552053452,
-0.01235138438642025,
0.15544642508029938,
-0.006990326568484306,
-0.027011670172214508,
-0.169451043009758,
0.17475630342960358,
0.005381989758461714,
0.03608616441488266,
-0.034223128110170364,
-0.08078961819410324,
-0.07263259589672089,
0.19251643121242523,
0.17134547233581543,
-0.06843128800392151,
-0.06969596445560455,
-0.03047134168446064,
-0.02391214482486248,
0.015551273711025715,
0.1903618723154068,
0.06605763733386993,
0.15132902562618256,
-0.021840795874595642,
-0.01201916765421629,
0.07546505331993103,
0.021745456382632256,
-0.0628138929605484,
0.04833019897341728,
0.024964820593595505,
0.005730653181672096,
-0.0340527668595314,
0.04685668647289276,
-0.009168816730380058,
-0.06600384414196014,
-0.11111870408058167,
-0.08020581305027008,
-0.09538327902555466,
0.0028905682265758514,
-0.12603577971458435,
-0.001176810241304338,
0.06825173646211624,
-0.08257515728473663,
0.043533291667699814,
0.1599123775959015,
-0.03872963786125183,
-0.20491032302379608,
-0.03707488253712654,
0.079724982380867,
0.029972020536661148,
0.14744393527507782,
-0.011327806860208511,
0.03271741047501564,
0.1055968850851059,
-0.11711321026086807,
-0.07418126612901688,
0.07316649705171585,
-0.04699680209159851,
-0.13080358505249023,
0.010996745899319649,
0.06450241804122925,
-0.004143752623349428,
-0.021001912653446198,
0.04528568312525749,
0.007584964390844107,
-0.05690225213766098,
-0.0851990282535553,
-0.014542766846716404,
-0.05457090586423874,
0.06057050824165344,
-0.11725963652133942,
0.08376894146203995,
0.14716754853725433,
-0.02045840583741665,
-0.03434936702251434,
-0.07047141343355179,
0.09476324915885925,
0.029806172475218773,
-0.03993954882025719,
-0.02469080314040184,
-0.11110375076532364,
-0.034950193017721176,
-0.056852687150239944,
0.008452635258436203,
-0.2296009510755539,
-0.028117166832089424,
-0.07251784950494766,
0.023878157138824463,
-0.05069325119256973,
0.12306424230337143,
0.05671890825033188,
0.03710494190454483,
-0.0070565915666520596,
-0.1573677808046341,
-0.05849636346101761,
0.03254846855998039,
-0.13103017210960388,
-0.04542567953467369
] |
null | null | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-cartpole", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "mean_reward", "value": "500.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | Jack51003/Reinforce-cartpole | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | 2024-02-09T12:57:13+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
39,
54
] | [
"passage: TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
0.007526164408773184,
-0.12498430907726288,
-0.0013541718944907188,
0.09601131081581116,
0.11848696321249008,
-0.04186001420021057,
0.11405468732118607,
0.05624859035015106,
0.09539441019296646,
0.04239490255713463,
0.13636724650859833,
0.06906966865062714,
-0.004102868959307671,
0.12412862479686737,
0.09840741008520126,
-0.26058563590049744,
0.07420794665813446,
-0.04403980076313019,
-0.009944677352905273,
0.10139261186122894,
0.07836852967739105,
-0.08325441926717758,
0.051592715084552765,
0.00009572553972247988,
-0.044259943068027496,
0.0321260429918766,
0.013628939166665077,
-0.053157225251197815,
0.1606452465057373,
-0.07313758134841919,
0.10494591295719147,
-0.03843724727630615,
0.14574295282363892,
-0.1126825287938118,
0.04758213832974434,
0.05111503228545189,
-0.04548581689596176,
0.03848232328891754,
-0.12538743019104004,
-0.06033875793218613,
0.026815801858901978,
-0.015865681692957878,
0.12249194830656052,
0.03647647053003311,
-0.1777559220790863,
-0.13461355865001678,
-0.0165896974503994,
0.12325166910886765,
0.1627800315618515,
0.00512364786118269,
0.014270431362092495,
0.16791965067386627,
-0.1761058121919632,
0.025937072932720184,
0.11400806158781052,
-0.37275227904319763,
-0.00034436015994288027,
0.2240462601184845,
0.06164427846670151,
0.1252165287733078,
-0.12646614015102386,
0.010440526530146599,
0.07403992861509323,
0.04368630796670914,
0.049784936010837555,
-0.015430688858032227,
-0.12260042130947113,
0.08455035835504532,
-0.1383819431066513,
-0.058066487312316895,
0.1495426446199417,
-0.019741326570510864,
-0.009476418606936932,
-0.016515808179974556,
-0.009238536469638348,
-0.050979889929294586,
-0.03430935740470886,
-0.11778499186038971,
0.10755524039268494,
0.04975730925798416,
0.0038771627005189657,
-0.04602450504899025,
-0.05612579360604286,
-0.09815777093172073,
-0.03123871050775051,
0.0372777059674263,
-0.013706400990486145,
0.01091629359871149,
0.027692900970578194,
0.09935613721609116,
-0.13446329534053802,
0.01825822703540325,
-0.028096558526158333,
-0.028040969744324684,
-0.1316804438829422,
-0.11984307318925858,
-0.026084421202540398,
0.004223645199090242,
0.03029833547770977,
0.20433813333511353,
0.020139509811997414,
0.059011414647102356,
-0.0022708347532898188,
0.09776382148265839,
0.029780851677060127,
0.13517548143863678,
-0.04466623440384865,
0.19488364458084106,
0.07711011171340942,
0.05364556983113289,
0.03204274922609329,
-0.05344729498028755,
-0.19369827210903168,
0.04861246794462204,
0.06659778952598572,
0.08274952322244644,
-0.1178959533572197,
0.0059632807970047,
-0.10316018015146255,
0.0028950648847967386,
-0.10474003106355667,
-0.0642905905842781,
-0.02892979420721531,
0.031841445714235306,
-0.10535725951194763,
0.028785312548279762,
0.025052599608898163,
0.04140377417206764,
0.0676041767001152,
-0.12253966927528381,
-0.07404746115207672,
-0.021733485162258148,
-0.12817098200321198,
-0.09923440217971802,
0.08802318572998047,
-0.026199497282505035,
-0.005110981408506632,
-0.1253623217344284,
-0.2661486268043518,
-0.05670225992798805,
0.06396034359931946,
-0.03231031447649002,
-0.08589376509189606,
-0.1633463054895401,
0.026403428986668587,
-0.07700273394584656,
0.05221332609653473,
0.04776721075177193,
-0.03665859252214432,
0.02023705095052719,
-0.07958202809095383,
0.12739010155200958,
0.049698662012815475,
0.00541001046076417,
-0.09916839748620987,
0.07882837951183319,
-0.3034103214740753,
-0.02581131085753441,
-0.15228183567523956,
0.0772043839097023,
-0.07893010973930359,
0.01308529730886221,
0.05044940114021301,
0.043790437281131744,
-0.016942394897341728,
0.16269747912883759,
-0.17043575644493103,
-0.05301272124052048,
0.026445282623171806,
-0.09261117875576019,
-0.09916394203901291,
0.07275339215993881,
-0.06339669227600098,
0.21263530850410461,
0.08751397579908371,
0.17006252706050873,
-0.011036526411771774,
-0.16256992518901825,
0.1207515075802803,
0.07522942125797272,
-0.1639646589756012,
0.004287737421691418,
0.061784300953149796,
-0.0016935690073296428,
0.02746843732893467,
-0.01872866041958332,
-0.07289361208677292,
0.06302516162395477,
-0.07825060933828354,
0.022581040859222412,
0.06258945167064667,
-0.09531243145465851,
0.23986859619617462,
-0.005434412509202957,
0.0862451046705246,
-0.025957979261875153,
-0.09802921861410141,
0.00908072479069233,
0.07164718210697174,
-0.0014321404742076993,
0.01703714393079281,
-0.14553219079971313,
0.23044352233409882,
-0.07965081930160522,
0.011176814325153828,
-0.11607582122087479,
-0.1256982982158661,
0.011873425915837288,
0.13336114585399628,
0.059921663254499435,
0.16569606959819794,
0.09518871456384659,
-0.032197169959545135,
0.017584815621376038,
-0.0023385772947221994,
-0.09040450304746628,
0.01580043137073517,
-0.0021571461111307144,
-0.12167251110076904,
-0.07353103160858154,
-0.08134473115205765,
0.12585052847862244,
-0.20988115668296814,
0.015492538921535015,
0.04099845886230469,
0.008103687316179276,
0.04467369243502617,
0.023746047168970108,
-0.013269703835248947,
-0.00007021807687124237,
0.03244573250412941,
-0.10098352283239365,
0.12937165796756744,
0.013381263241171837,
0.014676140621304512,
-0.006365173030644655,
-0.05572463944554329,
0.03720450773835182,
0.040439579635858536,
-0.11237845569849014,
-0.11330515146255493,
-0.009658765979111195,
-0.0015364213613793254,
0.02637762948870659,
-0.022321155294775963,
0.052120618522167206,
0.27587956190109253,
0.05387469753623009,
0.10401033610105515,
-0.05769326910376549,
0.015315087512135506,
-0.015322818420827389,
-0.07135670632123947,
0.06358719617128372,
0.025013601407408714,
0.08050397783517838,
-0.03531401976943016,
0.03759452700614929,
0.1675453782081604,
-0.015888912603259087,
0.11127935349941254,
-0.06545067578554153,
-0.03844274953007698,
-0.043109722435474396,
0.05627678707242012,
0.015021559782326221,
0.04564907029271126,
0.0000015355876712419558,
-0.08444724231958389,
-0.03503387048840523,
-0.03988509997725487,
-0.010637006722390652,
-0.12273643165826797,
-0.00499896751716733,
0.01265440508723259,
-0.021940499544143677,
0.04488934203982353,
0.07375624030828476,
-0.04849626496434212,
0.025821007788181305,
0.06070821359753609,
-0.10193055868148804,
0.08957115560770035,
0.015067169442772865,
-0.06946801394224167,
0.13769419491291046,
-0.07484805583953857,
-0.045293889939785004,
-0.1025395318865776,
-0.1568877100944519,
0.09384927153587341,
0.06704871356487274,
-0.05427970737218857,
-0.1503879576921463,
-0.0016851738328114152,
-0.008973666466772556,
0.09206123650074005,
-0.006399387493729591,
-0.12621140480041504,
0.01989075168967247,
0.08295059949159622,
-0.05633419007062912,
-0.09804849326610565,
-0.0075809285044670105,
-0.05280788615345955,
-0.17707788944244385,
-0.03888550028204918,
-0.06398582458496094,
-0.06734282523393631,
0.23586803674697876,
0.02017230913043022,
0.08274748176336288,
-0.044721852988004684,
0.04250151664018631,
-0.012231717817485332,
0.0006326579605229199,
0.10689259320497513,
-0.09043551236391068,
-0.017900818958878517,
-0.001320177922025323,
-0.024820495396852493,
-0.07327181100845337,
0.029733488336205482,
-0.04272191599011421,
-0.08249637484550476,
-0.1415451467037201,
-0.04993678629398346,
-0.011005163192749023,
0.10754310339689255,
0.07337497919797897,
0.0048001972027122974,
-0.11733713001012802,
0.062058478593826294,
0.13692134618759155,
0.031207585707306862,
0.004062763415277004,
0.028157465159893036,
0.14977529644966125,
-0.10706274956464767,
-0.022463621571660042,
-0.038119975477457047,
-0.054863203316926956,
0.004114252515137196,
0.016883620992302895,
0.08840765058994293,
0.1410384476184845,
0.11468084901571274,
0.047563645988702774,
0.0464191697537899,
0.06561273336410522,
0.1694946140050888,
0.059157438576221466,
-0.10448314249515533,
-0.044678982347249985,
-0.0040070898830890656,
-0.10903503000736237,
0.057307638227939606,
0.16030821204185486,
0.06326017528772354,
-0.14463356137275696,
0.021787412464618683,
-0.038982175290584564,
0.13649246096611023,
0.020638149231672287,
-0.2677258849143982,
-0.008139112964272499,
0.023630544543266296,
-0.0010347915813326836,
-0.012379839085042477,
0.10821118950843811,
-0.040134772658348083,
-0.233198344707489,
-0.12299054861068726,
0.010077533312141895,
0.031144635751843452,
-0.1509784311056137,
0.015542911365628242,
-0.14036494493484497,
0.08027976751327515,
-0.007007129956036806,
0.07418135553598404,
-0.025149788707494736,
0.15060245990753174,
-0.028731435537338257,
0.01628703810274601,
-0.07902143895626068,
-0.047717493027448654,
0.09898673743009567,
-0.0046631391160190105,
0.1931537538766861,
0.005480166990309954,
-0.023713182657957077,
-0.12098433077335358,
-0.05229806900024414,
-0.04967813938856125,
0.010598190128803253,
-0.05373382940888405,
0.0765683576464653,
-0.02441473677754402,
-0.0039579677395522594,
-0.010900177992880344,
0.08942947536706924,
-0.05291692912578583,
0.03636563941836357,
-0.11246588081121445,
-0.05034820735454559,
0.14550213515758514,
-0.09163831174373627,
-0.10174685716629028,
-0.16205860674381256,
0.14137998223304749,
0.15070600807666779,
0.058216437697410583,
-0.04001476243138313,
0.03867831453680992,
-0.019183965399861336,
-0.024241572245955467,
0.07880574464797974,
0.009653856977820396,
0.1324782371520996,
-0.08983246237039566,
0.014327390119433403,
0.14589735865592957,
-0.05275948345661163,
0.016191845759749413,
-0.02304735779762268,
0.12202176451683044,
0.04650457948446274,
0.06189403310418129,
0.018547222018241882,
0.06655703485012054,
0.06466961652040482,
-0.02262885868549347,
0.08456692099571228,
0.030712679028511047,
-0.18644161522388458,
0.058530256152153015,
-0.09805119782686234,
0.22581584751605988,
0.05066308751702309,
0.06047345697879791,
0.2993181645870209,
0.21986234188079834,
-0.05372472479939461,
0.1669820249080658,
0.044286344200372696,
-0.05891284719109535,
-0.21245966851711273,
-0.03684934973716736,
-0.030655447393655777,
0.09436552971601486,
0.15607263147830963,
-0.0981721356511116,
-0.04201313853263855,
-0.00972361396998167,
-0.032264553010463715,
0.020120708271861076,
-0.24663487076759338,
-0.01734781451523304,
0.14379777014255524,
0.10629188269376755,
0.2451348900794983,
-0.006132842972874641,
0.023609744384884834,
0.049030207097530365,
0.018605992197990417,
-0.02483358606696129,
-0.21013511717319489,
0.09079083055257797,
0.006071676965802908,
0.04935038834810257,
0.022885039448738098,
-0.006052911281585693,
0.04500092566013336,
-0.073696069419384,
0.08904470503330231,
-0.08561883866786957,
-0.08341272175312042,
0.2185351401567459,
-0.03945168852806091,
-0.00661163916811347,
0.12917985022068024,
-0.011526807211339474,
-0.1097102016210556,
-0.015364703722298145,
0.027403371408581734,
0.030678823590278625,
-0.030246863141655922,
-0.03609466925263405,
0.024012766778469086,
0.10202405601739883,
-0.04282205551862717,
0.04565315693616867,
0.10240072011947632,
-0.020902957767248154,
0.15945613384246826,
0.13205459713935852,
0.10420060157775879,
0.002927543595433235,
-0.06464727967977524,
0.014349685050547123,
-0.055471502244472504,
0.02962767891585827,
-0.17038846015930176,
-0.0070191239938139915,
0.055695805698633194,
0.04772466421127319,
0.0945243164896965,
0.11333164572715759,
-0.127106174826622,
0.0300484336912632,
0.028996523469686508,
-0.06286120414733887,
-0.06029998138546944,
-0.002275418024510145,
-0.016458535566926003,
-0.008173024281859398,
-0.09947093576192856,
0.07884971052408218,
-0.10555081814527512,
-0.03306307643651962,
0.05025126785039902,
-0.0607193186879158,
-0.12852220237255096,
-0.010904680006206036,
0.1252979338169098,
0.061709314584732056,
-0.05078592896461487,
0.14939077198505402,
0.06109785661101341,
-0.08055379986763,
0.037185851484537125,
0.027442200109362602,
-0.08008874952793121,
-0.10198270529508591,
-0.0004569833690766245,
0.31761088967323303,
0.06076094135642052,
-0.0329466350376606,
-0.11946453154087067,
-0.15002015233039856,
0.04840146750211716,
0.1035679280757904,
0.12359631806612015,
0.011757869273424149,
-0.05322748050093651,
0.02236519381403923,
-0.05275069922208786,
0.03814244270324707,
0.06910209357738495,
-0.03928454965353012,
-0.13761694729328156,
0.0077122850343585014,
0.026647454127669334,
0.10174071043729782,
-0.06771174818277359,
-0.09184598177671432,
-0.18085066974163055,
0.09208621084690094,
-0.03432070091366768,
-0.10890032351016998,
0.027215104550123215,
-0.017406610772013664,
0.014248576015233994,
0.07639352232217789,
-0.047281619161367416,
0.01244808267802,
-0.1517520695924759,
0.07082249224185944,
0.05706808716058731,
0.08926787972450256,
0.000014311663107946515,
-0.054843269288539886,
0.07618319988250732,
-0.05763502046465874,
0.06680037826299667,
-0.053477559238672256,
0.005539732985198498,
0.10781200975179672,
-0.23264040052890778,
-0.021164139732718468,
0.009476077742874622,
-0.04681631922721863,
0.08765807747840881,
-0.19047698378562927,
0.024190550670027733,
-0.08897756040096283,
-0.024605726823210716,
0.01802127994596958,
-0.1086471825838089,
-0.04306677728891373,
0.08475461602210999,
0.037119291722774506,
-0.031288959085941315,
-0.04612116143107414,
-0.019314980134367943,
-0.0914498046040535,
0.053634315729141235,
0.07442525774240494,
-0.0687926784157753,
0.08314394950866699,
-0.05507456883788109,
0.00841207429766655,
-0.052043743431568146,
0.06760627031326294,
-0.012366239912807941,
-0.12672528624534607,
-0.02123171091079712,
-0.044928714632987976,
0.11662110686302185,
-0.023402327671647072,
0.022080281749367714,
0.014599837362766266,
0.0323631577193737,
-0.012065601535141468,
0.05028461292386055,
0.1019197478890419,
0.05136820673942566,
0.014879679307341576,
0.02292765863239765,
0.055746350437402725,
0.0757644772529602,
-0.1134679913520813,
0.06457309424877167,
-0.02098844014108181,
-0.08620109409093857,
0.1013324111700058,
0.06909440457820892,
0.037490107119083405,
0.15593400597572327,
0.22674402594566345,
0.10539932548999786,
-0.03564648702740669,
-0.03126971051096916,
0.12967991828918457,
0.17799612879753113,
-0.07682197540998459,
0.015780627727508545,
-0.0020607721526175737,
-0.017265556380152702,
-0.09849067777395248,
-0.13722245395183563,
-0.060460351407527924,
-0.2453264594078064,
0.1078341007232666,
-0.03288164362311363,
-0.04169659689068794,
0.128489688038826,
0.027952738106250763,
0.03724630922079086,
0.08183616399765015,
-0.12909026443958282,
-0.013460557907819748,
0.07749562710523605,
-0.08914026618003845,
-0.033571500331163406,
-0.17521262168884277,
-0.06771576404571533,
-0.08741120994091034,
-0.15989220142364502,
-0.06844990700483322,
0.029948782175779343,
0.035394806414842606,
0.010386589914560318,
-0.039711855351924896,
-0.01962728053331375,
0.011063394136726856,
-0.0025537724141031504,
-0.04985455423593521,
-0.01753084547817707,
0.021317757666110992,
-0.11333847790956497,
-0.024336790665984154,
0.16320326924324036,
-0.03297848999500275,
-0.18396754562854767,
-0.0405106395483017,
0.2157316505908966,
0.025046708062291145,
0.0590171180665493,
-0.073721744120121,
-0.016323629766702652,
0.021523483097553253,
0.20813441276550293,
0.10171995311975479,
-0.10821312665939331,
0.015457749366760254,
-0.03655189648270607,
0.0013793212128803134,
-0.061893612146377563,
0.10775819420814514,
0.06519263982772827,
-0.07549984753131866,
-0.17567221820354462,
-0.04389495030045509,
-0.08628730475902557,
0.03370477631688118,
-0.14383791387081146,
-0.03786516562104225,
0.1168690100312233,
0.004516853019595146,
-0.053927481174468994,
0.07883694022893906,
-0.17713546752929688,
0.03441957011818886,
-0.04880853369832039,
-0.13215437531471252,
-0.09491758048534393,
-0.10123858600854874,
0.0027463934384286404,
0.08913854509592056,
0.15567956864833832,
-0.06151591241359711,
-0.07471925020217896,
-0.009579092264175415,
-0.028091613203287125,
-0.052700337022542953,
-0.07900123298168182,
0.059512585401535034,
0.0007560851518064737,
0.16147300601005554,
-0.07439453154802322,
0.09558981657028198,
0.09099138528108597,
-0.021246420219540596,
-0.00915549136698246,
0.032866667956113815,
-0.003863809397444129,
-0.07436864078044891,
-0.04970616102218628,
0.02312966249883175,
0.027639856562018394,
0.10846075415611267,
-0.030836544930934906,
-0.1934703141450882,
0.11230092495679855,
0.09140218049287796,
-0.04296138137578964,
-0.046487610787153244,
0.05351927503943443,
-0.07097935676574707,
0.1252279132604599,
0.03444884717464447,
-0.02163051813840866,
0.013762647286057472,
-0.06370721012353897,
0.08370721340179443,
0.11594565212726593,
-0.048265840858221054,
-0.08278503268957138,
-0.06164652109146118,
0.012770666740834713,
0.02961382456123829,
-0.13650155067443848,
-0.21160630881786346,
-0.10802312940359116,
-0.1383298933506012,
0.004740108735859394,
-0.04703504592180252,
0.08498300611972809,
0.12991970777511597,
0.09780163317918777,
-0.011416295543313026,
-0.004867587238550186,
0.018085451796650887,
0.13192623853683472,
-0.11232008039951324,
-0.08192373812198639
] |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-med-ner-2
This model is a fine-tuned version of [DeBERTa](https://huggingface.co/microsoft/deberta-v3-base) on the PubMED Dataset.
## Model description
Medical NER Model finetuned on BERT to recognize 41 Medical entities.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP
## Usage
The easiest way is to load the inference api from huggingface and second method is through the pipeline object offered by transformers library.
```python
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("token-classification", model="Clinical-AI-Apollo/Medical-NER", aggregation_strategy='simple')
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("Clinical-AI-Apollo/Medical-NER")
model = AutoModelForTokenClassification.from_pretrained("Clinical-AI-Apollo/Medical-NER")
```
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/deberta-v3-base", "widget": [{"text": "63 year old woman with history of CAD presented to ER", "example_title": "Example-1"}, {"text": "63 year old woman diagnosed with CAD", "example_title": "Example-2"}, {"text": "A 48 year-old female presented with vaginal bleeding and abnormal Pap smears. Upon diagnosis of invasive non-keratinizing SCC of the cervix, she underwent a radical hysterectomy with salpingo-oophorectomy which demonstrated positive spread to the pelvic lymph nodes and the parametrium. Pathological examination revealed that the tumour also extensively involved the lower uterine segment.", "example_title": "example 3"}], "model-index": [{"name": "deberta-med-ner-2", "results": []}]} | token-classification | Clinical-AI-Apollo/Medical-NER | [
"transformers",
"safetensors",
"deberta-v2",
"token-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-09T12:57:34+00:00 | [] | [] | TAGS
#transformers #safetensors #deberta-v2 #token-classification #generated_from_trainer #base_model-microsoft/deberta-v3-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# deberta-med-ner-2
This model is a fine-tuned version of DeBERTa on the PubMED Dataset.
## Model description
Medical NER Model finetuned on BERT to recognize 41 Medical entities.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP
## Usage
The easiest way is to load the inference api from huggingface and second method is through the pipeline object offered by transformers library.
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
| [
"# deberta-med-ner-2\n\nThis model is a fine-tuned version of DeBERTa on the PubMED Dataset.",
"## Model description\n\nMedical NER Model finetuned on BERT to recognize 41 Medical entities.",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 16\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 30\n- mixed_precision_training: Native AMP",
"## Usage\nThe easiest way is to load the inference api from huggingface and second method is through the pipeline object offered by transformers library.",
"### Framework versions\n\n- Transformers 4.37.0\n- Pytorch 2.1.2\n- Datasets 2.1.0\n- Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #safetensors #deberta-v2 #token-classification #generated_from_trainer #base_model-microsoft/deberta-v3-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# deberta-med-ner-2\n\nThis model is a fine-tuned version of DeBERTa on the PubMED Dataset.",
"## Model description\n\nMedical NER Model finetuned on BERT to recognize 41 Medical entities.",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 16\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 30\n- mixed_precision_training: Native AMP",
"## Usage\nThe easiest way is to load the inference api from huggingface and second method is through the pipeline object offered by transformers library.",
"### Framework versions\n\n- Transformers 4.37.0\n- Pytorch 2.1.2\n- Datasets 2.1.0\n- Tokenizers 0.15.1"
] | [
69,
29,
20,
142,
35,
30
] | [
"passage: TAGS\n#transformers #safetensors #deberta-v2 #token-classification #generated_from_trainer #base_model-microsoft/deberta-v3-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# deberta-med-ner-2\n\nThis model is a fine-tuned version of DeBERTa on the PubMED Dataset.## Model description\n\nMedical NER Model finetuned on BERT to recognize 41 Medical entities.### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 16\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 30\n- mixed_precision_training: Native AMP## Usage\nThe easiest way is to load the inference api from huggingface and second method is through the pipeline object offered by transformers library.### Framework versions\n\n- Transformers 4.37.0\n- Pytorch 2.1.2\n- Datasets 2.1.0\n- Tokenizers 0.15.1"
] | [
-0.08370990306138992,
0.11374296993017197,
-0.006201591342687607,
0.04999956116080284,
0.07969272136688232,
0.030708197504281998,
0.1390027403831482,
0.11516154557466507,
0.0631210133433342,
0.11993612349033356,
0.03891502693295479,
0.08635326474905014,
0.04869592562317848,
0.1578827053308487,
-0.018314369022846222,
-0.21864138543605804,
0.03485565632581711,
-0.04358144477009773,
-0.027774415910243988,
0.055486638098955154,
0.07505249977111816,
-0.10469675809144974,
0.05535788834095001,
0.05184846743941307,
-0.032728929072618484,
-0.01076323352754116,
-0.0017351595452055335,
-0.03901258111000061,
0.07878843694925308,
0.021678809076547623,
0.12622125446796417,
0.013416353613138199,
0.11530093103647232,
-0.1971523016691208,
-0.0016610774910077453,
0.0670187771320343,
0.03485571965575218,
0.09694800525903702,
0.09232888370752335,
-0.04519502446055412,
0.03881623223423958,
-0.12442070245742798,
0.12054162472486496,
0.02999032661318779,
-0.07506594806909561,
-0.08638086169958115,
-0.048007216304540634,
0.060249119997024536,
0.14633290469646454,
0.0781174972653389,
0.013808769173920155,
0.10638240724802017,
-0.0724976658821106,
0.05382099375128746,
0.12396202981472015,
-0.26158133149147034,
-0.02769072726368904,
0.02447529509663582,
0.027212943881750107,
0.048956241458654404,
-0.05776585638523102,
-0.035821180790662766,
-0.008094051852822304,
-0.0009421888971701264,
0.12158112972974777,
-0.0220392607152462,
-0.019000625237822533,
-0.012671351432800293,
-0.14224866032600403,
-0.03695015609264374,
0.06461774557828903,
0.0540880486369133,
-0.05711602419614792,
-0.12368442863225937,
-0.08513221144676208,
-0.03816838189959526,
-0.020116358995437622,
-0.052601754665374756,
0.042802393436431885,
-0.03220536559820175,
0.022613227367401123,
-0.05056009069085121,
-0.08476706594228745,
-0.04417668655514717,
-0.023788362741470337,
0.09876418113708496,
0.03758922219276428,
0.04424367472529411,
0.056282415986061096,
0.09859111905097961,
0.004841091576963663,
-0.08964690566062927,
-0.037401873618364334,
-0.02037261426448822,
-0.1938713788986206,
-0.08056342601776123,
0.006934488657861948,
-0.005076996982097626,
0.03198695927858353,
0.17444482445716858,
-0.015527662821114063,
0.04582614451646805,
0.04189392551779747,
-0.010725227184593678,
0.006247003562748432,
0.1272457391023636,
-0.0490323044359684,
-0.04538127779960632,
0.011474479921162128,
0.09314333647489548,
0.05750191584229469,
0.012815222144126892,
-0.06960275024175644,
-0.020998073741793633,
0.01472746767103672,
0.02060071937739849,
-0.059266265481710434,
0.033319804817438126,
-0.054847538471221924,
-0.0820239931344986,
0.1513328105211258,
-0.08226870000362396,
0.03779798373579979,
0.06564951688051224,
-0.06015373393893242,
0.04517519846558571,
0.06954769045114517,
0.014057649299502373,
-0.05355185642838478,
0.04269616678357124,
-0.06998147815465927,
0.0012504692422226071,
-0.06321369856595993,
-0.09630858898162842,
0.030731726437807083,
-0.07052963972091675,
-0.027705803513526917,
-0.15055835247039795,
-0.18988321721553802,
-0.021497346460819244,
0.05435250326991081,
-0.028304751962423325,
-0.02713496796786785,
-0.04104304313659668,
0.007600544951856136,
0.019687505438923836,
-0.01999575085937977,
-0.03913441672921181,
-0.07340269535779953,
0.00027545136981643736,
-0.08870556950569153,
0.041008561849594116,
0.018105095252394676,
0.015772301703691483,
-0.0633755624294281,
0.05087212473154068,
-0.21828751266002655,
0.06795510649681091,
-0.06899354606866837,
0.038074031472206116,
-0.13503354787826538,
-0.045639947056770325,
0.05066496506333351,
-0.04302036017179489,
0.04269372671842575,
0.08477147668600082,
-0.0740470439195633,
-0.017970168963074684,
0.0764007717370987,
-0.061646897345781326,
-0.06815041601657867,
0.08745058625936508,
-0.05095392093062401,
-0.02022499218583107,
0.04815872386097908,
0.14250729978084564,
0.2146891951560974,
-0.18814782798290253,
-0.03490552678704262,
0.040057554841041565,
0.03203919157385826,
0.013093003071844578,
0.055347099900245667,
0.018052412196993828,
0.03358202055096626,
0.0068592713214457035,
-0.0505535788834095,
0.09063246101140976,
-0.026665249839425087,
-0.04467111825942993,
-0.011092301458120346,
-0.12317752838134766,
-0.012683172710239887,
-0.03724396228790283,
0.03485913947224617,
-0.0008856489439494908,
-0.040266357362270355,
0.13629162311553955,
0.16144564747810364,
-0.054887354373931885,
-0.021357588469982147,
-0.08336164057254791,
-0.012321169488132,
-0.03396638110280037,
-0.01704416610300541,
-0.1541488766670227,
-0.1654738038778305,
0.07749030739068985,
-0.14090536534786224,
0.0005388017743825912,
0.02097412571310997,
0.043878525495529175,
0.1180083230137825,
0.003112135222181678,
-0.06616832315921783,
0.01512453705072403,
-0.0014561567222699523,
-0.06425867974758148,
-0.1497047394514084,
-0.03489489108324051,
-0.05901213735342026,
0.16689005494117737,
-0.16720692813396454,
0.03951350972056389,
-0.10715853422880173,
0.1385767012834549,
0.069582000374794,
-0.040690939873456955,
0.033251360058784485,
0.0016146119451150298,
-0.0031408206559717655,
-0.06979653984308243,
0.009683455340564251,
0.010333150625228882,
-0.05565807223320007,
0.02301272563636303,
-0.1267225295305252,
0.033660098910331726,
0.022029004991054535,
0.018435247242450714,
-0.05853593721985817,
-0.10545171797275543,
-0.06147586554288864,
-0.029566045850515366,
-0.09272488206624985,
-0.048264313489198685,
0.15875039994716644,
0.04876354709267616,
0.1345187872648239,
-0.035715240985155106,
-0.07122164219617844,
-0.0018253610469400883,
-0.012150200083851814,
0.026428846642374992,
0.055335454642772675,
0.053896281868219376,
-0.11920935660600662,
0.03914673626422882,
0.08307857066392899,
-0.08299604803323746,
0.08672481030225754,
-0.02812988869845867,
-0.07031150162220001,
-0.0364665649831295,
0.038129158318042755,
0.010085650719702244,
0.03137331083416939,
-0.05406128615140915,
0.021884366869926453,
0.03548996150493622,
0.02083054929971695,
-0.006379700731486082,
-0.10505597293376923,
0.03969651088118553,
-0.002787114819511771,
-0.04642762243747711,
0.03075828030705452,
-0.0374767892062664,
0.010184369049966335,
0.06188179925084114,
0.0051098475232720375,
-0.037101827561855316,
-0.011847703717648983,
-0.018895955756306648,
-0.11407653987407684,
0.16117925941944122,
-0.09202874451875687,
-0.16141277551651,
-0.1419515460729599,
-0.017694992944598198,
-0.04150168597698212,
0.02495834045112133,
-0.017742794007062912,
0.004913479555398226,
-0.08081131428480148,
-0.09724567085504532,
-0.033769089728593826,
0.057497069239616394,
0.02573195844888687,
-0.03964615985751152,
0.025418046861886978,
0.1043764129281044,
-0.0956033319234848,
-0.0059143793769180775,
-0.020805394276976585,
-0.03386572748422623,
0.026137210428714752,
0.014914496801793575,
0.06015286594629288,
0.14501941204071045,
0.020794153213500977,
-0.016846291720867157,
-0.010315416380763054,
0.20617076754570007,
-0.03988306224346161,
0.04971412569284439,
0.15579168498516083,
0.003532044356688857,
0.054234784096479416,
0.1230945736169815,
0.05202696472406387,
-0.0405389741063118,
0.031786806881427765,
0.05078781396150589,
0.011222089640796185,
-0.24256084859371185,
-0.06491149961948395,
-0.05123855173587799,
-0.0456649474799633,
0.13165698945522308,
0.04054783657193184,
-0.022809579968452454,
0.06629524379968643,
-0.01586160808801651,
0.06913632154464722,
0.0802140161395073,
0.09005957841873169,
0.1200733482837677,
0.03349728137254715,
0.10542160272598267,
-0.0065588438883423805,
-0.058099597692489624,
0.056703828275203705,
0.05163666978478432,
0.1976437121629715,
-0.02141696587204933,
0.13993039727210999,
0.018135322257876396,
0.05905217304825783,
0.018090665340423584,
0.07586850970983505,
0.05909497290849686,
0.01853562332689762,
0.005708134267479181,
-0.05184239521622658,
-0.025888927280902863,
0.076649010181427,
-0.004507501143962145,
0.00590740330517292,
0.00394744798541069,
0.07597724348306656,
0.05908658355474472,
0.2659769356250763,
0.08935872465372086,
-0.2861347794532776,
-0.04853518307209015,
0.030689092352986336,
-0.0654730498790741,
-0.07871580123901367,
0.0058949049562215805,
0.06595288217067719,
-0.07462194561958313,
0.07741375267505646,
-0.03455200791358948,
0.076097272336483,
-0.1311541348695755,
0.011291610077023506,
0.050401851534843445,
0.08469557762145996,
0.012042856775224209,
0.06828910112380981,
-0.1568068116903305,
0.10358808189630508,
0.02640354633331299,
0.06956169754266739,
-0.024801068007946014,
0.04445234686136246,
0.02811129204928875,
0.10000953823328018,
0.10180680453777313,
0.0060736373998224735,
-0.020606745034456253,
-0.15639013051986694,
-0.14281874895095825,
0.026592625305056572,
0.04770103096961975,
-0.06331000477075577,
0.07871571183204651,
-0.029321786016225815,
-0.02790633589029312,
0.007533028721809387,
-0.05546582490205765,
-0.12281534820795059,
-0.13672995567321777,
0.032628659158945084,
0.011439657770097256,
-0.008012144826352596,
-0.061964359134435654,
-0.03683948144316673,
-0.0675780400633812,
0.16987569630146027,
-0.06141611561179161,
-0.048177920281887054,
-0.1489146500825882,
0.04169723391532898,
0.0908435508608818,
-0.06671907007694244,
0.04690813273191452,
-0.0009851090144366026,
0.12259288132190704,
-0.015876123681664467,
-0.08277912437915802,
0.038579683750867844,
-0.0943758562207222,
-0.15203502774238586,
-0.07080559432506561,
0.07874153554439545,
0.07118275761604309,
0.03788599744439125,
0.011137131601572037,
0.005905103404074907,
0.006841944996267557,
-0.08569410443305969,
0.035779230296611786,
0.23584268987178802,
0.014918600209057331,
0.05615249276161194,
-0.07531953603029251,
-0.051515061408281326,
-0.018551982939243317,
0.0573127418756485,
0.06362432986497879,
0.2429869920015335,
-0.06258976459503174,
0.07780750840902328,
0.14195318520069122,
-0.04602741822600365,
-0.13176082074642181,
-0.001966449897736311,
0.15443728864192963,
0.001367144868709147,
-0.03634551540017128,
-0.18580949306488037,
0.1794845461845398,
0.09341635555028915,
-0.046072863042354584,
-0.015067210420966148,
-0.18520313501358032,
-0.1125154122710228,
0.10647599399089813,
0.07393188029527664,
0.02415047213435173,
-0.05960329249501228,
-0.036850862205028534,
-0.02905518002808094,
-0.0878266766667366,
0.1326228380203247,
-0.08267045766115189,
0.08050250262022018,
-0.005577781703323126,
0.028603726997971535,
0.039158087223768234,
-0.050367824733257294,
0.1087108701467514,
0.03878410905599594,
0.07206025719642639,
-0.004002661909908056,
0.08581732213497162,
0.03491273894906044,
-0.10904491692781448,
0.10547521710395813,
-0.03608333319425583,
0.034827787429094315,
-0.11898140609264374,
-0.05917825177311897,
-0.012762228958308697,
0.06711394339799881,
0.020290441811084747,
-0.07878905534744263,
-0.04050261899828911,
0.02397432178258896,
0.09028404206037521,
0.013946660794317722,
0.07172883301973343,
0.0246278066188097,
-0.024929944425821304,
0.14474688470363617,
0.09031601250171661,
0.022528626024723053,
-0.0830545425415039,
-0.01320051308721304,
-0.04420115426182747,
0.05051364004611969,
-0.11643898487091064,
0.04632582888007164,
0.12879739701747894,
0.009705443866550922,
0.14812345802783966,
0.022036109119653702,
-0.08464539796113968,
-0.01730344071984291,
0.03916940838098526,
-0.12408576905727386,
-0.07132027298212051,
-0.007273293565958738,
0.061135660856962204,
-0.12352510541677475,
0.006692591588944197,
0.19112400710582733,
-0.03167495131492615,
-0.005614529829472303,
0.0186656154692173,
0.0021840750705450773,
0.0009056308190338314,
0.161431685090065,
0.006926940754055977,
0.063934326171875,
-0.054166030138731,
0.08362141996622086,
0.11144983768463135,
-0.07761584967374802,
-0.009559009224176407,
0.09988744556903839,
-0.13510578870773315,
-0.060269612818956375,
-0.038256533443927765,
0.15672308206558228,
-0.07120048999786377,
-0.038635846227407455,
-0.048689667135477066,
-0.09607166796922684,
0.06091965362429619,
0.10134318470954895,
0.02096099779009819,
-0.008413977921009064,
-0.04522078484296799,
0.026318587362766266,
-0.0592682845890522,
0.13936978578567505,
0.022832978516817093,
0.09244740009307861,
-0.11892502754926682,
0.0804138109087944,
-0.013828778639435768,
0.004454682115465403,
-0.020881328731775284,
0.0347062349319458,
-0.09883084148168564,
-0.04041502624750137,
-0.1675623208284378,
0.07536330819129944,
-0.05828676000237465,
0.02318946272134781,
0.012286171317100525,
-0.0020707326475530863,
-0.010602583177387714,
0.017097849398851395,
-0.05316631868481636,
-0.03867990896105766,
-0.022943709045648575,
0.06885157525539398,
-0.11717469990253448,
-0.07431959360837936,
0.009850510396063328,
-0.12975633144378662,
0.13085319101810455,
0.02332732081413269,
0.016207700595259666,
0.0014900193782523274,
-0.07921077311038971,
0.0035326604265719652,
0.09275370091199875,
0.09196144342422485,
0.039194926619529724,
-0.09379328042268753,
0.012287627905607224,
-0.028869498521089554,
-0.05176016688346863,
-0.019205180928111076,
0.01827828958630562,
-0.06406418234109879,
-0.010233812034130096,
-0.023233938962221146,
-0.06394212692975998,
-0.1185089498758316,
-0.023704785853624344,
0.06468158960342407,
0.03691372275352478,
0.12667356431484222,
-0.060914792120456696,
0.031107239425182343,
-0.16482308506965637,
-0.015990234911441803,
0.026441996917128563,
-0.02328484132885933,
-0.06782373785972595,
-0.05453561246395111,
0.07374689728021622,
-0.04358744993805885,
0.10387542098760605,
-0.023264383897185326,
0.0541217066347599,
0.005297720432281494,
-0.043449487537145615,
0.0329839289188385,
0.05413069203495979,
0.07611613720655441,
0.05389275774359703,
-0.020666584372520447,
0.003472220152616501,
-0.019386237487196922,
0.06705854088068008,
0.06766296923160553,
0.07872853428125381,
0.14256983995437622,
-0.06143910810351372,
0.0420207753777504,
0.051884669810533524,
-0.12232191860675812,
-0.14941760897636414,
0.0585147850215435,
-0.08605920523405075,
0.09059159457683563,
-0.01495696883648634,
0.1459987610578537,
0.08037934452295303,
-0.15884153544902802,
-0.0200500451028347,
-0.0020561139099299908,
-0.08242813497781754,
-0.11487732082605362,
-0.11432725191116333,
-0.06123729422688484,
-0.09533141553401947,
-0.01859881542623043,
-0.08878641575574875,
-0.004234444350004196,
0.0012381522683426738,
0.02024718187749386,
0.05419863387942314,
0.07967213541269302,
-0.024707961827516556,
0.022008929401636124,
0.029562313109636307,
0.028865214437246323,
0.025359278544783592,
-0.010679972358047962,
-0.06189671531319618,
0.03889589011669159,
-0.0100508788600564,
0.029104800894856453,
-0.04094003885984421,
0.08349359035491943,
0.05427071079611778,
0.02430638112127781,
-0.06530468165874481,
0.033234428614377975,
-0.016264336183667183,
-0.0599752739071846,
0.08045140653848648,
0.075835682451725,
-0.008214361034333706,
-0.011767594143748283,
0.17429903149604797,
-0.07242842763662338,
-0.06505824625492096,
-0.17594857513904572,
0.22456341981887817,
-0.0015482663875445724,
0.02683641016483307,
0.030270906165242195,
-0.06867092102766037,
-0.049969810992479324,
0.1555144190788269,
0.09545370191335678,
-0.057392269372940063,
-0.0016717161051928997,
-0.00863431952893734,
-0.03207433968782425,
-0.012468413449823856,
0.08510807901620865,
0.08108016848564148,
0.12737531960010529,
-0.028808698058128357,
-0.00062707276083529,
0.0041790008544921875,
-0.03187837451696396,
-0.0544864758849144,
0.07018642872571945,
-0.0026114548090845346,
0.023583078756928444,
-0.03433876112103462,
0.027191611006855965,
-0.05546587333083153,
-0.1148342713713646,
0.06208109110593796,
-0.05794942378997803,
-0.10033514350652695,
-0.05166224390268326,
-0.008187469094991684,
-0.02452826499938965,
0.030120640993118286,
-0.0621308833360672,
-0.04679551348090172,
0.15010210871696472,
-0.04739227518439293,
-0.10881149023771286,
-0.024139730259776115,
0.05021737143397331,
-0.041151002049446106,
0.16861052811145782,
0.01562708616256714,
0.061341915279626846,
0.10748530179262161,
0.02085467055439949,
-0.14402465522289276,
0.06666679680347443,
0.028111567720770836,
-0.12882648408412933,
0.02939097210764885,
0.13547617197036743,
-0.027868149802088737,
0.12218865752220154,
0.05647491663694382,
-0.1518784463405609,
-0.03340381011366844,
-0.043546173721551895,
-0.05294017121195793,
-0.0955977514386177,
-0.009619061835110188,
-0.0299873985350132,
0.104153573513031,
0.12626735866069794,
-0.0009848106419667602,
0.009813530370593071,
-0.024047717452049255,
0.05522527918219566,
0.04757542163133621,
0.021174542605876923,
-0.013691427186131477,
-0.19348786771297455,
0.048748038709163666,
0.007269431836903095,
0.0680399239063263,
-0.14663034677505493,
-0.0975697860121727,
-0.03557334467768669,
-0.026288215070962906,
-0.05125311762094498,
0.12114644050598145,
0.04062306880950928,
0.03581417724490166,
-0.03639731928706169,
-0.11137568950653076,
-0.034508656710386276,
0.09409964084625244,
-0.12029097229242325,
-0.08632440119981766
] |
null | null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral-finetuned-samsum
This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.1-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 250
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "TheBloke/Mistral-7B-Instruct-v0.1-GPTQ", "model-index": [{"name": "mistral-finetuned-samsum", "results": []}]} | null | Purukoli/mistral-finetuned-samsum | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ",
"license:apache-2.0",
"region:us"
] | 2024-02-09T12:59:01+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-TheBloke/Mistral-7B-Instruct-v0.1-GPTQ #license-apache-2.0 #region-us
|
# mistral-finetuned-samsum
This model is a fine-tuned version of TheBloke/Mistral-7B-Instruct-v0.1-GPTQ on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 250
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1 | [
"# mistral-finetuned-samsum\n\nThis model is a fine-tuned version of TheBloke/Mistral-7B-Instruct-v0.1-GPTQ on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 250\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
"TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-TheBloke/Mistral-7B-Instruct-v0.1-GPTQ #license-apache-2.0 #region-us \n",
"# mistral-finetuned-samsum\n\nThis model is a fine-tuned version of TheBloke/Mistral-7B-Instruct-v0.1-GPTQ on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 250\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
62,
45,
6,
12,
8,
3,
102,
4,
44
] | [
"passage: TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-TheBloke/Mistral-7B-Instruct-v0.1-GPTQ #license-apache-2.0 #region-us \n# mistral-finetuned-samsum\n\nThis model is a fine-tuned version of TheBloke/Mistral-7B-Instruct-v0.1-GPTQ on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 250\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
-0.10875197499990463,
0.09050465375185013,
-0.002950237365439534,
0.07555120438337326,
0.11527957767248154,
0.03136107325553894,
0.09810794144868851,
0.14219079911708832,
-0.06912323087453842,
0.09345142543315887,
0.06834494322538376,
0.019139179959893227,
0.08463143557310104,
0.19333602488040924,
-0.026880910620093346,
-0.2263862043619156,
0.037407536059617996,
-0.04669421911239624,
-0.042224399745464325,
0.10796573013067245,
0.10401119291782379,
-0.10059069842100143,
0.05225154384970665,
-0.01893964596092701,
-0.13940821588039398,
-0.025803694501519203,
-0.01214022096246481,
-0.039354108273983,
0.09444190561771393,
-0.0012631325516849756,
0.13220573961734772,
0.03187096491456032,
0.1455622762441635,
-0.21901045739650726,
0.005379816982895136,
0.08486799150705338,
0.05435498058795929,
0.09373292326927185,
0.06169688329100609,
0.016123028472065926,
0.03414313495159149,
-0.1413487046957016,
0.10101285576820374,
0.01907677762210369,
-0.10735724866390228,
-0.11872468143701553,
-0.13482043147087097,
0.1058058813214302,
0.11269693076610565,
0.0909079909324646,
0.010603686794638634,
0.13049958646297455,
-0.07549100369215012,
0.055145926773548126,
0.18331539630889893,
-0.21148303151130676,
-0.08151106536388397,
0.06701254099607468,
0.06404054164886475,
0.08601274341344833,
-0.11568813025951385,
-0.03314314782619476,
0.028546448796987534,
0.026957912370562553,
0.08688035607337952,
-0.01924402453005314,
-0.08628526329994202,
-0.016188358888030052,
-0.1271159052848816,
-0.05104115232825279,
0.12528075277805328,
0.03944025933742523,
-0.051066141575574875,
-0.08386542648077011,
-0.040218643844127655,
-0.11861823499202728,
-0.016754180192947388,
-0.06237964704632759,
0.027684591710567474,
-0.02335493080317974,
-0.006492671091109514,
-0.055019836872816086,
-0.10948768258094788,
-0.06547235697507858,
0.0054114386439323425,
0.021357346326112747,
0.01783839613199234,
0.022128647193312645,
-0.01322607696056366,
0.13279329240322113,
-0.010475097224116325,
-0.10142145305871964,
-0.02340182662010193,
-0.017155475914478302,
-0.10940654575824738,
-0.07985567301511765,
-0.012774301692843437,
-0.0023144937586039305,
-0.012229274027049541,
0.14640650153160095,
-0.09666990488767624,
0.07495934516191483,
-0.007838523015379906,
0.029620354995131493,
-0.04755830392241478,
0.12155222147703171,
-0.03519273176789284,
-0.007218226324766874,
-0.0029238637071102858,
0.1246848851442337,
-0.005907422862946987,
-0.0148399593308568,
-0.05329812690615654,
-0.021779030561447144,
0.07968513667583466,
0.07006999105215073,
-0.030594782903790474,
0.0036154743283987045,
-0.07441848516464233,
-0.028136899694800377,
0.09395913034677505,
-0.12218059599399567,
0.056649100035429,
0.01112455315887928,
-0.05280740559101105,
-0.022113624960184097,
0.04140433669090271,
0.018064826726913452,
-0.028802983462810516,
0.08972633630037308,
-0.07231125980615616,
0.017733892425894737,
-0.07613152265548706,
-0.06369822472333908,
0.03918507322669029,
-0.07857971638441086,
-0.055521536618471146,
-0.07248485088348389,
-0.19527912139892578,
-0.06978531926870346,
0.03046610578894615,
-0.05389685928821564,
-0.03181975707411766,
-0.04590992256999016,
-0.056731365621089935,
0.020341631025075912,
-0.01235611829906702,
0.12799254059791565,
-0.04910726845264435,
0.07845389097929001,
-0.06931246817111969,
0.011105604469776154,
0.0036664640065282583,
0.02976953610777855,
-0.07216481864452362,
0.03468085080385208,
-0.15475445985794067,
0.06668293476104736,
-0.0891973227262497,
0.016512200236320496,
-0.1450483649969101,
-0.08925573527812958,
-0.010720223188400269,
-0.04368186742067337,
0.061006106436252594,
0.12704552710056305,
-0.19708473980426788,
-0.008796954527497292,
0.1838015913963318,
-0.09470459073781967,
-0.03663704916834831,
0.09717898070812225,
-0.04047761484980583,
0.04107245057821274,
0.055468909442424774,
0.14432933926582336,
0.11301950365304947,
-0.15575644373893738,
0.013171951286494732,
-0.015206610783934593,
0.0621228963136673,
0.04684382304549217,
0.06461331248283386,
-0.040050409734249115,
0.02830389328300953,
0.00016433840210083872,
-0.08148819953203201,
-0.006992563139647245,
-0.06612345576286316,
-0.0856713280081749,
-0.046060286462306976,
-0.05905086547136307,
0.04677553102374077,
0.03449626639485359,
0.017211664468050003,
-0.056670527905225754,
-0.1065894216299057,
0.06742698699235916,
0.13972772657871246,
-0.04360087960958481,
0.011632971465587616,
-0.06949766725301743,
0.025522198528051376,
0.015920832753181458,
-0.0338631235063076,
-0.19064874947071075,
-0.10132353752851486,
0.04291992634534836,
-0.03664014860987663,
0.0178673155605793,
0.04046499356627464,
0.05587942153215408,
0.060080766677856445,
-0.05304942652583122,
-0.020986003801226616,
-0.07919619232416153,
0.00032917654607445,
-0.11054366081953049,
-0.18993356823921204,
-0.03978400304913521,
-0.037909336388111115,
0.22319918870925903,
-0.24100056290626526,
0.019926918670535088,
0.01617545634508133,
0.1430215835571289,
0.03557562083005905,
-0.07201719284057617,
0.0038229378405958414,
0.06792152673006058,
0.01840406097471714,
-0.10279384255409241,
0.042129699140787125,
-0.0044009750708937645,
-0.06843911856412888,
-0.032851021736860275,
-0.15976180136203766,
-0.03542163595557213,
0.06852639466524124,
0.10808750987052917,
-0.13003601133823395,
-0.07226576656103134,
-0.06091370806097984,
-0.04940984025597572,
-0.08528982102870941,
0.0006151238339953125,
0.14704136550426483,
0.03266408294439316,
0.10540857166051865,
-0.048580653965473175,
-0.05960966646671295,
0.0016001260373741388,
0.017028557136654854,
0.006862674839794636,
0.073846735060215,
0.051661912351846695,
-0.1301954984664917,
0.094235360622406,
0.10380948334932327,
-0.04536626115441322,
0.11455881595611572,
-0.05774902552366257,
-0.09394486993551254,
-0.02591097727417946,
0.04897255077958107,
0.018226856365799904,
0.12919388711452484,
-0.010698720812797546,
0.022363808006048203,
0.031778838485479355,
0.02241540141403675,
0.006240049377083778,
-0.19724252820014954,
-0.009591824375092983,
0.00898104440420866,
-0.053065065294504166,
-0.008375831879675388,
-0.036809928715229034,
0.042740799486637115,
0.07888327538967133,
0.01851077377796173,
-0.019858121871948242,
0.005962541792541742,
-0.009165859781205654,
-0.10714122653007507,
0.1729808896780014,
-0.1461166888475418,
-0.16648538410663605,
-0.1187715008854866,
0.1118551567196846,
0.01970282755792141,
-0.02631359174847603,
0.008370038121938705,
-0.052105776965618134,
-0.03667720779776573,
-0.1127764880657196,
-0.06830286979675293,
-0.025653690099716187,
-0.017753753811120987,
0.02344554476439953,
0.017050061374902725,
0.07354563474655151,
-0.10637784004211426,
0.0010126804700121284,
0.005950795952230692,
-0.08936203271150589,
0.02900957688689232,
0.02653108350932598,
0.0690191313624382,
0.15330399572849274,
-0.005507814697921276,
-0.007819894701242447,
-0.06321436166763306,
0.17182807624340057,
-0.1001305803656578,
0.02847369946539402,
0.10478872805833817,
-0.002820839174091816,
0.0638858899474144,
0.1337907910346985,
0.022495077922940254,
-0.07167776674032211,
0.02111757919192314,
0.04053299501538277,
-0.012029420584440231,
-0.24670085310935974,
-0.04372677206993103,
-0.030914535745978355,
-0.021497245877981186,
0.10634882003068924,
0.054042115807533264,
0.034116026014089584,
0.043910443782806396,
-0.04110782966017723,
0.006529794540256262,
0.035770487040281296,
0.07211568206548691,
0.038445595651865005,
0.032427962869405746,
0.10158006101846695,
-0.017144570127129555,
0.011489569209516048,
0.07433616369962692,
0.02819948084652424,
0.25534242391586304,
-0.008451553992927074,
0.10761703550815582,
0.03288406878709793,
0.16976502537727356,
-0.015924211591482162,
0.03532999008893967,
0.057724419981241226,
-0.0022440156899392605,
-0.004335043020546436,
-0.06632586568593979,
-0.03673458844423294,
0.04887143149971962,
0.020637981593608856,
0.029460785910487175,
-0.07251305133104324,
0.018006186932325363,
0.019891006872057915,
0.30036085844039917,
0.04890323430299759,
-0.2817043960094452,
-0.0878630205988884,
0.021062597632408142,
-0.028215935453772545,
-0.05916747450828552,
-0.019136041402816772,
0.1493290364742279,
-0.1605784296989441,
0.05295335128903389,
-0.07070541381835938,
0.09434416145086288,
-0.03286546468734741,
-0.013320562429726124,
0.04175388067960739,
0.0836866945028305,
-0.0017231915844604373,
0.05956054478883743,
-0.19726382195949554,
0.2266620546579361,
0.015317777171730995,
0.10453964024782181,
-0.04329445958137512,
0.03813952952623367,
0.026055289432406425,
0.09288424998521805,
0.117429219186306,
0.0017565221060067415,
-0.08408039808273315,
-0.20451033115386963,
-0.11581243574619293,
0.01640971750020981,
0.10196252167224884,
-0.052347082644701004,
0.055385857820510864,
-0.04151664301753044,
0.007627890445291996,
0.02780470810830593,
-0.07857916504144669,
-0.17653533816337585,
-0.1110459715127945,
0.04023294523358345,
0.009630929678678513,
-0.009085566736757755,
-0.10041674226522446,
-0.09109512716531754,
0.001858446397818625,
0.16041722893714905,
-0.08275371789932251,
-0.02132461406290531,
-0.1497064083814621,
0.07269217818975449,
0.13180312514305115,
-0.0526326484978199,
0.01679978519678116,
0.015002666972577572,
0.11103975772857666,
0.012580005452036858,
-0.04488828033208847,
0.04964528977870941,
-0.07324438542127609,
-0.19739821553230286,
-0.08593481034040451,
0.13201755285263062,
0.08127421885728836,
0.06565220654010773,
0.0020670522935688496,
0.041248347610235214,
0.03293576091527939,
-0.08975725620985031,
0.015425578691065311,
0.15782472491264343,
0.08487817645072937,
0.054147765040397644,
-0.06522650271654129,
-0.013618013821542263,
-0.05590023472905159,
-0.04725160077214241,
0.09744472801685333,
0.21085615456104279,
-0.09813082218170166,
0.11039711534976959,
0.06403739750385284,
-0.08330642431974411,
-0.18415740132331848,
0.06837784498929977,
0.13054107129573822,
0.038121920078992844,
0.047182850539684296,
-0.15682603418827057,
0.06232607737183571,
0.1552528738975525,
-0.03179970756173134,
0.05246608704328537,
-0.3307322859764099,
-0.13028331100940704,
0.05007803812623024,
0.06768889725208282,
-0.038478922098875046,
-0.1315935254096985,
-0.04515504837036133,
0.0015430693747475743,
-0.1286855936050415,
0.08584358543157578,
-0.09905196726322174,
0.0916273444890976,
0.006412773858755827,
0.05429181829094887,
0.022601434960961342,
-0.03712010011076927,
0.17193633317947388,
0.01104159839451313,
0.07466943562030792,
-0.04856640845537186,
0.0015817669918760657,
0.07857546955347061,
-0.07793689519166946,
0.038451146334409714,
-0.007708773948252201,
0.05486837029457092,
-0.12677812576293945,
-0.0018500370206311345,
-0.0775575041770935,
0.05883023515343666,
-0.0521659180521965,
-0.047938767820596695,
-0.04872067645192146,
0.08546674251556396,
0.03942112624645233,
-0.03222677856683731,
0.04289155453443527,
0.0023146101739257574,
0.13032925128936768,
0.12415309995412827,
0.09139855951070786,
0.012857632711529732,
-0.09585443139076233,
-0.0061519998125731945,
-0.034279417246580124,
0.05846395343542099,
-0.10094811022281647,
0.009702368639409542,
0.13076069951057434,
0.03114786185324192,
0.15265099704265594,
0.023891523480415344,
-0.06957715004682541,
0.011789960786700249,
0.04317818582057953,
-0.10128481686115265,
-0.14586317539215088,
0.027594316750764847,
0.05483213812112808,
-0.12964604794979095,
-0.028901653364300728,
0.138497456908226,
-0.0551302470266819,
-0.030228622257709503,
0.0049959528259932995,
0.03634832799434662,
-0.03206583112478256,
0.20215563476085663,
0.012952763587236404,
0.07573917508125305,
-0.06729960441589355,
0.10340266674757004,
0.09976086020469666,
-0.1158614307641983,
0.031122544780373573,
0.08867087960243225,
-0.1089104413986206,
-0.028183959424495697,
0.08002959936857224,
0.0936269536614418,
-0.005043084733188152,
-0.0387575589120388,
-0.03823951631784439,
-0.08945494145154953,
0.044515740126371384,
0.10423585027456284,
0.01981762796640396,
0.024565663188695908,
-0.01184883713722229,
0.024039413779973984,
-0.11041609942913055,
0.061880093067884445,
0.04644466191530228,
0.07002299278974533,
-0.11554624885320663,
0.16083534061908722,
0.0020732744596898556,
0.012330520898103714,
-0.004126632586121559,
0.017947040498256683,
-0.0777183473110199,
-0.01748134382069111,
-0.09951097518205643,
0.02383211813867092,
-0.042945507913827896,
-0.014264975674450397,
0.008252440020442009,
-0.044676847755908966,
0.002350523602217436,
0.03687690943479538,
-0.07056424021720886,
-0.057833753526210785,
-0.024731803685426712,
0.06850874423980713,
-0.13494963943958282,
-0.022456733509898186,
0.037207942456007004,
-0.07945763319730759,
0.07223815470933914,
0.051103632897138596,
0.03188285231590271,
0.019054502248764038,
-0.14555472135543823,
0.0012790345354005694,
0.03527611866593361,
0.022600330412387848,
0.04393412545323372,
-0.0995808020234108,
-0.020949242636561394,
-0.04046875983476639,
0.015804734081029892,
0.03932779282331467,
0.052061304450035095,
-0.11114516109228134,
-0.03986196219921112,
-0.040852587670087814,
-0.06820068508386612,
-0.06840478628873825,
0.049005769193172455,
0.11345841735601425,
0.030060332268476486,
0.11891431361436844,
-0.08885346353054047,
0.0680771991610527,
-0.19835898280143738,
-0.03196874260902405,
-0.0098161231726408,
-0.005124383606016636,
-0.07677680999040604,
-0.036047667264938354,
0.09540972113609314,
-0.04257846251130104,
0.031219882890582085,
-0.03259935975074768,
0.09736566990613937,
0.03533453121781349,
-0.10147242248058319,
-0.02451365999877453,
0.03639201074838638,
0.1268618404865265,
0.08010295778512955,
-0.03460942208766937,
0.07911453396081924,
-0.03576859459280968,
0.026107508689165115,
0.0279021505266428,
0.20418913662433624,
0.15782612562179565,
0.011496683582663536,
0.061682965606451035,
0.04061863571405411,
-0.1138908639550209,
-0.11702350527048111,
0.09828921407461166,
-0.04394577071070671,
0.08764069527387619,
-0.0730801448225975,
0.17532870173454285,
0.08579272031784058,
-0.20957441627979279,
0.03861074522137642,
-0.07581094652414322,
-0.11293280124664307,
-0.1011435016989708,
-0.059645313769578934,
-0.06969191133975983,
-0.09869644045829773,
-0.0017846806440502405,
-0.10158710181713104,
0.04483197256922722,
0.09613001346588135,
0.024789586663246155,
0.030280673876404762,
0.10975168645381927,
-0.02575044147670269,
0.009316779673099518,
0.07024780660867691,
0.03945698216557503,
0.02653873711824417,
-0.0805148184299469,
-0.08417849987745285,
0.05895411968231201,
0.0007983677787706256,
0.08755229413509369,
-0.04042919725179672,
0.03960109502077103,
0.026823023334145546,
-0.00319976219907403,
-0.07043334096670151,
0.03933635354042053,
0.009634439833462238,
0.014797735027968884,
0.06650342792272568,
0.06698420643806458,
0.019394928589463234,
-0.026353739202022552,
0.26603925228118896,
-0.06620378792285919,
-0.08070648461580276,
-0.15616728365421295,
0.22479882836341858,
0.002010711468756199,
-0.009087069891393185,
0.06481923162937164,
-0.1110786646604538,
-0.0011757755419239402,
0.12296069413423538,
0.1405731439590454,
-0.053799599409103394,
-0.00031191238667815924,
-0.00325013673864305,
-0.01777097024023533,
-0.06869950890541077,
0.1113942340016365,
0.07297175377607346,
0.022317063063383102,
-0.08284658193588257,
0.019051680341362953,
0.02298331819474697,
-0.044862423092126846,
-0.07994133234024048,
0.005565904080867767,
-0.02007261849939823,
0.023087453097105026,
-0.047018881887197495,
0.08759530633687973,
0.04118731990456581,
-0.16947002708911896,
0.08224426954984665,
-0.1677943468093872,
-0.17344114184379578,
-0.00661909393966198,
0.0603574700653553,
-0.031175777316093445,
0.038032740354537964,
-0.01735275238752365,
-0.008087201043963432,
0.1650896817445755,
-0.03303956240415573,
-0.034405384212732315,
-0.11617541313171387,
0.07518094778060913,
-0.053167715668678284,
0.20212271809577942,
-0.01706691086292267,
0.08284800499677658,
0.08753857016563416,
0.0360441692173481,
-0.12334706634283066,
0.014260660856962204,
0.07435833662748337,
-0.04086349532008171,
0.011129924096167088,
0.16553886234760284,
-0.05399376153945923,
0.10000818967819214,
0.06619155406951904,
-0.1411086618900299,
-0.004996117204427719,
-0.057744868099689484,
-0.012785565108060837,
-0.07293923944234848,
0.0030242835637181997,
-0.059535764157772064,
0.16404952108860016,
0.19111746549606323,
-0.028849683701992035,
0.017233779653906822,
-0.05517370253801346,
0.01595316082239151,
0.03669178858399391,
0.10379702597856522,
-0.026286140084266663,
-0.18043309450149536,
0.03198210522532463,
-0.0030916056130081415,
0.05358930304646492,
-0.18937014043331146,
-0.11868709325790405,
0.026814881712198257,
-0.05718126893043518,
-0.045940253883600235,
0.13799650967121124,
0.00305541860871017,
0.02725352719426155,
-0.026918824762105942,
-0.16339802742004395,
-0.025861706584692,
0.14317838847637177,
-0.12649290263652802,
-0.03746026009321213
] |
null | null | peft |
# Mistral-7B-dbnl-v0.1
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the DBNL Public Domain dataset, featuring texts from the Dutch Literature that are in the public domain, specifically focusing on historical texts that are at least 140 years old.
## Model description
Mistral-7B-dbnl-v0.1 is designed to generate and understand Dutch literature, trained on a wide array of historical Dutch texts. This model leverages the LORA (Low-Rank Adaptation) technique for efficient parameter adaptation, providing a way to maintain high performance while being computationally efficient.
## Intended uses & limitations
I mostly created this for fun, cultural learnings and sharing with others.
This model is can be used by researchers, historians, and natural language processing practitioners interested in Dutch literature, historical text analysis, and language modeling. It can be used for tasks such as text generation, language modeling, and more.
### Limitations
- The model is trained on historical texts, which may contain biases and outdated language that do not reflect current norms or values.
- The model's performance and relevance may be limited to the context of Dutch literature and historical texts.
## Training and evaluation data
The model was trained on the DBNL Public Domain dataset, which includes a variety of texts such as books, poems, songs, and other documentation, ensuring a rich source of linguistic and cultural heritage.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
### Adapter configuration
The model uses LORA with the following configuration:
- lora_alpha: 2048
- r: 1024
- lora_dropout: 0.0
- inference_mode: true
- init_lora_weights: true
- peft_type: "LORA"
- target_modules: ["q_proj", "v_proj", "up_proj", "o_proj", "k_proj", "gate_proj"]
- task_type: "CAUSAL_LM"
This configuration allows the model to adapt the pre-trained layers specifically for the task of causal language modeling with an efficient use of parameters.
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.37.1
- Pytorch 2.1.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
The model is an innovative example of applying advanced NLP techniques to historical texts, offering a unique resource for exploring Dutch literature and linguistics. | {"language": ["nl"], "license": "cc0-1.0", "library_name": "peft", "tags": ["llama-factory", "lora", "generated_from_trainer"], "datasets": ["jvdgoltz/dbnl.org-dutch-public-domain"], "base_model": "mistralai/Mistral-7B-v0.1", "pipeline_tag": "text-generation", "model-index": [{"name": "Mistral-7B-dbnl-v0.1", "results": []}]} | text-generation | jvdgoltz/Mistral-7B-dbnl-v0.1 | [
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"text-generation",
"nl",
"dataset:jvdgoltz/dbnl.org-dutch-public-domain",
"base_model:mistralai/Mistral-7B-v0.1",
"license:cc0-1.0",
"region:us"
] | 2024-02-09T13:01:25+00:00 | [] | [
"nl"
] | TAGS
#peft #safetensors #llama-factory #lora #generated_from_trainer #text-generation #nl #dataset-jvdgoltz/dbnl.org-dutch-public-domain #base_model-mistralai/Mistral-7B-v0.1 #license-cc0-1.0 #region-us
|
# Mistral-7B-dbnl-v0.1
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the DBNL Public Domain dataset, featuring texts from the Dutch Literature that are in the public domain, specifically focusing on historical texts that are at least 140 years old.
## Model description
Mistral-7B-dbnl-v0.1 is designed to generate and understand Dutch literature, trained on a wide array of historical Dutch texts. This model leverages the LORA (Low-Rank Adaptation) technique for efficient parameter adaptation, providing a way to maintain high performance while being computationally efficient.
## Intended uses & limitations
I mostly created this for fun, cultural learnings and sharing with others.
This model is can be used by researchers, historians, and natural language processing practitioners interested in Dutch literature, historical text analysis, and language modeling. It can be used for tasks such as text generation, language modeling, and more.
### Limitations
- The model is trained on historical texts, which may contain biases and outdated language that do not reflect current norms or values.
- The model's performance and relevance may be limited to the context of Dutch literature and historical texts.
## Training and evaluation data
The model was trained on the DBNL Public Domain dataset, which includes a variety of texts such as books, poems, songs, and other documentation, ensuring a rich source of linguistic and cultural heritage.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
### Adapter configuration
The model uses LORA with the following configuration:
- lora_alpha: 2048
- r: 1024
- lora_dropout: 0.0
- inference_mode: true
- init_lora_weights: true
- peft_type: "LORA"
- target_modules: ["q_proj", "v_proj", "up_proj", "o_proj", "k_proj", "gate_proj"]
- task_type: "CAUSAL_LM"
This configuration allows the model to adapt the pre-trained layers specifically for the task of causal language modeling with an efficient use of parameters.
### Training results
!Training loss
### Framework versions
- PEFT 0.7.1
- Transformers 4.37.1
- Pytorch 2.1.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
The model is an innovative example of applying advanced NLP techniques to historical texts, offering a unique resource for exploring Dutch literature and linguistics. | [
"# Mistral-7B-dbnl-v0.1\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the DBNL Public Domain dataset, featuring texts from the Dutch Literature that are in the public domain, specifically focusing on historical texts that are at least 140 years old.",
"## Model description\n\nMistral-7B-dbnl-v0.1 is designed to generate and understand Dutch literature, trained on a wide array of historical Dutch texts. This model leverages the LORA (Low-Rank Adaptation) technique for efficient parameter adaptation, providing a way to maintain high performance while being computationally efficient.",
"## Intended uses & limitations\n\nI mostly created this for fun, cultural learnings and sharing with others.\n\nThis model is can be used by researchers, historians, and natural language processing practitioners interested in Dutch literature, historical text analysis, and language modeling. It can be used for tasks such as text generation, language modeling, and more.",
"### Limitations\n- The model is trained on historical texts, which may contain biases and outdated language that do not reflect current norms or values.\n- The model's performance and relevance may be limited to the context of Dutch literature and historical texts.",
"## Training and evaluation data\n\nThe model was trained on the DBNL Public Domain dataset, which includes a variety of texts such as books, poems, songs, and other documentation, ensuring a rich source of linguistic and cultural heritage.",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 2\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 16\n- total_eval_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 2000\n- num_epochs: 3.0",
"### Adapter configuration\n\nThe model uses LORA with the following configuration:\n- lora_alpha: 2048\n- r: 1024\n- lora_dropout: 0.0\n- inference_mode: true\n- init_lora_weights: true\n- peft_type: \"LORA\"\n- target_modules: [\"q_proj\", \"v_proj\", \"up_proj\", \"o_proj\", \"k_proj\", \"gate_proj\"]\n- task_type: \"CAUSAL_LM\"\n\nThis configuration allows the model to adapt the pre-trained layers specifically for the task of causal language modeling with an efficient use of parameters.",
"### Training results\n\n !Training loss",
"### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.37.1\n- Pytorch 2.1.1+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1\n\nThe model is an innovative example of applying advanced NLP techniques to historical texts, offering a unique resource for exploring Dutch literature and linguistics."
] | [
"TAGS\n#peft #safetensors #llama-factory #lora #generated_from_trainer #text-generation #nl #dataset-jvdgoltz/dbnl.org-dutch-public-domain #base_model-mistralai/Mistral-7B-v0.1 #license-cc0-1.0 #region-us \n",
"# Mistral-7B-dbnl-v0.1\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the DBNL Public Domain dataset, featuring texts from the Dutch Literature that are in the public domain, specifically focusing on historical texts that are at least 140 years old.",
"## Model description\n\nMistral-7B-dbnl-v0.1 is designed to generate and understand Dutch literature, trained on a wide array of historical Dutch texts. This model leverages the LORA (Low-Rank Adaptation) technique for efficient parameter adaptation, providing a way to maintain high performance while being computationally efficient.",
"## Intended uses & limitations\n\nI mostly created this for fun, cultural learnings and sharing with others.\n\nThis model is can be used by researchers, historians, and natural language processing practitioners interested in Dutch literature, historical text analysis, and language modeling. It can be used for tasks such as text generation, language modeling, and more.",
"### Limitations\n- The model is trained on historical texts, which may contain biases and outdated language that do not reflect current norms or values.\n- The model's performance and relevance may be limited to the context of Dutch literature and historical texts.",
"## Training and evaluation data\n\nThe model was trained on the DBNL Public Domain dataset, which includes a variety of texts such as books, poems, songs, and other documentation, ensuring a rich source of linguistic and cultural heritage.",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 2\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 16\n- total_eval_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 2000\n- num_epochs: 3.0",
"### Adapter configuration\n\nThe model uses LORA with the following configuration:\n- lora_alpha: 2048\n- r: 1024\n- lora_dropout: 0.0\n- inference_mode: true\n- init_lora_weights: true\n- peft_type: \"LORA\"\n- target_modules: [\"q_proj\", \"v_proj\", \"up_proj\", \"o_proj\", \"k_proj\", \"gate_proj\"]\n- task_type: \"CAUSAL_LM\"\n\nThis configuration allows the model to adapt the pre-trained layers specifically for the task of causal language modeling with an efficient use of parameters.",
"### Training results\n\n !Training loss",
"### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.37.1\n- Pytorch 2.1.1+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1\n\nThe model is an innovative example of applying advanced NLP techniques to historical texts, offering a unique resource for exploring Dutch literature and linguistics."
] | [
82,
71,
72,
79,
59,
54,
3,
158,
153,
8,
70
] | [
"passage: TAGS\n#peft #safetensors #llama-factory #lora #generated_from_trainer #text-generation #nl #dataset-jvdgoltz/dbnl.org-dutch-public-domain #base_model-mistralai/Mistral-7B-v0.1 #license-cc0-1.0 #region-us \n# Mistral-7B-dbnl-v0.1\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the DBNL Public Domain dataset, featuring texts from the Dutch Literature that are in the public domain, specifically focusing on historical texts that are at least 140 years old.## Model description\n\nMistral-7B-dbnl-v0.1 is designed to generate and understand Dutch literature, trained on a wide array of historical Dutch texts. This model leverages the LORA (Low-Rank Adaptation) technique for efficient parameter adaptation, providing a way to maintain high performance while being computationally efficient.## Intended uses & limitations\n\nI mostly created this for fun, cultural learnings and sharing with others.\n\nThis model is can be used by researchers, historians, and natural language processing practitioners interested in Dutch literature, historical text analysis, and language modeling. It can be used for tasks such as text generation, language modeling, and more.### Limitations\n- The model is trained on historical texts, which may contain biases and outdated language that do not reflect current norms or values.\n- The model's performance and relevance may be limited to the context of Dutch literature and historical texts.## Training and evaluation data\n\nThe model was trained on the DBNL Public Domain dataset, which includes a variety of texts such as books, poems, songs, and other documentation, ensuring a rich source of linguistic and cultural heritage.## Training procedure"
] | [
-0.07054857909679413,
-0.017162257805466652,
-0.0038452218286693096,
0.0890316590666771,
-0.0068632992915809155,
-0.0539713017642498,
0.04016445577144623,
0.03281830623745918,
0.003398740664124489,
0.06918645650148392,
-0.024488301947712898,
-0.03940917178988457,
0.03803513944149017,
-0.0029934742487967014,
0.04407666623592377,
-0.24622203409671783,
0.09603972733020782,
-0.07806278020143509,
0.06906288117170334,
0.07560565322637558,
0.12782007455825806,
-0.03446618840098381,
0.0676954835653305,
-0.03202076256275177,
0.008812417276203632,
-0.03824952244758606,
-0.02681412361562252,
-0.012956202030181885,
0.15749798715114594,
0.09257490932941437,
0.10930361598730087,
0.00048293519648723304,
0.018389247357845306,
-0.1674116551876068,
0.009105321019887924,
0.04348684847354889,
-0.020130254328250885,
-0.015587630681693554,
0.028383085504174232,
0.05795235559344292,
0.29631102085113525,
-0.033958036452531815,
-0.029617510735988617,
-0.009431148879230022,
-0.10453891009092331,
-0.0316290520131588,
-0.07400783151388168,
-0.0785336047410965,
0.07399826496839523,
0.06584528833627701,
0.0017188116908073425,
0.07372157275676727,
-0.1490717977285385,
0.043139081448316574,
0.09346344321966171,
-0.20999981462955475,
-0.05492817610502243,
0.06923697888851166,
0.08363599330186844,
0.142796590924263,
-0.11984734982252121,
0.05537976324558258,
-0.055217474699020386,
0.044122323393821716,
0.10745939612388611,
-0.05449069291353226,
0.0208234041929245,
-0.02601342648267746,
-0.09405481815338135,
-0.05011923983693123,
0.13414137065410614,
-0.030874568969011307,
-0.04634164646267891,
-0.14235946536064148,
-0.07247580587863922,
0.08534258604049683,
-0.03942381963133812,
-0.08624967932701111,
0.04347531870007515,
0.04117667302489281,
0.19550465047359467,
-0.09631967544555664,
-0.10445579886436462,
0.01694312132894993,
0.009857428260147572,
0.05450659245252609,
0.04133051261305809,
-0.015139893628656864,
0.018809443339705467,
0.08652275800704956,
-0.099534772336483,
-0.03585841879248619,
-0.08561999350786209,
-0.044650252908468246,
-0.03251178190112114,
0.015762934461236,
0.009539779275655746,
-0.0676175132393837,
-0.05087932571768761,
0.02523813582956791,
-0.05582728236913681,
0.0029277577996253967,
0.0105296541005373,
0.05900144949555397,
-0.007725537288933992,
0.05770178884267807,
-0.02178155444562435,
-0.11175300925970078,
0.09672435373067856,
-0.023418618366122246,
0.048180483281612396,
0.01816760003566742,
-0.046693749725818634,
-0.015682140365242958,
0.08194427937269211,
0.02632213942706585,
-0.04168391972780228,
0.012255119159817696,
-0.05059518665075302,
0.011094636283814907,
0.185708150267601,
-0.039086561650037766,
0.0033564544282853603,
-0.0034777119290083647,
-0.021491773426532745,
0.014914136379957199,
0.037673480808734894,
-0.028750967234373093,
-0.10786598920822144,
0.061884015798568726,
-0.004491306841373444,
0.023870941251516342,
-0.11433656513690948,
-0.18651393055915833,
0.03266938775777817,
-0.006243051495403051,
-0.042940765619277954,
-0.11662203073501587,
-0.3121032416820526,
-0.06539519131183624,
0.03818764165043831,
-0.09145515412092209,
0.04097016528248787,
-0.024387797340750694,
-0.10424225777387619,
-0.010069518350064754,
-0.0018067972268909216,
-0.05417172983288765,
-0.052948273718357086,
0.043035849928855896,
-0.13657793402671814,
0.007310319691896439,
-0.08475160598754883,
0.013228438794612885,
-0.13107886910438538,
-0.005623575299978256,
-0.19138552248477936,
0.08066294342279434,
-0.05783740431070328,
-0.11107120662927628,
-0.06793981045484543,
-0.02539954148232937,
-0.10425599664449692,
0.059831757098436356,
-0.000805019517429173,
0.1250806301832199,
-0.21273468434810638,
0.016853701323270798,
0.15880317986011505,
-0.1547238677740097,
-0.017266731709241867,
0.1650054156780243,
-0.02299432083964348,
0.17206750810146332,
0.12736056745052338,
0.047444820404052734,
0.17451685667037964,
-0.1531957983970642,
-0.051723673939704895,
0.025051463395357132,
-0.00379187916405499,
0.07026292383670807,
0.02154269814491272,
0.026885008439421654,
0.09899384528398514,
0.021756678819656372,
-0.09599989652633667,
0.001915689674206078,
0.02085527777671814,
-0.043615907430648804,
0.011755119077861309,
-0.007506888825446367,
0.0752926841378212,
0.0363907665014267,
0.05118558555841446,
-0.002256344072520733,
-0.0993252620100975,
0.06405716389417648,
0.027827028185129166,
-0.03262215107679367,
0.0619678869843483,
-0.029905082657933235,
-0.013431067578494549,
0.06684969365596771,
0.02288500778377056,
-0.14047537744045258,
-0.08410127460956573,
0.07523344457149506,
-0.0268765427172184,
0.0302157923579216,
0.15030048787593842,
0.06542552262544632,
0.08052282780408859,
-0.09564010798931122,
0.015793563798069954,
-0.1094832569360733,
-0.04430445656180382,
-0.12112567573785782,
-0.12894169986248016,
0.042225003242492676,
-0.047018978744745255,
0.08704188466072083,
-0.12765279412269592,
-0.025775467976927757,
0.03993433713912964,
0.05205339193344116,
0.05552477762103081,
-0.03985685855150223,
0.011555193923413754,
0.08904623985290527,
-0.013013120740652084,
-0.04452117905020714,
0.022664694115519524,
-0.005313449539244175,
0.040316689759492874,
0.09444799274206161,
-0.07142375409603119,
0.05238068476319313,
0.0364740788936615,
0.10983492434024811,
-0.02474275231361389,
-0.008206790313124657,
0.01942884363234043,
-0.058756791055202484,
-0.07531575858592987,
-0.14128872752189636,
0.1615498811006546,
0.02690870314836502,
0.054235052317380905,
-0.109873466193676,
-0.003036853391677141,
-0.034888856112957,
0.006428317632526159,
0.04660188406705856,
0.10517781227827072,
0.02791454829275608,
-0.09524925798177719,
0.07979587465524673,
0.01200677640736103,
-0.0148842204362154,
0.1869678646326065,
0.03359881788492203,
-0.05936095863580704,
0.006267769727855921,
0.09618660807609558,
0.00429122056812048,
0.118744395673275,
-0.02781190723180771,
-0.003618301125243306,
0.0063934484496712685,
0.07122731953859329,
0.018637873232364655,
-0.09769368171691895,
0.041588667780160904,
0.0003782325075007975,
-0.06972502171993256,
0.13231851160526276,
0.01295371726155281,
-0.020447170361876488,
0.08065414428710938,
-0.006194776389747858,
-0.07944735139608383,
-0.04729527607560158,
-0.038852449506521225,
-0.08325786143541336,
0.14800316095352173,
-0.11346698552370071,
-0.2190844714641571,
-0.13247789442539215,
0.1060730591416359,
-0.052930161356925964,
0.01125736627727747,
0.05124884098768234,
0.03601209074258804,
-0.08246077597141266,
-0.0876755341887474,
0.12548276782035828,
0.03713618591427803,
-0.1013878658413887,
-0.21409906446933746,
0.015087136998772621,
-0.07542701810598373,
-0.15915584564208984,
0.012181577272713184,
-0.023701416328549385,
-0.08840782195329666,
0.0706857442855835,
-0.012609689496457577,
0.06615805625915527,
0.024896271526813507,
0.06838379800319672,
-0.08600432425737381,
-0.040721338242292404,
0.1913108080625534,
-0.08700530976057053,
0.1591711938381195,
0.0866924598813057,
0.021520983427762985,
0.04692964628338814,
0.07540381699800491,
0.026674430817365646,
-0.08821868896484375,
-0.006465164944529533,
0.03654242679476738,
-0.0640006959438324,
-0.15086211264133453,
-0.14759497344493866,
-0.09310929477214813,
-0.0668639987707138,
-0.02589600160717964,
0.04813838750123978,
0.006842666305601597,
-0.020148783922195435,
-0.05008448287844658,
0.11137960106134415,
0.10772887617349625,
0.012866221368312836,
0.09680911898612976,
0.04451434686779976,
0.034235283732414246,
-0.04525980353355408,
0.0017228330252692103,
0.0691787451505661,
-0.020018747076392174,
0.30407193303108215,
-0.05882386490702629,
0.13021062314510345,
0.06511586159467697,
0.09533333033323288,
0.018605148419737816,
0.10291583091020584,
-0.021011915057897568,
0.014630011282861233,
-0.07445722073316574,
-0.0865466371178627,
-0.029287932440638542,
0.0926751121878624,
-0.07257706671953201,
-0.06247614696621895,
0.033420346677303314,
-0.04254206269979477,
0.06632684171199799,
0.12938746809959412,
-0.023265914991497993,
-0.007095344830304384,
-0.08998556435108185,
0.07251950353384018,
-0.03350839391350746,
-0.11266534775495529,
0.019963981583714485,
0.19773678481578827,
-0.15829278528690338,
-0.06403517723083496,
-0.04660637304186821,
0.08814291656017303,
-0.09870220720767975,
0.009284076280891895,
-0.12269529700279236,
0.0711272731423378,
0.020156892016530037,
0.0627705305814743,
-0.19066154956817627,
0.20491153001785278,
0.029542485252022743,
0.1755649447441101,
-0.05361999571323395,
0.007862572558224201,
0.02908601611852646,
0.05071337893605232,
0.11098314821720123,
0.0614810548722744,
0.04488343372941017,
0.03803420066833496,
-0.10420873761177063,
0.027695106342434883,
0.010899586603045464,
-0.0012273936299607158,
0.0673254206776619,
0.028185976669192314,
0.01348776463419199,
-0.02919696643948555,
-0.006687105633318424,
-0.12380605190992355,
-0.10196924209594727,
0.04749232903122902,
-0.07163622975349426,
0.02007368765771389,
-0.05147121846675873,
-0.01220870390534401,
-0.06756816804409027,
0.12961158156394958,
-0.19120684266090393,
-0.06589927524328232,
-0.08267641067504883,
-0.07025203108787537,
0.04833623394370079,
-0.08986157178878784,
0.008614350110292435,
0.02980130910873413,
0.0368468314409256,
-0.0750589370727539,
-0.0023785282392054796,
0.06865552067756653,
-0.07893455028533936,
-0.09154053032398224,
-0.028217075392603874,
0.04417598247528076,
0.13023242354393005,
0.05617483705282211,
0.008339712396264076,
0.0226249098777771,
0.009956994093954563,
-0.14613601565361023,
-0.06475304812192917,
0.15198715031147003,
-0.07529299706220627,
-0.007086690049618483,
-0.12794214487075806,
0.03323381394147873,
-0.0137570695951581,
-0.04906251281499863,
0.0741933062672615,
0.14903616905212402,
-0.049265384674072266,
0.16016310453414917,
0.16505689918994904,
-0.12317980825901031,
-0.3254326581954956,
-0.01886235736310482,
0.010835024528205395,
0.04807792603969574,
0.01924019679427147,
-0.16685812175273895,
0.028476886451244354,
0.12139847874641418,
-0.016680939123034477,
-0.05139422416687012,
-0.23515191674232483,
-0.1251654326915741,
0.035880036652088165,
0.045749083161354065,
0.1420752853155136,
-0.06402920931577682,
-0.017683658748865128,
0.0059234644286334515,
-0.12340142577886581,
0.00731456745415926,
-0.17347054183483124,
0.05230104178190231,
0.002056627767160535,
-0.07112661004066467,
0.006801642011851072,
-0.014915422536432743,
0.1187143623828888,
-0.012464125640690327,
0.07800853997468948,
-0.020574500784277916,
0.10715796053409576,
0.11718522757291794,
-0.02942194789648056,
0.16001012921333313,
-0.0037618400529026985,
-0.042954228818416595,
-0.19730332493782043,
-0.017450522631406784,
-0.12783314287662506,
0.09406320005655289,
-0.04138434678316116,
-0.05325005203485489,
-0.0846519023180008,
0.09130591154098511,
0.05849769338965416,
0.011187352240085602,
0.03309078514575958,
-0.00832958985120058,
-0.019635653123259544,
0.0850750133395195,
0.1819431632757187,
-0.02144140750169754,
-0.10274715721607208,
0.006252910941839218,
0.04172156751155853,
0.0993933454155922,
-0.03659664839506149,
-0.002623765729367733,
0.09696801751852036,
-0.028892356902360916,
0.10108084231615067,
-0.014318173751235008,
-0.11658591777086258,
0.01991902105510235,
0.05520385131239891,
-0.04601204767823219,
-0.2005484402179718,
-0.034051693975925446,
0.029345009475946426,
0.030784595757722855,
-0.06956835091114044,
0.15690091252326965,
-0.14537373185157776,
0.02672627568244934,
-0.025139525532722473,
0.06114610284566879,
0.04241741821169853,
0.11018572002649307,
-0.05641975626349449,
0.06033686175942421,
-0.06669626384973526,
0.0828498974442482,
0.05230379477143288,
-0.07110843062400818,
0.016005296260118484,
0.10516611486673355,
-0.12063466757535934,
-0.07071856409311295,
0.05463407561182976,
0.07271505147218704,
0.014048978686332703,
-0.033388812094926834,
-0.004289198666810989,
-0.09303426742553711,
-0.03542624041438103,
0.11895235627889633,
0.015172702260315418,
0.044246915727853775,
-0.047018881887197495,
0.00586450332775712,
-0.03568166121840477,
0.04560873657464981,
0.049967117607593536,
-0.0017658036667853594,
-0.00497758062556386,
0.070628322660923,
0.012366710230708122,
-0.035172030329704285,
-0.04457719996571541,
-0.019239772111177444,
-0.0681447759270668,
-0.014413912780582905,
-0.1481342315673828,
-0.00885650236159563,
-0.0645180493593216,
-0.01998842880129814,
-0.05268152803182602,
-0.026328369975090027,
-0.01632894203066826,
0.019412120804190636,
-0.06881408393383026,
0.01190976519137621,
-0.08509666472673416,
0.07182680815458298,
-0.07389985769987106,
-0.040701836347579956,
0.06390126049518585,
-0.07185511291027069,
0.05208094045519829,
0.02801545150578022,
-0.03113861382007599,
0.09690085053443909,
-0.11230900883674622,
0.07790786027908325,
0.030518073588609695,
0.03946744650602341,
0.048485077917575836,
-0.06105639785528183,
-0.027349431067705154,
0.008317404426634312,
0.04027131199836731,
0.0040096198208630085,
0.011063053272664547,
-0.0748540535569191,
0.12139667570590973,
-0.009798863902688026,
-0.02518581971526146,
-0.0316447988152504,
0.07707838714122772,
0.06169336661696434,
0.0026887021958827972,
0.12405607849359512,
-0.05687219649553299,
-0.03154260292649269,
-0.06663920730352402,
-0.01628042384982109,
0.026606883853673935,
-0.016586866229772568,
-0.018627360463142395,
-0.07149790972471237,
0.0250516626983881,
0.016195494681596756,
0.15221716463565826,
0.05416930839419365,
-0.06190012767910957,
0.04810863733291626,
-0.029841452836990356,
-0.14256428182125092,
-0.00048089251504279673,
-0.13073837757110596,
0.04715944081544876,
0.02989046648144722,
-0.06483402848243713,
-0.030195318162441254,
-0.03753799572587013,
0.0009334919159300625,
0.21163401007652283,
0.15010468661785126,
0.12330032140016556,
0.09920420497655869,
-0.03983360528945923,
0.00616835430264473,
-0.16760261356830597,
0.01484822016209364,
0.0198292825371027,
0.07320485264062881,
-0.08565129339694977,
0.06575796753168106,
0.122608982026577,
-0.1538647711277008,
0.1268613189458847,
-0.008470437489449978,
-0.06413997709751129,
-0.07738549262285233,
-0.056836050003767014,
-0.04984622821211815,
-0.013137656264007092,
-0.04992813244462013,
-0.1254214495420456,
0.04104021191596985,
0.04062221944332123,
0.04884105175733566,
-0.01720472425222397,
0.11258554458618164,
-0.03278147429227829,
-0.12113462388515472,
0.08693666756153107,
-0.01426109578460455,
0.08401203155517578,
-0.004961290862411261,
0.019075581803917885,
-0.07928252220153809,
0.007323362398892641,
0.010517244227230549,
0.09027154743671417,
0.023984627798199654,
-0.014848988503217697,
-0.056668881326913834,
-0.03257562592625618,
0.011206565424799919,
0.04184458777308464,
-0.005118229426443577,
0.16531220078468323,
0.09049050509929657,
-0.16171137988567352,
0.026933785527944565,
0.2233298122882843,
-0.0060992250218987465,
-0.1418951451778412,
-0.10015557706356049,
0.18574218451976776,
0.04245179891586304,
0.026198308914899826,
0.011444978415966034,
-0.03490869700908661,
0.08944366127252579,
0.18584729731082916,
0.0941331759095192,
-0.05341137945652008,
0.0071631427854299545,
-0.05065326392650604,
-0.012002912349998951,
0.012341972440481186,
0.11306562274694443,
-0.012131213210523129,
0.43142426013946533,
-0.08397558331489563,
0.07760355621576309,
-0.0380573533475399,
-0.02883683703839779,
-0.1769847720861435,
0.05911347270011902,
-0.06158445030450821,
-0.013381509110331535,
-0.10898436605930328,
0.0726485624909401,
-0.010575580410659313,
-0.23995286226272583,
-0.001188349910080433,
-0.027247712016105652,
-0.07786846160888672,
-0.017847895622253418,
-0.024896103888750076,
0.018241966143250465,
0.07314762473106384,
0.0016032219864428043,
0.007514112628996372,
0.08453038334846497,
0.02164473384618759,
-0.018282746896147728,
-0.04358132556080818,
0.07877864688634872,
0.0739731416106224,
0.12554515898227692,
0.016729358583688736,
0.03888409957289696,
0.07557490468025208,
-0.003782450221478939,
-0.0846228152513504,
0.11644794791936874,
0.03736014664173126,
-0.09151455760002136,
0.01595095731317997,
0.12154407799243927,
-0.030495015904307365,
0.15801610052585602,
0.10393784195184708,
0.011498363688588142,
0.05646026134490967,
0.1557067334651947,
-0.07866472005844116,
-0.02837182953953743,
0.07398007065057755,
-0.07070798426866531,
0.09716614335775375,
0.15933337807655334,
0.01265672966837883,
0.031396083533763885,
-0.07137909531593323,
0.04144538193941116,
-0.04334212839603424,
0.09840504080057144,
-0.04617977887392044,
-0.11605896800756454,
0.020194007083773613,
0.09585361927747726,
0.053306885063648224,
-0.14033697545528412,
-0.0847414955496788,
0.0007133897161111236,
0.03475780785083771,
-0.06346209347248077,
0.10517368465662003,
0.036679577082395554,
-0.026870256289839745,
-0.009575936943292618,
-0.12240348011255264,
-0.04377393797039986,
0.04780268669128418,
-0.026155419647693634,
-0.07513363659381866
] |
null | null | transformers |
## Model Description
This model is finetuned from [Viet-Mistral/Vistral-7B-Chat](https://huggingface.co/Viet-Mistral/Vistral-7B-Chat). The dataset is taken from [bkai-foundation-models/vi-self-chat-sharegpt-format](https://huggingface.co/datasets/bkai-foundation-models/vi-self-chat-sharegpt-format)
This is a **low rank** finetune to add support for chatml template. While the template does not affect model performance, it would be nice to support chatml since most of models based on Mistral already using it.
The format looks like this:
```
<|im_start|>system
Provide some context and/or instructions to the model.
<|im_end|>
<|im_start|>user
The user’s message goes here
<|im_end|>
<|im_start|>assistant
```
## How to use it
The recommended way is to use the GGUF `vistral-7b-chatml-Q4_K_M.gguf` file included in this repository. Run it via llama.cpp (remember to pass `-cml` argument to use chatml template)
```bash
./main -m vistral-7b-chatml-Q4_K_M.gguf -p "Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực." -cml
```
Additionally, you can run the `python3 run.py` inside this repository to try the model using transformers library. This it not the recommended way since you may need to change some params inside in order to make it work.
This is an example of a conversation using llama.cpp:
- <|im_start|>system
- Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực.<|im_end|>
- <|im_start|>user
- > xin chào
- Tôi là một trợ lý AI được tạo ra để hỗ trợ con người. Bạn có câu hỏi gì về chủ đề nào đó?<|im_end|>
- > trợ lý AI là gì? bạn giải thích được không?
- Trợ lý AI là một chương trình máy tính sử dụng trí tuệ nhân tạo và học máy để hỗ trợ con người trong nhiều công việc khác nhau. Nó có thể hiểu và xử lý ngôn ngữ tự nhiên, nhận biết hình ảnh và âm thanh cũng như đưa ra các đề xuất hay câu trả lời phù hợp cho các yêu cầu của bạn.<|im_end|>
- > ồ, cảm ơn, vậy bạn có thể làm gì?
- Tôi được thiết kế để giúp đỡ con người trong nhiều công việc như giải đáp các câu hỏi, trả lời những vấn đề liên quan đến cuộc sống hàng ngày của họ. Tôi cũng hỗ trợ nghiên cứu và phân tích dữ liệu để đưa ra dự đoán hay tư vấn cho doanh nghiệp hoặc cá nhân.<|im_end|>
- > tức là sao?
- Ví dụ, nếu bạn đang thắc mắc về một chủ đề nào đó và không biết phải bắt đầu tìm kiếm ở đâu hay làm thế nào để hiểu rõ hơn về nó thì tôi có thể giúp bạn. Tôi cũng có khả năng đọc và phân tích dữ liệu từ nhiều nguồn khác nhau như các tài liệu nghiên cứu khoa học hoặc thông tin trên mạng xã hội, sau đó đưa ra những giải thích chi tiết cho vấn đề của bạn.<|im_end|>
## Training
You can also look at the training code in the `finetune.py` file.
For tokenizer, I changed these things:
- Token `[INST]` to `<|im_start|>`, make it become special token
- Token `[/INST]` to `<|im_end|>`, make it become special token
- Change to `eos_token` to `<|im_end|>`
- Update `chat_template` to chatml, taken from [this example](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B/blob/main/tokenizer_config.json#L52)
Additionally, there is a checkpoint file in my repository if you want to merge the LORA yourself.
## More information
Disclaimer: I'm not expert in machine learning, my background is from cybersecurity so the making of this model is a "hobby" to me. Training is done using a VPS on Google Cloud, I paid with my own money.
If you want to discuss, feel free to contact me at `contact at ngxson dot com` - [ngxson.com](https://ngxson.com)
| {"language": ["vi"], "license": "mit", "library_name": "transformers", "tags": ["LLMs", "NLP", "Vietnamese"]} | text-generation | ngxson/Vistral-7B-ChatML | [
"transformers",
"safetensors",
"gguf",
"mistral",
"text-generation",
"LLMs",
"NLP",
"Vietnamese",
"conversational",
"vi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T13:01:34+00:00 | [] | [
"vi"
] | TAGS
#transformers #safetensors #gguf #mistral #text-generation #LLMs #NLP #Vietnamese #conversational #vi #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## Model Description
This model is finetuned from Viet-Mistral/Vistral-7B-Chat. The dataset is taken from bkai-foundation-models/vi-self-chat-sharegpt-format
This is a low rank finetune to add support for chatml template. While the template does not affect model performance, it would be nice to support chatml since most of models based on Mistral already using it.
The format looks like this:
## How to use it
The recommended way is to use the GGUF 'vistral-7b-chatml-Q4_K_M.gguf' file included in this repository. Run it via URL (remember to pass '-cml' argument to use chatml template)
Additionally, you can run the 'python3 URL' inside this repository to try the model using transformers library. This it not the recommended way since you may need to change some params inside in order to make it work.
This is an example of a conversation using URL:
- <|im_start|>system
- Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực.<|im_end|>
- <|im_start|>user
- > xin chào
- Tôi là một trợ lý AI được tạo ra để hỗ trợ con người. Bạn có câu hỏi gì về chủ đề nào đó?<|im_end|>
- > trợ lý AI là gì? bạn giải thích được không?
- Trợ lý AI là một chương trình máy tính sử dụng trí tuệ nhân tạo và học máy để hỗ trợ con người trong nhiều công việc khác nhau. Nó có thể hiểu và xử lý ngôn ngữ tự nhiên, nhận biết hình ảnh và âm thanh cũng như đưa ra các đề xuất hay câu trả lời phù hợp cho các yêu cầu của bạn.<|im_end|>
- > ồ, cảm ơn, vậy bạn có thể làm gì?
- Tôi được thiết kế để giúp đỡ con người trong nhiều công việc như giải đáp các câu hỏi, trả lời những vấn đề liên quan đến cuộc sống hàng ngày của họ. Tôi cũng hỗ trợ nghiên cứu và phân tích dữ liệu để đưa ra dự đoán hay tư vấn cho doanh nghiệp hoặc cá nhân.<|im_end|>
- > tức là sao?
- Ví dụ, nếu bạn đang thắc mắc về một chủ đề nào đó và không biết phải bắt đầu tìm kiếm ở đâu hay làm thế nào để hiểu rõ hơn về nó thì tôi có thể giúp bạn. Tôi cũng có khả năng đọc và phân tích dữ liệu từ nhiều nguồn khác nhau như các tài liệu nghiên cứu khoa học hoặc thông tin trên mạng xã hội, sau đó đưa ra những giải thích chi tiết cho vấn đề của bạn.<|im_end|>
## Training
You can also look at the training code in the 'URL' file.
For tokenizer, I changed these things:
- Token '[INST]' to '<|im_start|>', make it become special token
- Token '[/INST]' to '<|im_end|>', make it become special token
- Change to 'eos_token' to '<|im_end|>'
- Update 'chat_template' to chatml, taken from this example
Additionally, there is a checkpoint file in my repository if you want to merge the LORA yourself.
## More information
Disclaimer: I'm not expert in machine learning, my background is from cybersecurity so the making of this model is a "hobby" to me. Training is done using a VPS on Google Cloud, I paid with my own money.
If you want to discuss, feel free to contact me at 'contact at ngxson dot com' - URL
| [
"## Model Description \n\nThis model is finetuned from Viet-Mistral/Vistral-7B-Chat. The dataset is taken from bkai-foundation-models/vi-self-chat-sharegpt-format\n\nThis is a low rank finetune to add support for chatml template. While the template does not affect model performance, it would be nice to support chatml since most of models based on Mistral already using it.\n\nThe format looks like this:",
"## How to use it\n\nThe recommended way is to use the GGUF 'vistral-7b-chatml-Q4_K_M.gguf' file included in this repository. Run it via URL (remember to pass '-cml' argument to use chatml template)\n\n\n\nAdditionally, you can run the 'python3 URL' inside this repository to try the model using transformers library. This it not the recommended way since you may need to change some params inside in order to make it work.\n\nThis is an example of a conversation using URL:\n\n- <|im_start|>system\n- Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực.<|im_end|>\n- <|im_start|>user\n- > xin chào \n- Tôi là một trợ lý AI được tạo ra để hỗ trợ con người. Bạn có câu hỏi gì về chủ đề nào đó?<|im_end|>\n- > trợ lý AI là gì? bạn giải thích được không?\n- Trợ lý AI là một chương trình máy tính sử dụng trí tuệ nhân tạo và học máy để hỗ trợ con người trong nhiều công việc khác nhau. Nó có thể hiểu và xử lý ngôn ngữ tự nhiên, nhận biết hình ảnh và âm thanh cũng như đưa ra các đề xuất hay câu trả lời phù hợp cho các yêu cầu của bạn.<|im_end|>\n- > ồ, cảm ơn, vậy bạn có thể làm gì?\n- Tôi được thiết kế để giúp đỡ con người trong nhiều công việc như giải đáp các câu hỏi, trả lời những vấn đề liên quan đến cuộc sống hàng ngày của họ. Tôi cũng hỗ trợ nghiên cứu và phân tích dữ liệu để đưa ra dự đoán hay tư vấn cho doanh nghiệp hoặc cá nhân.<|im_end|>\n- > tức là sao?\n- Ví dụ, nếu bạn đang thắc mắc về một chủ đề nào đó và không biết phải bắt đầu tìm kiếm ở đâu hay làm thế nào để hiểu rõ hơn về nó thì tôi có thể giúp bạn. Tôi cũng có khả năng đọc và phân tích dữ liệu từ nhiều nguồn khác nhau như các tài liệu nghiên cứu khoa học hoặc thông tin trên mạng xã hội, sau đó đưa ra những giải thích chi tiết cho vấn đề của bạn.<|im_end|>",
"## Training\n\nYou can also look at the training code in the 'URL' file.\n\nFor tokenizer, I changed these things:\n- Token '[INST]' to '<|im_start|>', make it become special token\n- Token '[/INST]' to '<|im_end|>', make it become special token\n- Change to 'eos_token' to '<|im_end|>'\n- Update 'chat_template' to chatml, taken from this example\n\nAdditionally, there is a checkpoint file in my repository if you want to merge the LORA yourself.",
"## More information\n\nDisclaimer: I'm not expert in machine learning, my background is from cybersecurity so the making of this model is a \"hobby\" to me. Training is done using a VPS on Google Cloud, I paid with my own money.\n\nIf you want to discuss, feel free to contact me at 'contact at ngxson dot com' - URL"
] | [
"TAGS\n#transformers #safetensors #gguf #mistral #text-generation #LLMs #NLP #Vietnamese #conversational #vi #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Model Description \n\nThis model is finetuned from Viet-Mistral/Vistral-7B-Chat. The dataset is taken from bkai-foundation-models/vi-self-chat-sharegpt-format\n\nThis is a low rank finetune to add support for chatml template. While the template does not affect model performance, it would be nice to support chatml since most of models based on Mistral already using it.\n\nThe format looks like this:",
"## How to use it\n\nThe recommended way is to use the GGUF 'vistral-7b-chatml-Q4_K_M.gguf' file included in this repository. Run it via URL (remember to pass '-cml' argument to use chatml template)\n\n\n\nAdditionally, you can run the 'python3 URL' inside this repository to try the model using transformers library. This it not the recommended way since you may need to change some params inside in order to make it work.\n\nThis is an example of a conversation using URL:\n\n- <|im_start|>system\n- Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực.<|im_end|>\n- <|im_start|>user\n- > xin chào \n- Tôi là một trợ lý AI được tạo ra để hỗ trợ con người. Bạn có câu hỏi gì về chủ đề nào đó?<|im_end|>\n- > trợ lý AI là gì? bạn giải thích được không?\n- Trợ lý AI là một chương trình máy tính sử dụng trí tuệ nhân tạo và học máy để hỗ trợ con người trong nhiều công việc khác nhau. Nó có thể hiểu và xử lý ngôn ngữ tự nhiên, nhận biết hình ảnh và âm thanh cũng như đưa ra các đề xuất hay câu trả lời phù hợp cho các yêu cầu của bạn.<|im_end|>\n- > ồ, cảm ơn, vậy bạn có thể làm gì?\n- Tôi được thiết kế để giúp đỡ con người trong nhiều công việc như giải đáp các câu hỏi, trả lời những vấn đề liên quan đến cuộc sống hàng ngày của họ. Tôi cũng hỗ trợ nghiên cứu và phân tích dữ liệu để đưa ra dự đoán hay tư vấn cho doanh nghiệp hoặc cá nhân.<|im_end|>\n- > tức là sao?\n- Ví dụ, nếu bạn đang thắc mắc về một chủ đề nào đó và không biết phải bắt đầu tìm kiếm ở đâu hay làm thế nào để hiểu rõ hơn về nó thì tôi có thể giúp bạn. Tôi cũng có khả năng đọc và phân tích dữ liệu từ nhiều nguồn khác nhau như các tài liệu nghiên cứu khoa học hoặc thông tin trên mạng xã hội, sau đó đưa ra những giải thích chi tiết cho vấn đề của bạn.<|im_end|>",
"## Training\n\nYou can also look at the training code in the 'URL' file.\n\nFor tokenizer, I changed these things:\n- Token '[INST]' to '<|im_start|>', make it become special token\n- Token '[/INST]' to '<|im_end|>', make it become special token\n- Change to 'eos_token' to '<|im_end|>'\n- Update 'chat_template' to chatml, taken from this example\n\nAdditionally, there is a checkpoint file in my repository if you want to merge the LORA yourself.",
"## More information\n\nDisclaimer: I'm not expert in machine learning, my background is from cybersecurity so the making of this model is a \"hobby\" to me. Training is done using a VPS on Google Cloud, I paid with my own money.\n\nIf you want to discuss, feel free to contact me at 'contact at ngxson dot com' - URL"
] | [
73,
98,
478,
141,
76
] | [
"passage: TAGS\n#transformers #safetensors #gguf #mistral #text-generation #LLMs #NLP #Vietnamese #conversational #vi #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## Model Description \n\nThis model is finetuned from Viet-Mistral/Vistral-7B-Chat. The dataset is taken from bkai-foundation-models/vi-self-chat-sharegpt-format\n\nThis is a low rank finetune to add support for chatml template. While the template does not affect model performance, it would be nice to support chatml since most of models based on Mistral already using it.\n\nThe format looks like this:"
] | [
-0.04364587739109993,
-0.012654013931751251,
-0.0004832901176996529,
0.02932177670300007,
0.09959112107753754,
-0.039399903267621994,
0.1066879853606224,
0.05085661634802818,
0.07399380952119827,
-0.0313311330974102,
0.05193421617150307,
0.03486458584666252,
0.01194380596280098,
0.18014687299728394,
-0.0024093869142234325,
-0.29274648427963257,
0.11815370619297028,
0.0299615990370512,
0.06724905967712402,
0.050877250730991364,
0.09830301254987717,
-0.018427615985274315,
0.13397465646266937,
0.05583653599023819,
-0.0689828172326088,
-0.028818024322390556,
0.0021326441783457994,
-0.009289325214922428,
0.05329614505171776,
0.07027580589056015,
-0.01747189648449421,
0.0340125672519207,
-0.014519439078867435,
-0.1312670111656189,
0.050663262605667114,
-0.0002273052086820826,
-0.0000361347192665562,
0.034614160656929016,
-0.020498894155025482,
0.02509322576224804,
0.1882684826850891,
-0.03130382299423218,
-0.009985527023673058,
0.07305038720369339,
-0.06503331661224365,
-0.062371429055929184,
-0.07212302833795547,
0.010819405317306519,
0.13297948241233826,
0.06627693772315979,
-0.015981728211045265,
0.14779193699359894,
-0.059952303767204285,
0.07069001346826553,
0.12074105441570282,
-0.25807830691337585,
-0.05066701024770737,
0.2887968122959137,
0.07011231780052185,
0.0419633686542511,
-0.05655750259757042,
0.13003122806549072,
0.048695314675569534,
-0.005664817523211241,
-0.033008258789777756,
-0.0538819283246994,
-0.03699348866939545,
-0.046293921768665314,
-0.05851805955171585,
0.0516231544315815,
0.30110910534858704,
0.033985696732997894,
-0.009853442199528217,
-0.10768426954746246,
-0.042192209511995316,
0.09168829023838043,
-0.09902080148458481,
-0.07469237595796585,
0.008504197001457214,
0.07727356255054474,
0.13913096487522125,
-0.10446824133396149,
-0.06996849179267883,
-0.05011158064007759,
-0.05421961098909378,
-0.10192738473415375,
-0.010856404900550842,
0.029186220839619637,
-0.19649483263492584,
0.040595028549432755,
-0.178374245762825,
-0.07772372663021088,
-0.08703385293483734,
-0.10630042850971222,
0.009295087307691574,
0.013231507502496243,
0.032476745545864105,
-0.03275501728057861,
0.10497521609067917,
0.0273881908506155,
-0.030293548479676247,
0.13104644417762756,
-0.011618336662650108,
-0.002619105391204357,
-0.005171092227101326,
0.09546341001987457,
-0.018949298188090324,
-0.02244061417877674,
0.0961165502667427,
-0.03605795279145241,
0.09098289906978607,
-0.010456169955432415,
-0.14392931759357452,
-0.026850109919905663,
-0.048883553594350815,
-0.0031738867983222008,
-0.06220598518848419,
0.14189676940441132,
-0.011020734906196594,
-0.022730806842446327,
0.2733198404312134,
-0.032515816390514374,
0.031047727912664413,
0.011634644120931625,
0.0017576905665919185,
0.07459525763988495,
0.08444228768348694,
0.017414651811122894,
0.01585596613585949,
-0.13608472049236298,
-0.05264177545905113,
-0.015299196355044842,
-0.06657599657773972,
-0.025954723358154297,
0.0036066174507141113,
0.06683763861656189,
-0.025372670963406563,
-0.12496742606163025,
-0.19468170404434204,
0.018828604370355606,
0.09517862647771835,
-0.0316384881734848,
-0.013890593312680721,
-0.08724381029605865,
-0.0387028343975544,
-0.021615400910377502,
0.0284385047852993,
-0.024069737643003464,
-0.041532643139362335,
-0.04758286476135254,
-0.013331960886716843,
0.08053896576166153,
-0.20478762686252594,
0.02739710919559002,
-0.07473356276750565,
0.051862090826034546,
-0.20573852956295013,
0.1289101094007492,
-0.07360467314720154,
0.06849710643291473,
-0.024000387638807297,
-0.001589620136655867,
-0.028103571385145187,
-0.014555304311215878,
-0.016019251197576523,
0.12725748121738434,
-0.17615818977355957,
-0.039852075278759,
0.17785444855690002,
-0.19630251824855804,
-0.13111381232738495,
0.12819790840148926,
0.0017613465897738934,
0.01667807251214981,
0.13318534195423126,
0.19959992170333862,
0.18362371623516083,
-0.09558793157339096,
0.057884395122528076,
0.04916618391871452,
-0.036622002720832825,
-0.035928014665842056,
0.0012093930272385478,
0.054784100502729416,
-0.1786348670721054,
0.1030069962143898,
-0.09037593007087708,
0.07121630012989044,
-0.009801401756703854,
-0.04179772362112999,
-0.0818624272942543,
0.004118067678064108,
0.02547849342226982,
0.007231703959405422,
0.03507780283689499,
-0.06307791918516159,
-0.009601770900189877,
0.07126494497060776,
0.12578362226486206,
-0.03217914700508118,
-0.03456181660294533,
-0.12208879739046097,
0.1034783199429512,
-0.006154038477689028,
0.07073084264993668,
-0.010674904100596905,
-0.1330924928188324,
0.02796630933880806,
0.02129187248647213,
0.0004951593582518399,
0.10418391972780228,
0.07093987613916397,
0.0609709694981575,
-0.03692849352955818,
0.035173866897821426,
0.14771974086761475,
0.008042427711188793,
-0.01999780163168907,
-0.1301249861717224,
0.025534693151712418,
-0.07240890711545944,
0.1933857798576355,
-0.181340292096138,
0.02722025103867054,
0.0706467404961586,
0.15169742703437805,
0.00954898726195097,
0.014812052249908447,
0.10289982706308365,
-0.04811336472630501,
-0.012490984983742237,
-0.018764691427350044,
0.07152841985225677,
0.026465190574526787,
-0.05967435613274574,
0.17734938859939575,
-0.1431320756673813,
-0.048035092651844025,
0.12526126205921173,
0.09879244863986969,
0.021035294979810715,
-0.12689067423343658,
-0.002399223856627941,
0.0020653519313782454,
-0.01043651346117258,
0.004376246128231287,
0.17334216833114624,
-0.037614572793245316,
0.15186291933059692,
-0.05218205973505974,
-0.005799159873276949,
0.006907518953084946,
-0.07074283808469772,
-0.06871142238378525,
0.08217834681272507,
0.015246903523802757,
-0.1341991424560547,
0.06412651389837265,
0.01167147234082222,
0.05252902954816818,
0.1935502141714096,
0.011229963973164558,
0.05808156728744507,
-0.02266777493059635,
0.0660405084490776,
-0.022730261087417603,
0.10576970875263214,
-0.28024953603744507,
-0.018203169107437134,
0.03210507705807686,
0.04778038710355759,
0.04773738235235214,
-0.0830235555768013,
-0.003495891112834215,
-0.033469561487436295,
-0.09026826918125153,
0.06156383454799652,
0.12540732324123383,
-0.031215213239192963,
0.04969264566898346,
-0.03500954434275627,
-0.018724899739027023,
-0.00030560154118575156,
0.0011364277452230453,
-0.09683005511760712,
0.12205987423658371,
-0.14001908898353577,
-0.2572697103023529,
-0.047083642333745956,
-0.0571267269551754,
-0.15391583740711212,
0.012685672380030155,
0.060886405408382416,
-0.17988303303718567,
-0.0932416096329689,
-0.08774608373641968,
0.0103414012119174,
0.09603900462388992,
-0.03344753012061119,
0.04374624043703079,
-0.04612294211983681,
-0.04003658518195152,
-0.1286424994468689,
-0.04028797522187233,
-0.038367755711078644,
-0.05427972599864006,
0.04501128941774368,
-0.17379052937030792,
0.014646674506366253,
0.16706199944019318,
-0.025417592376470566,
0.09040944278240204,
0.013861028477549553,
0.22331669926643372,
-0.03308146446943283,
0.07864129543304443,
0.1617577224969864,
0.153779998421669,
0.030102092772722244,
0.15048083662986755,
-0.018346210941672325,
-0.09693174809217453,
0.05989697575569153,
-0.02265426702797413,
-0.07989580184221268,
-0.061603181064128876,
-0.16411583125591278,
-0.09057126939296722,
-0.02601192705333233,
-0.03963016718626022,
0.0756072923541069,
0.05458462983369827,
0.11349646747112274,
-0.030448365956544876,
0.054604221135377884,
0.14075171947479248,
0.05502041429281235,
0.07746940106153488,
-0.044743314385414124,
0.05661920830607414,
-0.0676245242357254,
-0.013172077015042305,
0.11628377437591553,
0.02282108925282955,
0.13127239048480988,
0.01670277677476406,
0.06631560623645782,
0.09441469609737396,
0.09931161254644394,
0.12565013766288757,
-0.013478949666023254,
-0.10503636300563812,
-0.05028822273015976,
-0.04404086247086525,
-0.09812942147254944,
-0.005863535683602095,
0.025312937796115875,
-0.13404539227485657,
-0.0026730732060968876,
-0.07243114709854126,
0.09655578434467316,
0.09207691997289658,
0.02585950493812561,
0.07761353999376297,
-0.20567485690116882,
-0.08721419423818588,
0.09773943573236465,
0.10129158198833466,
-0.03965196758508682,
0.045241229236125946,
0.19539877772331238,
-0.09803345799446106,
0.13700079917907715,
0.013704806566238403,
0.11960858106613159,
-0.004216262139379978,
0.012061946094036102,
-0.06733628362417221,
0.12213919311761856,
0.02387801557779312,
0.06655032932758331,
-0.11506388336420059,
0.14593970775604248,
0.00262458180077374,
0.024966774508357048,
-0.06693381071090698,
-0.015311955474317074,
0.07793092727661133,
0.30836281180381775,
0.07098915427923203,
0.04840163514018059,
-0.0569022037088871,
0.03735268861055374,
-0.09457521885633469,
0.07906486839056015,
-0.056327760219573975,
0.04031941667199135,
-0.03793258219957352,
-0.06324523687362671,
-0.08096913248300552,
-0.009812341071665287,
-0.061453673988580704,
-0.1922731250524521,
-0.1427379697561264,
-0.011921028606593609,
0.21771381795406342,
0.05636535584926605,
-0.02631499618291855,
-0.00784206297248602,
0.009012233465909958,
0.23411160707473755,
0.1573323756456375,
-0.07350829988718033,
-0.0768427774310112,
-0.07063109427690506,
0.030384209007024765,
-0.0498509481549263,
0.01872324012219906,
-0.10179811716079712,
0.15798351168632507,
0.0023619821295142174,
-0.14481745660305023,
0.048753246665000916,
-0.10968305170536041,
0.013215524144470692,
0.0019846681971102953,
0.11864007264375687,
0.02017907053232193,
-0.028508255258202553,
0.12710881233215332,
-0.03043779358267784,
-0.0020383987575769424,
-0.16419649124145508,
-0.13337348401546478,
0.2867279350757599,
-0.05335475504398346,
0.021828947588801384,
-0.11565788090229034,
-0.18470966815948486,
0.00037932387203909457,
-0.013652224093675613,
0.0848383903503418,
0.12013614922761917,
-0.08017057180404663,
0.14293520152568817,
0.15854668617248535,
-0.05276921018958092,
-0.16456934809684753,
-0.027432819828391075,
-0.06774070858955383,
-0.011696398258209229,
-0.08008003979921341,
-0.003207032335922122,
0.11010859161615372,
0.007396155968308449,
-0.030534986406564713,
0.018357329070568085,
-0.25778189301490784,
-0.10997821390628815,
0.08227821439504623,
0.06546496599912643,
0.29095137119293213,
-0.08037427067756653,
-0.09470049291849136,
-0.054561153054237366,
-0.17343513667583466,
0.1295015960931778,
-0.11704419553279877,
0.058500174432992935,
-0.015054389834403992,
0.04841207340359688,
0.016068680211901665,
-0.016704224050045013,
0.17527209222316742,
-0.014373809099197388,
0.03568856418132782,
-0.12025590986013412,
0.04978155344724655,
0.07653852552175522,
-0.04843652620911598,
0.24084123969078064,
-0.058009061962366104,
0.06895327568054199,
-0.055065739899873734,
-0.009509792551398277,
-0.07957907766103745,
0.015201651491224766,
0.024042516946792603,
-0.04603572189807892,
-0.060123782604932785,
0.039267994463443756,
0.005895133130252361,
0.030411118641495705,
0.07323724776506424,
-0.0588533990085125,
0.0851716473698616,
0.017678115516901016,
0.09470537304878235,
-0.28698012232780457,
0.02850983291864395,
0.003514094976708293,
0.002901477040722966,
0.07943235337734222,
-0.0897730141878128,
0.04165148362517357,
0.07067500799894333,
-0.04899812862277031,
0.14200201630592346,
-0.000820482091512531,
-0.008134015835821629,
0.021151872351765633,
0.09134827554225922,
-0.138075053691864,
-0.24658961594104767,
-0.0622234120965004,
0.18464095890522003,
0.08651571720838547,
0.11679211258888245,
0.137450709939003,
-0.09540684521198273,
-0.005701069254428148,
0.015590755268931389,
0.02339288219809532,
-0.04898018017411232,
0.005076008383184671,
-0.04904748126864433,
0.016991574317216873,
-0.13595901429653168,
-0.005698660388588905,
0.02303583361208439,
-0.029872288927435875,
0.02300548367202282,
0.1320105642080307,
-0.2095964103937149,
-0.11581245064735413,
-0.027983466163277626,
0.21979443728923798,
0.0400017611682415,
-0.04680754616856575,
-0.01685245707631111,
-0.13378320634365082,
-0.03072650171816349,
0.04599190130829811,
0.07466321438550949,
-0.0000598127517150715,
0.0011793059529736638,
-0.036894217133522034,
-0.06457901746034622,
0.031935207545757294,
0.04686085879802704,
0.02135315164923668,
-0.18950192630290985,
-0.055463165044784546,
0.06425091624259949,
0.06810308992862701,
-0.08365514129400253,
-0.10509712249040604,
-0.12407431751489639,
-0.03420314937829971,
-0.17185541987419128,
0.06342298537492752,
-0.17072898149490356,
0.012334858998656273,
-0.019467661157250404,
-0.07550898939371109,
-0.012880263850092888,
0.03130021691322327,
-0.03845546767115593,
0.0347466878592968,
-0.04778246581554413,
0.04944224655628204,
-0.07893270254135132,
-0.022846821695566177,
0.04347473010420799,
-0.023201214149594307,
0.09785628318786621,
0.046649493277072906,
-0.07608795166015625,
0.0320870578289032,
-0.28924280405044556,
-0.05289393290877342,
0.10789798200130463,
0.038067735731601715,
0.08108270913362503,
-0.06642059236764908,
-0.01609618030488491,
0.0639142096042633,
0.09508670121431351,
0.023523712530732155,
0.19478127360343933,
-0.08008027076721191,
-0.07579077780246735,
-0.07162889838218689,
-0.05619155243039131,
-0.02364729717373848,
0.027019832283258438,
0.0535336509346962,
0.07016110420227051,
0.07702026516199112,
-0.10422533005475998,
-0.024437181651592255,
-0.10146258026361465,
-0.028952764347195625,
-0.020912619307637215,
-0.11200450360774994,
-0.07246667146682739,
-0.02487354353070259,
-0.003658056491985917,
0.005795215256512165,
0.10148203372955322,
0.05098085105419159,
-0.0297563299536705,
0.015253565274178982,
0.02487686648964882,
0.08830633014440536,
-0.042116425931453705,
0.0874428004026413,
0.03461756929755211,
0.06841113418340683,
0.0011887591099366546,
0.0802391916513443,
0.05675024166703224,
-0.03389737382531166,
0.06540355086326599,
-0.003723525907844305,
-0.11982496082782745,
0.09465079009532928,
0.01981370709836483,
0.059049345552921295,
-0.07027886807918549,
0.007244401145726442,
-0.14390383660793304,
0.07598930597305298,
-0.1113317534327507,
0.03201207146048546,
0.16981743276119232,
-0.09132231026887894,
0.059979427605867386,
-0.020695427432656288,
-0.07713791728019714,
-0.12009934335947037,
-0.3021509647369385,
-0.10528583079576492,
-0.26043611764907837,
-0.036344852298498154,
-0.11884336173534393,
0.0028153390157967806,
-0.03451751545071602,
0.06455317884683609,
0.005937248468399048,
0.14229336380958557,
-0.10350142419338226,
-0.01641613058745861,
-0.0010086039546877146,
-0.0306516382843256,
-0.02964375540614128,
-0.024326160550117493,
0.04061447083950043,
0.04336066544055939,
0.002384027000516653,
-0.049279555678367615,
0.08287771791219711,
0.05531999468803406,
0.010044371709227562,
-0.012865598313510418,
-0.03990258648991585,
-0.06948628276586533,
0.02689226157963276,
-0.01873100735247135,
0.010714331641793251,
0.07956767082214355,
-0.09022130817174911,
0.02832440845668316,
0.20023222267627716,
-0.0007530617876909673,
-0.12014140188694,
-0.14652268588542938,
0.1561959832906723,
-0.07303982973098755,
0.013657274655997753,
-0.02090451493859291,
-0.014278844930231571,
-0.0728822574019432,
0.26577991247177124,
0.21600760519504547,
-0.015434317290782928,
-0.0074587296694517136,
-0.14256909489631653,
0.009684532880783081,
-0.03474158048629761,
0.11839308589696884,
0.07275831699371338,
0.25405871868133545,
-0.0112869618460536,
0.013378825969994068,
-0.03217718005180359,
-0.039187029004096985,
-0.12340493500232697,
-0.09316656738519669,
-0.002227461664006114,
-0.03651921823620796,
0.01492634229362011,
0.08305815607309341,
-0.1071355864405632,
0.0730416402220726,
-0.11336380988359451,
-0.13350144028663635,
-0.054387614130973816,
-0.07588747888803482,
0.04630901291966438,
0.04092573747038841,
0.03729971870779991,
-0.038493406027555466,
0.030918598175048828,
0.19782128930091858,
-0.014134639874100685,
-0.17763538658618927,
-0.15035443007946014,
0.0726388618350029,
0.011566662229597569,
0.16389723122119904,
0.013127276673913002,
-0.02005159854888916,
0.04317004606127739,
-0.03325027599930763,
-0.09688837081193924,
0.06774032860994339,
-0.04226306453347206,
0.0027947332710027695,
0.010052130557596684,
0.0009270037990063429,
-0.04930027946829796,
0.04540826752781868,
0.012376662343740463,
-0.1250343769788742,
0.016639508306980133,
-0.009326230734586716,
0.008182017132639885,
-0.037255555391311646,
0.13875946402549744,
-0.09475135058164597,
0.08315543830394745,
0.10026691854000092,
-0.023970680311322212,
-0.005749079864472151,
-0.04744366183876991,
0.10828591138124466,
-0.052344877272844315,
-0.10869333893060684,
-0.0036494906526058912,
-0.14709778130054474,
-0.029435565695166588,
0.06901728361845016,
0.008927889168262482,
-0.19871826469898224,
-0.07681088149547577,
-0.1244012638926506,
0.04993695765733719,
-0.02265181951224804,
0.1043907180428505,
0.1657598912715912,
0.06231262907385826,
-0.04176270216703415,
-0.10702923685312271,
0.008615812286734581,
0.051606424152851105,
-0.060158561915159225,
-0.09421619772911072
] |
null | null | transformers | # merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [hyeogi/SOLAR-10.7B-dpo-v1](https://huggingface.co/hyeogi/SOLAR-10.7B-dpo-v1)
* [LDCC/LDCC-SOLAR-10.7B](https://huggingface.co/LDCC/LDCC-SOLAR-10.7B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: LDCC/LDCC-SOLAR-10.7B
layer_range: [0, 48]
- model: hyeogi/SOLAR-10.7B-dpo-v1
layer_range: [0, 48]
merge_method: slerp
tokenizer_source: base
base_model: LDCC/LDCC-SOLAR-10.7B
embed_slerp: true
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## Datasets
Finetuned using LoRA with [kyujinpy/OpenOrca-KO](https://huggingface.co/datasets/kyujinpy/OpenOrca-KO) | {"language": ["ko"], "license": "apache-2.0", "tags": ["mergekit", "merge", "LDCC/LDCC-SOLAR-10.7B", "hyeogi/SOLAR-10.7B-dpo-v1"], "base_model": ["LDCC/LDCC-SOLAR-10.7B", "hyeogi/SOLAR-10.7B-dpo-v1"]} | text-generation | rrw-x2/KoSOLAR-10.9B-v0.5 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"LDCC/LDCC-SOLAR-10.7B",
"hyeogi/SOLAR-10.7B-dpo-v1",
"ko",
"base_model:LDCC/LDCC-SOLAR-10.7B",
"base_model:hyeogi/SOLAR-10.7B-dpo-v1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T13:05:53+00:00 | [] | [
"ko"
] | TAGS
#transformers #safetensors #llama #text-generation #mergekit #merge #LDCC/LDCC-SOLAR-10.7B #hyeogi/SOLAR-10.7B-dpo-v1 #ko #base_model-LDCC/LDCC-SOLAR-10.7B #base_model-hyeogi/SOLAR-10.7B-dpo-v1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # merge
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* hyeogi/SOLAR-10.7B-dpo-v1
* LDCC/LDCC-SOLAR-10.7B
### Configuration
The following YAML configuration was used to produce this model:
## Datasets
Finetuned using LoRA with kyujinpy/OpenOrca-KO | [
"# merge\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* hyeogi/SOLAR-10.7B-dpo-v1\n* LDCC/LDCC-SOLAR-10.7B",
"### Configuration\n\nThe following YAML configuration was used to produce this model:",
"## Datasets\n\nFinetuned using LoRA with kyujinpy/OpenOrca-KO"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #LDCC/LDCC-SOLAR-10.7B #hyeogi/SOLAR-10.7B-dpo-v1 #ko #base_model-LDCC/LDCC-SOLAR-10.7B #base_model-hyeogi/SOLAR-10.7B-dpo-v1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# merge\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* hyeogi/SOLAR-10.7B-dpo-v1\n* LDCC/LDCC-SOLAR-10.7B",
"### Configuration\n\nThe following YAML configuration was used to produce this model:",
"## Datasets\n\nFinetuned using LoRA with kyujinpy/OpenOrca-KO"
] | [
132,
18,
4,
18,
46,
17,
21
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #LDCC/LDCC-SOLAR-10.7B #hyeogi/SOLAR-10.7B-dpo-v1 #ko #base_model-LDCC/LDCC-SOLAR-10.7B #base_model-hyeogi/SOLAR-10.7B-dpo-v1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# merge\nThis is a merge of pre-trained language models created using mergekit.## Merge Details### Merge Method\n\nThis model was merged using the SLERP merge method.### Models Merged\n\nThe following models were included in the merge:\n* hyeogi/SOLAR-10.7B-dpo-v1\n* LDCC/LDCC-SOLAR-10.7B### Configuration\n\nThe following YAML configuration was used to produce this model:## Datasets\n\nFinetuned using LoRA with kyujinpy/OpenOrca-KO"
] | [
-0.05027922987937927,
-0.02658991329371929,
-0.0035859323106706142,
-0.0636521503329277,
0.11659843474626541,
0.03568644821643829,
0.18308036029338837,
0.05488472059369087,
0.015831466764211655,
0.0925271138548851,
-0.027008797973394394,
0.12269053608179092,
0.09501028060913086,
0.14729955792427063,
0.009523372165858746,
-0.1681404709815979,
0.07742597162723541,
-0.031967926770448685,
-0.11170454323291779,
0.05337309092283249,
0.08826649934053421,
-0.09362874180078506,
0.07815533876419067,
0.009125033393502235,
-0.13348661363124847,
0.024273952469229698,
-0.06529183685779572,
0.012913764454424381,
0.11238520592451096,
0.0915316715836525,
0.05763661861419678,
0.07259514927864075,
-0.005170167423784733,
-0.22448177635669708,
0.022889262065291405,
-0.0031064781360328197,
-0.039539188146591187,
0.04566672444343567,
0.06633227318525314,
0.029079020023345947,
0.15914086997509003,
-0.17081153392791748,
-0.03473644331097603,
0.039976175874471664,
-0.04216562211513519,
-0.17332515120506287,
-0.1125662624835968,
0.10242921113967896,
0.13772398233413696,
-0.0034868335351347923,
-0.017638280987739563,
0.026760434731841087,
0.04173848405480385,
0.04438014328479767,
0.10504286736249924,
-0.20554320514202118,
0.038887981325387955,
0.18687157332897186,
0.01847650110721588,
-0.12147217243909836,
0.0014257206348702312,
0.05306779593229294,
0.048831500113010406,
-0.034718625247478485,
0.042547594755887985,
-0.04131777212023735,
0.08150511980056763,
-0.014705312438309193,
-0.12001010030508041,
-0.046333931386470795,
0.22158671915531158,
-0.003935357090085745,
-0.007655973080545664,
-0.12661336362361908,
-0.1136811226606369,
0.06758522987365723,
-0.0077984570525586605,
0.026599999517202377,
0.014686452224850655,
-0.01722482219338417,
0.06569410115480423,
-0.06503686308860779,
-0.09186338633298874,
-0.01967989280819893,
-0.079732745885849,
0.26669982075691223,
0.0932069942355156,
0.010283180512487888,
-0.020443426445126534,
0.13019759953022003,
-0.04949698597192764,
-0.12877996265888214,
-0.009310523979365826,
0.005752275697886944,
-0.04303349554538727,
0.03748733922839165,
-0.08024099469184875,
-0.07942613214254379,
0.12379960715770721,
0.2055646777153015,
-0.07922469824552536,
-0.007758974563330412,
0.13069529831409454,
0.04563794657588005,
0.027635538950562477,
0.031991198658943176,
-0.13356496393680573,
-0.03989579528570175,
0.04230746254324913,
0.004300383385270834,
0.18309882283210754,
0.008174983784556389,
-0.10211006551980972,
-0.03415565937757492,
0.022231820970773697,
0.03538661077618599,
0.10545673221349716,
0.05197840556502342,
-0.052706029266119,
-0.05944322794675827,
0.17240339517593384,
-0.09680373966693878,
-0.014454654417932034,
0.018213843926787376,
-0.0025360179133713245,
-0.08567055314779282,
0.08355765789747238,
0.04695617035031319,
0.010023844428360462,
0.10280055552721024,
-0.00924642849713564,
-0.00037432584213092923,
-0.062136828899383545,
-0.05101826786994934,
-0.011402367614209652,
0.058514658361673355,
0.014769378118216991,
-0.08168993145227432,
-0.21316304802894592,
-0.01800399459898472,
-0.00816649105399847,
-0.06405819207429886,
-0.044942911714315414,
-0.003969334065914154,
-0.009249486960470676,
-0.005920407362282276,
-0.023226339370012283,
0.009123205207288265,
-0.005977391730993986,
0.00012709524889942259,
0.005903144832700491,
0.03270970657467842,
-0.10611594468355179,
0.017129432410001755,
-0.07095058262348175,
0.11875368654727936,
-0.10657915472984314,
0.10342895984649658,
-0.034043408930301666,
0.044626183807849884,
-0.0920703187584877,
-0.04521580785512924,
0.028331944718956947,
0.00629679998382926,
0.08041492849588394,
0.172336146235466,
-0.13140013813972473,
-0.05238882824778557,
0.09060981869697571,
-0.13398034870624542,
-0.08613479882478714,
0.025378504768013954,
0.03369487076997757,
0.035499460995197296,
0.006979570258408785,
0.15893559157848358,
0.09769149124622345,
-0.0590774230659008,
-0.12196283787488937,
-0.03073340840637684,
0.061443157494068146,
-0.018535619601607323,
0.0636526495218277,
-0.033439382910728455,
-0.06076841801404953,
0.03342369571328163,
-0.10003567487001419,
0.11253383010625839,
-0.04314494505524635,
-0.05135568231344223,
-0.06446480751037598,
-0.03197280690073967,
0.03691685199737549,
-0.0011755781015381217,
-0.013754405081272125,
-0.0054747858084738255,
-0.01441330835223198,
0.1287248283624649,
0.16575275361537933,
-0.09814805537462234,
0.04180904105305672,
-0.015235685743391514,
0.14841553568840027,
-0.07412048429250717,
0.012558799237012863,
-0.038110390305519104,
-0.09499210864305496,
0.016460182145237923,
-0.05464520677924156,
-0.04114200547337532,
0.020178480073809624,
0.022920861840248108,
0.04932660609483719,
-0.06394658982753754,
-0.09876542538404465,
0.021496998146176338,
0.02885477989912033,
0.007976728491485119,
-0.17105074226856232,
-0.1244925707578659,
-0.02008877694606781,
0.28737562894821167,
-0.09717968106269836,
0.0787649154663086,
0.0605851486325264,
0.22872082889080048,
-0.07553565502166748,
0.017218371853232384,
0.10560812056064606,
0.02085343562066555,
-0.03946083039045334,
-0.02490290068089962,
0.05347243696451187,
0.024684349074959755,
-0.20540110766887665,
0.1017175167798996,
-0.18871407210826874,
-0.15505777299404144,
0.0871502161026001,
0.02556312270462513,
0.01945536583662033,
-0.08858752995729446,
-0.028301578015089035,
-0.07536309957504272,
0.025297975167632103,
-0.07147081941366196,
-0.03878794610500336,
-0.020160675048828125,
0.09179017692804337,
-0.023766320198774338,
-0.03605762869119644,
-0.024692369624972343,
-0.020869791507720947,
-0.04910184442996979,
0.08167488873004913,
-0.00790298543870449,
-0.041506461799144745,
0.09891687333583832,
0.1104189082980156,
0.06083930283784866,
0.09918299317359924,
-0.024062475189566612,
-0.00890832208096981,
-0.060820482671260834,
0.052059292793273926,
0.00947149470448494,
0.07398579269647598,
0.021048707887530327,
0.06237475574016571,
0.0701143890619278,
-0.05764983594417572,
0.0632910504937172,
-0.138034388422966,
0.021280329674482346,
0.03416208177804947,
-0.01782204769551754,
0.14836205542087555,
0.10763028264045715,
-0.005454279016703367,
0.000057326666137669235,
-0.00860581174492836,
0.03703857958316803,
0.0219378974288702,
-0.023565534502267838,
-0.08528696745634079,
0.2025526911020279,
-0.12755681574344635,
-0.18838365375995636,
-0.18099676072597504,
-0.03485843911767006,
-0.11745169013738632,
-0.03095485456287861,
0.05248378589749336,
-0.05149722471833229,
-0.08180808275938034,
-0.06812699884176254,
0.17533040046691895,
0.032592177391052246,
0.012616022489964962,
-0.036067597568035126,
-0.0456634983420372,
0.03213513270020485,
-0.10870784521102905,
0.004588128998875618,
0.01600288413465023,
-0.0570836178958416,
0.07429937273263931,
0.04071702063083649,
0.12069042026996613,
0.0800243392586708,
0.03524848446249962,
-0.028566943481564522,
0.012984652072191238,
0.22407269477844238,
-0.06246078386902809,
0.08602399379014969,
0.207940474152565,
0.01888977736234665,
0.04793020710349083,
0.21890594065189362,
0.042686715722084045,
-0.034345321357250214,
0.0009178604232147336,
-0.03398304432630539,
-0.07031504064798355,
-0.17911067605018616,
-0.18401066958904266,
-0.07195619493722916,
-0.062465470284223557,
-0.004806128330528736,
0.0790194422006607,
0.09691982716321945,
0.07001032680273056,
-0.13433924317359924,
-0.025622373446822166,
-0.011584043502807617,
0.0738399475812912,
0.22122494876384735,
0.03154471889138222,
0.07298869639635086,
-0.05567668005824089,
0.025506243109703064,
0.041105542331933975,
0.029697857797145844,
0.10538201034069061,
-0.012131760828197002,
0.09475234895944595,
0.11689603328704834,
0.11996761709451675,
-0.004624626599252224,
0.03409852460026741,
-0.050568755716085434,
0.014504334889352322,
0.029627596959471703,
-0.11559761315584183,
0.038632504642009735,
0.06375252455472946,
-0.07552526146173477,
0.07602780312299728,
-0.03892218694090843,
-0.0534033328294754,
0.044919226318597794,
0.10067939013242722,
0.13734279572963715,
-0.17491267621517181,
-0.07252849638462067,
0.09192539751529694,
0.0750996470451355,
-0.02527298405766487,
-0.06696627289056778,
0.060554198920726776,
-0.07303168624639511,
0.2289285510778427,
-0.0072178286500275135,
0.055722229182720184,
-0.011819996871054173,
-0.008786697871983051,
0.006976948119699955,
0.14599475264549255,
0.02457229234278202,
0.06753960996866226,
-0.16664882004261017,
0.11023248732089996,
0.0456041619181633,
-0.00031483141356147826,
0.0005948007456026971,
0.05249444395303726,
-0.018786156550049782,
0.17700187861919403,
0.027331331744790077,
0.04100587218999863,
0.0332062691450119,
-0.02241516299545765,
-0.12086948752403259,
0.008739842101931572,
-0.014157680794596672,
-0.09417227655649185,
0.09287464618682861,
-0.014064067043364048,
-0.02597666345536709,
-0.02078760787844658,
0.10181792080402374,
-0.10389432311058044,
-0.13504523038864136,
0.10717012733221054,
0.09541227668523788,
0.11789995431900024,
-0.11224585771560669,
-0.030563808977603912,
-0.11851033568382263,
0.16968205571174622,
-0.07954378426074982,
-0.1440315544605255,
-0.07245930284261703,
0.015058123506605625,
0.17055974900722504,
-0.10155165195465088,
0.06822678446769714,
-0.023367825895547867,
0.017202353104948997,
-0.0319632962346077,
-0.15845124423503876,
0.020207956433296204,
-0.0907202735543251,
-0.1405634582042694,
0.058308135718107224,
0.14710786938667297,
-0.03043115884065628,
0.023691507056355476,
-0.009711922146379948,
0.05264478549361229,
-0.10633747279644012,
-0.08332665264606476,
0.009593702852725983,
0.20342309772968292,
0.06945422291755676,
0.1354082077741623,
-0.008309715427458286,
-0.13297419250011444,
-0.00800605770200491,
-0.10490155965089798,
0.07963911443948746,
0.2823222577571869,
-0.061041612178087234,
0.09410139918327332,
0.08665556460618973,
-0.06560558080673218,
-0.19055157899856567,
-0.11461025476455688,
-0.052725955843925476,
0.06133626773953438,
-0.01290922611951828,
-0.11520638316869736,
0.023380491882562637,
0.07638516277074814,
-0.018312156200408936,
-0.04682563245296478,
-0.3153823912143707,
-0.18367953598499298,
0.04793655127286911,
0.055686626583337784,
0.10211994498968124,
-0.1608767807483673,
-0.12125138193368912,
-0.056687723845243454,
-0.2066689431667328,
0.05193908140063286,
-0.1356372833251953,
0.05200899392366409,
-0.05870803818106651,
0.02455217018723488,
0.037236541509628296,
-0.030522413551807404,
0.2274216115474701,
-0.024294601753354073,
0.002399920020252466,
-0.06778142601251602,
0.021924976259469986,
0.11228279769420624,
-0.04263249784708023,
0.07526207715272903,
-0.09438157826662064,
0.03676629066467285,
-0.1203000470995903,
0.010996771045029163,
-0.09205199033021927,
0.07540968060493469,
-0.09292803704738617,
-0.06030182167887688,
-0.10222230851650238,
0.012185635045170784,
0.0703987255692482,
0.0036102994345128536,
0.1423756182193756,
-0.003262478159740567,
0.09296087920665741,
0.16846102476119995,
0.06389978528022766,
0.043483465909957886,
-0.013264365494251251,
0.0005328914267010987,
-0.0301645677536726,
0.0369107685983181,
-0.09280768036842346,
-0.008271992206573486,
0.0733913704752922,
0.02237733080983162,
0.050300851464271545,
0.017563659697771072,
-0.0906895101070404,
0.0021771437022835016,
0.09734339267015457,
-0.15242503583431244,
-0.3558131456375122,
-0.045635148882865906,
0.06422369927167892,
-0.02670723758637905,
0.13513703644275665,
0.12310801446437836,
-0.056907087564468384,
-0.052559174597263336,
0.03141818568110466,
0.03974742814898491,
-0.05341176688671112,
0.11427522450685501,
0.03847796469926834,
0.07593289762735367,
-0.12133954465389252,
0.04162644222378731,
0.0628761351108551,
-0.007143449503928423,
-0.03495915234088898,
0.04624766856431961,
-0.1048223227262497,
-0.04272258281707764,
-0.0465436726808548,
0.012359442189335823,
-0.1368020474910736,
-0.11148254573345184,
-0.09919694066047668,
-0.11412345618009567,
-0.012440824881196022,
-0.018211914226412773,
0.05339503288269043,
0.010465609841048717,
0.06187653914093971,
-0.05836264044046402,
-0.05406707897782326,
0.06136160343885422,
-0.006903297733515501,
0.10341153293848038,
-0.12729014456272125,
0.08427749574184418,
-0.026103008538484573,
0.10219202190637589,
-0.031105780974030495,
-0.008806516416370869,
-0.07302939891815186,
-0.020875344052910805,
-0.10252396017313004,
-0.05616709217429161,
-0.16752518713474274,
-0.05238991603255272,
-0.020436767488718033,
-0.0405440591275692,
0.020175915211439133,
0.05747469142079353,
-0.01760307513177395,
-0.046985212713479996,
-0.07257000356912613,
0.02425629086792469,
-0.0740843415260315,
-0.029141878709197044,
0.02026662975549698,
-0.10683678835630417,
0.09503921121358871,
0.011270356364548206,
0.003231093054637313,
-0.03766046464443207,
-0.05660489574074745,
-0.049488816410303116,
0.04464855417609215,
-0.0022468571551144123,
0.0039983224123716354,
-0.13621382415294647,
-0.0856107771396637,
-0.012730843387544155,
-0.09455669671297073,
-0.051413629204034805,
0.07961035519838333,
-0.0670606791973114,
0.01517449226230383,
-0.0963636115193367,
0.04218415915966034,
-0.05443314462900162,
0.058704081922769547,
-0.0052982522174716,
0.06557594984769821,
0.0591881163418293,
-0.052836693823337555,
-0.00842653401196003,
-0.13793152570724487,
-0.0347304493188858,
-0.04449659213423729,
-0.0925467237830162,
-0.07015135884284973,
-0.06403644382953644,
0.029698388651013374,
0.026637639850378036,
0.11538432538509369,
-0.07867585867643356,
-0.0890679806470871,
0.0049660345539450645,
0.09825287014245987,
0.05740273743867874,
0.03600747510790825,
0.25485092401504517,
-0.0045409356243908405,
0.05973801016807556,
0.011683141812682152,
0.035819657146930695,
-0.003000773023813963,
0.016259465366601944,
0.04267038777470589,
0.08117002248764038,
0.038032952696084976,
0.028487207368016243,
0.059648558497428894,
-0.13427042961120605,
-0.06402336806058884,
-0.0453445129096508,
-0.07494273781776428,
0.06929539889097214,
-0.02776751108467579,
0.14913341403007507,
0.1149873211979866,
-0.19321925938129425,
0.07273988425731659,
0.07744872570037842,
-0.039274200797080994,
-0.07426352798938751,
-0.05991000682115555,
-0.07552583515644073,
-0.1509978473186493,
-0.02478589490056038,
-0.07927580177783966,
-0.03423468396067619,
0.08890650421380997,
-0.0383150689303875,
0.020266218110919,
0.14893345534801483,
0.1426004022359848,
-0.047044895589351654,
-0.05471647530794144,
-0.0052354466170072556,
0.011321628466248512,
-0.04065322130918503,
-0.04078754037618637,
0.041304707527160645,
-0.0005893093766644597,
0.007479999214410782,
0.05402877926826477,
0.11047637462615967,
0.09612792730331421,
-0.0007912222063168883,
-0.08054731041193008,
0.034161195158958435,
0.019568804651498795,
0.0922766774892807,
-0.02220872789621353,
0.02038215659558773,
-0.0036586683709174395,
-0.020877163857221603,
0.1203627660870552,
-0.04471822828054428,
-0.09717058390378952,
-0.011007887311279774,
0.0839940533041954,
-0.05480518192052841,
-0.0018809445900842547,
0.09157132357358932,
-0.13254289329051971,
-0.01982564851641655,
0.09754186868667603,
0.2099451720714569,
0.08219394832849503,
0.025135114789009094,
-0.002698321361094713,
0.0031992814037948847,
0.013793460093438625,
0.051722850650548935,
0.03809960559010506,
0.1392531543970108,
-0.06951861083507538,
0.08562204986810684,
-0.03511853888630867,
-0.06427083164453506,
-0.12446114420890808,
0.04264162480831146,
-0.0901595950126648,
-0.09213753789663315,
0.06432390958070755,
0.0555882453918457,
-0.0046454970724880695,
-0.11741550266742706,
0.1158791109919548,
-0.15302513539791107,
-0.09528058022260666,
-0.10418909043073654,
0.15543581545352936,
-0.005657147150486708,
0.005397479049861431,
-0.08234003931283951,
0.046755559742450714,
0.14510220289230347,
0.030599776655435562,
-0.07532230019569397,
-0.029138335958123207,
0.07596323639154434,
-0.08326093852519989,
0.028038589283823967,
-0.02380274422466755,
0.06112868711352348,
0.0972389280796051,
-0.018538648262619972,
-0.13984514772891998,
0.028025835752487183,
0.07574652880430222,
-0.07360862195491791,
0.06316443532705307,
0.06058104708790779,
0.006457298528403044,
0.016783975064754486,
-0.0015608022222295403,
-0.14778415858745575,
0.027886513620615005,
0.1517375260591507,
-0.023564999923110008,
-0.057117823511362076,
0.02184818685054779,
-0.07989461719989777,
0.09361688792705536,
0.08156471699476242,
-0.08479895442724228,
0.0040720487013459206,
-0.04116572439670563,
0.016453001648187637,
0.04065176844596863,
0.17803271114826202,
0.028524817898869514,
-0.13220003247261047,
0.041259922087192535,
-0.004422958008944988,
0.08904135972261429,
-0.16913741827011108,
-0.05637454614043236,
-0.05662380903959274,
-0.049980856478214264,
-0.06918742507696152,
0.134177103638649,
0.12307236343622208,
-0.008005647920072079,
-0.032346226274967194,
-0.11233597248792648,
-0.059514693915843964,
0.08994699269533157,
-0.11322475969791412,
-0.060030337423086166
] |
null | null | diffusers | # Olaf Scholz
<Gallery />
## Model description
Its just an LORA of Olaf Scholz
## Trigger words
You should use `osh` to trigger the image generation.
You should use `bre` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/gz8iz/Olaf_Scholz/tree/main) them in the Files & versions tab.
| {"tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora"], "widget": [{"text": "-", "output": {"url": "images/2024-02-07_21-18-47_8347.png"}}], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "osh, bre"} | text-to-image | gz8iz/Olaf_Scholz | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"has_space",
"region:us"
] | 2024-02-09T13:07:05+00:00 | [] | [] | TAGS
#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us
| # Olaf Scholz
<Gallery />
## Model description
Its just an LORA of Olaf Scholz
## Trigger words
You should use 'osh' to trigger the image generation.
You should use 'bre' to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
| [
"# Olaf Scholz\n\n<Gallery />",
"## Model description \n\nIts just an LORA of Olaf Scholz",
"## Trigger words\n\nYou should use 'osh' to trigger the image generation.\n\nYou should use 'bre' to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
"TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us \n",
"# Olaf Scholz\n\n<Gallery />",
"## Model description \n\nIts just an LORA of Olaf Scholz",
"## Trigger words\n\nYou should use 'osh' to trigger the image generation.\n\nYou should use 'bre' to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
60,
10,
14,
28,
28
] | [
"passage: TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us \n# Olaf Scholz\n\n<Gallery />## Model description \n\nIts just an LORA of Olaf Scholz## Trigger words\n\nYou should use 'osh' to trigger the image generation.\n\nYou should use 'bre' to trigger the image generation.## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
-0.14285707473754883,
-0.08469190448522568,
0.00081135134678334,
0.02356678433716297,
0.08743148297071457,
0.012922062538564205,
0.13851137459278107,
-0.017476512119174004,
0.16861338913440704,
0.1351773738861084,
0.11634024977684021,
-0.014471887610852718,
0.023027785122394562,
0.2045396864414215,
-0.10733269900083542,
-0.22559857368469238,
0.08051323145627975,
-0.01838267594575882,
-0.10618996620178223,
0.009577459655702114,
0.03176843374967575,
-0.07068148255348206,
0.11839643120765686,
-0.02580408751964569,
-0.023309729993343353,
0.03910258039832115,
0.010186021216213703,
-0.02406153455376625,
-0.005940037313848734,
0.07786393165588379,
-0.013268690556287766,
0.15681591629981995,
0.12406280636787415,
-0.09371673315763474,
0.05788591131567955,
-0.010964900255203247,
-0.08076424151659012,
0.04006991907954216,
-0.009063253179192543,
-0.06387221068143845,
0.19140833616256714,
-0.1287182718515396,
-0.0791354849934578,
-0.017230041325092316,
-0.012019963935017586,
-0.0038661835715174675,
0.00850148219615221,
0.0023307236842811108,
0.07610180228948593,
-0.04911443963646889,
-0.015601711347699165,
0.012083885259926319,
-0.02403627522289753,
0.05994056537747383,
0.23142878711223602,
-0.1972728669643402,
-0.05550047755241394,
0.1891457736492157,
0.030998436734080315,
0.19306333363056183,
-0.07089968025684357,
0.14016325771808624,
0.09601563960313797,
-0.05208699777722359,
0.08403433859348297,
-0.06044499948620796,
0.1223641112446785,
0.005305830854922533,
-0.10518176108598709,
0.05858428403735161,
0.26629307866096497,
0.06187509745359421,
-0.052757378667593,
-0.04664548113942146,
-0.08273160457611084,
0.1075885221362114,
-0.0603276789188385,
0.008521548472344875,
0.041054822504520416,
-0.02247435413300991,
0.0032919275108724833,
-0.10188940912485123,
-0.10056577622890472,
-0.0437300018966198,
-0.048855677247047424,
0.26594704389572144,
-0.032654404640197754,
0.07688754051923752,
0.06302976608276367,
0.07259353995323181,
-0.20997610688209534,
-0.1325283944606781,
-0.010024646297097206,
-0.06575531512498856,
0.022029267624020576,
0.09118729084730148,
-0.04175847768783569,
-0.11368538439273834,
0.10876157134771347,
0.04984420910477638,
0.029445067048072815,
-0.03493275120854378,
0.009615955874323845,
0.10842651128768921,
0.07156643271446228,
-0.003977062180638313,
-0.014225059188902378,
-0.15637092292308807,
0.0674152746796608,
0.10862638801336288,
0.09061388671398163,
-0.06651895493268967,
-0.13488061726093292,
-0.03227163478732109,
-0.045502521097660065,
-0.08317779004573822,
0.00051566754700616,
0.0046184672974050045,
-0.009672482497990131,
-0.007375833112746477,
0.13333548605442047,
-0.02928330935537815,
-0.01604546420276165,
0.01681702956557274,
-0.037755511701107025,
0.13265101611614227,
0.1226876825094223,
0.02091675065457821,
0.0796351432800293,
0.07347827404737473,
-0.06311667710542679,
-0.017653651535511017,
-0.02761325053870678,
-0.06459373980760574,
-0.049838997423648834,
-0.15406534075737,
0.03901025280356407,
-0.130558043718338,
-0.24633614718914032,
-0.03511834517121315,
0.04652619734406471,
-0.0192476324737072,
0.03694888949394226,
-0.04476078599691391,
-0.0434735082089901,
0.015954358503222466,
-0.013308054767549038,
-0.0440395213663578,
-0.060519251972436905,
0.06122470274567604,
-0.05218604952096939,
0.12558972835540771,
-0.21292464435100555,
0.004271047655493021,
-0.0860576406121254,
0.05178341269493103,
-0.16614656150341034,
0.04259886220097542,
-0.10642751306295395,
-0.010121199302375317,
0.02532832883298397,
-0.026970762759447098,
-0.10248233377933502,
0.08781440556049347,
0.03686041757464409,
0.20288723707199097,
-0.15606141090393066,
-0.06136449798941612,
-0.035207249224185944,
-0.20584636926651,
-0.024933047592639923,
0.09647338092327118,
-0.012691465206444263,
0.008800573647022247,
0.05009198188781738,
0.19933153688907623,
0.005734160542488098,
-0.12459667026996613,
0.01614321582019329,
0.026307955384254456,
-0.022964488714933395,
-0.029328245669603348,
0.0885777473449707,
0.09102900326251984,
-0.09916635602712631,
0.08410769701004028,
-0.21882066130638123,
0.0425914041697979,
-0.04974593594670296,
0.013618582859635353,
-0.023788996040821075,
-0.08492627739906311,
0.08462119102478027,
0.059625014662742615,
0.0559375062584877,
-0.033005766570568085,
-0.017312802374362946,
0.05012967064976692,
0.08090271800756454,
-0.08587177097797394,
-0.010823220014572144,
0.027761423960328102,
0.23550324141979218,
-0.19764696061611176,
-0.016038166359066963,
-0.05367503687739372,
-0.06193644925951958,
-0.03908587992191315,
0.16108648478984833,
0.072361521422863,
0.09980491548776627,
0.11347466707229614,
0.12067143619060516,
-0.06188840791583061,
-0.021581489592790604,
0.053996164351701736,
-0.017317427322268486,
-0.059531088918447495,
-0.13634054362773895,
-0.08550935238599777,
-0.09498024731874466,
0.10962911695241928,
-0.2612292468547821,
0.04120473563671112,
-0.028310338035225868,
0.05262745916843414,
0.04651022329926491,
0.005506908521056175,
-0.0342249795794487,
-0.04385659471154213,
-0.09944822639226913,
-0.014743738807737827,
-0.0025811181403696537,
0.008434783667325974,
-0.07269895076751709,
0.05959508940577507,
-0.10374999046325684,
0.10956484079360962,
0.12700505554676056,
0.0708494633436203,
0.02123691327869892,
-0.20472480356693268,
-0.015380891971290112,
0.04392937198281288,
-0.03422251716256142,
-0.05141478404402733,
-0.08311500400304794,
-0.049268949776887894,
0.0280147697776556,
-0.045996859669685364,
0.0673946812748909,
0.041085366159677505,
-0.06941227614879608,
-0.08055930584669113,
0.09820973128080368,
0.12712745368480682,
-0.009011629037559032,
0.11104081571102142,
0.12967389822006226,
-0.10675077885389328,
0.16923290491104126,
0.015027245506644249,
-0.11682803928852081,
0.02161353826522827,
0.017186280339956284,
0.013214423321187496,
0.22941099107265472,
0.012855463661253452,
-0.06057106703519821,
0.028416374698281288,
-0.10886027663946152,
0.02267572283744812,
-0.07451140135526657,
-0.015155010856688023,
0.023838987573981285,
-0.018960820510983467,
0.0624588243663311,
0.04272383823990822,
-0.08462601900100708,
0.07711172103881836,
-0.11188871413469315,
-0.09142705798149109,
-0.04459947720170021,
-0.007313286419957876,
-0.026340490207076073,
0.09271575510501862,
0.0004831218393519521,
-0.022031154483556747,
-0.12984077632427216,
0.1359802484512329,
-0.17870797216892242,
0.06544438004493713,
0.03562826290726662,
0.007120446301996708,
-0.07601804286241531,
-0.11972179263830185,
0.08462464809417725,
0.1471206545829773,
0.003692370606586337,
-0.05182633921504021,
-0.08323174715042114,
-0.0919777974486351,
-0.04608796536922455,
-0.061234086751937866,
-0.11805401742458344,
0.010111012496054173,
0.08227817714214325,
-0.06003442779183388,
0.15965282917022705,
0.03876858204603195,
0.011153018102049828,
0.00974631030112505,
0.04305615276098251,
0.08405771851539612,
-0.03040805086493492,
0.1229543387889862,
0.12663163244724274,
0.013093383051455021,
0.033848147839307785,
0.06300599128007889,
0.001254147500731051,
-0.03442928567528725,
0.014479353092610836,
-0.01150735467672348,
-0.12656427919864655,
0.02290692925453186,
-0.12183953821659088,
-0.09399227052927017,
0.005463017616420984,
0.025301817804574966,
0.047254424542188644,
0.04903544485569,
0.19351685047149658,
0.0022549526765942574,
-0.05556093528866768,
0.05732542648911476,
0.03620048612356186,
-0.059928614646196365,
0.009832849726080894,
0.0952991396188736,
-0.021561073139309883,
-0.033706892281770706,
0.15160924196243286,
-0.024182118475437164,
0.19014112651348114,
-0.07228148728609085,
-0.017454471439123154,
0.014261342585086823,
0.08356597274541855,
0.0811769887804985,
0.14161230623722076,
-0.008256074972450733,
-0.0275496207177639,
-0.04930553212761879,
-0.10291771590709686,
0.009657122194766998,
0.1482854187488556,
0.015926064923405647,
0.011817803606390953,
-0.043446045368909836,
0.06099966913461685,
0.019789548590779305,
0.04712392017245293,
0.04078603908419609,
-0.3021770715713501,
0.002210655715316534,
0.12055612355470657,
0.11975990235805511,
-0.03760148212313652,
0.036677196621894836,
0.22621001303195953,
0.02072104811668396,
0.03200574591755867,
-0.014033306390047073,
0.051311980932950974,
0.09974931925535202,
-0.06434577703475952,
-0.0903279036283493,
0.14755693078041077,
-0.05723176151514053,
-0.028977705165743828,
-0.017556147649884224,
0.07389136403799057,
-0.016378458589315414,
-0.028005382046103477,
-0.042202215641736984,
-0.03319248929619789,
0.1209404245018959,
0.17431522905826569,
0.15600049495697021,
0.031472839415073395,
-0.06681554764509201,
-0.03499699756503105,
-0.09164402633905411,
0.023449350148439407,
0.0020559169352054596,
-0.15471802651882172,
0.049798570573329926,
0.023883001878857613,
-0.019305787980556488,
0.0003406632458791137,
0.1128404289484024,
-0.0808911845088005,
-0.07038980722427368,
-0.04544645920395851,
0.09370860457420349,
-0.050498295575380325,
-0.03636982664465904,
-0.09189145267009735,
-0.14869624376296997,
0.05060567334294319,
0.10226243734359741,
-0.07438282668590546,
-0.13421376049518585,
0.03824722394347191,
0.055576570332050323,
-0.02799239195883274,
-0.01308382023125887,
0.026426510885357857,
0.08967962116003036,
-0.0658993124961853,
-0.15665392577648163,
0.061696141958236694,
-0.05706151947379112,
-0.06838907301425934,
-0.011607160791754723,
0.06277777999639511,
0.037149637937545776,
-0.01793542504310608,
0.00268231681548059,
0.059945520013570786,
0.00802336074411869,
-0.09143128991127014,
0.037222426384687424,
0.05232878774404526,
0.02218267135322094,
0.13096210360527039,
-0.033914659172296524,
-0.18102367222309113,
0.04753609374165535,
0.1021750196814537,
0.009570146910846233,
0.2319464385509491,
-0.06323719769716263,
0.012984552420675755,
0.15434199571609497,
0.02302817441523075,
-0.23029372096061707,
0.04922925680875778,
-0.043631020933389664,
0.08800770342350006,
0.08841767907142639,
0.027433890849351883,
0.16613881289958954,
0.11493698507547379,
-0.03679307550191879,
0.23727715015411377,
-0.2035660594701767,
-0.10716656595468521,
0.10714433342218399,
0.14877192676067352,
0.32559189200401306,
-0.22121751308441162,
-0.009734873659908772,
-0.06337235122919083,
-0.0841815397143364,
0.029771409928798676,
-0.22840933501720428,
0.017917724326252937,
-0.0068182931281626225,
-0.03508523479104042,
0.0240751001983881,
-0.0521879605948925,
0.19532354176044464,
-0.00013254313671495765,
0.07082153111696243,
-0.08885356783866882,
-0.055495042353868484,
0.09818452596664429,
-0.04344014450907707,
0.08808010071516037,
-0.22507904469966888,
0.017127249389886856,
-0.11775629222393036,
-0.050094544887542725,
-0.011436287313699722,
0.13001000881195068,
0.00513186352327466,
-0.054904755204916,
-0.012387811206281185,
0.03804824873805046,
0.0213308148086071,
0.045400723814964294,
0.099623903632164,
-0.0970073938369751,
0.033702362328767776,
0.20772910118103027,
-0.0484946109354496,
0.031253017485141754,
-0.0004366062639746815,
-0.037604864686727524,
-0.03595335781574249,
0.08476556092500687,
-0.10990597307682037,
-0.03496808931231499,
0.10440907627344131,
0.05411548539996147,
0.09375440329313278,
0.03719136118888855,
0.05204727500677109,
0.10282236337661743,
0.0952126756310463,
-0.13044902682304382,
-0.061338163912296295,
-0.05553615838289261,
-0.11368121951818466,
0.06218075752258301,
0.09652777761220932,
0.13879531621932983,
-0.0920441523194313,
0.04763168841600418,
-0.04539436101913452,
0.050704509019851685,
-0.05756256729364395,
0.03846730291843414,
0.07834193110466003,
-0.014669653959572315,
-0.08941557258367538,
0.048963095992803574,
-0.053794875741004944,
0.02156934142112732,
-0.07682044804096222,
0.00019870192045345902,
-0.0821424052119255,
-0.0441238172352314,
-0.00501798652112484,
0.12474789470434189,
-0.09535353630781174,
0.00195711269043386,
-0.06173611804842949,
-0.12940272688865662,
-0.051376570016145706,
0.05922946706414223,
0.0690300464630127,
-0.1077447235584259,
0.015269762836396694,
0.04159867390990257,
-0.01846938394010067,
0.04052583873271942,
0.13725776970386505,
0.10666079074144363,
-0.14516504108905792,
-0.09367556124925613,
-0.07804819941520691,
0.0007319020805880427,
-0.12381868809461594,
-0.02063438855111599,
-0.08836238086223602,
-0.017810052260756493,
-0.13347133994102478,
0.09276404231786728,
-0.12416626513004303,
-0.022296719253063202,
-0.008070625364780426,
-0.13900858163833618,
-0.06688512861728668,
0.022791430354118347,
-0.08871617168188095,
0.034481778740882874,
0.009017586708068848,
-0.008783970959484577,
-0.03794889524579048,
-0.07101187109947205,
0.01041395589709282,
-0.09267028421163559,
0.04421057552099228,
0.08925412595272064,
-0.0732099860906601,
0.013828408904373646,
-0.19653303921222687,
0.0015832147328183055,
0.08080574870109558,
0.02121605910360813,
0.00879345741122961,
0.13362973928451538,
0.02816454879939556,
0.025098903104662895,
0.028067203238606453,
-0.0561370849609375,
0.01372700184583664,
-0.03802661970257759,
0.09796048700809479,
-0.08601068705320358,
-0.05163934826850891,
-0.01609925925731659,
-0.007720839232206345,
0.06757058948278427,
0.1105126366019249,
0.07950525730848312,
-0.06610675156116486,
-0.013424788601696491,
-0.1135174110531807,
0.029200799763202667,
0.059141457080841064,
-0.10413569211959839,
-0.035775672644376755,
-0.05913855880498886,
0.036761119961738586,
-0.007147292606532574,
0.12268010526895523,
0.04535095766186714,
-0.0511663593351841,
-0.04250310733914375,
0.026216957718133926,
0.21661600470542908,
0.03374597430229187,
0.22139768302440643,
0.04156934469938278,
0.06176422908902168,
-0.14408548176288605,
0.08898959308862686,
0.11528697609901428,
-0.11337202787399292,
0.08002504706382751,
0.08180499821901321,
-0.07627119868993759,
0.1378241330385208,
0.04437138885259628,
0.016327207908034325,
-0.012323843315243721,
-0.001211483497172594,
-0.10525109618902206,
0.02284085378050804,
-0.05567888170480728,
0.05080590024590492,
0.18101580440998077,
-0.12525738775730133,
-0.024834230542182922,
0.08487139642238617,
0.003475792473182082,
-0.08401757478713989,
-0.24325120449066162,
-0.0751938745379448,
-0.20594903826713562,
0.04014964401721954,
-0.06261332333087921,
-0.02509879507124424,
0.12337100505828857,
0.01946325972676277,
0.06963347643613815,
0.12428738921880722,
-0.09261632710695267,
-0.017519861459732056,
0.09012527763843536,
-0.03471560776233673,
-0.04045609384775162,
0.007107005454599857,
-0.11332003027200699,
0.08497389405965805,
-0.025367215275764465,
-0.023004036396741867,
0.01378783117979765,
0.07861297577619553,
0.041257523000240326,
0.0010341370943933725,
-0.05447894334793091,
-0.05587150901556015,
-0.0017552933422848582,
-0.009406236931681633,
0.10200952738523483,
0.044508907943964005,
-0.03129516541957855,
-0.008082046173512936,
0.12813124060630798,
-0.07562364637851715,
-0.015139557421207428,
-0.09327449649572372,
-0.06029988452792168,
-0.07430857419967651,
0.0683763176202774,
-0.03816096484661102,
-0.13178429007530212,
0.025971489027142525,
0.08303163200616837,
0.1779462695121765,
-0.09470885992050171,
0.03430841490626335,
-0.054164912551641464,
-0.0005136404070071876,
-0.02003796398639679,
0.01885363645851612,
-0.0324910432100296,
0.25805655121803284,
-0.057309340685606,
-0.030141470953822136,
-0.06107093021273613,
-0.019011734053492546,
-0.0351618193089962,
-0.07751226425170898,
-0.022071968764066696,
-0.0926562026143074,
-0.11003691703081131,
0.05795992538332939,
-0.05747806280851364,
-0.1151922345161438,
0.10052213817834854,
-0.06181693449616432,
-0.016453733667731285,
-0.10472860932350159,
0.01093232911080122,
0.07235374301671982,
0.0003660949587356299,
-0.12122112512588501,
-0.0051495106890797615,
-0.09633345901966095,
-0.015129056759178638,
-0.06486532837152481,
0.026106378063559532,
0.020433276891708374,
-0.06025778874754906,
0.12686552107334137,
-0.05103055760264397,
0.018003413453698158,
-0.00678031612187624,
-0.00045340872020460665,
-0.005746588576585054,
0.05102802440524101,
-0.020722413435578346,
-0.17027801275253296,
-0.017624303698539734,
0.08975015580654144,
-0.07337354123592377,
0.03127387911081314,
0.02053486742079258,
-0.10040458291769028,
-0.0009553022682666779,
0.12642091512680054,
-0.06334831565618515,
-0.036652907729148865,
0.0409332700073719,
-0.12356635183095932,
0.09840908646583557,
0.002066235523670912,
0.0353965163230896,
-0.04484323039650917,
-0.02809753268957138,
0.11657226830720901,
0.06407050788402557,
0.04835353419184685,
0.09438291192054749,
-0.07382286339998245,
-0.02465604804456234,
-0.015562709420919418,
-0.026245415210723877,
-0.23684915900230408,
-0.027307642623782158,
-0.18935230374336243,
0.02383388951420784,
-0.0004862219502683729,
0.019568119198083878,
0.26916468143463135,
-0.02521326020359993,
-0.03646577522158623,
-0.19771312177181244,
0.03259018063545227,
0.08602530509233475,
-0.06546128541231155,
-0.040154144167900085
] |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1639
- F1: 0.8590
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2894 | 1.0 | 715 | 0.1794 | 0.8253 |
| 0.1468 | 2.0 | 1430 | 0.1554 | 0.8477 |
| 0.093 | 3.0 | 2145 | 0.1639 | 0.8590 |
### Framework versions
- Transformers 4.16.2
- Pytorch 2.1.0+cu121
- Datasets 1.16.1
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de-fr", "results": []}]} | token-classification | gewissta/xlm-roberta-base-finetuned-panx-de-fr | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-09T13:12:14+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de-fr
=====================================
This model is a fine-tuned version of xlm-roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1639
* F1: 0.8590
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 24
* eval\_batch\_size: 24
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.16.2
* Pytorch 2.1.0+cu121
* Datasets 1.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 2.1.0+cu121\n* Datasets 1.16.1\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 2.1.0+cu121\n* Datasets 1.16.1\n* Tokenizers 0.15.1"
] | [
53,
98,
4,
33
] | [
"passage: TAGS\n#transformers #pytorch #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 2.1.0+cu121\n* Datasets 1.16.1\n* Tokenizers 0.15.1"
] | [
-0.1027127131819725,
0.07110948860645294,
-0.0016597781796008348,
0.12235049903392792,
0.2076919823884964,
0.032158903777599335,
0.10010475665330887,
0.10481873899698257,
-0.10083566606044769,
0.009431972168385983,
0.12439412623643875,
0.20683923363685608,
-0.004402651451528072,
0.11724357306957245,
-0.06539022922515869,
-0.2587703764438629,
-0.021938364952802658,
0.0370807982981205,
-0.08429288119077682,
0.13615809381008148,
0.10070626437664032,
-0.14856049418449402,
0.08407723158597946,
-0.004146394785493612,
-0.2564338147640228,
0.019015321508049965,
0.03579212725162506,
-0.05172530189156532,
0.15361027419567108,
0.010196829214692116,
0.13735438883304596,
-0.00036895525408908725,
0.0950482189655304,
-0.1636432409286499,
0.009345017373561859,
0.03878822922706604,
0.01098261121660471,
0.0824856236577034,
0.06175515055656433,
-0.007015143986791372,
0.09724541008472443,
-0.08317051082849503,
0.04512191191315651,
0.01982126384973526,
-0.13365992903709412,
-0.22211478650569916,
-0.08674794435501099,
0.0011138409608975053,
0.06155717372894287,
0.09253018349409103,
0.0026299050077795982,
0.16030816733837128,
-0.11300809681415558,
0.0943358987569809,
0.22312778234481812,
-0.2888677418231964,
-0.0681627169251442,
0.04482655227184296,
0.0012575595173984766,
0.07728103548288345,
-0.11259090155363083,
-0.019135840237140656,
0.06078917533159256,
0.04655556008219719,
0.11859197914600372,
-0.04559788852930069,
-0.10281182080507278,
0.029660334810614586,
-0.14409089088439941,
-0.020351314917206764,
0.11040225625038147,
0.03005308099091053,
-0.03845898434519768,
-0.016072722151875496,
-0.045623261481523514,
-0.16287340223789215,
-0.03204641491174698,
-0.009343355894088745,
0.046135835349559784,
-0.05057694390416145,
-0.0776410773396492,
0.017621198669075966,
-0.09466052055358887,
-0.07100827246904373,
-0.06923599541187286,
0.19495466351509094,
0.0396987646818161,
0.015996178612113,
-0.014586900360882282,
0.10951574146747589,
0.008764189667999744,
-0.1186760663986206,
0.027378106489777565,
0.027958255261182785,
-0.013086793012917042,
-0.06386487185955048,
-0.0758536159992218,
-0.027114739641547203,
0.003071202663704753,
0.1028715968132019,
-0.05935715511441231,
0.03251918777823448,
0.05934557691216469,
0.034431733191013336,
-0.0920361578464508,
0.19163298606872559,
-0.02534094825387001,
-0.006640315055847168,
0.0060995277017354965,
0.03225673735141754,
-0.025226309895515442,
0.003040904412046075,
-0.11559233069419861,
-0.009699273854494095,
0.11340636014938354,
0.02834283374249935,
-0.0937783494591713,
0.060221944004297256,
-0.06018715724349022,
-0.03277136757969856,
-0.0014040516689419746,
-0.09600700438022614,
0.03853859007358551,
-0.021312054246664047,
-0.09178762137889862,
-0.036160681396722794,
-0.009703888557851315,
0.017414763569831848,
-0.00848716963082552,
0.12230783700942993,
-0.09537157416343689,
0.05289008468389511,
-0.10688720643520355,
-0.11059383302927017,
-0.011273934505879879,
-0.08782363682985306,
0.03688672557473183,
-0.09914842247962952,
-0.15096460282802582,
-0.024707654491066933,
0.04209957271814346,
-0.022063927724957466,
-0.06344226747751236,
-0.0383976586163044,
-0.06517104804515839,
-0.008951532654464245,
-0.01256698276847601,
0.15562285482883453,
-0.05435722693800926,
0.11300373822450638,
0.04677261412143707,
0.06223917379975319,
-0.05674271285533905,
0.06346592307090759,
-0.10414736717939377,
-0.0018924511969089508,
-0.18626901507377625,
0.04133547842502594,
-0.04898475855588913,
0.08032327145338058,
-0.0802936926484108,
-0.12007664144039154,
0.04633413255214691,
-0.011091265827417374,
0.06571336835622787,
0.07511921226978302,
-0.15535178780555725,
-0.07275819033384323,
0.13503578305244446,
-0.05307872220873833,
-0.096690833568573,
0.1000385656952858,
-0.07220055162906647,
0.05048513039946556,
0.07168447226285934,
0.15065404772758484,
0.07354292273521423,
-0.06405987590551376,
0.047394149005413055,
-0.019845880568027496,
0.034187398850917816,
-0.08069103956222534,
0.05747750774025917,
0.01898164115846157,
-0.035749584436416626,
0.03784683719277382,
-0.04566569998860359,
0.06317682564258575,
-0.11599242687225342,
-0.09354499727487564,
-0.03410130366683006,
-0.10696598887443542,
0.05032629892230034,
0.0739249587059021,
0.09504815936088562,
-0.10566050559282303,
-0.04192954674363136,
0.07528224587440491,
0.07225023955106735,
-0.03592212498188019,
0.013757091946899891,
-0.05095594748854637,
0.055060431361198425,
-0.05126701667904854,
-0.039147742092609406,
-0.1955900937318802,
-0.02170548401772976,
0.013138248585164547,
0.024751806631684303,
0.03394237160682678,
0.03516890108585358,
0.058080386370420456,
0.06824522465467453,
-0.04731723293662071,
-0.018232427537441254,
-0.026942331343889236,
-0.0056580412201583385,
-0.14605972170829773,
-0.17998884618282318,
-0.030986707657575607,
-0.01369097363203764,
0.10717237740755081,
-0.20239576697349548,
0.023639889433979988,
-0.04973519966006279,
0.06393636763095856,
0.0029120256658643484,
-0.007422389462590218,
-0.05692535266280174,
0.11049791425466537,
-0.022807808592915535,
-0.04159758985042572,
0.08195825666189194,
-0.009245573543012142,
-0.07255245745182037,
-0.05290194973349571,
-0.07731594890356064,
0.2132805436849594,
0.12794393301010132,
-0.1424258053302765,
-0.09904841333627701,
-0.00677386624738574,
-0.05766553431749344,
-0.018979387357831,
-0.05224516987800598,
0.0675799697637558,
0.1872778981924057,
-0.02715640515089035,
0.15420100092887878,
-0.06508420407772064,
-0.041116878390312195,
0.017633548006415367,
-0.038493745028972626,
0.04438076540827751,
0.12321897596120834,
0.13753211498260498,
-0.11727481335401535,
0.13455499708652496,
0.1535990834236145,
-0.08260095864534378,
0.11968714743852615,
-0.038162436336278915,
-0.0595564991235733,
-0.02662627026438713,
-0.025205060839653015,
-0.007634625304490328,
0.09076584130525589,
-0.09636073559522629,
-0.008487625047564507,
0.014665821567177773,
0.025471670553088188,
0.01755448989570141,
-0.23557588458061218,
-0.05903762951493263,
0.03061925806105137,
-0.02336740493774414,
-0.0137206194922328,
-0.012480296194553375,
0.021195750683546066,
0.11224399507045746,
0.002129521919414401,
-0.10022483766078949,
0.041780900210142136,
0.01073758490383625,
-0.07279723137617111,
0.21269769966602325,
-0.08347766846418381,
-0.12522542476654053,
-0.11547548323869705,
-0.07824647426605225,
-0.051686257123947144,
0.011899972334504128,
0.04571777582168579,
-0.09344030916690826,
-0.02408851869404316,
-0.029216598719358444,
0.017025554552674294,
-0.02070261724293232,
0.04164501652121544,
-0.013566190376877785,
0.0038000785280019045,
0.06278663128614426,
-0.10176114737987518,
-0.014133511111140251,
-0.06210700422525406,
-0.08767940104007721,
0.061827402561903,
0.04706132411956787,
0.1140650287270546,
0.14911797642707825,
-0.04478422924876213,
0.003760645864531398,
-0.03032735362648964,
0.25861769914627075,
-0.06330295652151108,
-0.05211939662694931,
0.1233031153678894,
0.00508192228153348,
0.05255946144461632,
0.10448545217514038,
0.07933437824249268,
-0.10191319137811661,
0.012596799992024899,
0.025190405547618866,
-0.03428378701210022,
-0.21225184202194214,
-0.05030003935098648,
-0.05514611303806305,
-0.05936906114220619,
0.09444060176610947,
0.0243113674223423,
0.044858913868665695,
0.07003291696310043,
0.054805830121040344,
0.08118293434381485,
-0.07727966457605362,
0.05527249723672867,
0.10721570253372192,
0.05338708311319351,
0.12714527547359467,
-0.037257883697748184,
-0.08372797071933746,
0.028211764991283417,
-0.015086567029356956,
0.24069631099700928,
0.0222002062946558,
0.09087348729372025,
0.05847413092851639,
0.19786956906318665,
0.012141241692006588,
0.08725139498710632,
0.014065418392419815,
-0.05177970603108406,
-0.012638300657272339,
-0.024964015930891037,
-0.0385303758084774,
0.016400108113884926,
-0.04115908220410347,
0.04714427888393402,
-0.13143183290958405,
-0.031032852828502655,
0.04625565931200981,
0.24698784947395325,
0.01921917498111725,
-0.3140440881252289,
-0.0713248997926712,
-0.006595053244382143,
-0.036744374781847,
-0.01159204263240099,
0.011225605383515358,
0.07847116142511368,
-0.1130422055721283,
0.010740728117525578,
-0.06146474555134773,
0.09467763453722,
-0.02887643314898014,
0.0435938686132431,
0.06939655542373657,
0.09388746321201324,
0.005326677579432726,
0.07773517817258835,
-0.3075057566165924,
0.2843548059463501,
0.008138247765600681,
0.06621625274419785,
-0.0711032971739769,
-0.013794688507914543,
0.035325828939676285,
0.06508854031562805,
0.041941944509744644,
-0.007098232861608267,
-0.04651116207242012,
-0.24595995247364044,
-0.015074481256306171,
0.030880214646458626,
0.11028382182121277,
-0.03530959039926529,
0.09424730390310287,
-0.030181096866726875,
0.013755440711975098,
0.06922462582588196,
-0.02813783660531044,
-0.054872989654541016,
-0.06732393056154251,
-0.021991873160004616,
0.017015917226672173,
-0.030709093436598778,
-0.05633331090211868,
-0.10777514427900314,
-0.1335986852645874,
0.15153124928474426,
0.006609352771192789,
-0.025802873075008392,
-0.12259592115879059,
0.08953860402107239,
0.0732305571436882,
-0.08805444836616516,
0.046014655381441116,
0.010089859366416931,
0.04363838955760002,
0.03655610233545303,
-0.06349588185548782,
0.10822179913520813,
-0.05090070143342018,
-0.16019278764724731,
-0.05587424337863922,
0.08758226037025452,
0.029108554124832153,
0.0638640820980072,
-0.007305073086172342,
0.0189705491065979,
-0.03782520070672035,
-0.09925556927919388,
0.03422379866242409,
-0.047227926552295685,
0.06962529569864273,
0.04279612377285957,
-0.05918257310986519,
0.026590412482619286,
-0.06966190040111542,
-0.02981416881084442,
0.19045747816562653,
0.24454694986343384,
-0.1056305319070816,
0.004251378588378429,
0.011269792914390564,
-0.06480646133422852,
-0.18634767830371857,
0.06421471387147903,
0.07133211940526962,
0.006299980916082859,
0.05243484675884247,
-0.17487670481204987,
0.14315427839756012,
0.11056685447692871,
-0.0060201771557331085,
0.1198897734284401,
-0.3074885606765747,
-0.1237473115324974,
0.10304710268974304,
0.1677982211112976,
0.15813620388507843,
-0.12547667324543,
-0.008259654976427555,
-0.014119683764874935,
-0.10597752034664154,
0.10053371638059616,
-0.03213173523545265,
0.1288401186466217,
-0.03991486504673958,
0.10923795402050018,
0.003400312038138509,
-0.049691371619701385,
0.11660654097795486,
0.03628000617027283,
0.12657681107521057,
-0.050527919083833694,
-0.06308358907699585,
0.01896185427904129,
-0.020711638033390045,
-0.006548308301717043,
-0.04981357231736183,
0.03229739889502525,
-0.10114356875419617,
-0.015284567140042782,
-0.08820796012878418,
0.04966164752840996,
-0.026238368824124336,
-0.06567754596471786,
-0.03230075538158417,
0.03529726713895798,
0.029871143400669098,
-0.021328188478946686,
0.1053081676363945,
0.0163385309278965,
0.16050288081169128,
0.08946958184242249,
0.05589070916175842,
-0.06507446616888046,
-0.05036310479044914,
-0.01649780571460724,
-0.01185641810297966,
0.06253497302532196,
-0.11006288975477219,
0.023436952382326126,
0.14844228327274323,
0.025705713778734207,
0.13068054616451263,
0.08820894360542297,
0.0022519202902913094,
0.016972020268440247,
0.0668724849820137,
-0.15390899777412415,
-0.061871107667684555,
-0.00975347775965929,
-0.07817593961954117,
-0.07763694226741791,
0.054301146417856216,
0.08146949857473373,
-0.06664292514324188,
-0.025538118556141853,
-0.02345854602754116,
-0.013859827071428299,
-0.07241199165582657,
0.208836629986763,
0.07700831443071365,
0.05169523134827614,
-0.11288794130086899,
0.05104589834809303,
0.06194396689534187,
-0.042122967541217804,
-0.020453592762351036,
0.06707152724266052,
-0.07944007962942123,
-0.038066815584897995,
0.10422925651073456,
0.18038009107112885,
-0.09805438667535782,
-0.022983822971582413,
-0.13559073209762573,
-0.11907633394002914,
0.07206489145755768,
0.16503052413463593,
0.12546563148498535,
0.005812839604914188,
-0.048503048717975616,
0.0018400374101474881,
-0.12925809621810913,
0.061991386115550995,
0.029236333444714546,
0.07692933082580566,
-0.14486493170261383,
0.19207069277763367,
-0.000027053451049141586,
0.06329067796468735,
-0.025308487936854362,
0.02200031839311123,
-0.10813447833061218,
0.019191818311810493,
-0.09984724968671799,
-0.047082435339689255,
-0.00977710448205471,
0.008772121742367744,
-0.000016430549294454977,
-0.07277736812829971,
-0.05562631040811539,
0.01883423887193203,
-0.1355990618467331,
-0.02334599941968918,
0.04442645609378815,
0.0535881444811821,
-0.09839563071727753,
-0.051796041429042816,
0.018438195809721947,
-0.04124597832560539,
0.04950467497110367,
0.04772118851542473,
0.024964259937405586,
0.06428801268339157,
-0.14091727137565613,
-0.007971826009452343,
0.07103745639324188,
0.0047719599679112434,
0.0846419483423233,
-0.08036274462938309,
0.0011289003305137157,
0.004328498151153326,
0.08574968576431274,
0.024972710758447647,
0.06193925440311432,
-0.15023495256900787,
-0.012243067845702171,
-0.027270779013633728,
-0.09462975710630417,
-0.06633318215608597,
0.00785987451672554,
0.08652924746274948,
0.011959053575992584,
0.19275930523872375,
-0.0733930766582489,
0.0511779822409153,
-0.20516157150268555,
-0.014375641010701656,
-0.026793478056788445,
-0.11335399001836777,
-0.1429639309644699,
-0.07083722949028015,
0.06788356602191925,
-0.03996273875236511,
0.14412537217140198,
0.055543091148138046,
0.05588097870349884,
0.02232113853096962,
-0.009803126566112041,
0.01913880929350853,
0.021597430109977722,
0.20421245694160461,
0.050516534596681595,
-0.03170451521873474,
0.07903104275465012,
0.07582221925258636,
0.08596903830766678,
0.09979528188705444,
0.19641605019569397,
0.15471793711185455,
-0.010731173679232597,
0.06669577211141586,
0.02322756126523018,
-0.057507243007421494,
-0.15273256599903107,
0.014442646875977516,
-0.035659704357385635,
0.07825957983732224,
-0.027968859300017357,
0.1979956328868866,
0.05019264668226242,
-0.17561529576778412,
0.050355639308691025,
-0.051768891513347626,
-0.08411271125078201,
-0.1087426170706749,
-0.009956037625670433,
-0.08621294051408768,
-0.13993224501609802,
0.00901302695274353,
-0.10122200846672058,
0.019199132919311523,
0.12139906734228134,
-0.005463836714625359,
-0.02415573224425316,
0.14237941801548004,
0.031056974083185196,
0.03092845156788826,
0.05630049854516983,
-0.00487668439745903,
-0.015163091942667961,
-0.10755273699760437,
-0.0620482861995697,
-0.03231169655919075,
-0.032393958419561386,
0.035120487213134766,
-0.07464057952165604,
-0.09432382881641388,
0.02260226383805275,
-0.03381389379501343,
-0.09853193163871765,
0.0199257992208004,
0.024458251893520355,
0.05981169268488884,
0.04281820356845856,
0.004396823700517416,
0.021608199924230576,
-0.02105080522596836,
0.21709047257900238,
-0.07055540382862091,
-0.07845428586006165,
-0.07981365919113159,
0.3159596920013428,
0.05677396431565285,
0.0033750145230442286,
0.036545466631650925,
-0.05496683716773987,
0.020025750622153282,
0.22966331243515015,
0.19505009055137634,
-0.11095172166824341,
-0.0003215121105313301,
-0.007117870729416609,
-0.016876280307769775,
-0.015284252353012562,
0.1376984566450119,
0.12676742672920227,
0.03478134423494339,
-0.10615888237953186,
-0.031017178669571877,
-0.07307856529951096,
-0.0037183668464422226,
-0.04384387657046318,
0.04381534084677696,
0.05303487181663513,
0.006757913623005152,
-0.046722572296857834,
0.058217715471982956,
-0.061465151607990265,
-0.07388638705015182,
0.09636307507753372,
-0.18631435930728912,
-0.16267889738082886,
-0.013739495538175106,
0.08318932354450226,
0.001685751718468964,
0.06764984875917435,
-0.04122389480471611,
0.0015409761108458042,
0.04471314698457718,
-0.024140503257513046,
-0.08432615548372269,
-0.098670594394207,
0.11705731600522995,
-0.10982643812894821,
0.1746300905942917,
-0.04677069932222366,
0.08263237029314041,
0.11606843024492264,
0.06833373755216599,
-0.06505829840898514,
0.07663772255182266,
0.040028516203165054,
-0.09062203764915466,
0.037654977291822433,
0.09788461029529572,
-0.02388649433851242,
0.05616458132863045,
0.02700151689350605,
-0.13243025541305542,
0.03492648899555206,
-0.07835988700389862,
-0.043743766844272614,
-0.039142437279224396,
-0.046584710478782654,
-0.05153173580765724,
0.12644334137439728,
0.22851765155792236,
-0.020169442519545555,
0.01956871524453163,
-0.08168339729309082,
0.009538967162370682,
0.06687787175178528,
0.019356494769454002,
-0.09952566027641296,
-0.22596676647663116,
0.00901204627007246,
0.0938330590724945,
-0.03742099180817604,
-0.20992349088191986,
-0.08570697903633118,
-0.010104144923388958,
-0.0807998850941658,
-0.08427752554416656,
0.08973393589258194,
0.054396480321884155,
0.04866868630051613,
-0.04618031531572342,
-0.12284529954195023,
-0.0830516591668129,
0.153325155377388,
-0.14084230363368988,
-0.09148380905389786
] |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlmv3-testCUSTOMds09_02
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Precision: 1.0
- Recall: 1.0
- F1: 1.0
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
| No log | 1.25 | 100 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
| No log | 2.5 | 200 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
| No log | 3.75 | 300 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 |
| No log | 5.0 | 400 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0185 | 6.25 | 500 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0185 | 7.5 | 600 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0185 | 8.75 | 700 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0185 | 10.0 | 800 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0185 | 11.25 | 900 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0001 | 12.5 | 1000 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "microsoft/layoutlmv3-base", "model-index": [{"name": "layoutlmv3-testCUSTOMds09_02", "results": []}]} | token-classification | eliascc5/layoutlmv3-testCUSTOMds09_02 | [
"transformers",
"tensorboard",
"safetensors",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"base_model:microsoft/layoutlmv3-base",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-09T13:14:34+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #layoutlmv3 #token-classification #generated_from_trainer #base_model-microsoft/layoutlmv3-base #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
| layoutlmv3-testCUSTOMds09\_02
=============================
This model is a fine-tuned version of microsoft/layoutlmv3-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0000
* Precision: 1.0
* Recall: 1.0
* F1: 1.0
* Accuracy: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 1000
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 1000",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #layoutlmv3 #token-classification #generated_from_trainer #base_model-microsoft/layoutlmv3-base #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 1000",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
79,
97,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #layoutlmv3 #token-classification #generated_from_trainer #base_model-microsoft/layoutlmv3-base #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 1000### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
-0.11703933030366898,
0.08645466715097427,
-0.0013011887203902006,
0.1055634617805481,
0.13416984677314758,
0.009120453149080276,
0.15011374652385712,
0.10430759191513062,
-0.04540260136127472,
0.03991725295782089,
0.15317338705062866,
0.11761010438203812,
0.018710626289248466,
0.15899567306041718,
-0.04973648115992546,
-0.2029043287038803,
0.014973205514252186,
0.035571251064538956,
-0.05392963066697121,
0.12346097081899643,
0.08950772881507874,
-0.130258709192276,
0.09715234488248825,
0.004418430384248495,
-0.18668678402900696,
-0.01721995323896408,
0.03312976658344269,
-0.04272979870438576,
0.14204338192939758,
0.03535379841923714,
0.12761326134204865,
0.03577118739485741,
0.09188234806060791,
-0.18041911721229553,
0.013161834329366684,
0.05095139890909195,
0.004850024823099375,
0.09398075193166733,
0.04047493636608124,
0.016389697790145874,
0.003399195848032832,
-0.07992738485336304,
0.053259316831827164,
0.02834346517920494,
-0.13789954781532288,
-0.21886128187179565,
-0.08253268152475357,
0.04242496192455292,
0.08920924365520477,
0.06850676983594894,
-0.0038352650590240955,
0.15651775896549225,
-0.02155454270541668,
0.08554285764694214,
0.19083021581172943,
-0.3120008111000061,
-0.07024388760328293,
0.09664840996265411,
0.06610846519470215,
0.07096352428197861,
-0.10530168563127518,
-0.004316248465329409,
0.05411672964692116,
0.015092353336513042,
0.1568988561630249,
-0.024893494322896004,
0.028584586456418037,
0.003352087689563632,
-0.13563303649425507,
-0.02334078587591648,
0.14705447852611542,
0.06944377720355988,
-0.0445978082716465,
-0.0662900060415268,
-0.04920082539319992,
-0.13471153378486633,
-0.03059590794146061,
-0.01164154801517725,
0.0343979075551033,
-0.02440406009554863,
-0.1171128898859024,
-0.042158693075180054,
-0.10813935101032257,
-0.08404215425252914,
-0.02958029881119728,
0.14906221628189087,
0.0018794361967593431,
0.0009486460476182401,
0.007241389714181423,
0.11355464160442352,
-0.036676157265901566,
-0.13515515625476837,
0.012163884937763214,
0.015520614571869373,
-0.0328286811709404,
-0.06962909549474716,
-0.043684378266334534,
-0.05312672257423401,
0.008268962614238262,
0.12178675085306168,
-0.00022891607659403235,
0.041641559451818466,
0.028863197192549706,
0.049851421266794205,
-0.11517716199159622,
0.20058496296405792,
-0.05171017721295357,
-0.04819944500923157,
0.0027945879846811295,
0.11000435054302216,
0.04335147142410278,
-0.01154064666479826,
-0.1460292637348175,
0.011408478952944279,
0.10867052525281906,
0.015454495325684547,
-0.04537319764494896,
0.06815709918737411,
-0.07279865443706512,
-0.02217940241098404,
0.050505172461271286,
-0.073655866086483,
0.0182324331253767,
-0.02330014295876026,
-0.047111302614212036,
-0.09267470985651016,
0.015491055324673653,
0.036470040678977966,
0.04367309808731079,
0.08629333227872849,
-0.10991520434617996,
0.010733517818152905,
-0.08620281517505646,
-0.10712718218564987,
0.008112220093607903,
-0.09635835886001587,
0.0229061059653759,
-0.10979987680912018,
-0.1795603483915329,
-0.011714644730091095,
0.05634373053908348,
-0.030482811853289604,
-0.04181649535894394,
-0.04894623905420303,
-0.06366624683141708,
0.006006624549627304,
-0.013667300343513489,
0.09111480414867401,
-0.06586944311857224,
0.10004056990146637,
0.05421849340200424,
0.06152348220348358,
-0.0629383996129036,
0.021779056638479233,
-0.08970264345407486,
0.058875296264886856,
-0.16364158689975739,
0.026611730456352234,
-0.04830627515912056,
0.08741024136543274,
-0.10120949149131775,
-0.06555309146642685,
-0.0058898162096738815,
-0.013556835241615772,
0.062347859144210815,
0.07817896455526352,
-0.1755574643611908,
-0.05623529851436615,
0.14913038909435272,
-0.06840328127145767,
-0.16224734485149384,
0.11640463769435883,
-0.04695389047265053,
0.0780380517244339,
0.056506991386413574,
0.1750534623861313,
0.08236756175756454,
-0.10273469239473343,
0.01451578363776207,
-0.0009345718426629901,
0.05959322676062584,
-0.0675349310040474,
0.09798140078783035,
-0.008966096676886082,
0.009225125424563885,
0.006047804839909077,
-0.07040690630674362,
0.06104990467429161,
-0.07619177550077438,
-0.08290286362171173,
-0.026490366086363792,
-0.11187860369682312,
0.07142505794763565,
0.044570114463567734,
0.05196848511695862,
-0.09433328360319138,
-0.08341392874717712,
0.0706351026892662,
0.07278133928775787,
-0.05798298120498657,
0.010583520866930485,
-0.09628229588270187,
0.07252738624811172,
-0.11943337321281433,
-0.030566031113266945,
-0.14453457295894623,
-0.06935770809650421,
0.010033275932073593,
0.016979390755295753,
0.013487746939063072,
0.004776956979185343,
0.07716099917888641,
0.07482481002807617,
-0.07186039537191391,
-0.04009521007537842,
-0.024772992357611656,
0.023980967700481415,
-0.12497742474079132,
-0.17720730602741241,
-0.05107759311795235,
-0.03771296516060829,
0.15850284695625305,
-0.22397421300411224,
0.04402800649404526,
0.013121156021952629,
0.08979100733995438,
0.05500011891126633,
-0.028949199244379997,
-0.024489877745509148,
0.0665929913520813,
-0.027809226885437965,
-0.07684987783432007,
0.06941197067499161,
0.02303510531783104,
-0.1480833739042282,
-0.0280538871884346,
-0.16194941103458405,
0.17955194413661957,
0.11841931939125061,
-0.053775299340486526,
-0.06641945987939835,
-0.02934684231877327,
-0.028493937104940414,
-0.031567029654979706,
-0.042167097330093384,
-0.018250666558742523,
0.10760718584060669,
0.019036440178751945,
0.14996954798698425,
-0.0853860154747963,
-0.0506717823445797,
0.023507166653871536,
-0.04078123718500137,
-0.002110660308972001,
0.09013167023658752,
0.053013596683740616,
-0.13639096915721893,
0.14594976603984833,
0.1967300921678543,
-0.06113893538713455,
0.13493213057518005,
-0.02942572720348835,
-0.051646217703819275,
-0.05278991162776947,
0.005793177988380194,
0.02980865351855755,
0.14623378217220306,
-0.07494276016950607,
0.0004036291502416134,
0.006079312413930893,
0.013882179744541645,
0.00026894418988376856,
-0.21893557906150818,
-0.04127788543701172,
0.04705264791846275,
-0.04434609413146973,
-0.005696906242519617,
-0.019855685532093048,
-0.023784946650266647,
0.07453379780054092,
0.03287376090884209,
-0.06630432605743408,
0.05568027123808861,
-0.006612862925976515,
-0.0780886858701706,
0.19516490399837494,
-0.06138148531317711,
-0.14621223509311676,
-0.16699275374412537,
-0.07727749645709991,
-0.048231057822704315,
0.04026756435632706,
0.028751976788043976,
-0.05266297981142998,
-0.03044825792312622,
-0.1030813530087471,
-0.020173469558358192,
0.015143536031246185,
0.02252879925072193,
0.03021790273487568,
0.0063895536586642265,
0.10087279975414276,
-0.08212891221046448,
-0.0003390085475984961,
-0.017495514824986458,
-0.02712329663336277,
0.030717968940734863,
0.013695508241653442,
0.12254651635885239,
0.13887298107147217,
-0.010279985144734383,
0.004712739493697882,
-0.038847099989652634,
0.2256743609905243,
-0.08303870260715485,
-0.02349947951734066,
0.14797720313072205,
-0.03770763799548149,
0.054266557097435,
0.14010092616081238,
0.06393958628177643,
-0.09820234775543213,
0.02153620682656765,
0.004363686311990023,
-0.04339972138404846,
-0.1618340015411377,
-0.020244944840669632,
-0.04498201981186867,
-0.0013417230220511556,
0.1006990447640419,
0.019345955923199654,
0.02505522035062313,
0.0715804398059845,
0.0317344032227993,
0.07437615096569061,
-0.016659369692206383,
0.08965255320072174,
0.08713969588279724,
0.04177859053015709,
0.13499507308006287,
-0.03664606064558029,
-0.04821532219648361,
0.022030478343367577,
0.042925406247377396,
0.19274704158306122,
0.03984460607171059,
0.18148605525493622,
0.03809721767902374,
0.16361159086227417,
0.014464017003774643,
0.05900164693593979,
0.01261912751942873,
-0.0503871813416481,
-0.0066146706230938435,
-0.04165021330118179,
-0.03321772813796997,
0.039352308958768845,
-0.03302338346838951,
0.05692749843001366,
-0.07559314370155334,
0.01215831097215414,
0.04820942133665085,
0.2316134124994278,
0.06686199456453323,
-0.36299896240234375,
-0.09444601088762283,
0.02323971875011921,
-0.007716715801507235,
-0.03591613844037056,
-0.0054268743842840195,
0.1429533213376999,
-0.06891024112701416,
0.03461460396647453,
-0.08544345945119858,
0.07805052399635315,
-0.05638984590768814,
0.03968869894742966,
0.06129051372408867,
0.07121655344963074,
-0.013057450763881207,
0.059883829206228256,
-0.24790985882282257,
0.28157079219818115,
0.02844909206032753,
0.06012275069952011,
-0.0400310680270195,
-0.016759192571043968,
0.024903660640120506,
0.0863870233297348,
0.08822842687368393,
-0.01660740002989769,
-0.0435529425740242,
-0.24136461317539215,
-0.07369747012853622,
0.010065468028187752,
0.09114181995391846,
-0.06640138477087021,
0.1055859699845314,
-0.04263174533843994,
0.0009279772057197988,
0.073505699634552,
0.009696259163320065,
-0.06032562255859375,
-0.08733441680669785,
-0.0003884306934196502,
0.04373423382639885,
0.01140563189983368,
-0.09058555215597153,
-0.09178929030895233,
-0.10060538351535797,
0.153279110789299,
-0.06016926467418671,
-0.017895853146910667,
-0.11851438134908676,
0.058821696788072586,
0.06726400554180145,
-0.07428286224603653,
0.0444817841053009,
0.001013677567243576,
0.12171857804059982,
0.016140654683113098,
-0.028451526537537575,
0.12418504804372787,
-0.0645732581615448,
-0.1711159646511078,
-0.0696234405040741,
0.11727946996688843,
0.0007748962962068617,
0.05269543081521988,
0.005942969582974911,
0.02732456475496292,
-0.02180284634232521,
-0.06718659400939941,
0.043585725128650665,
-0.01795162260532379,
0.03969714045524597,
-0.009979159571230412,
-0.014948729425668716,
0.03209001570940018,
-0.05360531806945801,
-0.044009122997522354,
0.15423277020454407,
0.3093528151512146,
-0.09892379492521286,
-0.016222033649683,
0.03607548028230667,
-0.04088817909359932,
-0.18700625002384186,
0.048829495906829834,
0.03346725180745125,
0.022795911878347397,
0.07261163741350174,
-0.11133516579866409,
0.0719994530081749,
0.08858823776245117,
-0.035987451672554016,
0.09231120347976685,
-0.24999982118606567,
-0.14456340670585632,
0.0885200947523117,
0.15210871398448944,
0.09272536635398865,
-0.13062024116516113,
-0.0550721250474453,
-0.04214683175086975,
-0.11650001257658005,
0.08700180798768997,
-0.08877350389957428,
0.11342132091522217,
0.00011907555744983256,
0.04188762605190277,
0.009007333777844906,
-0.05581008270382881,
0.14720463752746582,
-0.020538782700896263,
0.10233020037412643,
-0.05438439920544624,
-0.024082541465759277,
0.06269016116857529,
-0.07011059671640396,
-0.0006563638453371823,
-0.08503354340791702,
0.02688714861869812,
-0.053800422698259354,
-0.031157180666923523,
-0.049563609063625336,
0.02430151030421257,
-0.025798672810196877,
-0.062465593218803406,
-0.024204101413488388,
0.05304204300045967,
0.016362808644771576,
-0.020381992682814598,
0.16813094913959503,
-0.01271765772253275,
0.14618483185768127,
0.15351790189743042,
0.10031312704086304,
-0.0711008831858635,
-0.02477264031767845,
-0.003244300838559866,
-0.04484562948346138,
0.05119302496314049,
-0.13252940773963928,
0.03896750137209892,
0.10885488986968994,
0.010136616416275501,
0.14589157700538635,
0.06564267724752426,
-0.03089476376771927,
0.023616129532456398,
0.0777474194765091,
-0.1480083465576172,
-0.12621162831783295,
-0.016468361020088196,
-0.013060277327895164,
-0.14783184230327606,
0.0517105795443058,
0.13149523735046387,
-0.05732257664203644,
0.008106076158583164,
-0.0014272487023845315,
-0.00723011652007699,
-0.04302403703331947,
0.17038454115390778,
0.06879334896802902,
0.06439477205276489,
-0.06262180954217911,
0.0638742595911026,
0.05193130671977997,
-0.04359385371208191,
-0.022402193397283554,
-0.000710933527443558,
-0.10238367319107056,
-0.033811304718256,
0.01086349692195654,
0.1537250578403473,
-0.05772711709141731,
-0.041989460587501526,
-0.15541812777519226,
-0.100855253636837,
0.03121701255440712,
0.18058761954307556,
0.09604641795158386,
0.02175593003630638,
-0.014704051427543163,
0.007726965472102165,
-0.09074723720550537,
0.1107620969414711,
0.022712871432304382,
0.09277980029582977,
-0.18367770314216614,
0.13104505836963654,
-0.010865206830203533,
0.027549490332603455,
-0.02375783771276474,
0.04105113446712494,
-0.1048046201467514,
0.005105804186314344,
-0.10866429656744003,
-0.01613004319369793,
-0.03682331740856171,
-0.0011729508405551314,
0.0035460712388157845,
-0.06767406314611435,
-0.049358025193214417,
0.0063093495555222034,
-0.09516826272010803,
-0.031473152339458466,
0.0422649085521698,
0.045591842383146286,
-0.09738612174987793,
-0.039485543966293335,
0.03415457159280777,
-0.06585635989904404,
0.06426121294498444,
-0.02591984160244465,
0.03363311290740967,
0.03044256754219532,
-0.11708540469408035,
0.042763903737068176,
0.04818281903862953,
0.005104658659547567,
0.044226765632629395,
-0.0980902835726738,
-0.028288690373301506,
-0.020494982600212097,
0.04616379365324974,
0.027934635058045387,
0.10011453926563263,
-0.12547191977500916,
-0.006449762266129255,
-0.015840668231248856,
-0.04261672496795654,
-0.05699770897626877,
0.029397055506706238,
0.0818651095032692,
0.02850807085633278,
0.2052118331193924,
-0.07895065099000931,
0.010991298593580723,
-0.1999295949935913,
0.00407377490773797,
-0.006802757270634174,
-0.11076308786869049,
-0.10158149152994156,
-0.06519864499568939,
0.04964158311486244,
-0.05709031969308853,
0.11817722767591476,
-0.021311135962605476,
0.05855482071638107,
0.04004158824682236,
-0.016420142725110054,
0.052756164222955704,
0.024965448305010796,
0.20751412212848663,
0.026629284024238586,
-0.03157961741089821,
0.06108171492815018,
0.046400465071201324,
0.08519548922777176,
0.07661103457212448,
0.15367074310779572,
0.14183664321899414,
-0.03397313132882118,
0.09299544990062714,
0.05210376903414726,
-0.024405866861343384,
-0.15838174521923065,
0.042286016047000885,
-0.04503491520881653,
0.08135014027357101,
-0.022869901731610298,
0.19561797380447388,
0.10161112993955612,
-0.17504863440990448,
-0.0022282996214926243,
-0.04973738640546799,
-0.07591726630926132,
-0.082600899040699,
-0.09190263599157333,
-0.09317485243082047,
-0.13623370230197906,
-0.0030464280862361193,
-0.08966505527496338,
-0.012329583056271076,
0.13343316316604614,
-0.003838229924440384,
-0.005980161018669605,
0.17691883444786072,
-0.008732471615076065,
0.03166380152106285,
0.03971364349126816,
0.0033123232424259186,
-0.020808566361665726,
-0.0884644091129303,
-0.0836387574672699,
0.005788057576864958,
-0.023913810029625893,
0.02790757641196251,
-0.06029016524553299,
-0.035674482583999634,
0.02577049843966961,
0.0018897542031481862,
-0.1219959408044815,
0.0025945366360247135,
0.03170359879732132,
0.04231477156281471,
0.02295631170272827,
0.0013317950069904327,
0.014031101949512959,
-0.012690301984548569,
0.2233218103647232,
-0.0837097316980362,
-0.05412224680185318,
-0.1036890521645546,
0.19563737511634827,
-0.010322961024940014,
-0.0015657346230000257,
-0.0027671298012137413,
-0.09071623533964157,
0.059678852558135986,
0.2174597978591919,
0.1506115198135376,
-0.10816556960344315,
0.0064642466604709625,
-0.010772024281322956,
-0.011994443833827972,
-0.038284484297037125,
0.08618347346782684,
0.07027161866426468,
-0.031200503930449486,
-0.08329825103282928,
-0.046023182570934296,
-0.047845449298620224,
-0.017554109916090965,
-0.012930270284414291,
0.03215397894382477,
0.023373564705252647,
0.03067876026034355,
-0.07042109221220016,
0.06563539803028107,
-0.023166581988334656,
-0.08301018923521042,
0.07156719267368317,
-0.18834038078784943,
-0.14505258202552795,
-0.02219197526574135,
0.09806285053491592,
-0.03219928592443466,
0.04979245364665985,
-0.034117694944143295,
0.023585962131619453,
0.02824064902961254,
-0.020900439471006393,
-0.04934843257069588,
-0.07225015014410019,
0.0694698616862297,
-0.05662136524915695,
0.21933379769325256,
-0.05370321124792099,
0.04089074209332466,
0.13030675053596497,
0.03768794983625412,
-0.09549833834171295,
0.08773492276668549,
0.04649713635444641,
-0.05034182220697403,
0.042351480573415756,
0.0861448273062706,
-0.030684618279337883,
0.15270811319351196,
0.04986320808529854,
-0.14234748482704163,
0.025331562384963036,
-0.0575655996799469,
-0.05939246714115143,
-0.0487779825925827,
-0.0495988093316555,
-0.044868532568216324,
0.15739606320858002,
0.1666773408651352,
-0.048928871750831604,
-0.016505742445588112,
-0.0386645682156086,
0.020294837653636932,
0.08070575445890427,
0.041959188878536224,
-0.05704423040151596,
-0.23042838275432587,
0.017745548859238625,
0.0529911145567894,
-0.02339612878859043,
-0.26690614223480225,
-0.10346027463674545,
-0.004803963005542755,
-0.05356614291667938,
-0.056667860597372055,
0.1026865616440773,
0.11529556661844254,
0.05361533910036087,
-0.06488952040672302,
-0.08361440151929855,
-0.06476591527462006,
0.13937686383724213,
-0.13913126289844513,
-0.10488422214984894
] |
null | null | diffusers | # Svenja Schulze
<Gallery />
## Model description
just an LORA of Svenja Schulze
## Trigger words
You should use `ssh` to trigger the image generation.
You should use `bre` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/gz8iz/Svenja_Schulze/tree/main) them in the Files & versions tab.
| {"tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora"], "widget": [{"text": "\tssh bre in front of a green background, highly detailed, magic, perfect composition, beautiful intricate stunning amazing detail, sharp focus, creative, ambient light, colorful, very inspirational, innocent, fine, balanced colors, cinematic, elegant, complex, epic, professional, winning, new, color, clear, crisp, artistic, novel, surreal, intriguing, awesome, illuminated", "parameters": {"negative_prompt": "\tunrealistic, saturated, high contrast, big nose, painting, drawing, sketch, cartoon, anime, manga, render, CG, 3d, watermark, signature, label"}, "output": {"url": "images/2024-02-09_14-16-28_4027.png"}}], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "ssh, bre"} | text-to-image | gz8iz/Svenja_Schulze | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] | 2024-02-09T13:18:40+00:00 | [] | [] | TAGS
#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #region-us
| # Svenja Schulze
<Gallery />
## Model description
just an LORA of Svenja Schulze
## Trigger words
You should use 'ssh' to trigger the image generation.
You should use 'bre' to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
| [
"# Svenja Schulze\n\n<Gallery />",
"## Model description \n\njust an LORA of Svenja Schulze",
"## Trigger words\n\nYou should use 'ssh' to trigger the image generation.\n\nYou should use 'bre' to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
"TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #region-us \n",
"# Svenja Schulze\n\n<Gallery />",
"## Model description \n\njust an LORA of Svenja Schulze",
"## Trigger words\n\nYou should use 'ssh' to trigger the image generation.\n\nYou should use 'bre' to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
56,
10,
12,
29,
28
] | [
"passage: TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #region-us \n# Svenja Schulze\n\n<Gallery />## Model description \n\njust an LORA of Svenja Schulze## Trigger words\n\nYou should use 'ssh' to trigger the image generation.\n\nYou should use 'bre' to trigger the image generation.## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
-0.1621837019920349,
-0.0858660414814949,
0.0008446716237813234,
-0.0014361693756654859,
0.11764093488454819,
0.027297809720039368,
0.1715935915708542,
-0.008898427709937096,
0.2017478048801422,
0.11301884055137634,
0.1474153995513916,
-0.014456732198596,
0.013773784041404724,
0.26449108123779297,
-0.07686024159193039,
-0.22208108007907867,
0.1016342043876648,
-0.0402703583240509,
-0.0681689903140068,
0.023157162591814995,
0.05090450495481491,
-0.09253369271755219,
0.1454094648361206,
-0.07673400640487671,
-0.031885869801044464,
0.027467405423521996,
0.036854930222034454,
0.006227855104953051,
0.0365188829600811,
0.04861069470643997,
-0.007158917374908924,
0.15545961260795593,
0.15344218909740448,
-0.1028488352894783,
0.0628863126039505,
-0.014463035389780998,
-0.03336627408862114,
0.04558306559920311,
-0.03112473338842392,
-0.06275654584169388,
0.14652998745441437,
-0.1340574473142624,
-0.09572570770978928,
-0.01214553415775299,
-0.014570648781955242,
-0.0023975111544132233,
0.021984633058309555,
-0.005246583838015795,
0.08561848849058151,
-0.07795245200395584,
-0.009719953872263432,
0.006426252890378237,
-0.011415494605898857,
0.0574529729783535,
0.2129228413105011,
-0.15957076847553253,
-0.05295766144990921,
0.22729532420635223,
0.00981786847114563,
0.21411988139152527,
-0.05099881812930107,
0.1952863335609436,
0.09462649375200272,
-0.06652574241161346,
0.07800393551588058,
-0.051868144422769547,
0.1412108987569809,
-0.013901127502322197,
-0.08098969608545303,
0.03238783776760101,
0.2628391683101654,
0.042678285390138626,
-0.03822550177574158,
-0.10109023749828339,
-0.09938080608844757,
0.19780948758125305,
-0.07260441035032272,
0.0019815871492028236,
0.014575951732695103,
-0.02021006867289543,
-0.02566431649029255,
-0.13671627640724182,
-0.07443366199731827,
-0.1202901229262352,
0.036453716456890106,
0.19267268478870392,
-0.014896495267748833,
0.09908325225114822,
0.035768091678619385,
0.09673827886581421,
-0.1915203034877777,
-0.15374666452407837,
0.0021362649276852608,
-0.10386946052312851,
-0.009219297207891941,
0.11722739040851593,
0.0031467117369174957,
-0.11627250909805298,
0.08422840386629105,
0.0733383446931839,
0.04989420995116234,
-0.009250300005078316,
0.0649043545126915,
0.0957496166229248,
0.022276729345321655,
-0.0039285095408558846,
-0.06123894825577736,
-0.13423648476600647,
0.056770917028188705,
0.07876961678266525,
0.10233161598443985,
-0.06390207260847092,
-0.12750621140003204,
-0.04411773383617401,
-0.0754256397485733,
-0.01609523594379425,
-0.03183262422680855,
0.02367544174194336,
-0.0015545218484476209,
-0.011680781841278076,
0.16029655933380127,
-0.012066627852618694,
-0.058115534484386444,
0.001261108205653727,
-0.014438011683523655,
0.13020288944244385,
0.09177947044372559,
-0.0001372273691231385,
0.08270429074764252,
0.06464267522096634,
-0.07540347427129745,
-0.04329469054937363,
-0.040657587349414825,
-0.060411237180233,
-0.046259805560112,
-0.2118278443813324,
0.02282859943807125,
-0.11814741790294647,
-0.3055950701236725,
0.006226533558219671,
0.017111865803599358,
-0.04683378338813782,
0.07482291758060455,
-0.05849983170628548,
-0.019155699759721756,
0.014095904305577278,
0.0014731407864019275,
-0.04594193026423454,
-0.059941962361335754,
0.06085771322250366,
-0.04703778773546219,
0.1368783712387085,
-0.18742816150188446,
-0.021573958918452263,
-0.08880189806222916,
0.059685513377189636,
-0.24369122087955475,
0.0797821581363678,
-0.1321442872285843,
0.011170726269483566,
-0.028284817934036255,
-0.03648123890161514,
-0.09058764576911926,
0.06136060878634453,
0.0519522987306118,
0.24495702981948853,
-0.19438289105892181,
-0.04734860733151436,
0.04339926317334175,
-0.24076281487941742,
-0.05797708407044411,
0.06608781218528748,
-0.00935364980250597,
0.08001373708248138,
0.0908542275428772,
0.213417649269104,
-0.00013533653691411018,
-0.1337195783853531,
0.042970068752765656,
0.08823514729738235,
-0.044070709496736526,
-0.040300797671079636,
0.09959763288497925,
0.0891161561012268,
-0.04751535505056381,
0.09796957671642303,
-0.27256184816360474,
0.055592913180589676,
-0.05347399786114693,
-0.004139212425798178,
-0.014221766032278538,
-0.11639288812875748,
0.09550803899765015,
0.05762862414121628,
0.057468172162771225,
-0.004795218352228403,
-0.006282663904130459,
0.08815842121839523,
0.10073161125183105,
-0.09921131283044815,
-0.013601263985037804,
0.027670525014400482,
0.20283541083335876,
-0.13649407029151917,
-0.0007374871638603508,
-0.048237401992082596,
-0.11683505773544312,
-0.018259389325976372,
0.21084892749786377,
0.06705750524997711,
0.04259659722447395,
0.10124438256025314,
0.11219852417707443,
-0.08773970603942871,
-0.021558774635195732,
0.08818446844816208,
-0.026552656665444374,
-0.03158961981534958,
-0.14409303665161133,
-0.08629119396209717,
-0.057326484471559525,
0.1120179072022438,
-0.2327214926481247,
0.030028490349650383,
0.015041415579617023,
0.07797116786241531,
0.0469810850918293,
-0.023572998121380806,
0.007608393672853708,
-0.06419255584478378,
-0.05805675685405731,
-0.013612555339932442,
0.02727840654551983,
-0.02208109386265278,
-0.08763061463832855,
0.12114926427602768,
-0.07092109322547913,
0.11567658185958862,
0.09173024445772171,
0.07503227144479752,
0.02316582202911377,
-0.1505924016237259,
0.026667824015021324,
0.023026471957564354,
-0.04021149501204491,
-0.0864216610789299,
-0.11544506996870041,
-0.002916674828156829,
0.044093772768974304,
-0.08401847630739212,
0.07087040692567825,
0.024559620767831802,
-0.06385716050863266,
-0.020458072423934937,
0.10348621755838394,
0.1389261782169342,
-0.03235142305493355,
0.07096584886312485,
0.08802655339241028,
-0.07609604299068451,
0.20182764530181885,
-0.0016165039269253612,
-0.10921023786067963,
0.03653109818696976,
0.010738611221313477,
-0.0020772789139300585,
0.23842768371105194,
0.05859080329537392,
-0.08830267935991287,
0.03492211923003197,
-0.0832229033112526,
0.00022876987350173295,
-0.06890777498483658,
-0.026139188557863235,
-0.007096658460795879,
-0.06360964477062225,
0.0479322224855423,
0.06820962578058243,
-0.09944428503513336,
0.07221416383981705,
-0.10460632294416428,
-0.062344636768102646,
-0.05659032240509987,
-0.021155085414648056,
-0.06549914181232452,
0.10826347768306732,
0.0031419943552464247,
-0.03307845816016197,
-0.1592111736536026,
0.1558382213115692,
-0.1363823115825653,
0.05069448798894882,
0.011501958593726158,
-0.01772134378552437,
-0.07954005897045135,
-0.07434087246656418,
0.06202966347336769,
0.14994707703590393,
-0.02691577561199665,
-0.06026388332247734,
-0.06387514621019363,
-0.09105636924505234,
-0.069025419652462,
-0.043503936380147934,
-0.09735573828220367,
0.0333673395216465,
0.0546012781560421,
-0.09821439534425735,
0.16503897309303284,
0.057098329067230225,
0.009912910871207714,
0.023807790130376816,
0.007812784053385258,
0.11802803725004196,
-0.037555061280727386,
0.1446809023618698,
0.1951722502708435,
0.009645689278841019,
0.048408739268779755,
0.06746821850538254,
0.0173395574092865,
-0.05803273245692253,
0.017933756113052368,
-0.034266311675310135,
-0.15854224562644958,
0.004013371653854847,
-0.13776765763759613,
-0.06376052647829056,
0.014431409537792206,
-0.009578351862728596,
0.01175830326974392,
-0.01101552415639162,
0.20677389204502106,
-0.03375572711229324,
-0.11436139792203903,
0.06659722328186035,
0.0578281506896019,
-0.09194201231002808,
-0.014877020381391048,
0.1168992891907692,
-0.06483496725559235,
0.002101796679198742,
0.13854293525218964,
-0.03631019592285156,
0.16424021124839783,
-0.09816368669271469,
0.06309909373521805,
0.042309828102588654,
0.08575824648141861,
0.08496119827032089,
0.1251695454120636,
-0.045511383563280106,
-0.024948621168732643,
-0.060901347547769547,
-0.11609269678592682,
-0.007903308607637882,
0.14458823204040527,
-0.06924140453338623,
-0.012867162004113197,
-0.036640431731939316,
0.1171499565243721,
0.05275087431073189,
0.041803836822509766,
0.08646892756223679,
-0.3009328842163086,
-0.02248591184616089,
0.11772496998310089,
0.13140869140625,
-0.021284667775034904,
0.01955055259168148,
0.2280178666114807,
-0.03918452560901642,
0.03533582016825676,
-0.007683811243623495,
0.0622003935277462,
0.07598254084587097,
-0.029897276312112808,
-0.0910547524690628,
0.1393570601940155,
-0.03943127766251564,
0.008514364250004292,
0.015174895524978638,
0.13159163296222687,
-0.006398703437298536,
-0.04821117967367172,
-0.047049690037965775,
-0.05470242351293564,
0.11192598938941956,
0.15608741343021393,
0.17224422097206116,
0.036515623331069946,
-0.07915053516626358,
-0.06388840079307556,
-0.07971250265836716,
0.030528198927640915,
0.05933305621147156,
-0.12625053524971008,
0.012247320264577866,
0.006369933485984802,
-0.02297629788517952,
-0.006230015307664871,
0.05393093079328537,
-0.07320651412010193,
-0.0397784523665905,
-0.06713264435529709,
0.0768762156367302,
-0.023338496685028076,
-0.04609907791018486,
-0.06473933160305023,
-0.07080952078104019,
0.05628526583313942,
0.08841893821954727,
-0.11724550276994705,
-0.12211407721042633,
0.001061679213307798,
0.05994086340069771,
-0.05888454616069794,
0.03383653983473778,
0.01057793851941824,
0.10844133794307709,
-0.06686294823884964,
-0.12894195318222046,
0.04010662063956261,
-0.062005285173654556,
-0.08806068450212479,
0.0015658038901165128,
0.10140878707170486,
0.030108924955129623,
-0.043842412531375885,
0.017382416874170303,
0.05264504998922348,
0.04285937920212746,
-0.11209143698215485,
0.012912402860820293,
0.06714978069067001,
-0.006029697600752115,
0.13575805723667145,
-0.02120775356888771,
-0.12136417627334595,
0.028391659259796143,
0.08016671240329742,
0.019060134887695312,
0.22801868617534637,
-0.08508003503084183,
0.0517222061753273,
0.21357384324073792,
0.018432533368468285,
-0.32352346181869507,
0.016050778329372406,
-0.043065086007118225,
0.05908496677875519,
0.07041486352682114,
-0.024536533281207085,
0.17221574485301971,
0.07346469163894653,
-0.0448133759200573,
0.13020358979701996,
-0.22922509908676147,
-0.13477078080177307,
0.06357983499765396,
0.13737550377845764,
0.29887765645980835,
-0.17829278111457825,
-0.04932843893766403,
-0.09268210083246231,
-0.08561736345291138,
-0.010043750517070293,
-0.1611907184123993,
-0.002273484133183956,
0.0018399229738861322,
-0.02933606691658497,
0.015954503789544106,
-0.043359436094760895,
0.20295147597789764,
0.0026872826274484396,
0.08682391792535782,
-0.06166762486100197,
-0.04024296626448631,
0.1347733438014984,
-0.0527675524353981,
0.13510257005691528,
-0.2346716672182083,
0.07570505142211914,
-0.08419685810804367,
-0.02984062023460865,
-0.019980674609541893,
0.06428748369216919,
0.01944410614669323,
-0.07371324300765991,
-0.03923691064119339,
0.018899666145443916,
-0.015331590548157692,
0.039908267557621,
0.08956069499254227,
-0.09556028246879578,
-0.006730903405696154,
0.1542009860277176,
-0.00801541842520237,
-0.03666681796312332,
-0.011273326352238655,
-0.05095214396715164,
-0.05455178767442703,
0.08987864851951599,
-0.14744602143764496,
-0.020822005346417427,
0.08836785703897476,
0.0826481431722641,
0.06694577634334564,
0.017210859805345535,
0.04059157148003578,
0.08639026433229446,
0.1043986901640892,
-0.13335774838924408,
-0.09996415674686432,
-0.07240114361047745,
-0.08089195191860199,
0.05665900558233261,
0.08306272327899933,
0.14168180525302887,
-0.05434803664684296,
0.045119814574718475,
-0.05051359161734581,
0.036746568977832794,
-0.041380662471055984,
0.07122588902711868,
0.0825369656085968,
0.018185142427682877,
-0.10729269683361053,
0.07162933051586151,
-0.07317434996366501,
0.0872819647192955,
-0.05646316707134247,
0.03958630934357643,
-0.1454455703496933,
-0.03370792418718338,
-0.0478413924574852,
0.08435618132352829,
-0.13407589495182037,
0.0035366802476346493,
-0.08460313826799393,
-0.09713543951511383,
-0.0851534977555275,
0.10243666172027588,
0.09708108007907867,
-0.08197327703237534,
0.011708919890224934,
0.014822673052549362,
0.005477798171341419,
0.062246765941381454,
0.12744726240634918,
0.09194830805063248,
-0.17762599885463715,
-0.06723435968160629,
-0.044054750353097916,
0.007215612567961216,
-0.12750646471977234,
-0.012826040387153625,
-0.0988968089222908,
0.013263795524835587,
-0.15741820633411407,
0.10582002997398376,
-0.11231706291437149,
-0.05382630228996277,
-0.034886546432971954,
-0.11148864775896072,
-0.03240314498543739,
0.01881350576877594,
-0.060382481664419174,
0.04899318516254425,
0.005725763272494078,
0.02279694378376007,
-0.07405279576778412,
-0.044124193489551544,
0.02848587930202484,
-0.06917908042669296,
0.0543443001806736,
0.06665503233671188,
-0.0517713688313961,
0.06463661044836044,
-0.17570026218891144,
-0.00019277275714557618,
0.09530393779277802,
0.024203667417168617,
-0.030247926712036133,
0.11372930556535721,
0.026630332693457603,
0.007121857721358538,
0.049756523221731186,
-0.028377968817949295,
0.005474410485476255,
-0.023139160126447678,
0.1680215448141098,
-0.059159714728593826,
-0.013602852821350098,
-0.02206682786345482,
0.03195975348353386,
0.08116190135478973,
0.11468829214572906,
0.046701580286026,
-0.09116952866315842,
-0.023807162418961525,
-0.10613926500082016,
0.04223164916038513,
0.03357614576816559,
-0.1215583086013794,
-0.12318768352270126,
-0.08111785352230072,
0.02540585771203041,
-0.0349065400660038,
0.11210393905639648,
0.023750392720103264,
-0.06992130726575851,
-0.029874751344323158,
0.10830646753311157,
0.19815166294574738,
0.053283773362636566,
0.2298135608434677,
0.043007414788007736,
0.0908438190817833,
-0.1454506814479828,
0.10585232079029083,
0.0665697380900383,
-0.06589364260435104,
0.08933704346418381,
0.10460761934518814,
-0.05089319869875908,
0.11469916254281998,
0.040768515318632126,
0.057416655123233795,
-0.02693040855228901,
-0.01641150191426277,
-0.09218120574951172,
0.007802399341017008,
-0.05856535583734512,
0.02981780283153057,
0.1875903308391571,
-0.12716327607631683,
-0.023258887231349945,
0.08176542073488235,
-0.01900697685778141,
-0.11703012883663177,
-0.2734379172325134,
-0.06259056180715561,
-0.18845947086811066,
0.03624773398041725,
-0.0672844871878624,
-0.057092297822237015,
0.13794730603694916,
0.005794483236968517,
0.04864555597305298,
0.13512589037418365,
-0.11962636560201645,
-0.008127105422317982,
0.06056997925043106,
-0.019931387156248093,
-0.056726448237895966,
0.04494277387857437,
-0.08837394416332245,
0.11295086145401001,
0.003094868268817663,
-0.02662660740315914,
-0.000700428441632539,
0.03916873782873154,
0.011989240534603596,
0.022762620821595192,
-0.07085063308477402,
-0.05754903331398964,
0.023520277813076973,
-0.020088104531168938,
0.1176762506365776,
0.03719532489776611,
-0.030158277601003647,
-0.027027543634176254,
0.1500159651041031,
-0.05488140881061554,
-0.04549495875835419,
-0.08971156179904938,
0.0824408084154129,
-0.10519332438707352,
0.08748403191566467,
-0.02681582421064377,
-0.12500669062137604,
0.033067502081394196,
0.1221003606915474,
0.24115855991840363,
-0.10744316130876541,
0.028565699234604836,
-0.08272819966077805,
0.006412670947611332,
-0.032232630997896194,
0.07436385750770569,
-0.06950929760932922,
0.25858765840530396,
-0.049597639590501785,
0.010389292612671852,
-0.033783283084630966,
-0.059423428028821945,
-0.03893930837512016,
-0.09309663623571396,
-0.014062447473406792,
-0.077565997838974,
-0.10605742037296295,
0.08663773536682129,
-0.09097597002983093,
-0.09668201208114624,
0.04750825837254524,
-0.0677967444062233,
0.011425911448895931,
-0.09189053624868393,
-0.0665007084608078,
0.07384412735700607,
-0.01973951980471611,
-0.13387684524059296,
0.03790596127510071,
-0.06747765094041824,
0.009380779229104519,
-0.09325186163187027,
-0.03619998320937157,
0.024694614112377167,
-0.07412566989660263,
0.10479163378477097,
-0.07247920334339142,
0.01774037629365921,
-0.007364415563642979,
-0.039888374507427216,
-0.022410834208130836,
0.04274841770529747,
-0.009772821329534054,
-0.11727139353752136,
-0.06067417189478874,
0.09837637841701508,
-0.08639416098594666,
0.08442740142345428,
0.007176591549068689,
-0.15981639921665192,
-0.006334374658763409,
0.16527196764945984,
-0.09417233616113663,
-0.045169807970523834,
0.05376526340842247,
-0.12344121187925339,
0.09065312892198563,
0.0049497028812766075,
0.02982540801167488,
-0.04118325188755989,
-0.013135120272636414,
0.11939579248428345,
0.0647025927901268,
-0.013964888639748096,
0.08874501287937164,
-0.08814245462417603,
-0.04510946199297905,
-0.02850355952978134,
-0.006954626180231571,
-0.23541299998760223,
-0.021240148693323135,
-0.2035217136144638,
0.06213536858558655,
0.01671471819281578,
0.06790239363908768,
0.24308478832244873,
-0.013972725719213486,
0.006561410613358021,
-0.27536314725875854,
0.03701018542051315,
0.07265974581241608,
-0.06709268689155579,
-0.07438209652900696
] |
null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.1 | {"library_name": "peft", "base_model": "mistralai/Mistral-7B-Instruct-v0.1"} | null | Mahdish720/mistral_7b_Enlighten | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"region:us"
] | 2024-02-09T13:19:55+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-mistralai/Mistral-7B-Instruct-v0.1 #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
### Framework versions
- PEFT 0.7.1 | [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.7.1"
] | [
"TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-mistralai/Mistral-7B-Instruct-v0.1 #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"### Framework versions\n\n- PEFT 0.7.1"
] | [
42,
6,
3,
54,
28,
3,
4,
9,
9,
10,
42,
20,
3,
4,
5,
9,
11,
13,
3,
12,
5,
4,
5,
3,
4,
9,
53,
9,
8,
6,
3,
14,
8,
7,
9,
4,
11
] | [
"passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-mistralai/Mistral-7B-Instruct-v0.1 #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.7.1"
] | [
-0.12370278686285019,
0.20035040378570557,
-0.0028664683923125267,
0.026737522333860397,
0.08292152732610703,
0.018904531374573708,
0.052758291363716125,
0.13020724058151245,
0.030153056606650352,
0.11460290104150772,
0.07297997921705246,
0.125077024102211,
0.11139491200447083,
0.20324043929576874,
0.005670811980962753,
-0.16467395424842834,
0.027827592566609383,
-0.08089073747396469,
0.016619721427559853,
0.12726931273937225,
0.1432514190673828,
-0.10117144137620926,
0.07835434377193451,
-0.01981494575738907,
0.004349906928837299,
-0.0352393202483654,
-0.06790929287672043,
-0.01725205034017563,
0.05307446047663689,
0.026189720258116722,
0.060790322721004486,
-0.01654665172100067,
0.09133651852607727,
-0.2673582136631012,
0.018790800124406815,
0.042303938418626785,
0.008864170871675014,
0.08461890369653702,
0.10190901905298233,
-0.0401630736887455,
0.11709907650947571,
-0.02111041359603405,
0.13962113857269287,
0.09187621623277664,
-0.09330180287361145,
-0.23279143869876862,
-0.06278906017541885,
0.07683935016393661,
0.18940827250480652,
0.0885700061917305,
-0.046312667429447174,
0.13281314074993134,
-0.06543084979057312,
0.024073533713817596,
0.05883748084306717,
-0.10321591049432755,
-0.06624127924442291,
0.06951114535331726,
0.13933871686458588,
0.06950996816158295,
-0.12006407976150513,
-0.03875116631388664,
0.03487453982234001,
0.046696025878190994,
0.05153792351484299,
0.008108798414468765,
0.16088861227035522,
0.030887849628925323,
-0.14920790493488312,
-0.05668804794549942,
0.14405126869678497,
0.0061280382797122,
-0.04395770654082298,
-0.22031866014003754,
-0.0014274041168391705,
-0.09189210832118988,
-0.030347395688295364,
-0.05682741850614548,
0.03325285390019417,
0.013215325772762299,
0.12968207895755768,
-0.04165268689393997,
-0.09483686834573746,
-0.02256331779062748,
0.10175339877605438,
0.0535091757774353,
0.024332737550139427,
-0.023581869900226593,
0.013872253708541393,
0.12344539165496826,
0.07622259855270386,
-0.13235871493816376,
-0.0656445175409317,
-0.07497408986091614,
-0.03990820050239563,
-0.03307846933603287,
0.03774069994688034,
0.016567891463637352,
0.06369028240442276,
0.27186739444732666,
-0.02032889612019062,
0.06180655211210251,
0.051992181688547134,
0.019275100901722908,
0.03823855146765709,
0.10761357843875885,
-0.0336335226893425,
-0.15962094068527222,
-0.014103583991527557,
0.10286331921815872,
-0.003113945247605443,
-0.030916297808289528,
-0.04647998884320259,
0.02985381707549095,
0.044948551803827286,
0.11923704296350479,
0.11463913321495056,
-0.021387897431850433,
-0.08143356442451477,
-0.05728275328874588,
0.1943773329257965,
-0.15572194755077362,
0.042495567351579666,
0.024921024218201637,
-0.0033351753372699022,
-0.06424922496080399,
0.0048318421468138695,
0.020588012412190437,
-0.030128972604870796,
0.06736154854297638,
-0.06642592698335648,
-0.042259231209754944,
-0.1269172728061676,
-0.029898149892687798,
0.03407588228583336,
0.01950664632022381,
-0.04628675803542137,
-0.045495033264160156,
-0.08219289779663086,
-0.11140318214893341,
0.10866383463144302,
-0.05315598100423813,
-0.05640609934926033,
-0.027365704998373985,
-0.08438503742218018,
0.020764144137501717,
0.03094005025923252,
0.07275574654340744,
-0.02639390341937542,
0.04901900514960289,
-0.0015930673107504845,
0.06265150755643845,
0.08636108040809631,
0.03098663128912449,
-0.08630451560020447,
0.06676739454269409,
-0.19480600953102112,
0.07410793751478195,
-0.08039084821939468,
0.038452692329883575,
-0.16597723960876465,
-0.009889614768326283,
0.006371681112796068,
0.03127526491880417,
0.04245782271027565,
0.16118645668029785,
-0.21772393584251404,
-0.023482652381062508,
0.15748928487300873,
-0.11233942210674286,
-0.1353442370891571,
0.045124154537916183,
-0.03782976046204567,
0.18286290764808655,
0.028059909120202065,
0.014213744550943375,
0.08345969021320343,
-0.16088691353797913,
-0.02538415975868702,
-0.028932303190231323,
0.007194234523922205,
0.07530750334262848,
0.08823250234127045,
-0.0928809717297554,
-0.005320941098034382,
0.009460599161684513,
-0.0641336739063263,
-0.014634702354669571,
-0.04288903623819351,
-0.10526223480701447,
0.003635958069935441,
-0.08504176884889603,
0.021388394758105278,
0.006543965544551611,
-0.10083941370248795,
-0.009539654478430748,
-0.15418311953544617,
-0.06140757352113724,
0.08830468356609344,
0.003567253239452839,
-0.020644040778279305,
-0.10190993547439575,
0.06527101248502731,
-0.038788534700870514,
-0.02331450954079628,
-0.1473758965730667,
-0.02070821076631546,
0.017912713810801506,
-0.14163626730442047,
-0.007488899398595095,
-0.11959235370159149,
0.06726401299238205,
0.004162535071372986,
-0.04673616960644722,
-0.045705441385507584,
-0.0020731822587549686,
-0.0022424899507313967,
-0.058903906494379044,
-0.22974854707717896,
-0.026553241536021233,
-0.054226357489824295,
0.15803958475589752,
-0.23175272345542908,
0.04267347976565361,
-0.0010480774799361825,
0.11390642821788788,
0.0021672246512025595,
-0.06564372777938843,
0.021235911175608635,
-0.063203364610672,
-0.026735886931419373,
-0.06913445889949799,
-0.003182758344337344,
-0.0008994291420094669,
-0.022463258355855942,
0.015847524628043175,
-0.11952517181634903,
-0.06208924949169159,
0.09589242190122604,
0.056939251720905304,
-0.15059207379817963,
0.0067536914721131325,
-0.03717174381017685,
-0.05953634902834892,
-0.07090888172388077,
-0.07041006535291672,
0.0873391181230545,
0.057509057223796844,
0.03875390440225601,
-0.0776306539773941,
-0.06510437279939651,
0.003132033860310912,
-0.02849433571100235,
-0.01442405954003334,
0.12258235365152359,
0.07661592960357666,
-0.0992024764418602,
0.09103231132030487,
0.07663650065660477,
0.01855170913040638,
0.06890179961919785,
-0.024938492104411125,
-0.10686193406581879,
-0.028095947578549385,
0.05887025594711304,
0.012643139809370041,
0.16700907051563263,
-0.07496246695518494,
0.055384714156389236,
0.0480702668428421,
-0.049459122121334076,
0.044436633586883545,
-0.08651945739984512,
0.00534125417470932,
0.002256769919767976,
-0.01260102167725563,
0.037849217653274536,
-0.02024589665234089,
0.0044150142930448055,
0.08240605890750885,
0.06033923476934433,
0.02244850993156433,
0.016600288450717926,
-0.03540618345141411,
-0.14307618141174316,
0.18113619089126587,
-0.09209266304969788,
-0.23790551722049713,
-0.15470270812511444,
0.05668915435671806,
0.05676816776394844,
-0.011487090960144997,
0.03155234083533287,
-0.05600937083363533,
-0.09920097142457962,
-0.08538676053285599,
0.00846089981496334,
0.03588230535387993,
-0.061135634779930115,
-0.0669591948390007,
0.03875943645834923,
0.038996897637844086,
-0.11785195767879486,
0.027490578591823578,
0.0645693689584732,
-0.011004988104104996,
-0.0012869960628449917,
0.04929900914430618,
0.09721426665782928,
0.19361133873462677,
0.000901883642654866,
0.0007843973580747843,
0.061695706099271774,
0.27732670307159424,
-0.15649685263633728,
0.10655525326728821,
0.15169976651668549,
-0.06692352145910263,
0.07140392810106277,
0.18600508570671082,
0.02430180460214615,
-0.0954776257276535,
0.024817224591970444,
0.02864665351808071,
-0.018556173890829086,
-0.2733447551727295,
-0.05303938686847687,
-0.01734212413430214,
-0.0805187076330185,
0.08043716847896576,
0.08757654577493668,
0.08786943554878235,
0.038641925901174545,
-0.059812385588884354,
-0.10790043324232101,
0.02836349606513977,
0.11362870782613754,
-0.011019483208656311,
0.001266577048227191,
0.08276102691888809,
-0.04663781449198723,
0.0045586517080664635,
0.08485372364521027,
-0.019431179389357567,
0.13427883386611938,
0.05759093910455704,
0.09974581748247147,
0.08115866780281067,
0.09051993489265442,
-0.009729122743010521,
0.0349062979221344,
0.009065065532922745,
0.021392004564404488,
0.02106170542538166,
-0.08727344870567322,
0.0074804676696658134,
0.11245371401309967,
0.02189219370484352,
0.022210033610463142,
0.01864168979227543,
-0.05482006072998047,
0.03701046109199524,
0.19767166674137115,
0.029051819816231728,
-0.21730954945087433,
-0.08690407872200012,
0.05128907784819603,
-0.07657357305288315,
-0.16056251525878906,
-0.008120291866362095,
0.017406661063432693,
-0.1603481024503708,
0.012141123414039612,
-0.044478222727775574,
0.10524789988994598,
-0.07417912036180496,
-0.03885636478662491,
0.11008059233427048,
0.04778419807553291,
-0.023537637665867805,
0.051884859800338745,
-0.1957838535308838,
0.10655397921800613,
0.026618560776114464,
0.0715516209602356,
-0.08758996427059174,
0.09202957153320312,
-0.0020062047988176346,
-0.019470280036330223,
0.16865241527557373,
-0.0034830227959901094,
-0.061765849590301514,
-0.0852428749203682,
-0.08692706376314163,
-0.004706318955868483,
0.07805825769901276,
-0.12762640416622162,
0.07818645983934402,
-0.03894205391407013,
-0.02466706931591034,
-0.0063107870519161224,
-0.08469512313604355,
-0.12892921268939972,
-0.15310350060462952,
0.05844602361321449,
-0.09324400871992111,
0.031347304582595825,
-0.08298609405755997,
-0.055441707372665405,
0.02714557759463787,
0.18025870621204376,
-0.2073221057653427,
-0.10543274134397507,
-0.14177606999874115,
-0.11035151034593582,
0.16223160922527313,
-0.04230796918272972,
0.0853738859295845,
-0.005455038510262966,
0.15964040160179138,
0.005695420783013105,
-0.010506672784686089,
0.08627717196941376,
-0.09498977661132812,
-0.19084462523460388,
-0.0495232455432415,
0.16464851796627045,
0.1388905942440033,
0.02703903615474701,
-0.008604643866419792,
0.03289229795336723,
-0.06965181231498718,
-0.10905351489782333,
0.028459668159484863,
0.15366631746292114,
0.07128261774778366,
-0.019316500052809715,
-0.037592459470033646,
-0.10516300797462463,
-0.06535998731851578,
-0.044599749147892,
-0.004951389040797949,
0.20285101234912872,
-0.06565795838832855,
0.14458344876766205,
0.11325106769800186,
-0.059198178350925446,
-0.21112823486328125,
0.0324893519282341,
0.0421038419008255,
0.019354723393917084,
0.040842290967702866,
-0.18751487135887146,
0.09186435490846634,
-0.013077317737042904,
-0.08374954015016556,
0.17306528985500336,
-0.1877415031194687,
-0.13175731897354126,
0.11343307048082352,
0.024745330214500427,
-0.20683524012565613,
-0.1476408839225769,
-0.10166526585817337,
-0.01732979714870453,
-0.12219797074794769,
0.03704017400741577,
0.02099565789103508,
0.009065298363566399,
0.013637312687933445,
0.01977173611521721,
0.04356249049305916,
-0.05383962020277977,
0.21152369678020477,
-0.043212488293647766,
-0.006023569032549858,
-0.05656985938549042,
-0.07577444612979889,
0.015732431784272194,
-0.053005632013082504,
0.12249618768692017,
-0.010248116217553616,
0.03554583713412285,
-0.1642766296863556,
-0.04463906213641167,
-0.05448657646775246,
0.03617704287171364,
-0.09280227869749069,
-0.0805022343993187,
-0.041006654500961304,
0.09064701199531555,
0.08763451874256134,
-0.016695482656359673,
0.011437056586146355,
-0.09623496234416962,
0.08952117711305618,
0.19673748314380646,
0.19496296346187592,
0.07887765765190125,
-0.05398334190249443,
0.031081456691026688,
-0.037137992680072784,
0.04427868872880936,
-0.20811378955841064,
0.04050343856215477,
0.06476634740829468,
0.019105326384305954,
0.06585721671581268,
-0.007553173694759607,
-0.1583329290151596,
-0.08206351101398468,
0.0857742577791214,
-0.05750631168484688,
-0.169158935546875,
-0.027007587254047394,
0.03877558559179306,
-0.2142086625099182,
-0.04726789891719818,
0.035324301570653915,
-0.01575626991689205,
-0.04139605909585953,
0.024461369961500168,
0.08286121487617493,
-0.02372562699019909,
0.09759712964296341,
0.0881190374493599,
0.09178051352500916,
-0.0922088772058487,
0.05351301655173302,
0.07710833102464676,
-0.03156385198235512,
0.027531340718269348,
0.1272287368774414,
-0.03701621666550636,
-0.04943053051829338,
0.08205670118331909,
0.11730652302503586,
-0.0031431992538273335,
-0.05571317300200462,
0.0059396810829639435,
-0.044459082186222076,
0.058875150978565216,
0.10354732722043991,
0.02866894192993641,
-0.004953955765813589,
0.07670622318983078,
0.027615563943982124,
-0.09115344285964966,
0.12349691241979599,
0.058530181646347046,
0.02515581250190735,
-0.046529125422239304,
-0.04071366414427757,
-0.01710675284266472,
-0.008196691982448101,
-0.016874056309461594,
-0.0004825816722586751,
-0.08640732616186142,
0.005192128010094166,
-0.12605956196784973,
0.017159389331936836,
-0.07686612010002136,
0.0020915744826197624,
0.033357325941324234,
-0.05013074353337288,
0.0024690001737326384,
-0.006117428187280893,
-0.07397367060184479,
-0.05543849244713783,
-0.02083675190806389,
0.08052675426006317,
-0.141737699508667,
0.03507783263921738,
0.07900284230709076,
-0.10412218421697617,
0.06689432263374329,
-0.008511376567184925,
0.009678062051534653,
-0.0002041303232545033,
-0.14669060707092285,
0.058784786611795425,
-0.02638368494808674,
-0.006965274922549725,
-0.002235287567600608,
-0.19063380360603333,
-0.007376522291451693,
-0.03567146137356758,
-0.06874005496501923,
0.017242509871721268,
0.0005309163243509829,
-0.12218218296766281,
0.11178095638751984,
0.006337318103760481,
-0.0643099769949913,
-0.018900711089372635,
0.03969648480415344,
0.08298396319150925,
-0.008065382950007915,
0.12090074270963669,
-0.029606271535158157,
0.08300649374723434,
-0.17657309770584106,
-0.00695727439597249,
-0.01679816283285618,
0.05701522156596184,
-0.015903102234005928,
-0.04050637036561966,
0.05750119313597679,
-0.02242201752960682,
0.16389547288417816,
-0.005783791188150644,
0.07125482708215714,
0.05149891600012779,
0.01401308923959732,
0.04700535908341408,
0.07403795421123505,
0.060738347470760345,
-0.011577424593269825,
-0.0033360468223690987,
0.03445012867450714,
0.00017389636195730418,
-0.05280710384249687,
-0.14837250113487244,
0.061915744096040726,
0.18357045948505402,
0.0640801414847374,
0.025119999423623085,
0.006532660685479641,
-0.12755054235458374,
-0.07323126494884491,
0.1071096882224083,
-0.0200025737285614,
-0.032921768724918365,
-0.06479515880346298,
0.22533392906188965,
0.14427705109119415,
-0.19130322337150574,
0.07297994941473007,
-0.0626438781619072,
-0.03860287740826607,
-0.1420411616563797,
-0.1742783933877945,
-0.05855550989508629,
-0.0473085455596447,
-0.027834570035338402,
-0.056837793439626694,
0.04759860038757324,
0.041906002908945084,
-0.004924839362502098,
-0.023956110700964928,
0.1037825271487236,
0.03004089556634426,
-0.03445086255669594,
0.0424635112285614,
0.05550241097807884,
0.041676923632621765,
-0.09570235013961792,
0.009082134813070297,
0.0028888608794659376,
0.013162201270461082,
0.06738482415676117,
0.02329901233315468,
-0.07427312433719635,
0.029987765476107597,
-0.0212028156965971,
-0.12332458049058914,
0.04458942636847496,
-0.004646174609661102,
-0.028072578832507133,
0.15024931728839874,
0.03822214528918266,
0.009783972054719925,
-0.011765716597437859,
0.23648670315742493,
-0.07234831154346466,
-0.08266238123178482,
-0.1347910761833191,
0.07890105992555618,
-0.07221031934022903,
0.024701779708266258,
0.019658474251627922,
-0.12174785137176514,
0.015260335057973862,
0.17033754289150238,
0.11511067301034927,
-0.016284793615341187,
0.0062955995090305805,
0.04841290041804314,
0.005869951099157333,
-0.04166676849126816,
0.013583788648247719,
0.05681830272078514,
0.20316162705421448,
-0.07927606254816055,
0.06052232161164284,
-0.019173189997673035,
-0.07300607860088348,
-0.025550900027155876,
0.09625755995512009,
-0.013276228681206703,
-0.006936253048479557,
-0.06201295554637909,
0.14970196783542633,
-0.07605934143066406,
-0.206947922706604,
0.057998497039079666,
-0.06496080011129379,
-0.13954423367977142,
-0.045308344066143036,
0.0396728515625,
-0.025949202477931976,
0.0061432719230651855,
0.06307854503393173,
-0.047478269785642624,
0.17515292763710022,
0.02778705023229122,
-0.039971183985471725,
-0.09506432712078094,
0.05743950232863426,
-0.1424650251865387,
0.28488701581954956,
0.024561459198594093,
0.0537935346364975,
0.11226409673690796,
-0.022886648774147034,
-0.14597029983997345,
0.015693269670009613,
0.11252783983945847,
-0.06473895162343979,
0.07074819505214691,
0.15857942402362823,
0.004244922660291195,
0.12740738689899445,
0.06748330593109131,
-0.05165703967213631,
0.03744286671280861,
-0.07961764931678772,
-0.04919426143169403,
-0.12402968108654022,
0.07888796925544739,
-0.09394027292728424,
0.1554749757051468,
0.11642409861087799,
-0.07159163057804108,
0.0023055642377585173,
-0.02246461808681488,
0.08548121154308319,
0.016938326880335808,
0.11434356123209,
0.007570695132017136,
-0.19086255133152008,
0.04210053011775017,
0.008819335140287876,
0.09640710055828094,
-0.2035098373889923,
-0.05383281409740448,
0.04344426095485687,
-0.022018374875187874,
-0.07827964425086975,
0.11350562423467636,
0.03736168518662453,
0.02048356458544731,
-0.03632144629955292,
-0.04064676910638809,
0.01596824638545513,
0.15690405666828156,
-0.10772421956062317,
-0.01819542981684208
] |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# safety-utcustom-train-SF30-RGBD-b0
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/safety-utcustom-TRAIN-30 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3227
- Mean Iou: 0.5786
- Mean Accuracy: 0.6222
- Overall Accuracy: 0.9658
- Accuracy Unlabeled: nan
- Accuracy Safe: 0.2552
- Accuracy Unsafe: 0.9891
- Iou Unlabeled: nan
- Iou Safe: 0.1917
- Iou Unsafe: 0.9655
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Safe | Accuracy Unsafe | Iou Unlabeled | Iou Safe | Iou Unsafe |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:---------------:|:-------------:|:--------:|:----------:|
| 0.9925 | 5.0 | 10 | 1.0612 | 0.3101 | 0.5355 | 0.8847 | nan | 0.1625 | 0.9085 | 0.0 | 0.0462 | 0.8841 |
| 0.8589 | 10.0 | 20 | 0.9441 | 0.3303 | 0.5181 | 0.9537 | nan | 0.0529 | 0.9833 | 0.0 | 0.0373 | 0.9537 |
| 0.7016 | 15.0 | 30 | 0.7764 | 0.3274 | 0.5069 | 0.9654 | nan | 0.0172 | 0.9965 | 0.0 | 0.0169 | 0.9654 |
| 0.6093 | 20.0 | 40 | 0.6213 | 0.3339 | 0.5219 | 0.9603 | nan | 0.0538 | 0.9901 | 0.0 | 0.0415 | 0.9603 |
| 0.5281 | 25.0 | 50 | 0.5431 | 0.3355 | 0.5213 | 0.9650 | nan | 0.0476 | 0.9951 | 0.0 | 0.0417 | 0.9649 |
| 0.5077 | 30.0 | 60 | 0.5043 | 0.3361 | 0.5231 | 0.9638 | nan | 0.0524 | 0.9938 | 0.0 | 0.0444 | 0.9638 |
| 0.5197 | 35.0 | 70 | 0.4579 | 0.3379 | 0.5249 | 0.9657 | nan | 0.0543 | 0.9956 | 0.0 | 0.0481 | 0.9656 |
| 0.4477 | 40.0 | 80 | 0.4340 | 0.3395 | 0.5271 | 0.9662 | nan | 0.0583 | 0.9960 | 0.0 | 0.0523 | 0.9661 |
| 0.4371 | 45.0 | 90 | 0.4033 | 0.3407 | 0.5287 | 0.9669 | nan | 0.0607 | 0.9967 | 0.0 | 0.0553 | 0.9669 |
| 0.3972 | 50.0 | 100 | 0.3975 | 0.3420 | 0.5292 | 0.9686 | nan | 0.0600 | 0.9985 | 0.0 | 0.0574 | 0.9686 |
| 0.4101 | 55.0 | 110 | 0.3777 | 0.5215 | 0.5381 | 0.9691 | nan | 0.0778 | 0.9983 | nan | 0.0740 | 0.9690 |
| 0.3528 | 60.0 | 120 | 0.3625 | 0.5360 | 0.5587 | 0.9668 | nan | 0.1229 | 0.9945 | nan | 0.1054 | 0.9667 |
| 0.3552 | 65.0 | 130 | 0.3733 | 0.5550 | 0.5829 | 0.9671 | nan | 0.1726 | 0.9932 | nan | 0.1430 | 0.9669 |
| 0.3798 | 70.0 | 140 | 0.3444 | 0.5598 | 0.5753 | 0.9722 | nan | 0.1515 | 0.9991 | nan | 0.1476 | 0.9720 |
| 0.3235 | 75.0 | 150 | 0.3461 | 0.5651 | 0.6041 | 0.9650 | nan | 0.2187 | 0.9895 | nan | 0.1656 | 0.9647 |
| 0.3457 | 80.0 | 160 | 0.3335 | 0.5638 | 0.5880 | 0.9695 | nan | 0.1806 | 0.9954 | nan | 0.1582 | 0.9693 |
| 0.318 | 85.0 | 170 | 0.3334 | 0.5739 | 0.6114 | 0.9667 | nan | 0.2321 | 0.9908 | nan | 0.1814 | 0.9665 |
| 0.32 | 90.0 | 180 | 0.3307 | 0.5779 | 0.6112 | 0.9684 | nan | 0.2299 | 0.9926 | nan | 0.1877 | 0.9681 |
| 0.3122 | 95.0 | 190 | 0.3263 | 0.5778 | 0.6175 | 0.9667 | nan | 0.2447 | 0.9904 | nan | 0.1891 | 0.9664 |
| 0.3554 | 100.0 | 200 | 0.3227 | 0.5786 | 0.6222 | 0.9658 | nan | 0.2552 | 0.9891 | nan | 0.1917 | 0.9655 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
| {"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "safety-utcustom-train-SF30-RGBD-b0", "results": []}]} | image-segmentation | sam1120/safety-utcustom-train-SF30-RGBD-b0 | [
"transformers",
"pytorch",
"tensorboard",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"license:other",
"endpoints_compatible",
"region:us"
] | 2024-02-09T13:20:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us
| safety-utcustom-train-SF30-RGBD-b0
==================================
This model is a fine-tuned version of nvidia/mit-b0 on the sam1120/safety-utcustom-TRAIN-30 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3227
* Mean Iou: 0.5786
* Mean Accuracy: 0.6222
* Overall Accuracy: 0.9658
* Accuracy Unlabeled: nan
* Accuracy Safe: 0.2552
* Accuracy Unsafe: 0.9891
* Iou Unlabeled: nan
* Iou Safe: 0.1917
* Iou Unsafe: 0.9655
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 100
### Training results
### Framework versions
* Transformers 4.30.2
* Pytorch 2.0.1+cu117
* Datasets 2.13.1
* Tokenizers 0.13.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 100",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3"
] | [
"TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 100",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3"
] | [
48,
117,
4,
33
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 100### Training results### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3"
] | [
-0.10660994052886963,
0.032675497233867645,
-0.002019572304561734,
0.11479292809963226,
0.1714918464422226,
0.028135446831583977,
0.11643318831920624,
0.11429917812347412,
-0.10586902499198914,
0.03235301002860069,
0.1052609533071518,
0.1443265825510025,
0.01723969541490078,
0.09979120641946793,
-0.020393380895256996,
-0.3044509291648865,
-0.025196360424160957,
0.03224802017211914,
-0.08301959186792374,
0.1253855675458908,
0.06450270861387253,
-0.16415537893772125,
0.09240162372589111,
-0.0046122316271066666,
-0.22154869139194489,
0.015598209574818611,
-0.00466264458373189,
-0.032839708030223846,
0.15968042612075806,
0.022707315161824226,
0.11539807170629501,
0.008385791443288326,
0.1143365278840065,
-0.2042301446199417,
0.017542308196425438,
0.05639766529202461,
-0.0034147382248193026,
0.06737568229436874,
0.06484885513782501,
0.0014013053150847554,
0.1481672078371048,
-0.10662989318370819,
0.06846962869167328,
0.0019910894334316254,
-0.14498072862625122,
-0.2125127762556076,
-0.07424520701169968,
0.02228745073080063,
0.08017740398645401,
0.09490053355693817,
-0.006120819132775068,
0.11308383196592331,
-0.0915009081363678,
0.11304999142885208,
0.27025482058525085,
-0.24352288246154785,
-0.08631893992424011,
0.04120531678199768,
0.003988782409578562,
0.06807348877191544,
-0.13222795724868774,
0.009121859446167946,
0.033936794847249985,
0.046208955347537994,
0.12007936090230942,
-0.03370574116706848,
-0.09829529374837875,
0.026571592316031456,
-0.13777972757816315,
-0.03384276479482651,
0.05729811638593674,
0.05404794588685036,
-0.020593944936990738,
-0.035161975771188736,
-0.06715637445449829,
-0.1847996860742569,
-0.06646668910980225,
0.010469309985637665,
0.06724265217781067,
-0.05968353897333145,
-0.11568494886159897,
-0.014745449647307396,
-0.11014527082443237,
-0.08549944311380386,
-0.04827827215194702,
0.12808933854103088,
0.03372448682785034,
0.018694188445806503,
-0.034620802849531174,
0.12636232376098633,
-0.028448253870010376,
-0.1394587904214859,
0.017120765522122383,
0.028844431042671204,
-0.04387075453996658,
-0.03278953954577446,
-0.04943745583295822,
-0.0658559575676918,
-0.010960361920297146,
0.1099226176738739,
-0.05685316398739815,
0.06757368892431259,
0.03709050640463829,
0.05071469768881798,
-0.11196263879537582,
0.19037678837776184,
-0.06718780845403671,
-0.011348443105816841,
-0.03731472045183182,
0.05793622136116028,
0.004363871645182371,
-0.02320598065853119,
-0.1051001250743866,
0.005048905033618212,
0.07208380848169327,
-0.00832862313836813,
-0.08815854787826538,
0.06960891932249069,
-0.039639681577682495,
-0.011020093224942684,
-0.004918300546705723,
-0.0761382132768631,
0.04683351516723633,
-0.0006643507513217628,
-0.0823325514793396,
-0.02980782650411129,
0.04824423789978027,
0.014941179193556309,
0.012589311227202415,
0.16650371253490448,
-0.08636433631181717,
0.06296328455209732,
-0.11238839477300644,
-0.10147636383771896,
-0.0001616202644072473,
-0.08858970552682877,
0.036357007920742035,
-0.07849534600973129,
-0.14790679514408112,
-0.009840060956776142,
0.07131689786911011,
-0.04060016945004463,
0.002357464749366045,
-0.05429467186331749,
-0.09163182973861694,
0.001695713377557695,
-0.008956803940236568,
0.16411375999450684,
-0.06519337743520737,
0.12199977040290833,
0.0372159518301487,
0.07323546707630157,
-0.06287305802106857,
0.039027560502290726,
-0.0850762128829956,
0.01969297230243683,
-0.22015629708766937,
0.04394318163394928,
-0.04998564347624779,
0.06824952363967896,
-0.06116855889558792,
-0.12153353542089462,
0.006529869046062231,
0.0023963209241628647,
0.09192132204771042,
0.1074763759970665,
-0.22442464530467987,
-0.07685289531946182,
0.1515221744775772,
-0.07213780283927917,
-0.10005009174346924,
0.11257003992795944,
-0.06397062540054321,
0.012650379911065102,
0.06265987455844879,
0.19945833086967468,
0.051220521330833435,
-0.13931064307689667,
0.022178610786795616,
-0.015611594542860985,
0.04956120252609253,
-0.02757536619901657,
0.04945487529039383,
0.021805405616760254,
0.08810900896787643,
0.01967213675379753,
-0.06629392504692078,
0.0676313042640686,
-0.12459244579076767,
-0.09756287187337875,
-0.0264261607080698,
-0.08666153252124786,
0.04165005311369896,
0.08856207132339478,
0.06237226724624634,
-0.10465779155492783,
-0.07721006125211716,
0.08849596232175827,
0.07450436055660248,
-0.06875042617321014,
0.03952685371041298,
-0.06538017094135284,
0.0451570525765419,
-0.018342385068535805,
-0.03515284135937691,
-0.17475217580795288,
-0.022749628871679306,
-0.020006032660603523,
0.03332129493355751,
0.02782047912478447,
0.020526545122265816,
0.0916178822517395,
0.08794313669204712,
-0.06999839842319489,
-0.025262178853154182,
-0.06573190540075302,
0.002913098782300949,
-0.12309687584638596,
-0.22825199365615845,
-0.04418036341667175,
-0.008248005993664265,
0.08714036643505096,
-0.2117336094379425,
0.024948880076408386,
0.023230962455272675,
0.08738879859447479,
0.02502964437007904,
-0.0324564054608345,
-0.05303780734539032,
0.0774141401052475,
-0.010501109063625336,
-0.06486332416534424,
0.0683775395154953,
-0.005597016774117947,
-0.06868989765644073,
-0.05763876065611839,
-0.11304925382137299,
0.16495318710803986,
0.13359901309013367,
-0.14767196774482727,
-0.09296377003192902,
-0.01312874536961317,
-0.06336091458797455,
-0.03276076912879944,
-0.04418261721730232,
0.03855151683092117,
0.1791233867406845,
0.0015355880605056882,
0.13266755640506744,
-0.06013854965567589,
-0.03636866807937622,
0.030028825625777245,
-0.026668677106499672,
0.027541562914848328,
0.12785904109477997,
0.1252460777759552,
-0.059260036796331406,
0.1239924505352974,
0.12523682415485382,
-0.07960369437932968,
0.1472863107919693,
-0.0333004891872406,
-0.0808156430721283,
-0.019248999655246735,
-0.014334888197481632,
-0.009533149190247059,
0.17689624428749084,
-0.15281562507152557,
-0.018961161375045776,
-0.004448987543582916,
0.01215657964348793,
0.013320904225111008,
-0.24980664253234863,
-0.05587894842028618,
0.03972950577735901,
-0.044791240245103836,
-0.00851476937532425,
-0.0243929885327816,
-0.004252621438354254,
0.10369039326906204,
-0.006969279143959284,
-0.07393141090869904,
0.0001591225591255352,
-0.008137120865285397,
-0.049103859812021255,
0.20756325125694275,
-0.05690593272447586,
-0.11909423768520355,
-0.08753391355276108,
-0.0767134577035904,
-0.03573555871844292,
0.002747634192928672,
0.058593619614839554,
-0.10869083553552628,
-0.018396614119410515,
-0.05982052907347679,
0.019369110465049744,
0.006106136832386255,
0.03666252642869949,
0.0006280781235545874,
-0.008939306251704693,
0.054127197712659836,
-0.09670034050941467,
-0.010041685774922371,
-0.06630489230155945,
-0.053051676601171494,
0.05522671714425087,
0.0607864148914814,
0.1483849138021469,
0.1348426640033722,
-0.024489639326930046,
0.02162122167646885,
-0.03322962298989296,
0.26108983159065247,
-0.09604258835315704,
-0.027632441371679306,
0.11551687866449356,
-0.011270022951066494,
0.05483314022421837,
0.10887512564659119,
0.08298485726118088,
-0.10986989736557007,
-0.0029082114342600107,
0.06376782804727554,
-0.05148618295788765,
-0.15392470359802246,
-0.014111863449215889,
-0.057976361364126205,
-0.030541641637682915,
0.07823701202869415,
0.027061911299824715,
-0.0002654750715009868,
0.056850701570510864,
0.04891524091362953,
0.042994897812604904,
-0.022758224979043007,
0.05083301663398743,
0.08742766082286835,
0.03123549558222294,
0.10970334708690643,
-0.04346964880824089,
-0.06754498183727264,
0.030796436592936516,
0.002575437305495143,
0.24606376886367798,
-0.013605552725493908,
0.09677509218454361,
0.0734155997633934,
0.16177119314670563,
-0.01293492317199707,
0.04849853366613388,
-0.013725928030908108,
-0.067111074924469,
-0.01871170662343502,
-0.044555407017469406,
-0.01571524515748024,
0.01059585902839899,
-0.05171755328774452,
0.038850221782922745,
-0.12635116279125214,
0.006222804542630911,
0.06906817108392715,
0.25234168767929077,
0.0282395351678133,
-0.3170398771762848,
-0.0640183687210083,
-0.005432880017906427,
-0.0117470882833004,
-0.00904144998639822,
0.005930027924478054,
0.15288777649402618,
-0.0831848531961441,
0.0567900575697422,
-0.0856810212135315,
0.08589634299278259,
-0.03673901408910751,
0.05127604305744171,
0.07515357434749603,
0.073655866086483,
-0.004514987580478191,
0.054174184799194336,
-0.28857675194740295,
0.30131953954696655,
0.001492456765845418,
0.08602875471115112,
-0.06501688808202744,
-0.03161504864692688,
0.031689245253801346,
0.08027275651693344,
0.08828148990869522,
-0.01547609455883503,
-0.02210496924817562,
-0.21683603525161743,
-0.021990971639752388,
0.03110329993069172,
0.12936024367809296,
-0.01959308423101902,
0.10411037504673004,
-0.00920826755464077,
-0.005530028138309717,
0.07391431927680969,
0.0005270353285595775,
-0.035126928240060806,
-0.09005743265151978,
-0.026381688192486763,
-0.023694824427366257,
-0.04670599848031998,
-0.058579038828611374,
-0.10567212849855423,
-0.11385250836610794,
0.11122038960456848,
0.012974703684449196,
-0.01396902184933424,
-0.12025371193885803,
0.0993705615401268,
0.0786958709359169,
-0.07541239261627197,
0.042035169899463654,
0.032132916152477264,
0.05850665643811226,
0.0318448469042778,
-0.05765334144234657,
0.11760324239730835,
-0.06003198027610779,
-0.15932632982730865,
-0.05538855865597725,
0.09217821061611176,
0.051202449947595596,
0.056274086236953735,
-0.02296561375260353,
0.016102638095617294,
-0.017540045082569122,
-0.09316810965538025,
0.05591177940368652,
-0.040732912719249725,
0.06287019699811935,
0.009451493620872498,
-0.020336156710982323,
0.0503668412566185,
-0.05715738236904144,
-0.012446556240320206,
0.1482238918542862,
0.2857440412044525,
-0.08995916694402695,
0.013553746975958347,
0.0167715884745121,
-0.06695480644702911,
-0.18865862488746643,
0.07846468687057495,
0.05792601406574249,
0.0007681610877625644,
0.08554795384407043,
-0.1675618439912796,
0.09795200824737549,
0.1038854643702507,
0.0008370618452318013,
0.11544113606214523,
-0.37093377113342285,
-0.12862847745418549,
0.08211181312799454,
0.1919938623905182,
0.07867167890071869,
-0.15510578453540802,
-0.00011176060797879472,
-0.0028610643930733204,
-0.14605604112148285,
0.09124358743429184,
-0.07626846432685852,
0.13461105525493622,
-0.01945161633193493,
0.08493716269731522,
0.016978615894913673,
-0.06133326515555382,
0.12196870893239975,
-0.0032684230245649815,
0.1416185200214386,
-0.06903201341629028,
-0.040259815752506256,
0.05433999374508858,
-0.03700830042362213,
-0.012420356273651123,
-0.04787350818514824,
0.025279436260461807,
-0.05747457966208458,
-0.012762964703142643,
-0.10448557138442993,
0.01277830358594656,
-0.03855343535542488,
-0.06800572574138641,
-0.04645482078194618,
0.04176213592290878,
0.0463670939207077,
-0.0036748098209500313,
0.1510414481163025,
-0.00968459527939558,
0.1145990714430809,
0.04770314320921898,
0.059280913323163986,
-0.06348926573991776,
-0.10843576490879059,
-0.016558002680540085,
0.007804223336279392,
0.048778384923934937,
-0.13045434653759003,
0.014708277769386768,
0.15256713330745697,
0.05049031227827072,
0.12274622172117233,
0.08678314089775085,
-0.03212818130850792,
0.03110666386783123,
0.06913309544324875,
-0.15666621923446655,
-0.11634542793035507,
0.0023739542812108994,
-0.06879975646734238,
-0.07302096486091614,
0.052515339106321335,
0.0771617591381073,
-0.07573367655277252,
0.011169357225298882,
-0.007966145873069763,
0.0071372841484844685,
-0.06754299998283386,
0.20695100724697113,
0.0545637309551239,
0.04137936234474182,
-0.10414726287126541,
0.0734362006187439,
0.018949327990412712,
-0.08668924868106842,
-0.00035997875966131687,
0.089802585542202,
-0.06940478831529617,
-0.02480441704392433,
0.08119627833366394,
0.19208359718322754,
-0.0744159147143364,
-0.021500742062926292,
-0.1492874175310135,
-0.10773063451051712,
0.06965663284063339,
0.1852685511112213,
0.0997857078909874,
-0.006852035876363516,
-0.052309583872556686,
0.047562532126903534,
-0.1166948676109314,
0.07790990173816681,
0.025948842987418175,
0.0806824117898941,
-0.1493142545223236,
0.1847759336233139,
0.011230225674808025,
0.054166775196790695,
-0.02634350396692753,
0.03196238353848457,
-0.12025861442089081,
0.04111311957240105,
-0.1111764907836914,
-0.03625573590397835,
-0.016673164442181587,
0.003873947774991393,
-0.013364416547119617,
-0.06205492094159126,
-0.06331786513328552,
0.004762549884617329,
-0.12717236578464508,
-0.02274029701948166,
0.04512079432606697,
0.022709982469677925,
-0.12714233994483948,
-0.03904116526246071,
0.027060598134994507,
-0.06429996341466904,
0.05575820803642273,
0.03596414253115654,
0.01328605879098177,
0.06547798961400986,
-0.17546631395816803,
-0.0209851935505867,
0.07102987915277481,
-0.007744347210973501,
0.06251636147499084,
-0.037272363901138306,
-0.02568928897380829,
-0.029291631653904915,
0.08728961646556854,
0.012985867448151112,
0.06178117170929909,
-0.13721366226673126,
0.002810081699863076,
-0.03241370618343353,
-0.09278498589992523,
-0.05929005518555641,
0.053110603243112564,
0.06301525235176086,
0.03664340823888779,
0.16244369745254517,
-0.08288025110960007,
0.04479287192225456,
-0.2191222459077835,
-0.015937333926558495,
0.00202799285762012,
-0.10731321573257446,
-0.08252818137407303,
-0.0730869472026825,
0.08348237723112106,
-0.07544390112161636,
0.11410060524940491,
0.03713355213403702,
0.06359545886516571,
0.031113190576434135,
-0.03362719342112541,
-0.0032631789799779654,
0.0354541577398777,
0.21424254775047302,
0.012340485118329525,
-0.034166429191827774,
0.08752661198377609,
0.08021105825901031,
0.10060592740774155,
0.13626006245613098,
0.22629684209823608,
0.15481792390346527,
-0.026662059128284454,
0.09012451022863388,
0.05106517672538757,
-0.06622818112373352,
-0.1693616807460785,
0.03694596886634827,
-0.052259378135204315,
0.09920284152030945,
-0.06186109036207199,
0.20538504421710968,
0.08569173514842987,
-0.18439432978630066,
0.06693881005048752,
-0.04419959709048271,
-0.10160865634679794,
-0.08131001889705658,
-0.03915918245911598,
-0.07065689563751221,
-0.14840343594551086,
0.025416284799575806,
-0.1031806543469429,
0.043456677347421646,
0.14711858332157135,
0.010155091062188148,
-0.012746226973831654,
0.21377602219581604,
0.03512504696846008,
0.035766225308179855,
0.059588730335235596,
0.014354579150676727,
-0.03063793107867241,
-0.09406313300132751,
-0.0600128136575222,
0.01703435555100441,
-0.030159493908286095,
0.018819203600287437,
-0.06865417957305908,
-0.07846879214048386,
0.02695588581264019,
0.004655054304748774,
-0.09344297647476196,
0.02405374124646187,
0.01946035958826542,
0.08967771381139755,
0.025753097608685493,
0.0070464168675243855,
0.014329634606838226,
-0.02849762514233589,
0.2464027851819992,
-0.09319140017032623,
-0.08165532350540161,
-0.08241334557533264,
0.21302610635757446,
0.03310370445251465,
0.0006426202016882598,
0.008420380763709545,
-0.08092903345823288,
0.008994552306830883,
0.23008455336093903,
0.17307040095329285,
-0.12951551377773285,
-0.010242275893688202,
-0.0006955375429242849,
0.001054303371347487,
-0.029452940449118614,
0.11772407591342926,
0.1198752224445343,
0.05248787999153137,
-0.11410818994045258,
-0.047448594123125076,
-0.051164235919713974,
-0.01999732106924057,
-0.02774493396282196,
0.05126718804240227,
0.06744267791509628,
0.020989039912819862,
-0.07034638524055481,
0.07672252506017685,
-0.05790045112371445,
-0.1393292248249054,
0.10645309835672379,
-0.2265191674232483,
-0.1554369032382965,
-0.00764375738799572,
0.11897280067205429,
0.00454573193565011,
0.060032062232494354,
-0.0409059040248394,
0.0016843709163367748,
0.050496120005846024,
-0.004987520165741444,
-0.07919853180646896,
-0.10609086602926254,
0.08455881476402283,
-0.11671741306781769,
0.2176869660615921,
-0.058255232870578766,
0.03406360000371933,
0.11312799155712128,
0.0628201887011528,
-0.049348779022693634,
0.0567750409245491,
0.0419490709900856,
-0.12164729833602905,
-0.003138959174975753,
0.12411979585886002,
-0.038650188595056534,
0.0540214478969574,
0.032733142375946045,
-0.13141517341136932,
0.0331757515668869,
-0.057601481676101685,
-0.04077790305018425,
-0.028646891936659813,
-0.05058128759264946,
-0.06371022015810013,
0.11471356451511383,
0.20815813541412354,
-0.009221695363521576,
0.024721385911107063,
-0.08742568641901016,
0.016561279073357582,
0.06502149254083633,
0.04875164479017258,
-0.0784115344285965,
-0.21654684841632843,
0.007773635443300009,
0.07286423444747925,
-0.041109099984169006,
-0.20264039933681488,
-0.11179840564727783,
0.03608325123786926,
-0.05323829874396324,
-0.07170888781547546,
0.09010615199804306,
0.0919748917222023,
0.05627740919589996,
-0.0549747534096241,
-0.10621871054172516,
-0.05750739946961403,
0.17133770883083344,
-0.14648397266864777,
-0.07660423219203949
] |
null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MRR_image_classification_dit_29_jan_small75-finetuned-eurosat
This model is a fine-tuned version of [microsoft/dit-large](https://huggingface.co/microsoft/dit-large) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5785
- Accuracy: 0.4756
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.8795 | 0.98 | 10 | 1.6437 | 0.3049 |
| 1.6681 | 1.95 | 20 | 1.6446 | 0.4146 |
| 1.5603 | 2.93 | 30 | 1.5785 | 0.4756 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/dit-large", "model-index": [{"name": "MRR_image_classification_dit_29_jan_small75-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.47560975609756095, "name": "Accuracy"}]}]}]} | image-classification | am-infoweb/MRR_image_classification_dit_29_jan_small75-finetuned | [
"transformers",
"tensorboard",
"safetensors",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/dit-large",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-09T13:22:56+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/dit-large #model-index #autotrain_compatible #endpoints_compatible #region-us
| MRR\_image\_classification\_dit\_29\_jan\_small75-finetuned-eurosat
===================================================================
This model is a fine-tuned version of microsoft/dit-large on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5785
* Accuracy: 0.4756
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/dit-large #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
71,
144,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/dit-large #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
-0.1493881791830063,
0.08664309233427048,
-0.0008360652718693018,
0.08833731710910797,
0.1622215360403061,
0.023625846952199936,
0.11739461869001389,
0.12349363416433334,
-0.10814955085515976,
0.07641339302062988,
0.10929476469755173,
0.08809585869312286,
0.03733423724770546,
0.13516364991664886,
-0.0339677594602108,
-0.3042217195034027,
-0.00024850384215824306,
0.017163021489977837,
-0.16270363330841064,
0.11738637834787369,
0.09701336920261383,
-0.1375306099653244,
0.07948319613933563,
0.02620013989508152,
-0.18793649971485138,
-0.008419252000749111,
-0.01036869641393423,
-0.05534951016306877,
0.1190539225935936,
0.038199637085199356,
0.12075649201869965,
0.02831948548555374,
0.12043095380067825,
-0.1694788932800293,
0.006316637620329857,
0.07224477082490921,
0.024063020944595337,
0.09932762384414673,
0.08374731242656708,
-0.0016657240921631455,
0.09723203629255295,
-0.07852476835250854,
0.07748380303382874,
0.047902293503284454,
-0.10814741253852844,
-0.288629412651062,
-0.09161686152219772,
0.07636884599924088,
0.13058674335479736,
0.0737413689494133,
-0.011521784588694572,
0.10623425245285034,
-0.06837400794029236,
0.09419871121644974,
0.23705318570137024,
-0.26064953207969666,
-0.09570923447608948,
0.044649574905633926,
0.05489829182624817,
0.03459731861948967,
-0.12810276448726654,
-0.005992026999592781,
0.06204698979854584,
0.022656932473182678,
0.10369139909744263,
0.011802298948168755,
0.016041794791817665,
0.003443978726863861,
-0.14594347774982452,
-0.029364274814724922,
0.12552399933338165,
0.08208711445331573,
-0.04833770915865898,
-0.0794048085808754,
-0.0379922091960907,
-0.21245510876178741,
-0.04345021769404411,
-0.0032515942584723234,
0.04154210537672043,
-0.07626129686832428,
-0.1416161209344864,
0.0011185379698872566,
-0.0979856327176094,
-0.10483288019895554,
0.011621021665632725,
0.1714356541633606,
0.04334394633769989,
-0.0006790922489017248,
-0.010626699775457382,
0.138785719871521,
0.03945573791861534,
-0.15508650243282318,
-0.005751829594373703,
0.014947179704904556,
-0.06863612681627274,
-0.03508424013853073,
-0.03799708932638168,
-0.02222180739045143,
-0.007953007705509663,
0.15338820219039917,
-0.07103482633829117,
0.04259511083364487,
0.05071736127138138,
0.030673904344439507,
-0.09200643748044968,
0.20515672862529755,
-0.08217965066432953,
-0.043788909912109375,
-0.03137148544192314,
0.10992243140935898,
0.0060051437467336655,
-0.0017929975874722004,
-0.09301967173814774,
0.026138894259929657,
0.1047661155462265,
0.033078886568546295,
-0.034440431743860245,
0.045101847499608994,
-0.04902790114283562,
-0.029927467927336693,
0.07645708322525024,
-0.07625094056129456,
0.03495590016245842,
0.007142499554902315,
-0.10228493064641953,
-0.024429259821772575,
0.027438674122095108,
0.014791490510106087,
0.039060261100530624,
0.1458495706319809,
-0.1111089214682579,
0.0008701291517354548,
-0.10930190235376358,
-0.10749131441116333,
0.010111946612596512,
-0.06839590519666672,
0.0025680873077362776,
-0.08032745122909546,
-0.13814550638198853,
-0.03308446332812309,
0.04856117442250252,
-0.04930160567164421,
-0.04614778235554695,
-0.03002089075744152,
-0.10309887677431107,
0.02966426871716976,
-0.0018870825879275799,
0.14641538262367249,
-0.04788332059979439,
0.11605294048786163,
0.06668514013290405,
0.06364186108112335,
0.006433592643588781,
0.04194704070687294,
-0.06969717890024185,
0.050124526023864746,
-0.20582515001296997,
0.04228236898779869,
-0.08090969175100327,
0.0861322358250618,
-0.0979773998260498,
-0.13016808032989502,
0.01252177357673645,
-0.0011009378358721733,
0.07580374926328659,
0.11193683743476868,
-0.15106558799743652,
-0.09190312772989273,
0.1434469372034073,
-0.08758042007684708,
-0.13826347887516022,
0.10450539737939835,
-0.01798894628882408,
0.011515968479216099,
0.03228534385561943,
0.10299713164567947,
0.06977110356092453,
-0.10733065009117126,
-0.027145886793732643,
-0.028112782165408134,
0.1002759113907814,
-0.004979421384632587,
0.09374087303876877,
-0.0045412336476147175,
0.04326456040143967,
0.015791185200214386,
-0.06166277080774307,
0.04301903024315834,
-0.1186104491353035,
-0.08196860551834106,
-0.032305993139743805,
-0.10137715935707092,
0.04950442537665367,
0.07603371888399124,
0.06096765398979187,
-0.09297803044319153,
-0.13060900568962097,
0.08473499119281769,
0.11247441917657852,
-0.06653676182031631,
0.0014139077393338084,
-0.07597155123949051,
0.074675053358078,
-0.06676751375198364,
-0.030316678807139397,
-0.16859519481658936,
-0.09648234397172928,
0.01045950222760439,
-0.016924628987908363,
-0.007472038269042969,
-0.007906507700681686,
0.0828094407916069,
0.0904298722743988,
-0.07928849011659622,
-0.06499471515417099,
-0.07754362374544144,
-0.011461750604212284,
-0.10147573053836823,
-0.23280905187129974,
-0.09921067953109741,
-0.0200838390737772,
0.1815343201160431,
-0.2434293031692505,
0.034640517085790634,
0.022328220307826996,
0.14686763286590576,
0.0632997676730156,
-0.037414100021123886,
-0.027571648359298706,
0.056264717131853104,
-0.03952077403664589,
-0.08038262277841568,
0.03077194280922413,
-0.0008703453931957483,
-0.10349272191524506,
-0.04208214953541756,
-0.11204229295253754,
0.1524588167667389,
0.11659780144691467,
-0.025935892015695572,
-0.11294816434383392,
-0.02591208927333355,
-0.09772659838199615,
-0.04550297558307648,
-0.03501957654953003,
-0.01589595153927803,
0.08414484560489655,
0.0255383662879467,
0.13871720433235168,
-0.07685395330190659,
-0.06553683429956436,
0.03900950029492378,
-0.01133155170828104,
-0.013144541531801224,
0.10335124284029007,
0.09396689385175705,
-0.07770488411188126,
0.12657895684242249,
0.1089552566409111,
-0.08585667610168457,
0.1527380794286728,
-0.050439734011888504,
-0.09532488137483597,
-0.01583162136375904,
0.004554322455078363,
0.03105306625366211,
0.1742701679468155,
-0.09388449043035507,
-0.013154068030416965,
0.019330443814396858,
0.004651554860174656,
0.02055293880403042,
-0.21130356192588806,
-0.016156328842043877,
0.031177202239632607,
-0.03554818779230118,
-0.0011555003002285957,
-0.021739456802606583,
0.0014447327703237534,
0.09886842221021652,
0.013409835286438465,
-0.0389900878071785,
0.012503904290497303,
0.006517038214951754,
-0.06605639308691025,
0.21239762008190155,
-0.06473024189472198,
-0.13622990250587463,
-0.1589428335428238,
0.007110981270670891,
-0.06166783720254898,
-0.002282271161675453,
0.02782873995602131,
-0.10810821503400803,
-0.033487316220998764,
-0.05675828456878662,
0.04363453760743141,
0.006830713711678982,
0.034239962697029114,
0.0036061075516045094,
0.02755533531308174,
0.10018331557512283,
-0.11428866535425186,
0.027224544435739517,
-0.03112207166850567,
-0.0696340873837471,
0.027915850281715393,
0.037809230387210846,
0.11663766205310822,
0.15019552409648895,
0.001545618986710906,
0.01825803518295288,
-0.02959861420094967,
0.19508355855941772,
-0.1122644916176796,
-0.01755554974079132,
0.12556155025959015,
0.00042490509804338217,
0.04279228672385216,
0.12217355519533157,
0.06557139754295349,
-0.09398306906223297,
0.02322356216609478,
0.06883552670478821,
-0.021831687539815903,
-0.20251020789146423,
-0.01934814266860485,
-0.0271778404712677,
0.004777534864842892,
0.0979660153388977,
0.038877636194229126,
0.027431033551692963,
0.06671153753995895,
-0.0179289523512125,
0.035351842641830444,
-0.03987801820039749,
0.08218622952699661,
0.037027888000011444,
0.042733512818813324,
0.1408405900001526,
-0.0305985938757658,
-0.04205823689699173,
0.033279143273830414,
-0.01362881064414978,
0.22096316516399384,
-0.03593209758400917,
0.11082467436790466,
0.04627243056893349,
0.15412966907024384,
-0.002645245986059308,
0.07699894905090332,
0.007606289815157652,
-0.05615082383155823,
0.019631147384643555,
-0.052543818950653076,
-0.00832886528223753,
0.04321857541799545,
0.013533234596252441,
0.08962385356426239,
-0.14144909381866455,
0.02925555408000946,
0.05587386339902878,
0.3017377555370331,
0.0761246383190155,
-0.36031574010849,
-0.1331409215927124,
0.00011171538062626496,
-0.04035451263189316,
-0.04454989731311798,
0.0007048051338642836,
0.13477392494678497,
-0.09849583357572556,
0.06142584979534149,
-0.0960325300693512,
0.085564985871315,
-0.031694646924734116,
0.004924722481518984,
0.11777753382921219,
0.1017465889453888,
-0.013727225363254547,
0.059502311050891876,
-0.22182628512382507,
0.2815926671028137,
0.00624873349443078,
0.07282272726297379,
-0.04183041676878929,
0.026551740244030952,
0.038621995598077774,
0.059625111520290375,
0.06638424843549728,
-0.024232517927885056,
-0.0347190760076046,
-0.2259298861026764,
-0.07481959462165833,
0.017343344166874886,
0.13493138551712036,
-0.09132847934961319,
0.13385222852230072,
-0.02635190449655056,
-0.02923070266842842,
0.055384404957294464,
-0.05182071030139923,
-0.08877874910831451,
-0.08100009709596634,
0.0081497086212039,
-0.037166837602853775,
0.03457430750131607,
-0.10918036103248596,
-0.12857221066951752,
-0.08315269649028778,
0.17224442958831787,
-0.06193196401000023,
-0.035616740584373474,
-0.14821109175682068,
0.12517234683036804,
0.14385893940925598,
-0.06459662318229675,
0.056609027087688446,
-0.00007835804717615247,
0.14359979331493378,
0.03271905705332756,
-0.020390193909406662,
0.11430102586746216,
-0.08705100417137146,
-0.25657474994659424,
-0.056761305779218674,
0.1305193454027176,
0.03218233585357666,
0.05031505599617958,
-0.025446107611060143,
0.030056385323405266,
-0.0034710976760834455,
-0.08488661050796509,
0.051834385842084885,
-0.02146901935338974,
0.049409717321395874,
0.02939889393746853,
-0.0372871458530426,
0.027427423745393753,
-0.03978606313467026,
-0.03181585669517517,
0.1018238440155983,
0.2858773469924927,
-0.10087044537067413,
-0.0313502699136734,
0.026712600141763687,
-0.026741109788417816,
-0.16484281420707703,
0.08127126097679138,
0.12341306358575821,
0.03160562366247177,
0.032697729766368866,
-0.16754236817359924,
0.10654245316982269,
0.11088038235902786,
-0.043228380382061005,
0.1381831169128418,
-0.2655330002307892,
-0.14087805151939392,
0.08190692216157913,
0.13813959062099457,
-0.03269490972161293,
-0.17049477994441986,
-0.04878292232751846,
-0.01530865952372551,
-0.12762366235256195,
0.09270886331796646,
-0.04939171299338341,
0.11073416471481323,
-0.008773187175393105,
0.03692332282662392,
0.019452273845672607,
-0.061118368059396744,
0.15019024908542633,
-0.01577540673315525,
0.09512708336114883,
-0.01994716189801693,
-0.013611321337521076,
0.07137500494718552,
-0.06793078035116196,
0.013108018785715103,
-0.054622337222099304,
0.042380191385746,
-0.08862588554620743,
-0.019107656553387642,
-0.09743717312812805,
0.04456968232989311,
-0.06263257563114166,
-0.05829036608338356,
-0.026724878698587418,
0.062121279537677765,
-0.002069575944915414,
-0.01623118482530117,
0.16197042167186737,
-0.005870631895959377,
0.1783096343278885,
0.10378317534923553,
0.07339253276586533,
-0.02842508628964424,
-0.06942673772573471,
-0.01026162039488554,
-0.016350887715816498,
0.06966733187437057,
-0.14680317044258118,
0.02269684337079525,
0.13449639081954956,
0.05715819448232651,
0.13119614124298096,
0.06879624724388123,
-0.05352131277322769,
0.022781740874052048,
0.10243386775255203,
-0.10795928537845612,
-0.09557148069143295,
-0.028101064264774323,
-0.02038438804447651,
-0.16426029801368713,
0.07873038947582245,
0.10426273196935654,
-0.06506476551294327,
-0.003311382606625557,
0.005293269641697407,
0.004110798705369234,
-0.030034717172384262,
0.21677176654338837,
0.0792759582400322,
0.0886630192399025,
-0.08975684642791748,
0.07214105874300003,
0.030093731358647346,
-0.11785157769918442,
-0.014137486927211285,
0.0693824514746666,
-0.060742657631635666,
-0.008852681145071983,
0.020410694181919098,
0.1008235365152359,
-0.06159098446369171,
-0.04228926822543144,
-0.1635405570268631,
-0.11723845452070236,
0.04992937669157982,
0.1263585388660431,
0.07521893829107285,
0.028269508853554726,
-0.006901830900460482,
0.061514683067798615,
-0.13496407866477966,
0.12664081156253815,
0.06259102374315262,
0.10676013678312302,
-0.1885576695203781,
0.1643122136592865,
0.011625014245510101,
0.030626516789197922,
-0.016649244353175163,
0.031457941979169846,
-0.11624275892972946,
-0.008802102878689766,
-0.1236080527305603,
-0.05325011536478996,
-0.04161854088306427,
-0.004678579047322273,
0.0030559829901903868,
-0.05877513810992241,
-0.06409241259098053,
0.015780620276927948,
-0.116620272397995,
-0.05210567265748978,
0.027439920231699944,
0.03974444419145584,
-0.11422476172447205,
-0.008350839838385582,
0.03677913919091225,
-0.10679273307323456,
0.0898931697010994,
0.028060702607035637,
0.05111946538090706,
0.04491042718291283,
-0.07450959086418152,
0.021049346774816513,
0.051785945892333984,
-0.023112060502171516,
0.06332585960626602,
-0.09434159100055695,
-0.006359601393342018,
-0.04841604456305504,
0.05415591225028038,
0.0011285226792097092,
0.032715216279029846,
-0.14577868580818176,
-0.006854156032204628,
-0.025897234678268433,
-0.04908560961484909,
-0.056487537920475006,
0.04502736032009125,
0.06244644150137901,
0.01630384288728237,
0.17339453101158142,
-0.07379639893770218,
0.02530962973833084,
-0.2425510138273239,
-0.00552392890676856,
-0.025222910568118095,
-0.08402955532073975,
-0.0898657888174057,
-0.018638497218489647,
0.07918556779623032,
-0.06774011254310608,
0.09338890016078949,
-0.022618167102336884,
0.09474019706249237,
0.042930640280246735,
-0.05056655406951904,
0.041285283863544464,
0.04772167280316353,
0.1808660924434662,
0.03194436803460121,
-0.00467871967703104,
0.059097856283187866,
0.04498109966516495,
0.0775497555732727,
0.06096874922513962,
0.2019040733575821,
0.11356581002473831,
-0.05741947144269943,
0.1019238606095314,
0.07196108251810074,
-0.09988534450531006,
-0.16934077441692352,
0.06690335273742676,
-0.04984629154205322,
0.11668067425489426,
-0.029272887855768204,
0.1450277715921402,
0.1234624981880188,
-0.18759362399578094,
0.015439718030393124,
-0.038767922669649124,
-0.07684154063463211,
-0.08703495562076569,
-0.0006800643168389797,
-0.07557376474142075,
-0.18528933823108673,
0.021506886929273605,
-0.12089110910892487,
0.003957297652959824,
0.09195811301469803,
0.01918269507586956,
0.007914385758340359,
0.19186539947986603,
0.025823114439845085,
0.036807745695114136,
0.07773738354444504,
0.029493216425180435,
-0.015871083363890648,
-0.04960612207651138,
-0.08255832642316818,
0.011381412856280804,
-0.015729913488030434,
0.034243952482938766,
-0.08373904973268509,
-0.1122484803199768,
0.06250117719173431,
0.04518696665763855,
-0.11190417408943176,
0.021304093301296234,
0.01146390289068222,
0.058468110859394073,
0.022541126236319542,
-0.007489602547138929,
0.03156065568327904,
-0.039735957980155945,
0.24663971364498138,
-0.11520127207040787,
-0.03833157941699028,
-0.14471757411956787,
0.2606668472290039,
0.0034532607533037663,
-0.01847195439040661,
0.03188890218734741,
-0.11151068657636642,
-0.00937199778854847,
0.15815651416778564,
0.15960201621055603,
-0.045159097760915756,
-0.022076280787587166,
0.02250293642282486,
-0.021379346027970314,
-0.06972883641719818,
0.08074526488780975,
0.09815473109483719,
0.08629745244979858,
-0.07842697948217392,
-0.05485253781080246,
-0.031654588878154755,
-0.05653132498264313,
0.003319320734590292,
0.06687034666538239,
0.03626703470945358,
0.009478696621954441,
-0.05599284917116165,
0.08316338062286377,
-0.03130912780761719,
-0.1385982483625412,
0.102695032954216,
-0.19130882620811462,
-0.18970853090286255,
-0.03248082101345062,
0.07332029193639755,
-0.000010322896741854493,
0.07697363942861557,
-0.005621781572699547,
-0.03525208681821823,
0.09777625650167465,
-0.013474170118570328,
-0.05225815996527672,
-0.12814396619796753,
0.0903390571475029,
-0.07006596773862839,
0.23250071704387665,
-0.061285894364118576,
0.019345268607139587,
0.12815679609775543,
0.02437979355454445,
-0.09523804485797882,
0.016508813947439194,
0.06767643243074417,
-0.12962624430656433,
0.031269222497940063,
0.1739276945590973,
-0.03297930955886841,
0.11632836610078812,
0.03033033385872841,
-0.175114706158638,
0.01607961766421795,
-0.0876830667257309,
-0.06299230456352234,
-0.07425466179847717,
-0.00827897060662508,
-0.043945975601673126,
0.13133718073368073,
0.2460067719221115,
-0.05036098510026932,
-0.006807561032474041,
-0.059304673224687576,
0.042793337255716324,
0.07609421759843826,
0.09235113859176636,
-0.028719959780573845,
-0.286332368850708,
0.03673582524061203,
0.0225208792835474,
-0.03245670348405838,
-0.25646325945854187,
-0.08329503238201141,
0.04969839006662369,
-0.06375298649072647,
-0.061419352889060974,
0.0931764543056488,
0.0929124727845192,
0.057817354798316956,
-0.052662093192338943,
-0.053468938916921616,
-0.0755418911576271,
0.17069865763187408,
-0.18252971768379211,
-0.07860970497131348
] |
null | null | diffusers | # Klara Geywitz
<Gallery />
## Model description
just a LORA of Klara Geywitz
## Trigger words
You should use `kge` to trigger the image generation.
You should use `bre` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/gz8iz/Klara_Geywitz/tree/main) them in the Files & versions tab.
| {"tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora"], "widget": [{"text": "kge bre in front of a green background, set very highly detailed, cinematic, dynamic light, ambient, thought, beautiful composition, intricate, elegant, rich deep colors, perfect sharp focus, professional, winning, open color, best, fair, artistic, innocent, pure, aesthetic, cool, confident, passionate, vibrant, illuminated, pretty, focused, extremely detail", "parameters": {"negative_prompt": "unrealistic, saturated, high contrast, big nose, painting, drawing, sketch, cartoon, anime, manga, render, CG, 3d, watermark, signature, label"}, "output": {"url": "images/2024-02-09_14-26-38_8966.png"}}], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "kge, bre"} | text-to-image | gz8iz/Klara_Geywitz | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] | 2024-02-09T13:28:11+00:00 | [] | [] | TAGS
#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #region-us
| # Klara Geywitz
<Gallery />
## Model description
just a LORA of Klara Geywitz
## Trigger words
You should use 'kge' to trigger the image generation.
You should use 'bre' to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
| [
"# Klara Geywitz\n\n<Gallery />",
"## Model description \n\njust a LORA of Klara Geywitz",
"## Trigger words\n\nYou should use 'kge' to trigger the image generation.\n\nYou should use 'bre' to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
"TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #region-us \n",
"# Klara Geywitz\n\n<Gallery />",
"## Model description \n\njust a LORA of Klara Geywitz",
"## Trigger words\n\nYou should use 'kge' to trigger the image generation.\n\nYou should use 'bre' to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
56,
11,
13,
29,
28
] | [
"passage: TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #region-us \n# Klara Geywitz\n\n<Gallery />## Model description \n\njust a LORA of Klara Geywitz## Trigger words\n\nYou should use 'kge' to trigger the image generation.\n\nYou should use 'bre' to trigger the image generation.## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
-0.10242660343647003,
-0.07081566005945206,
0.0007135311607271433,
0.00984695740044117,
0.12898920476436615,
0.05579351633787155,
0.10839357227087021,
0.07339206337928772,
0.2064797431230545,
0.06914462149143219,
0.09714668244123459,
-0.044462621212005615,
0.04120830073952675,
0.23997057974338531,
-0.0558222271502018,
-0.2970646023750305,
-0.008541439659893513,
0.0055859750136733055,
-0.0029769432730972767,
0.017006728798151016,
0.015736522153019905,
-0.11172959208488464,
0.08784748613834381,
-0.03322271257638931,
-0.02813487872481346,
0.03871101513504982,
0.03706265240907669,
-0.030541308224201202,
0.03351864591240883,
0.02358705922961235,
-0.006273989100009203,
0.12928396463394165,
0.05614533647894859,
-0.10404613614082336,
0.0728507861495018,
0.03647889196872711,
-0.01750844717025757,
0.003988421522080898,
-0.06733602285385132,
-0.08324146270751953,
0.17183683812618256,
-0.246539905667305,
-0.11020851135253906,
-0.01825723983347416,
-0.009901865385472775,
-0.12545858323574066,
0.00011678619193844497,
0.005765801761299372,
0.062397103756666183,
-0.07839030027389526,
-0.01453499123454094,
-0.05653313174843788,
0.017043493688106537,
0.045114126056432724,
0.28540828824043274,
-0.2111998051404953,
-0.008236878551542759,
0.22680902481079102,
-0.016610387712717056,
0.11989358067512512,
-0.06391983479261398,
0.19328074157238007,
0.06576986610889435,
-0.12833622097969055,
0.07026854157447815,
-0.07662955671548843,
0.1067451685667038,
-0.018504753708839417,
-0.08411625772714615,
0.06214376911520958,
0.27563977241516113,
0.028384873643517494,
0.004811926744878292,
-0.08267435431480408,
-0.06706018000841141,
0.17165012657642365,
-0.08306486904621124,
0.041522108018398285,
0.0033018970862030983,
-0.09903248399496078,
-0.05907826125621796,
-0.17109905183315277,
-0.0547567754983902,
-0.1488959789276123,
-0.02528081089258194,
0.29296138882637024,
0.07624774426221848,
0.04595980420708656,
0.03519812971353531,
0.1381639689207077,
-0.26691734790802,
-0.14821216464042664,
-0.0015666424296796322,
-0.05735941231250763,
0.03244602680206299,
0.10194520652294159,
-0.03220786899328232,
-0.09677648544311523,
0.13136576116085052,
-0.05640562251210213,
-0.0034427703358232975,
-0.00041060190415009856,
0.08360147476196289,
0.0851348340511322,
0.057400889694690704,
0.02797476388514042,
-0.06856755912303925,
-0.1061524897813797,
0.12524795532226562,
0.055073533207178116,
0.08198089152574539,
-0.04661859571933746,
-0.1063804030418396,
-0.10790546983480453,
-0.03529532253742218,
0.01913728564977646,
-0.06326603889465332,
0.07614408433437347,
-0.0010690540075302124,
-0.02133777365088463,
0.04013579338788986,
-0.04224330931901932,
-0.049534887075424194,
0.0009416412212885916,
-0.07648279517889023,
0.08576233685016632,
0.06567537039518356,
0.022790322080254555,
0.08904869109392166,
0.053387027233839035,
-0.07193578779697418,
-0.046807751059532166,
-0.05377309396862984,
-0.050668712705373764,
-0.08412749320268631,
-0.19170734286308289,
0.06766918301582336,
-0.10875651240348816,
-0.2806139290332794,
0.041211120784282684,
0.04759638011455536,
-0.01857418566942215,
0.023879824206233025,
-0.04797358065843582,
0.006597509142011404,
0.0174795500934124,
0.006905942223966122,
0.011437914334237576,
-0.06521741300821304,
0.07636026293039322,
-0.04756110534071922,
0.14804671704769135,
-0.10687491297721863,
0.02968631498515606,
-0.05867001786828041,
0.07576927542686462,
-0.28500044345855713,
0.06816122680902481,
-0.11563622951507568,
-0.08588845282793045,
-0.05424436926841736,
-0.031559351831674576,
-0.028725292533636093,
0.013716980814933777,
0.057125017046928406,
0.12955009937286377,
-0.08174318820238113,
-0.05619635805487633,
0.06064114347100258,
-0.13348859548568726,
-0.04456641897559166,
-0.021703405305743217,
0.000216582790017128,
0.08294655382633209,
0.11909977346658707,
0.26339438557624817,
0.11908145993947983,
-0.16012082993984222,
0.031595587730407715,
0.08308558166027069,
-0.05379291996359825,
-0.022959843277931213,
0.05459721386432648,
0.08279330283403397,
-0.14160333573818207,
0.09087050706148148,
-0.2945307195186615,
0.11706679314374924,
-0.05687062442302704,
0.01815146766602993,
-0.018145468086004257,
-0.13567118346691132,
0.17962336540222168,
0.010350139811635017,
0.057765133678913116,
0.03839028626680374,
-0.0007358636357821524,
0.10660334676504135,
0.11433777213096619,
-0.11344560235738754,
-0.01154362317174673,
0.047441575676202774,
0.17535512149333954,
-0.1294383406639099,
0.015299854800105095,
0.019630171358585358,
-0.15595099329948425,
0.020524751394987106,
0.07019667327404022,
0.06845148652791977,
0.05686803534626961,
0.05710643529891968,
0.07818564772605896,
-0.11051115393638611,
-0.02283693663775921,
0.030209491029381752,
0.00788305327296257,
-0.008876225911080837,
-0.14071470499038696,
0.03334389254450798,
-0.007914572022855282,
0.0054671247489750385,
-0.11168825626373291,
0.022691214457154274,
0.05355424806475639,
-0.006758621893823147,
-0.006437626201659441,
0.03706199675798416,
0.02947378158569336,
-0.06615051627159119,
-0.0005554301314987242,
-0.0028249784372746944,
0.06118171662092209,
-0.009298686869442463,
-0.057482779026031494,
0.15678948163986206,
-0.04587115719914436,
-0.018625253811478615,
0.09401357918977737,
0.20777443051338196,
0.012038406915962696,
-0.20943205058574677,
0.0029066361021250486,
-0.022426271811127663,
-0.0013744723983108997,
-0.04753689095377922,
0.06155708059668541,
0.005508797708898783,
0.05213727056980133,
-0.07892146706581116,
0.00880822166800499,
0.06415966153144836,
-0.0346127487719059,
0.00456930510699749,
0.08930344134569168,
0.10366940498352051,
-0.03650176525115967,
0.002362338826060295,
0.093961700797081,
-0.14306403696537018,
0.24420616030693054,
-0.02126546949148178,
-0.09318949282169342,
0.018605073913931847,
-0.0020217150449752808,
-0.030902108177542686,
0.2831259071826935,
-0.039081767201423645,
-0.058583974838256836,
0.007619738578796387,
-0.09258144348859787,
0.02093653753399849,
-0.07045367360115051,
-0.028833501040935516,
-0.04457544535398483,
-0.059680741280317307,
0.08938625454902649,
0.15984013676643372,
-0.09011183679103851,
0.07061032205820084,
-0.04455692321062088,
-0.027964703738689423,
-0.052813343703746796,
-0.030620183795690536,
0.01736328937113285,
0.15009495615959167,
0.008160930126905441,
0.03172352537512779,
-0.021251318976283073,
0.036287128925323486,
-0.14679734408855438,
-0.03244330734014511,
0.020414216443896294,
-0.021144727244973183,
-0.01851344108581543,
-0.05430099740624428,
0.14599871635437012,
0.06989642977714539,
0.022420134395360947,
-0.0912109985947609,
-0.10543779283761978,
-0.11650577187538147,
-0.08277327567338943,
-0.07026556134223938,
-0.10270186513662338,
-0.028142593801021576,
0.06773655861616135,
-0.0014912278857082129,
0.13226094841957092,
0.13142715394496918,
-0.047021590173244476,
0.04200148954987526,
-0.03603942319750786,
0.05418684706091881,
-0.04025445878505707,
0.13888156414031982,
0.10716131329536438,
0.07143817842006683,
0.07491179555654526,
0.07450425624847412,
0.0131024569272995,
-0.017726847901940346,
0.057503826916217804,
0.05766303092241287,
-0.10737454891204834,
-0.013144239783287048,
-0.12007132172584534,
-0.03614736348390579,
-0.008935557678341866,
0.01142913568764925,
-0.013431031256914139,
-0.025000281631946564,
0.20123736560344696,
0.009230845607817173,
-0.027315877377986908,
0.05029318109154701,
0.02079048752784729,
0.055077895522117615,
-0.025437286123633385,
0.05082584545016289,
-0.006261922884732485,
-0.0033838748931884766,
0.10157622396945953,
0.07851263880729675,
0.29307013750076294,
-0.17774945497512817,
-0.00938898604363203,
0.12578441202640533,
0.04009999334812164,
0.08655387163162231,
0.05690731108188629,
-0.12069874256849289,
-0.008860728703439236,
-0.026839306578040123,
-0.10186530649662018,
0.1500236690044403,
0.07415470480918884,
-0.05043669044971466,
-0.010535907000303268,
-0.05293319374322891,
0.011813544668257236,
0.017437538132071495,
0.03398416191339493,
0.09382803738117218,
-0.1842779815196991,
0.021719759330153465,
0.09240953624248505,
0.16536246240139008,
-0.04333449900150299,
0.03772517293691635,
0.11273077130317688,
-0.06363562494516373,
0.04079568013548851,
-0.0793164074420929,
0.011902913451194763,
-0.020674381405115128,
-0.0928926169872284,
-0.13178770244121552,
0.20583182573318481,
-0.0655628964304924,
0.049194011837244034,
-0.06062254682183266,
0.12800613045692444,
-0.02562491036951542,
0.011059468612074852,
-0.03851521015167236,
-0.09712346643209457,
0.12515954673290253,
0.09549099951982498,
0.12498973309993744,
0.027016909793019295,
0.09778629243373871,
-0.10144348442554474,
-0.08399207144975662,
0.09153678268194199,
0.038633838295936584,
-0.14145229756832123,
0.04133099690079689,
0.007677196525037289,
-0.03032325953245163,
-0.0016164160333573818,
0.030817916616797447,
-0.21506792306900024,
-0.02530423179268837,
-0.06638485938310623,
0.10418681055307388,
-0.0009505341295152903,
-0.034896548837423325,
-0.08538857847452164,
-0.13924364745616913,
0.053968705236911774,
0.15109264850616455,
-0.16504736244678497,
-0.0290985070168972,
-0.009938647970557213,
0.10696759819984436,
-0.11493887007236481,
0.07052025198936462,
-0.038285572081804276,
0.04451407492160797,
-0.07931695878505707,
-0.1657077819108963,
0.028353730216622353,
-0.010317917913198471,
-0.10866806656122208,
0.017716770991683006,
0.06938431411981583,
0.0659727156162262,
-0.07442151755094528,
0.017432190477848053,
0.041118741035461426,
0.001296526868827641,
-0.1321505606174469,
0.06821831315755844,
0.08386707305908203,
0.05018656700849533,
0.0712052658200264,
-0.0647435411810875,
-0.025134751573204994,
-0.04871106147766113,
0.045664429664611816,
0.09562703222036362,
0.2462061494588852,
-0.08649694174528122,
0.04083705693483353,
0.15316453576087952,
0.0013079537311568856,
-0.3030935823917389,
0.010602863505482674,
-0.01235198974609375,
0.013733022846281528,
0.0024043850135058165,
-0.11304952204227448,
0.12513786554336548,
0.07694694399833679,
-0.07274831086397171,
0.25518596172332764,
-0.3223539888858795,
-0.1805729866027832,
0.1117236316204071,
0.2502921521663666,
0.40855106711387634,
-0.175228551030159,
-0.04347725585103035,
-0.030368464067578316,
0.06519685685634613,
0.05301118269562721,
-0.23184983432292938,
0.10169822722673416,
-0.06775006651878357,
-0.0766712874174118,
-0.007320857606828213,
-0.0271547120064497,
0.12585750222206116,
-0.00413540517911315,
0.05415075272321701,
-0.07566799968481064,
-0.10504242777824402,
0.22073310613632202,
-0.11310982704162598,
0.10453978925943375,
-0.25617772340774536,
0.1273440271615982,
-0.05829053372144699,
-0.0446503721177578,
-0.07519818097352982,
0.16339565813541412,
0.03413787856698036,
-0.1283121407032013,
-0.08900698274374008,
0.034797657281160355,
-0.042607977986335754,
0.027236400172114372,
0.014759482815861702,
-0.033959340304136276,
-0.08545288443565369,
-0.000983609352260828,
-0.08136391639709473,
0.10687952488660812,
-0.037015970796346664,
-0.019123468548059464,
-0.0547969676554203,
0.11906251311302185,
-0.18512092530727386,
-0.07058493793010712,
0.08525262027978897,
0.10144069790840149,
0.013483135960996151,
0.007554560899734497,
-0.05199141055345535,
0.1422390341758728,
0.07814701646566391,
-0.12345317751169205,
-0.15251760184764862,
-0.11273809522390366,
-0.10918376594781876,
0.06681545078754425,
0.1115974560379982,
0.11622285842895508,
-0.05925491079688072,
0.008063719607889652,
-0.026706166565418243,
-0.017890412360429764,
0.003679735353216529,
0.05189206451177597,
0.13559922575950623,
0.005090297199785709,
-0.07545306533575058,
0.0903669074177742,
-0.05939982458949089,
0.05781838670372963,
-0.030270911753177643,
-0.01573384553194046,
-0.1181306466460228,
-0.05735306441783905,
-0.004277471918612719,
0.13904860615730286,
-0.06463940441608429,
0.020448602735996246,
-0.03157471865415573,
-0.06626907736063004,
-0.06801865994930267,
0.1343364268541336,
0.055457666516304016,
-0.06032520532608032,
0.002027899958193302,
0.007858101278543472,
-0.014249241910874844,
0.16909535229206085,
0.08206731826066971,
0.08659754693508148,
-0.17884595692157745,
-0.17016762495040894,
0.006133753340691328,
0.09912889450788498,
-0.11169184744358063,
-0.07586979120969772,
-0.06909079104661942,
-0.0195624902844429,
-0.060945648699998856,
0.08582282811403275,
-0.07628961652517319,
-0.028531305491924286,
-0.09177093207836151,
-0.08030292391777039,
-0.005776423029601574,
0.06921502947807312,
-0.026975305750966072,
0.022747213020920753,
-0.005445872433483601,
0.052227310836315155,
-0.05124553665518761,
-0.09628293663263321,
0.034998442977666855,
-0.05119495093822479,
0.09817630052566528,
0.019748413935303688,
-0.006284133531153202,
0.08270037174224854,
-0.240293949842453,
0.0593537874519825,
0.062271926552057266,
-0.01213029958307743,
-0.06946320831775665,
0.02016022801399231,
0.04637547954916954,
-0.07988711446523666,
0.09442897886037827,
-0.03503996133804321,
-0.06084921583533287,
-0.06876463443040848,
0.09205605089664459,
-0.032531872391700745,
0.01091534923762083,
0.0028715981170535088,
0.00891873612999916,
0.04658094421029091,
0.14601677656173706,
0.03029008023440838,
-0.09079224616289139,
-0.0031509026885032654,
-0.05174499750137329,
0.047844577580690384,
0.0296802818775177,
-0.121002197265625,
-0.027898035943508148,
-0.0684475377202034,
0.015520638786256313,
-0.018294529989361763,
0.17350119352340698,
0.04663059487938881,
-0.10750511288642883,
-0.0664290115237236,
0.0816611498594284,
0.13500656187534332,
0.05088241770863533,
0.36946865916252136,
0.02538134530186653,
0.06470004469156265,
-0.14595907926559448,
0.15287666022777557,
0.04335983470082283,
-0.06383979320526123,
0.08798147737979889,
0.03916579857468605,
-0.05758833885192871,
0.022528335452079773,
0.04916037619113922,
0.0017055966891348362,
-0.011670315638184547,
0.05403806269168854,
-0.10899415612220764,
0.015786094591021538,
-0.06460799276828766,
0.08797399699687958,
0.1571550965309143,
-0.13268905878067017,
-0.011366930790245533,
0.11767785996198654,
-0.06467131525278091,
-0.0879376232624054,
-0.311797559261322,
-0.09225986152887344,
-0.2136194258928299,
0.08157274127006531,
-0.04910404980182648,
-0.04196988791227341,
0.07513930648565292,
-0.005603432655334473,
0.0395609587430954,
0.21250465512275696,
0.01880360022187233,
-0.07585778832435608,
0.15185080468654633,
-0.00986762810498476,
-0.11068389564752579,
0.06273218989372253,
-0.03529820591211319,
0.0772223174571991,
-0.1049102246761322,
-0.05558614060282707,
0.03184231370687485,
-0.03819504752755165,
0.0011824470711871982,
0.0452372208237648,
-0.032958030700683594,
-0.06774802505970001,
0.04589862748980522,
0.03000357747077942,
0.026981506496667862,
0.05424416437745094,
-0.04018700495362282,
-0.03435659036040306,
0.19392536580562592,
0.030245814472436905,
-0.02275879681110382,
0.018069468438625336,
0.08171527087688446,
-0.04917332902550697,
0.03991469368338585,
-0.029243595898151398,
-0.1522977501153946,
0.025392767041921616,
0.1500380039215088,
0.15777254104614258,
-0.0003110832767561078,
0.053262293338775635,
-0.08955622464418411,
0.0023668454959988594,
0.014230157248675823,
0.07241596281528473,
-0.027560871094465256,
0.2191489338874817,
-0.07914058119058609,
0.002474813722074032,
-0.032740525901317596,
-0.017261473461985588,
-0.035394053906202316,
-0.0492713525891304,
0.01783779449760914,
-0.11253207176923752,
-0.12483295053243637,
0.06801541894674301,
-0.09030604362487793,
-0.07910685241222382,
0.007808139082044363,
-0.07714436203241348,
0.00221146154217422,
-0.11985880881547928,
-0.06759282201528549,
0.05404356122016907,
-0.00634456193074584,
-0.125543013215065,
0.04821188747882843,
-0.13479536771774292,
0.0330035537481308,
-0.14456602931022644,
-0.02805091254413128,
0.005212726537138224,
0.026289694011211395,
0.11083514243364334,
-0.07871101796627045,
0.09280601143836975,
0.0038867087569087744,
-0.055699996650218964,
-0.02493961900472641,
0.10708324611186981,
-0.02938413992524147,
-0.12576812505722046,
-0.008131658658385277,
0.11196667701005936,
-0.0396159291267395,
-0.03966917097568512,
0.011609201319515705,
0.005591440014541149,
0.048984549939632416,
0.17435187101364136,
-0.05521726608276367,
-0.12898166477680206,
0.0645165741443634,
-0.1442490965127945,
0.08670052886009216,
-0.015617583878338337,
0.015695154666900635,
-0.027763567864894867,
-0.04433950409293175,
0.04015904292464256,
0.05992169678211212,
0.044887058436870575,
0.07724315673112869,
0.007926054298877716,
-0.041309721767902374,
0.014479539357125759,
0.0214912798255682,
-0.2351093888282776,
0.010815913788974285,
-0.1797521859407425,
0.06867242604494095,
0.003290087915956974,
0.05963899567723274,
0.2227398306131363,
-0.016521014273166656,
0.009701409377157688,
-0.3138138949871063,
0.012988528236746788,
0.04650108888745308,
-0.19676566123962402,
-0.05600419268012047
] |
null | null | stable-baselines3 |
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "PandaReachDense-v3", "type": "PandaReachDense-v3"}, "metrics": [{"type": "mean_reward", "value": "-0.19 +/- 0.11", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | thisiswooyeol/a2c-PandaReachDense-v3 | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-09T13:35:05+00:00 | [] | [] | TAGS
#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing PandaReachDense-v3
This is a trained model of a A2C agent playing PandaReachDense-v3
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
41,
45,
17
] | [
"passage: TAGS\n#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
0.028780510649085045,
0.06549051403999329,
-0.004174588713794947,
0.028733979910612106,
0.12748076021671295,
-0.010029550641775131,
0.16130082309246063,
0.07903143763542175,
0.052706290036439896,
-0.055043965578079224,
0.09157051891088486,
-0.079488605260849,
0.04699381813406944,
0.3393711447715759,
0.029525093734264374,
-0.186785027384758,
0.08573613315820694,
0.015584449283778667,
0.018966808915138245,
0.09867662936449051,
0.03466832637786865,
-0.08736564218997955,
0.04568251967430115,
0.03800429776310921,
-0.07686931639909744,
-0.04319252818822861,
-0.03975098207592964,
-0.06744661927223206,
0.10361767560243607,
-0.044310007244348526,
0.1670169234275818,
-0.03489987552165985,
0.10219604521989822,
-0.12577489018440247,
0.031373992562294006,
-0.04813149571418762,
-0.05141052231192589,
0.002818689215928316,
-0.011371237225830555,
0.05937984213232994,
0.04167760908603668,
0.05197896435856819,
0.07366002351045609,
0.04871916025876999,
-0.08704962581396103,
-0.11396265029907227,
-0.006845315918326378,
0.07931416481733322,
0.17974808812141418,
0.04054044932126999,
-0.02474738284945488,
0.09696658700704575,
-0.11350683122873306,
0.01657135598361492,
-0.019304286688566208,
-0.4018571078777313,
0.006876560393720865,
0.15550047159194946,
0.04677277058362961,
0.010903568007051945,
-0.0061170910485088825,
-0.004642391111701727,
0.02805398777127266,
-0.037410516291856766,
0.08670840412378311,
-0.09000635892152786,
0.06153826415538788,
-0.019131680950522423,
-0.04113767296075821,
-0.01751464419066906,
0.2419518232345581,
0.01633240468800068,
-0.08024721592664719,
-0.07922019064426422,
0.009968155063688755,
-0.028026137501001358,
-0.0877801775932312,
-0.06134319305419922,
0.07644549012184143,
0.057131536304950714,
0.10696670413017273,
-0.030399860814213753,
-0.058683689683675766,
-0.04541248828172684,
0.08352918922901154,
-0.03953780233860016,
-0.017566127702593803,
-0.01754307933151722,
-0.06739802658557892,
-0.003707833355292678,
0.015629740431904793,
-0.06615205854177475,
-0.015486059710383415,
-0.044966671615839005,
-0.1556774228811264,
-0.009128551930189133,
-0.0599384643137455,
0.03310214728116989,
0.10073909163475037,
0.13065455853939056,
0.06838785856962204,
0.09685135632753372,
-0.08001106232404709,
0.0389438234269619,
0.06625691801309586,
0.09461154788732529,
-0.044509198516607285,
-0.011874453164637089,
0.14630302786827087,
0.10327376425266266,
0.09657767415046692,
-0.09182082861661911,
-0.12403369694948196,
0.04173071309924126,
0.10965418070554733,
0.03382069617509842,
0.0046537998132407665,
0.04452834278345108,
-0.14144757390022278,
0.023916395381093025,
0.0006972529226914048,
-0.045244041830301285,
-0.03088594414293766,
0.06111180782318115,
-0.04433412477374077,
0.02348744124174118,
-0.012718633748590946,
0.10830001533031464,
0.10152670741081238,
-0.023899899795651436,
-0.052799396216869354,
-0.04201658070087433,
-0.0440504252910614,
-0.05507666990160942,
0.04012975096702576,
0.01289378758519888,
0.04624854028224945,
-0.1184653639793396,
-0.13997629284858704,
0.051258668303489685,
0.019622454419732094,
-0.026321161538362503,
-0.13472233712673187,
-0.09338399767875671,
-0.03747362270951271,
-0.011210841126739979,
0.0030350966844707727,
-0.19588395953178406,
-0.02434816211462021,
-0.03428230062127113,
0.13725687563419342,
0.10810749977827072,
-0.06433141976594925,
-0.06369391083717346,
-0.12834231555461884,
0.06795675307512283,
-0.23485252261161804,
0.038750845938920975,
-0.09932064265012741,
0.12411006540060043,
0.007471752353012562,
0.023616313934326172,
0.1410844624042511,
0.02330038882791996,
0.004575210623443127,
0.1702503114938736,
-0.18833371996879578,
-0.046672217547893524,
0.17527204751968384,
-0.0857074186205864,
-0.17703735828399658,
0.05021136254072189,
-0.02124672941863537,
-0.013779462315142155,
0.06350992619991302,
0.09937554597854614,
-0.01727774553000927,
-0.17061583697795868,
0.02558896690607071,
-0.0014508399181067944,
-0.05959303304553032,
0.021542999893426895,
0.12072649598121643,
0.08040176331996918,
-0.027203790843486786,
-0.0016989230643957853,
-0.15452547371387482,
0.09701786935329437,
-0.023543400689959526,
-0.08447092026472092,
0.022736359387636185,
-0.10411997884511948,
0.10016260296106339,
-0.015677137300372124,
0.10591494292020798,
-0.02265925332903862,
-0.018805475905537605,
-0.032891299575567245,
0.10408006608486176,
-0.0068649593740701675,
0.039593957364559174,
-0.17728297412395477,
0.1326225996017456,
0.02176543138921261,
0.046730607748031616,
-0.10109715908765793,
-0.10202061384916306,
0.06674831360578537,
0.15375585854053497,
0.05606463924050331,
0.03833417221903801,
0.07328703999519348,
0.03443831577897072,
-0.0030986627098172903,
-0.1205538883805275,
-0.12789975106716156,
0.019881807267665863,
0.06068658083677292,
-0.08039596676826477,
-0.05172275751829147,
-0.10460081696510315,
0.21138279139995575,
-0.10705634206533432,
0.012047823518514633,
-0.09333895146846771,
0.010153836570680141,
0.08388294279575348,
0.01348812971264124,
0.08132237941026688,
0.02585482969880104,
-0.04426883906126022,
0.009419471956789494,
0.0882885605096817,
0.044275086373090744,
-0.1379590630531311,
0.03784618154168129,
0.024114131927490234,
0.23272188007831573,
0.15174852311611176,
-0.016499420627951622,
-0.055556558072566986,
0.006534850224852562,
0.03740030899643898,
0.03533044084906578,
0.034956689924001694,
0.06951800733804703,
0.1090264692902565,
0.07713755965232849,
0.1276414394378662,
-0.05066131055355072,
0.17763042449951172,
-0.006530070677399635,
-0.14888496696949005,
0.02993084490299225,
-0.07033783197402954,
0.0941668227314949,
-0.06030277907848358,
0.048379335552453995,
0.05410725995898247,
0.0304675605148077,
0.08504439890384674,
-0.00693494314327836,
0.022639812901616096,
-0.04341154545545578,
0.04943868890404701,
0.06790532171726227,
0.06545940041542053,
0.06452376395463943,
-0.007423467002809048,
0.015456308610737324,
-0.05288444459438324,
-0.0518295019865036,
-0.10519610345363617,
-0.12370408326387405,
0.037892695516347885,
-0.015912096947431564,
-0.04463989660143852,
-0.01629551686346531,
-0.07266248762607574,
0.050321705639362335,
0.05250744894146919,
-0.07199236750602722,
0.028561361134052277,
-0.007090074475854635,
-0.09633425623178482,
0.1130511462688446,
-0.14269201457500458,
-0.31355980038642883,
-0.02000165916979313,
-0.13154496252536774,
-0.02077566273510456,
0.15819574892520905,
-0.057956792414188385,
-0.1681092083454132,
0.03305667266249657,
-0.02401961199939251,
-0.09238096326589584,
0.04225420579314232,
-0.018061356619000435,
0.10221174359321594,
0.0857708528637886,
0.043082691729068756,
0.00862243864685297,
-0.01184127852320671,
-0.03903079405426979,
-0.08788500726222992,
0.07608162611722946,
-0.06721128523349762,
0.1173204705119133,
0.13519366085529327,
0.04123268276453018,
-0.015909500420093536,
-0.02043113484978676,
0.06215733662247658,
0.012027861550450325,
-0.036599598824977875,
0.13453175127506256,
-0.03608042374253273,
-0.00864011887460947,
0.04470202699303627,
0.008029532618820667,
-0.10533943772315979,
0.09432658553123474,
-0.05022074654698372,
-0.06974482536315918,
-0.017500806599855423,
-0.08790571242570877,
-0.09950723499059677,
0.18995612859725952,
0.0490412712097168,
0.007856572046875954,
-0.05151839926838875,
0.036120012402534485,
0.07772433012723923,
0.044773608446121216,
0.007161281071603298,
0.03985898196697235,
-0.005716364365071058,
-0.013170693069696426,
0.05278664082288742,
-0.023887991905212402,
0.009960537776350975,
-0.007844919338822365,
0.13077811896800995,
-0.015673788264393806,
0.10317149013280869,
0.0030158995650708675,
0.008619097992777824,
0.08018261194229126,
0.12394148856401443,
0.08064290136098862,
0.019240466877818108,
-0.11554506421089172,
-0.04732639715075493,
-0.030522609129548073,
-0.18181301653385162,
0.11669926345348358,
0.10738886147737503,
0.05268440023064613,
-0.05564067140221596,
0.22832486033439636,
0.0012100599706172943,
0.10802210867404938,
0.03496129810810089,
-0.17664514482021332,
0.024751557037234306,
0.03574612736701965,
0.050895314663648605,
0.007034227252006531,
0.062039270997047424,
-0.09453237801790237,
-0.1839483082294464,
0.03968557342886925,
0.018860090523958206,
0.05523261800408363,
-0.018427258357405663,
0.018512532114982605,
-0.12044285237789154,
-0.05746040865778923,
0.02161633037030697,
0.02076297253370285,
-0.3029120862483978,
0.06816349923610687,
-0.04133946821093559,
0.07392577081918716,
0.009542034938931465,
0.01343793235719204,
0.06604447960853577,
0.01652485318481922,
0.1375029981136322,
-0.017935138195753098,
0.1707022786140442,
-0.1572514772415161,
-0.16084668040275574,
0.025680551305413246,
-0.059293005615472794,
0.07245437800884247,
0.082563117146492,
0.017692390829324722,
0.0069250138476490974,
-0.00047057756455615163,
0.20794180035591125,
-0.13032017648220062,
-0.0346711240708828,
-0.035274047404527664,
0.019543148577213287,
0.022580156102776527,
-0.03844551369547844,
-0.021310672163963318,
0.06112392246723175,
0.1489492505788803,
0.07546767592430115,
-0.02780069410800934,
-0.04611911624670029,
-0.03938353434205055,
-0.09507237374782562,
-0.044778671115636826,
0.10472412407398224,
-0.07841785997152328,
0.10144548118114471,
-0.07513871043920517,
-0.04432075098156929,
0.11707907915115356,
-0.09250949323177338,
-0.053160861134529114,
-0.07627046853303909,
0.05462219938635826,
0.008296831510961056,
0.13374868035316467,
0.03642493113875389,
0.02114485390484333,
0.10089845955371857,
-0.05001259222626686,
0.08662480860948563,
0.03777577355504036,
-0.03541218861937523,
0.03517242521047592,
-0.05375073477625847,
-0.04829130321741104,
-0.010828596539795399,
0.03814345970749855,
0.24244728684425354,
0.302570104598999,
-0.012830551713705063,
0.1897524893283844,
0.09193363785743713,
0.029696941375732422,
-0.16292639076709747,
-0.1200476586818695,
0.05548451840877533,
0.059938978403806686,
0.06154406815767288,
-0.2788083851337433,
0.057189684361219406,
-0.053967077285051346,
-0.08999616652727127,
-0.06829255819320679,
-0.08560561388731003,
-0.07613074034452438,
0.088682159781456,
0.08794322609901428,
0.09100460261106491,
-0.12551987171173096,
0.015924450010061264,
-0.012671655975282192,
-0.1664767563343048,
0.12128932029008865,
-0.039350032806396484,
0.07007917016744614,
-0.025050386786460876,
-0.06438229978084564,
0.025165842846035957,
-0.02775278501212597,
0.04424511641263962,
-0.1206880658864975,
0.0005293674184940755,
-0.04527926817536354,
-0.03749620169401169,
0.1088484600186348,
0.020565982908010483,
-0.0028168195858597755,
-0.09558401256799698,
-0.011945599690079689,
-0.3103867173194885,
0.01988539844751358,
0.02114551141858101,
-0.039148375391960144,
-0.0012507046340033412,
-0.08678091317415237,
-0.042053963989019394,
0.10508828610181808,
0.03930897265672684,
0.08641290664672852,
0.15335260331630707,
-0.005581455305218697,
-0.021082017570734024,
0.17506572604179382,
0.05701295658946037,
-0.014002309180796146,
0.10069113969802856,
-0.06732672452926636,
-0.06576105207204819,
0.04418903961777687,
-0.1016126498579979,
-0.005435575265437365,
0.005642053205519915,
-0.007821558974683285,
0.07107745110988617,
0.09962856024503708,
-0.03340476378798485,
0.18194207549095154,
0.09798844903707504,
-0.15048468112945557,
0.0030947427731007338,
0.052597809582948685,
-0.032650984823703766,
0.04424609988927841,
-0.04443032294511795,
0.05541829764842987,
-0.07521786540746689,
-0.03790169581770897,
0.02031708136200905,
-0.01010141521692276,
-0.07618512213230133,
0.00011962707503698766,
0.03176301345229149,
0.029956085607409477,
-0.08340912312269211,
0.14036758244037628,
0.016359949484467506,
0.0652431845664978,
0.11902019381523132,
0.019259776920080185,
-0.10460162162780762,
-0.014167122542858124,
-0.02339506521821022,
0.2028627097606659,
-0.007937151938676834,
-0.018536100164055824,
-0.11391238868236542,
-0.12847240269184113,
0.018047582358121872,
-0.10348039865493774,
0.10282431542873383,
-0.052032727748155594,
-0.06570395082235336,
-0.03704213351011276,
-0.05561172217130661,
0.031932998448610306,
0.017090078443288803,
-0.015642894431948662,
-0.16111870110034943,
-0.04170334339141846,
0.06846143305301666,
0.039452772587537766,
-0.06145704537630081,
-0.06289087235927582,
-0.16302458941936493,
0.03506235405802727,
-0.1278870701789856,
0.0010145133128389716,
-0.047339316457509995,
-0.05002537742257118,
-0.05195476487278938,
0.01521157007664442,
-0.0177876316010952,
0.008817745372653008,
-0.05148332938551903,
0.03292781487107277,
0.011250603944063187,
0.0014076961670070887,
-0.06952075660228729,
-0.04419080913066864,
0.032172493636608124,
-0.04430563375353813,
0.0661356970667839,
0.04131564497947693,
-0.005653871223330498,
0.021474739536643028,
-0.07005896419286728,
-0.10248169302940369,
0.10313672572374344,
-0.014939527027308941,
0.050572704523801804,
-0.0603681318461895,
-0.012018447741866112,
0.007195405196398497,
-0.07569561898708344,
-0.007751014549285173,
0.24328774213790894,
-0.010914106853306293,
-0.05394120141863823,
-0.07426224648952484,
-0.036970075219869614,
-0.09100507944822311,
-0.0004900419735349715,
0.1948854625225067,
0.05477539822459221,
0.14600017666816711,
-0.0532439760863781,
0.08785777539014816,
-0.06481330841779709,
-0.01534446980804205,
-0.08259234577417374,
0.030320849269628525,
-0.157977893948555,
-0.08130980283021927,
-0.028043894097208977,
-0.03728124126791954,
0.13441862165927887,
-0.19242097437381744,
0.0032852457370609045,
-0.010904400609433651,
-0.04910553991794586,
0.11381126195192337,
0.0557032972574234,
0.24474471807479858,
0.1050342544913292,
-0.035265225917100906,
0.10503548383712769,
0.12215624749660492,
0.0929517149925232,
-0.03347417712211609,
0.058777112513780594,
-0.05078745633363724,
-0.0868106484413147,
0.09736774861812592,
0.012061800807714462,
0.036776214838027954,
-0.08157306164503098,
0.022900743409991264,
-0.10047483444213867,
0.002025678288191557,
0.02005080319941044,
0.2473200410604477,
0.1967000812292099,
-0.09632564336061478,
-0.012216159142553806,
-0.05708231031894684,
-0.032561756670475006,
-0.04091155156493187,
-0.002459051087498665,
-0.07821618020534515,
-0.21873407065868378,
0.051539067178964615,
-0.0930585265159607,
-0.07632365822792053,
-0.06189138814806938,
-0.04064059257507324,
-0.02870149537920952,
0.046939339488744736,
0.03212931379675865,
0.04136762022972107,
0.05070297420024872,
-0.0371626541018486,
-0.09345480799674988,
0.06879863888025284,
-0.11172787100076675,
-0.042014576494693756,
-0.03408866748213768,
0.014045859687030315,
0.032319605350494385,
-0.07429610192775726,
0.07487598061561584,
-0.012149554677307606,
-0.07710553705692291,
0.036456044763326645,
-0.03482281416654587,
0.02153356932103634,
0.07482071220874786,
0.04184282198548317,
-0.09644174575805664,
0.015602846629917622,
0.18867559731006622,
0.020273970440030098,
0.008802177384495735,
-0.14742465317249298,
0.2000039666891098,
-0.02619965374469757,
0.07266447693109512,
-0.03337041288614273,
-0.015141828916966915,
-0.10115411877632141,
0.19129611551761627,
0.11998134851455688,
-0.24376079440116882,
0.024953339248895645,
-0.12912821769714355,
0.022151969373226166,
-0.13376696407794952,
0.20840151607990265,
0.05465596541762352,
0.10847201198339462,
-0.06020665541291237,
-0.02479162998497486,
-0.1493310034275055,
-0.09408020973205566,
-0.08478302508592606,
-0.0414455346763134,
0.10249399393796921,
0.0031611735466867685,
-0.05072701349854469,
-0.00887248944491148,
-0.1566619724035263,
0.10201162099838257,
-0.048264030367136,
-0.11855816096067429,
-0.0679796114563942,
-0.059141192585229874,
-0.06102965027093887,
0.11088541150093079,
0.11637356877326965,
-0.01684124954044819,
0.024554423987865448,
-0.07280154526233673,
-0.012559473514556885,
0.011003518477082253,
0.005383014678955078,
0.0626269057393074,
-0.04783647879958153,
0.1594477891921997,
-0.021524829789996147,
0.0008918871753849089,
0.04285505786538124,
0.05263057351112366,
-0.07584847509860992,
0.06380704790353775,
0.02512199431657791,
0.028178859502077103,
-0.006920731160789728,
0.059795111417770386,
-0.0196672473102808,
0.08964395523071289,
0.08038042485713959,
-0.007235884666442871,
0.09868589043617249,
-0.03191833570599556,
0.006547331809997559,
-0.057698819786310196,
0.06932510435581207,
-0.12982366979122162,
0.05436630919575691,
0.043436627835035324,
-0.10945180803537369,
0.03841061517596245,
0.02560393325984478,
0.11603125184774399,
0.058632634580135345,
-0.040632184594869614,
-0.10494323819875717,
-0.13799439370632172,
0.023235952481627464,
0.058803655207157135,
-0.06312531977891922,
-0.13800419867038727,
-0.052970461547374725,
-0.2062724232673645,
0.04198472201824188,
-0.07393307238817215,
0.06842854619026184,
0.045238204300403595,
0.01849091611802578,
-0.05578908324241638,
-0.06200101599097252,
0.01771395653486252,
0.13669656217098236,
-0.06059794872999191,
-0.13932769000530243
] |
null | null | diffusers | # Karl Lauterbach
<Gallery />
## Model description
just a LORA of Karl Lauterbach
## Trigger words
You should use `klb` to trigger the image generation.
You should use `bre` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/gz8iz/Karl_Lauterbach/tree/main) them in the Files & versions tab.
| {"tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora"], "widget": [{"text": "klb bre in front of a green background, strong cinematic light, detailed, romantic, glowing, infinite, beautiful, symmetry, illuminated, intricate, full color, shiny, highly complex, elegant, dynamic, futuristic, sublime, sharp focus, epic, joyful,, professional, artistic, thought, iconic, creative, amazing, awesome, cool, perfect, gorgeous, best", "parameters": {"negative_prompt": "\tunrealistic, saturated, high contrast, big nose, painting, drawing, sketch, cartoon, anime, manga, render, CG, 3d, watermark, signature, label"}, "output": {"url": "images/2024-02-09_14-34-26_4736.png"}}], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "klb, bre"} | text-to-image | gz8iz/Karl_Lauterbach | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"has_space",
"region:us"
] | 2024-02-09T13:35:38+00:00 | [] | [] | TAGS
#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us
| # Karl Lauterbach
<Gallery />
## Model description
just a LORA of Karl Lauterbach
## Trigger words
You should use 'klb' to trigger the image generation.
You should use 'bre' to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
| [
"# Karl Lauterbach\n\n<Gallery />",
"## Model description \n\njust a LORA of Karl Lauterbach",
"## Trigger words\n\nYou should use 'klb' to trigger the image generation.\n\nYou should use 'bre' to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
"TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us \n",
"# Karl Lauterbach\n\n<Gallery />",
"## Model description \n\njust a LORA of Karl Lauterbach",
"## Trigger words\n\nYou should use 'klb' to trigger the image generation.\n\nYou should use 'bre' to trigger the image generation.",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
60,
10,
12,
29,
28
] | [
"passage: TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us \n# Karl Lauterbach\n\n<Gallery />## Model description \n\njust a LORA of Karl Lauterbach## Trigger words\n\nYou should use 'klb' to trigger the image generation.\n\nYou should use 'bre' to trigger the image generation.## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
-0.11719875782728195,
-0.11213003098964691,
0.002237255685031414,
0.03332362696528435,
0.049386054277420044,
0.013918599113821983,
0.09881123155355453,
0.04832522198557854,
0.14009258151054382,
0.08531307429075241,
0.09877654910087585,
-0.03542007878422737,
0.008958011865615845,
0.25493893027305603,
-0.07958761602640152,
-0.26811420917510986,
0.03281796723604202,
0.007013342808932066,
-0.005753683857619762,
-0.0072462852112948895,
0.031477976590394974,
-0.12494015693664551,
0.11082255840301514,
-0.08471772074699402,
-0.0024841425474733114,
0.0648684948682785,
0.03462875261902809,
-0.02175399661064148,
0.040257684886455536,
0.06290998309850693,
0.031168781220912933,
0.1499728262424469,
0.09208457171916962,
-0.08575071394443512,
0.06467852741479874,
-0.00023579495609737933,
-0.016278423368930817,
0.04058583453297615,
-0.055677760392427444,
-0.06796878576278687,
0.13085481524467468,
-0.18278741836547852,
-0.07322584837675095,
-0.0473855584859848,
-0.02572805993258953,
-0.07890661060810089,
-0.05075348913669586,
0.02153315208852291,
0.05193917080760002,
-0.07881279289722443,
0.009275930002331734,
-0.02754312753677368,
-0.03985893353819847,
0.05998782441020012,
0.2653317451477051,
-0.2248547375202179,
-0.03642892837524414,
0.2444087713956833,
0.05861548334360123,
0.1368436962366104,
-0.08154243975877762,
0.17507542669773102,
0.10939140617847443,
-0.08817021548748016,
0.09009645879268646,
-0.09306655824184418,
0.01950700581073761,
-0.034957438707351685,
-0.07876326888799667,
0.060869667679071426,
0.3292767405509949,
0.03104138746857643,
-0.03656952083110809,
-0.08408022671937943,
-0.039976127445697784,
0.15805183351039886,
-0.0721312090754509,
0.027022531256079674,
0.01729622296988964,
-0.09091120213270187,
-0.02249172516167164,
-0.11841796338558197,
-0.04582468420267105,
-0.11589369177818298,
-0.0044302381575107574,
0.2402930110692978,
0.012775857001543045,
0.045943260192871094,
0.03974618762731552,
0.13434240221977234,
-0.2623658776283264,
-0.15501385927200317,
0.01946372166275978,
-0.09483199566602707,
0.0636960119009018,
0.08775518834590912,
-0.033803027123212814,
-0.06903987377882004,
0.08412293344736099,
0.04734380915760994,
0.010632576420903206,
-0.014827496372163296,
0.034478142857551575,
0.10108968615531921,
0.019200649112462997,
0.050280407071113586,
-0.023774268105626106,
-0.21720805764198303,
0.09115499258041382,
0.08960041403770447,
0.08575144410133362,
-0.026722703129053116,
-0.12859390676021576,
-0.09619541466236115,
-0.038502052426338196,
-0.0005503366119228303,
-0.041646115481853485,
0.0454215407371521,
0.008683223277330399,
-0.008969021961092949,
0.10418600589036942,
-0.051244303584098816,
-0.02516235038638115,
-0.00936032459139824,
-0.0932699665427208,
0.152861088514328,
0.1336178183555603,
-0.025095447897911072,
0.10198917239904404,
0.091065913438797,
-0.02548419125378132,
-0.056099649518728256,
-0.08838118612766266,
-0.1333799809217453,
-0.04271724820137024,
-0.1668313890695572,
0.0709681585431099,
-0.12859834730625153,
-0.2043743133544922,
0.024277515709400177,
0.04437832161784172,
-0.005660956259816885,
0.021078690886497498,
-0.04431180655956268,
-0.0322427898645401,
0.015967847779393196,
0.00030735062318854034,
0.05659687891602516,
-0.058672480285167694,
0.06676150113344193,
-0.0595865324139595,
0.18554794788360596,
-0.17677204310894012,
0.02427125908434391,
-0.055496130138635635,
0.08146819472312927,
-0.34882664680480957,
0.04140383005142212,
-0.12412688136100769,
-0.052353378385305405,
-0.03145661577582359,
-0.042268671095371246,
-0.09552451968193054,
0.03943149000406265,
0.054918937385082245,
0.13774657249450684,
-0.20086708664894104,
-0.030189769342541695,
0.027326740324497223,
-0.1654880791902542,
-0.024834532290697098,
-0.0035711906384676695,
-0.027422329410910606,
0.08309880644083023,
0.09454920887947083,
0.1942465901374817,
0.10121487081050873,
-0.2209809124469757,
0.0035489315632730722,
0.11103014647960663,
-0.03872818499803543,
-0.015587028115987778,
0.03329598158597946,
0.11430126428604126,
-0.14350634813308716,
0.093016117811203,
-0.2348552644252777,
0.06373288482427597,
-0.055302925407886505,
0.014482242055237293,
-0.005862117744982243,
-0.12497130781412125,
0.16732554137706757,
-0.0050705852918326855,
0.06403211504220963,
-0.016853736713528633,
-0.013758708722889423,
0.13668814301490784,
0.12309516221284866,
-0.09175506234169006,
0.0051124440506100655,
0.04891075938940048,
0.21113304793834686,
-0.1455039381980896,
0.007940341718494892,
-0.02769768051803112,
-0.1463007926940918,
0.013202647678554058,
0.11660400778055191,
0.05436720326542854,
0.14367087185382843,
0.06766616553068161,
0.07606600970029831,
-0.12645822763442993,
-0.03177453204989433,
0.03542225435376167,
-0.0019059026381000876,
-0.056212302297353745,
-0.14439454674720764,
-0.06393615901470184,
-0.0683613121509552,
0.015522120520472527,
-0.17884452641010284,
0.012521624565124512,
-0.004381401464343071,
0.033235836774110794,
0.051558803766965866,
0.0766650065779686,
-0.006941161118447781,
-0.012021142058074474,
-0.03837031498551369,
0.012838000431656837,
0.02540242299437523,
-0.05190497264266014,
-0.060030922293663025,
0.14114020764827728,
-0.05216179043054581,
0.011720268055796623,
0.13553248345851898,
0.11034239083528519,
-0.0208856463432312,
-0.15742778778076172,
0.02333020605146885,
0.0001461745414417237,
-0.022815970703959465,
-0.08313339948654175,
-0.013996059074997902,
-0.011952866800129414,
0.04977276548743248,
-0.08112926036119461,
0.02417660877108574,
0.03472458943724632,
-0.02088220790028572,
-0.03678964823484421,
0.11931299418210983,
0.09326115250587463,
0.08340200036764145,
0.010132537223398685,
0.18661117553710938,
-0.12413394451141357,
0.1687832623720169,
-0.003096252214163542,
-0.1047305315732956,
0.024694837629795074,
0.019422102719545364,
-0.005834819283336401,
0.2663590908050537,
0.06253854185342789,
-0.05302746593952179,
0.006522638723254204,
-0.08245235681533813,
0.017600974068045616,
-0.07269226014614105,
-0.023497814312577248,
-0.03554293140769005,
-0.04673955962061882,
0.1316586583852768,
0.12641723453998566,
-0.047353360801935196,
0.09058567136526108,
-0.04837669059634209,
-0.09666261821985245,
-0.043882861733436584,
-0.02847178280353546,
0.02986222133040428,
0.10581705719232559,
-0.00817002821713686,
-0.016945431008934975,
-0.06115894392132759,
0.06392203271389008,
-0.12984253466129303,
-0.024772735312581062,
0.01108250766992569,
0.03331059589982033,
-0.04841405525803566,
-0.09690321236848831,
0.09813756495714188,
0.1310795396566391,
0.000047648096369812265,
-0.08418896049261093,
-0.07578754425048828,
-0.06944970041513443,
-0.10201788693666458,
-0.058158934116363525,
-0.10239558666944504,
-0.0069605568423867226,
0.06343725323677063,
-0.025765303522348404,
0.1494908332824707,
0.08325227349996567,
-0.062209684401750565,
0.011519747786223888,
-0.0063810646533966064,
0.06868965178728104,
-0.014136580750346184,
0.1633015125989914,
0.14246810972690582,
0.05016341060400009,
0.06183391809463501,
0.11153219640254974,
0.007864563725888729,
-0.03495031222701073,
0.060028597712516785,
0.03607173636555672,
-0.10710602253675461,
0.035270072519779205,
-0.12691311538219452,
-0.05750252306461334,
-0.006331874988973141,
0.013992201536893845,
0.003659539856016636,
-0.07235411554574966,
0.14622190594673157,
0.03467462956905365,
-0.01026089582592249,
0.06864206492900848,
0.04371578246355057,
0.011725878342986107,
-0.05367632582783699,
0.0827607810497284,
-0.040181126445531845,
-0.028936000540852547,
0.1206040158867836,
0.03660563752055168,
0.20995034277439117,
-0.1413961499929428,
-0.12538906931877136,
0.07735815644264221,
0.0983315110206604,
0.10314048826694489,
0.10707379132509232,
-0.11111024022102356,
-0.013383942656219006,
-0.05972746014595032,
-0.13324807584285736,
0.10431893169879913,
0.09776002168655396,
-0.04518745839595795,
0.039634592831134796,
-0.05478277429938316,
-0.007131770718842745,
0.006315660197287798,
0.03208712488412857,
0.14804716408252716,
-0.22803126275539398,
-0.011686752550303936,
0.11554858833551407,
0.13029897212982178,
-0.029496105387806892,
0.04210516810417175,
0.18216095864772797,
-0.03579536825418472,
0.038303859531879425,
-0.019292740151286125,
0.05393965169787407,
0.036950528621673584,
-0.07269809395074844,
-0.13560765981674194,
0.16092456877231598,
-0.06649114936590195,
0.004209436010569334,
-0.07222742587327957,
0.06418727338314056,
-0.014756575226783752,
-0.021778186783194542,
-0.021084899082779884,
-0.06429558247327805,
0.11934996396303177,
0.15918424725532532,
0.10197535157203674,
0.02169893868267536,
-0.031849343329668045,
-0.12714700400829315,
-0.09655017405748367,
0.030049631372094154,
0.03699556738138199,
-0.08653135597705841,
0.053129300475120544,
0.02914203703403473,
-0.041439034044742584,
0.0010562199167907238,
0.08152569830417633,
-0.18732137978076935,
-0.011600797064602375,
-0.07233627885580063,
0.07985005527734756,
-0.01945895329117775,
-0.051178134977817535,
-0.10632207244634628,
-0.1254391074180603,
0.004907198715955019,
0.13616904616355896,
-0.08776462078094482,
-0.05739255249500275,
-0.04037052020430565,
0.11041282117366791,
-0.07721684128046036,
0.03266303241252899,
-0.005314326379448175,
0.054443541914224625,
-0.1007506400346756,
-0.1508541703224182,
0.052674517035484314,
-0.026624711230397224,
-0.09590031206607819,
0.010698702186346054,
0.07581569999456406,
0.023632658645510674,
-0.042437370866537094,
0.01305487658828497,
0.021587777882814407,
0.049938950687646866,
-0.12030327320098877,
-0.01691599190235138,
0.07875831425189972,
0.011004583910107613,
0.08845454454421997,
-0.1223168596625328,
-0.11628077924251556,
0.014262628741562366,
0.11767981946468353,
0.04923120513558388,
0.2393244057893753,
-0.11300355941057205,
0.047307636588811874,
0.1607540398836136,
0.006672983523458242,
-0.3382298946380615,
0.06030323728919029,
-0.06334928423166275,
0.0359070785343647,
0.04765883833169937,
-0.026037193834781647,
0.16275127232074738,
0.04582073912024498,
-0.04192981868982315,
0.272398442029953,
-0.2594805657863617,
-0.16167451441287994,
0.06485814601182938,
0.1812629997730255,
0.2908347249031067,
-0.13229477405548096,
-0.019080575555562973,
-0.04670342803001404,
-0.03292682394385338,
0.017505129799246788,
-0.17979049682617188,
0.054783549159765244,
-0.03254424408078194,
-0.03263884410262108,
-0.005201137159019709,
-0.013929796405136585,
0.13670365512371063,
-0.020719116553664207,
0.09914681315422058,
-0.08080395311117172,
-0.12206058204174042,
0.1635267734527588,
-0.10649745911359787,
0.12571419775485992,
-0.23611439764499664,
0.07344571501016617,
-0.12930743396282196,
-0.010561796836555004,
-0.052434246987104416,
0.1732994168996811,
-0.027873018756508827,
-0.08717426657676697,
-0.05876713618636131,
0.062410127371549606,
-0.03305334970355034,
0.041447412222623825,
0.006590638309717178,
-0.0737563744187355,
-0.06034272164106369,
0.12214718014001846,
-0.06444279104471207,
0.09840892255306244,
0.03522583097219467,
0.03572589531540871,
-0.05320355296134949,
0.1263401210308075,
-0.16692639887332916,
-0.03092062659561634,
0.09923459589481354,
0.05354367569088936,
0.049837302416563034,
0.003214110154658556,
-0.01772061549127102,
0.11020523309707642,
0.07504819333553314,
-0.1276719570159912,
-0.030543480068445206,
-0.07122907042503357,
-0.12717920541763306,
0.06675272434949875,
0.06975772231817245,
0.14210714399814606,
-0.103941909968853,
0.035077013075351715,
-0.042395126074552536,
-0.0075850836001336575,
-0.0490310899913311,
0.04440928250551224,
0.14176641404628754,
0.00616380525752902,
-0.06829556822776794,
0.08908461034297943,
-0.04857474938035011,
0.012271072715520859,
-0.07073237001895905,
0.03239456191658974,
-0.09338316321372986,
-0.05181766673922539,
-0.03825072944164276,
0.19795042276382446,
-0.048158563673496246,
0.03154001384973526,
-0.03418896719813347,
-0.11783032864332199,
-0.042935967445373535,
0.13795414566993713,
0.06620948016643524,
-0.0802665576338768,
0.03277422487735748,
0.001565919374115765,
-0.015773208811879158,
0.14434140920639038,
0.1276400238275528,
0.09701798856258392,
-0.12125129997730255,
-0.13141348958015442,
-0.0023072753101587296,
0.0669664666056633,
-0.11999501287937164,
-0.05400354415178299,
-0.10280171781778336,
-0.023346364498138428,
-0.14607255160808563,
0.10532151907682419,
-0.05622788146138191,
0.0046871500089764595,
-0.10999301820993423,
-0.0805433914065361,
-0.027141330763697624,
0.02653777413070202,
-0.0693911537528038,
0.029513094574213028,
-0.009886409156024456,
0.04617086052894592,
-0.04927369952201843,
-0.11263791471719742,
0.025818565860390663,
-0.061224982142448425,
0.06700032949447632,
-0.006031533237546682,
-0.0033673483412712812,
0.07287854701280594,
-0.16882860660552979,
0.011550317518413067,
0.04885036125779152,
0.024988658726215363,
0.011629669927060604,
0.04865449666976929,
0.03081441856920719,
-0.061046164482831955,
0.09466153383255005,
-0.0437375046312809,
-0.062127165496349335,
-0.08472829312086105,
0.09091141074895859,
-0.05636199563741684,
-0.029538124799728394,
0.0019399552838876843,
0.049514882266521454,
0.08254643529653549,
0.12501467764377594,
0.044136036187410355,
-0.09490988403558731,
-0.014915668405592442,
-0.06893175095319748,
0.021381523460149765,
0.023922862485051155,
-0.15271899104118347,
-0.04128142446279526,
-0.045132968574762344,
0.03890184685587883,
-0.026524733752012253,
0.15205875039100647,
0.06610135734081268,
-0.09831708669662476,
-0.04629070684313774,
-0.005761699751019478,
0.07375471293926239,
0.06103508919477463,
0.2866128981113434,
0.019468950107693672,
0.09658627212047577,
-0.17545956373214722,
0.14763179421424866,
0.06647779047489166,
-0.021640095859766006,
0.11153727769851685,
0.17918716371059418,
0.000892472977284342,
0.06482154875993729,
0.06834446638822556,
0.022098524495959282,
0.008285492658615112,
0.04037283733487129,
-0.08526778221130371,
0.04840397462248802,
-0.09970768541097641,
0.042120326310396194,
0.12332158535718918,
-0.13790982961654663,
-0.004487775731831789,
0.09179099649190903,
-0.03441288694739342,
-0.09111558645963669,
-0.28512516617774963,
-0.07694742828607559,
-0.14571477472782135,
0.028747493401169777,
-0.07012736797332764,
-0.03519565612077713,
0.06819526851177216,
-0.003800684120506048,
0.05302756279706955,
0.18188413977622986,
-0.11158393323421478,
-0.029881425201892853,
0.1415461301803589,
-0.000592363125178963,
-0.05723528563976288,
0.04430568218231201,
-0.0472051240503788,
0.06997109204530716,
-0.052522897720336914,
-0.0910172164440155,
0.03078199364244938,
0.006014402490109205,
-0.02057483419775963,
0.06275121122598648,
-0.04412589222192764,
-0.05549905076622963,
0.025551537051796913,
0.06596164405345917,
0.07851497083902359,
0.05801726132631302,
-0.032668955624103546,
-0.04166929051280022,
0.18837517499923706,
-0.04483022168278694,
-0.05942758917808533,
-0.003178746672347188,
0.04393346235156059,
-0.07675706595182419,
0.0779440775513649,
-0.052153680473566055,
-0.10989340394735336,
0.010089660063385963,
0.1774328202009201,
0.24091273546218872,
-0.05175860971212387,
0.05822386220097542,
-0.0726512223482132,
0.0038437831681221724,
0.0009492796962149441,
0.07660608738660812,
-0.017747368663549423,
0.18730176985263824,
-0.04893910512328148,
0.0182100348174572,
-0.05984633415937424,
-0.004111617337912321,
-0.032518427819013596,
-0.06064405292272568,
-0.016236035153269768,
-0.12509950995445251,
-0.13395260274410248,
0.050829555839300156,
-0.054099176079034805,
-0.16318798065185547,
0.0836058184504509,
-0.1319969892501831,
-0.02131970040500164,
-0.12362882494926453,
-0.019516199827194214,
0.06620652973651886,
-0.010045993141829967,
-0.1440066248178482,
0.04171660542488098,
-0.0585809201002121,
-0.012605869211256504,
-0.06066319718956947,
-0.08971954882144928,
0.010086927562952042,
-0.0015778589295223355,
0.07696530967950821,
-0.05917397513985634,
0.05396280065178871,
0.007821406237781048,
-0.03201475739479065,
-0.012319781817495823,
0.10410359501838684,
0.0007881465135142207,
-0.1540459394454956,
-0.04454796761274338,
0.09933718293905258,
-0.07283297926187515,
0.15726222097873688,
0.011068254709243774,
-0.08427152037620544,
0.0401134267449379,
0.14406825602054596,
-0.10434729605913162,
-0.1170658990740776,
0.054124463349580765,
-0.12187635153532028,
0.10555075109004974,
0.07031386345624924,
0.017259065061807632,
-0.03183741867542267,
-0.03154176473617554,
0.05361813306808472,
0.059024982154369354,
-0.004482334479689598,
0.04236508905887604,
0.005762472748756409,
-0.018002429977059364,
0.0020827986299991608,
-0.000244349503191188,
-0.2436944842338562,
-0.013263641856610775,
-0.20141799747943878,
0.05452433228492737,
0.013840964064002037,
0.07201910763978958,
0.20993857085704803,
-0.005438775755465031,
0.004122747574001551,
-0.3401554226875305,
0.004904477391391993,
0.06306725740432739,
-0.15091674029827118,
-0.05004817992448807
] |
null | null | transformers | Finetune of miqu-70b-sf dequant of miqudev's leak of Mistral-70B (allegedly an early mistral medium). My diffs are available under CC-0, this is a merge with the leaked model, you can use the other repository to save bandwidth.
EQ-Bench: 84.89
Will run more benches later. | {"license": "cc-by-2.0"} | text-generation | LoneStriker/Senku-70B-Full-2.4bpw-h6-exl2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:cc-by-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-09T13:35:42+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #license-cc-by-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Finetune of miqu-70b-sf dequant of miqudev's leak of Mistral-70B (allegedly an early mistral medium). My diffs are available under CC-0, this is a merge with the leaked model, you can use the other repository to save bandwidth.
EQ-Bench: 84.89
Will run more benches later. | [] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-cc-by-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
60
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-cc-by-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
-0.006207775790244341,
0.05186443030834198,
-0.005766681861132383,
0.0034429291263222694,
0.09202545881271362,
-0.026229875162243843,
0.21461424231529236,
0.09413789957761765,
-0.012125303968787193,
-0.027240416035056114,
0.15366454422473907,
0.20107468962669373,
-0.030660197138786316,
0.0625392273068428,
-0.12453678250312805,
-0.15658724308013916,
0.07951316237449646,
-0.0074092973954975605,
0.0468166321516037,
0.08121465146541595,
0.11726208031177521,
-0.05624562129378319,
0.07327274978160858,
-0.05116863548755646,
-0.10777227580547333,
0.01719590090215206,
0.07997997105121613,
-0.1362335979938507,
0.10389529168605804,
0.070436492562294,
0.09596491605043411,
0.09478230774402618,
-0.026663783937692642,
-0.2320748120546341,
0.024640627205371857,
-0.013039902783930302,
-0.08545025438070297,
0.02922174707055092,
0.03788268566131592,
-0.042137615382671356,
0.05930780619382858,
0.04187946766614914,
-0.012704534456133842,
0.0843532532453537,
-0.12006022036075592,
0.03492521494626999,
-0.054476991295814514,
0.008457338437438011,
0.11366608738899231,
0.07924187183380127,
0.002242166781798005,
0.1153382733464241,
-0.05578871816396713,
0.08845382928848267,
0.06245912238955498,
-0.3711334466934204,
0.014689184725284576,
0.13522937893867493,
0.07748636603355408,
0.06413708627223969,
-0.05252519249916077,
0.11249673366546631,
0.06629528105258942,
-0.03810290992259979,
0.04312480613589287,
-0.07572442293167114,
-0.08102772384881973,
0.034424908459186554,
-0.04833368957042694,
-0.030284471809864044,
0.23118719458580017,
-0.03052249550819397,
0.012902313843369484,
-0.08178368955850601,
-0.06274984031915665,
0.003528473898768425,
-0.027013633400201797,
0.033650096505880356,
-0.007114062085747719,
0.09828754514455795,
0.0036289477720856667,
-0.04431737959384918,
-0.15287499129772186,
-0.017103472724556923,
-0.17628958821296692,
0.10724018514156342,
-0.0038528726436197758,
0.03643316775560379,
-0.1433914750814438,
0.03731580823659897,
0.01849939487874508,
-0.09380249679088593,
-0.01922903209924698,
-0.06579022109508514,
0.07399444282054901,
-0.015354927629232407,
-0.04418032616376877,
-0.04420563578605652,
0.146459698677063,
0.11814041435718536,
-0.01364491693675518,
0.003440734464675188,
-0.11379846185445786,
0.0978090688586235,
-0.019500313326716423,
-0.0008985437452793121,
0.047480762004852295,
-0.007553805597126484,
0.1015886589884758,
-0.08041958510875702,
0.08773300796747208,
-0.04154345020651817,
-0.16899511218070984,
0.014984861016273499,
-0.009690387174487114,
0.14272445440292358,
0.0067246644757688046,
0.08934520930051804,
-0.03848949074745178,
0.06400565803050995,
0.07495175302028656,
-0.06882821768522263,
0.002561564091593027,
0.008464166894555092,
0.05782800912857056,
0.020744536072015762,
0.0357230082154274,
0.05749142915010452,
-0.053222328424453735,
0.034963082522153854,
-0.05974286422133446,
-0.030262604355812073,
-0.04071192815899849,
-0.05133391171693802,
0.07166485488414764,
-0.026888888329267502,
0.023412950336933136,
-0.1889147162437439,
-0.17520999908447266,
0.017266716808080673,
0.006569101009517908,
-0.016733024269342422,
-0.0409536249935627,
-0.05526142567396164,
-0.029290098696947098,
0.025005020201206207,
-0.08925633877515793,
-0.08324684202671051,
-0.08618094772100449,
0.08566243946552277,
-0.034463070333004,
0.052008256316185,
-0.17173472046852112,
0.030293233692646027,
-0.09709536284208298,
0.015842683613300323,
-0.024641036987304688,
0.05080806463956833,
-0.03994987532496452,
0.1493525505065918,
-0.03130904585123062,
0.028674336150288582,
-0.049635149538517,
0.06748075783252716,
-0.04132431373000145,
0.20219798386096954,
-0.13369248807430267,
-0.02732842043042183,
0.23248150944709778,
-0.1203412264585495,
-0.2215833067893982,
0.09494956582784653,
-0.007244576700031757,
0.06883425265550613,
0.1192723736166954,
0.16428819298744202,
-0.018312968313694,
-0.084824338555336,
0.043344683945178986,
0.10571722686290741,
-0.05737517401576042,
-0.10720136761665344,
0.012378843501210213,
-0.02602921798825264,
-0.10123564302921295,
0.021436169743537903,
0.054138898849487305,
0.03870760649442673,
-0.010303346440196037,
-0.06431794166564941,
-0.04900350421667099,
-0.04556714743375778,
-0.019114533439278603,
-0.05745845288038254,
0.04891825467348099,
-0.10103435069322586,
-0.004564722999930382,
0.02500876598060131,
-0.0006331629119813442,
-0.017695071175694466,
0.03802654147148132,
-0.1019439622759819,
0.05491343140602112,
-0.01049356535077095,
0.05139922350645065,
-0.08506914973258972,
-0.09540282934904099,
-0.01487639732658863,
0.11419088393449783,
0.04237206280231476,
0.03597130998969078,
0.03009572997689247,
-0.0029274821281433105,
-0.016321944072842598,
0.019669566303491592,
0.1798858940601349,
0.027921929955482483,
-0.06157265976071358,
-0.10305018723011017,
0.10064617544412613,
-0.04469102993607521,
0.06838524341583252,
-0.12369509041309357,
0.021130846813321114,
0.09049873054027557,
0.08164099603891373,
0.011007205583155155,
0.06817352026700974,
-0.016652852296829224,
0.012067300267517567,
-0.08803333342075348,
0.018720898777246475,
0.09845291078090668,
0.023675929754972458,
-0.12399162352085114,
0.24828600883483887,
-0.22121906280517578,
0.23131431639194489,
0.20286236703395844,
-0.2072221040725708,
0.034866541624069214,
-0.11290061473846436,
0.008944046683609486,
0.009122604504227638,
0.00658374885097146,
-0.04158162698149681,
0.02772163227200508,
-0.017428645864129066,
0.17939260601997375,
-0.07259617745876312,
-0.01289988774806261,
-0.015334650874137878,
-0.07770753651857376,
-0.0461907684803009,
0.04808717593550682,
0.10923753678798676,
-0.17211377620697021,
0.18076997995376587,
0.2573873698711395,
0.016237052157521248,
0.145608589053154,
-0.02829170413315296,
0.011588740162551403,
0.031093206256628036,
0.051330193877220154,
0.017952319234609604,
-0.03206970542669296,
-0.09656007587909698,
-0.01615116000175476,
0.06039080768823624,
0.00738911610096693,
0.05942045524716377,
-0.14498206973075867,
-0.06179048866033554,
-0.010635633021593094,
-0.054405391216278076,
0.006760948803275824,
0.05542760342359543,
-0.006918448954820633,
0.12766680121421814,
-0.04561823606491089,
-0.06787645071744919,
0.1212611123919487,
-0.018814176321029663,
-0.11172524094581604,
0.16348372399806976,
-0.1353582739830017,
-0.2530854046344757,
-0.1794736236333847,
-0.16009610891342163,
-0.05283679813146591,
0.06249964237213135,
0.12733663618564606,
-0.021347085013985634,
-0.06735935807228088,
-0.08513124287128448,
-0.025516794994473457,
-0.004286123439669609,
-0.0013990295119583607,
-0.030478790402412415,
0.0740688145160675,
-0.04530498385429382,
-0.11495137214660645,
-0.04566733539104462,
0.02995317615568638,
-0.08298885077238083,
0.11527245491743088,
-0.08257944881916046,
0.09218067675828934,
0.14615941047668457,
0.013489138334989548,
-0.0007384112104773521,
-0.06414268910884857,
0.11027345061302185,
-0.05689837411046028,
-0.021911391988396645,
0.19550743699073792,
-0.06030816212296486,
0.054297950118780136,
0.17741087079048157,
0.019415782764554024,
-0.11608056724071503,
0.05627991259098053,
-0.04948503151535988,
-0.08943437039852142,
-0.2322319746017456,
-0.11231391131877899,
-0.09151040762662888,
0.09372707456350327,
0.02010270394384861,
0.06644773483276367,
0.1561785638332367,
0.07071852684020996,
-0.03820497542619705,
-0.003707042895257473,
0.09500505030155182,
0.10332844406366348,
0.26925957202911377,
-0.03648235276341438,
0.1326010376214981,
-0.09864376485347748,
-0.09166394174098969,
0.08177606761455536,
0.09666159003973007,
0.08463814854621887,
0.1308884173631668,
0.10701965540647507,
0.06300177425146103,
0.06616055965423584,
0.13583797216415405,
0.0930822342634201,
0.04747668653726578,
-0.027060847729444504,
-0.021886298432946205,
-0.06314191222190857,
-0.03154398128390312,
0.06433441489934921,
-0.0616009458899498,
-0.1419346034526825,
-0.01669052243232727,
-0.04656673222780228,
0.08677151799201965,
0.12965995073318481,
0.049826931208372116,
-0.20373210310935974,
0.03404708579182625,
0.12731851637363434,
-0.014891417697072029,
-0.0784522220492363,
0.11266084015369415,
0.041655853390693665,
-0.0492335706949234,
0.0968889519572258,
-0.022453399375081062,
0.10560388118028641,
-0.04346970468759537,
0.06621389091014862,
-0.09852857887744904,
-0.09218709915876389,
0.008176117204129696,
0.10175062716007233,
-0.32708150148391724,
0.19553345441818237,
0.025147762149572372,
0.003030276857316494,
-0.07397007197141647,
-0.00860549882054329,
0.006451844237744808,
0.1687123030424118,
0.14335986971855164,
-0.03559787943959236,
-0.11925935745239258,
-0.036741066724061966,
-0.04582417383790016,
0.021652963012456894,
0.114947110414505,
0.004123690538108349,
0.006702055223286152,
-0.060284681618213654,
-0.005426459014415741,
0.013130738399922848,
-0.03866162151098251,
-0.05004815012216568,
-0.18432049453258514,
0.036161795258522034,
0.15424391627311707,
0.105919748544693,
-0.038923926651477814,
0.025927383452653885,
-0.1276562660932541,
0.19439037144184113,
-0.1566786915063858,
-0.05010409653186798,
-0.10357701778411865,
-0.13866664469242096,
-0.010155638679862022,
-0.015465851873159409,
0.06662318855524063,
-0.05540037900209427,
0.05461542308330536,
-0.0985378548502922,
-0.16780197620391846,
0.11566402018070221,
-0.10529130697250366,
-0.03816051036119461,
-0.04050680622458458,
0.15799902379512787,
-0.10722509026527405,
-0.015996212139725685,
0.0695413127541542,
0.027312718331813812,
-0.04470572620630264,
-0.10069151222705841,
-0.01407475396990776,
0.022582462057471275,
0.03730671480298042,
-0.004337325692176819,
-0.1392161101102829,
-0.12033824622631073,
-0.007708914577960968,
-0.08476556092500687,
0.26667433977127075,
0.2823437750339508,
-0.052704282104969025,
0.1610942929983139,
0.18729859590530396,
-0.10833743214607239,
-0.3613292872905731,
-0.1156543493270874,
-0.19261738657951355,
-0.05812794342637062,
-0.0019929534755647182,
-0.09389892220497131,
0.0683603510260582,
0.03696022555232048,
-0.06046358868479729,
0.10693113505840302,
-0.20837023854255676,
-0.11674803495407104,
0.13628779351711273,
0.022366341203451157,
0.3236163854598999,
-0.1799478828907013,
-0.10869350284337997,
-0.1288607120513916,
-0.09091421216726303,
0.17097102105617523,
-0.11236412078142166,
0.09014905989170074,
0.038183145225048065,
0.05064551532268524,
0.019322404637932777,
-0.028820998966693878,
0.11060543358325958,
-0.05023352429270744,
0.05936712771654129,
-0.125066876411438,
-0.007887703366577625,
0.055251557379961014,
-0.021825825795531273,
0.047600388526916504,
-0.17949266731739044,
0.010565537959337234,
-0.02346070110797882,
-0.03863802179694176,
-0.00010827439837157726,
0.07584916800260544,
-0.0018114936538040638,
-0.05403559282422066,
-0.0338921919465065,
-0.07518398016691208,
0.021392593160271645,
-0.005685644224286079,
0.2621508240699768,
-0.09497393667697906,
0.143461674451828,
0.2097931206226349,
0.18348653614521027,
-0.11326921731233597,
0.10850489139556885,
-0.03179696202278137,
-0.10264644771814346,
0.06764788925647736,
-0.13615255057811737,
0.06894791126251221,
0.07885490357875824,
-0.05221731215715408,
0.09386573731899261,
0.07263122498989105,
0.024896983057260513,
0.011904848739504814,
0.15288308262825012,
-0.19621852040290833,
-0.06335753947496414,
-0.02627270668745041,
0.128641277551651,
0.07603052258491516,
0.07438892126083374,
0.19964289665222168,
-0.02363588474690914,
0.024383772164583206,
0.004766607191413641,
0.0470099002122879,
-0.051470812410116196,
0.03084268979728222,
-0.0036850213073194027,
0.009037865325808525,
-0.11732205748558044,
0.11256258189678192,
0.014299282804131508,
-0.13298094272613525,
0.018583929166197777,
0.10019229352474213,
-0.12479248642921448,
-0.1322273164987564,
-0.06397988647222519,
0.09235106408596039,
-0.1650686413049698,
-0.09092073142528534,
-0.035492803901433945,
-0.18309658765792847,
0.023424223065376282,
0.22705577313899994,
0.03624091297388077,
0.09397074580192566,
0.025309177115559578,
-0.050275810062885284,
-0.03333280235528946,
0.044489964842796326,
-0.0776023343205452,
0.026683444157242775,
-0.09567009657621384,
0.016401100903749466,
-0.0327458456158638,
0.04273902624845505,
-0.08503364771604538,
-0.01675247587263584,
-0.13592562079429626,
0.026447130367159843,
-0.1429377794265747,
0.016726532950997353,
-0.09382633119821548,
-0.022313788533210754,
0.025396376848220825,
-0.011300059035420418,
-0.044356830418109894,
-0.0456419438123703,
-0.08871109038591385,
0.022159568965435028,
-0.04314287006855011,
0.0626419335603714,
-0.09819427877664566,
-0.043954771012067795,
0.04525887221097946,
-0.036994993686676025,
0.11698898673057556,
0.04370922967791557,
-0.0972345620393753,
0.07480837404727936,
-0.24783827364444733,
-0.02655193954706192,
0.13125064969062805,
0.015579255297780037,
0.014812005683779716,
0.06315375864505768,
-0.011836273595690727,
0.1417415887117386,
0.009809926152229309,
0.052264366298913956,
0.013304419815540314,
-0.1011492908000946,
-0.001961590489372611,
-0.04560624808073044,
-0.10192413628101349,
-0.018009837716817856,
-0.0727795958518982,
0.1012018471956253,
-0.024938881397247314,
0.17435556650161743,
-0.092584989964962,
0.03382148593664169,
-0.01980471797287464,
0.03883249685168266,
0.005250109359622002,
-0.15618106722831726,
-0.14053024351596832,
-0.07882187515497208,
-0.001336804125458002,
-0.015131816267967224,
0.27824896574020386,
0.008481241762638092,
-0.06852035224437714,
0.09475342184305191,
0.022017354145646095,
0.034207794815301895,
0.04192714765667915,
0.2883480489253998,
0.08536436408758163,
-0.01155257411301136,
-0.15340177714824677,
0.016927950084209442,
0.0375761017203331,
-0.09943119436502457,
0.06783804297447205,
0.09215667098760605,
-0.07752043753862381,
0.10896968841552734,
0.07114557921886444,
0.0072129033505916595,
-0.018362488597631454,
-0.06760571897029877,
-0.055142998695373535,
0.046072885394096375,
-0.049081407487392426,
0.07889051735401154,
0.20583051443099976,
-0.03462229669094086,
-0.015487121418118477,
-0.04577798396348953,
-0.03612710162997246,
-0.18539372086524963,
-0.13403363525867462,
-0.1109817773103714,
-0.11150851845741272,
0.01701277866959572,
-0.08830517530441284,
0.055540017783641815,
0.05017261207103729,
0.054319605231285095,
-0.04291510581970215,
0.06990589201450348,
-0.05907499045133591,
-0.04540880024433136,
0.0442875400185585,
-0.02594553679227829,
0.06306269764900208,
-0.035188764333724976,
-0.06933852285146713,
-0.053066983819007874,
-0.04323925822973251,
-0.02944331243634224,
0.08734598755836487,
0.050012290477752686,
0.06000626087188721,
-0.13524575531482697,
-0.06855671852827072,
-0.032354552298784256,
0.0697886273264885,
-0.008020002394914627,
0.1599365472793579,
0.022465955466032028,
-0.0408162996172905,
0.08315412700176239,
0.1788318008184433,
-0.06562280654907227,
-0.12326370179653168,
-0.031882673501968384,
0.1764252483844757,
0.0044775111600756645,
0.13507580757141113,
-0.040701255202293396,
-0.013290504924952984,
-0.012223204597830772,
0.3287609815597534,
0.25894030928611755,
-0.09469550848007202,
0.03137436881661415,
-0.07478570938110352,
0.04246385395526886,
0.05169496685266495,
0.11560044437646866,
0.0689227506518364,
0.25144103169441223,
-0.03857225552201271,
-0.03432202711701393,
-0.00518826674669981,
0.017930803820490837,
-0.13905346393585205,
0.0863647311925888,
-0.04497753456234932,
-0.05007867515087128,
-0.02601996622979641,
0.09876758605241776,
-0.15639275312423706,
0.06847500801086426,
-0.04330084100365639,
-0.09984272718429565,
0.007430274039506912,
0.007269448135048151,
0.14150775969028473,
-0.019860094413161278,
0.029579713940620422,
-0.03768281638622284,
-0.07121695578098297,
0.01726885512471199,
-0.005519684869796038,
-0.18614327907562256,
0.03858964890241623,
0.023371171206235886,
-0.005911254324018955,
0.07187901437282562,
0.000053612515330314636,
0.06824749708175659,
0.07142221182584763,
0.030734332278370857,
-0.051403749734163284,
0.15974754095077515,
0.026775244623422623,
-0.0868566483259201,
0.050569936633110046,
-0.040603864938020706,
-0.026959538459777832,
0.04548517242074013,
0.06765088438987732,
-0.09669996052980423,
0.06760630011558533,
0.009751532226800919,
-0.11920495331287384,
-0.03518637642264366,
0.010957258753478527,
-0.07562252134084702,
0.07157810032367706,
0.007729232311248779,
-0.028513247147202492,
0.004583296831697226,
-0.016401249915361404,
0.010413174517452717,
-0.018589364364743233,
-0.15951070189476013,
-0.017387747764587402,
-0.12323169410228729,
-0.05553334206342697,
0.11709286272525787,
0.03462449461221695,
-0.23784784972667694,
0.011705098673701286,
-0.0969860851764679,
0.06600788980722427,
-0.19047285616397858,
0.05791420489549637,
0.2114742547273636,
-0.005059413146227598,
-0.031710248440504074,
-0.17269471287727356,
0.06433253735303879,
0.061398617923259735,
-0.036462731659412384,
-0.10163070261478424
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.