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ftopal
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huggingface-models-embeddings

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train · 241k rows

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null	null	transformers	# RobertaNER This model fine-tuned for the Named Entity Recognition (NER) task on a mixed NER dataset collected from [ARMAN](https://github.com/HaniehP/PersianNER), [PEYMA](http://nsurl.org/2019-2/tasks/task-7-named-entity-recognition-ner-for-farsi/), and [WikiANN](https://elisa-ie.github.io/wikiann/) that covered ten types of entities: - Date (DAT) - Event (EVE) - Facility (FAC) - Location (LOC) - Money (MON) - Organization (ORG) - Percent (PCT) - Person (PER) - Product (PRO) - Time (TIM) ## Dataset Information \| \| Records \| B-DAT \| B-EVE \| B-FAC \| B-LOC \| B-MON \| B-ORG \| B-PCT \| B-PER \| B-PRO \| B-TIM \| I-DAT \| I-EVE \| I-FAC \| I-LOC \| I-MON \| I-ORG \| I-PCT \| I-PER \| I-PRO \| I-TIM \| \|:------\|----------:\|--------:\|--------:\|--------:\|--------:\|--------:\|--------:\|--------:\|--------:\|--------:\|--------:\|--------:\|--------:\|--------:\|--------:\|--------:\|--------:\|--------:\|--------:\|--------:\|--------:\| \| Train \| 29133 \| 1423 \| 1487 \| 1400 \| 13919 \| 417 \| 15926 \| 355 \| 12347 \| 1855 \| 150 \| 1947 \| 5018 \| 2421 \| 4118 \| 1059 \| 19579 \| 573 \| 7699 \| 1914 \| 332 \| \| Valid \| 5142 \| 267 \| 253 \| 250 \| 2362 \| 100 \| 2651 \| 64 \| 2173 \| 317 \| 19 \| 373 \| 799 \| 387 \| 717 \| 270 \| 3260 \| 101 \| 1382 \| 303 \| 35 \| \| Test \| 6049 \| 407 \| 256 \| 248 \| 2886 \| 98 \| 3216 \| 94 \| 2646 \| 318 \| 43 \| 568 \| 888 \| 408 \| 858 \| 263 \| 3967 \| 141 \| 1707 \| 296 \| 78 \| ## Evaluation The following tables summarize the scores obtained by model overall and per each class. Overall \| Model \| accuracy \| precision \| recall \| f1 \| \|:----------:\|:--------:\|:---------:\|:--------:\|:--------:\| \| Roberta \| 0.994849 \| 0.949816 \| 0.960235 \| 0.954997 \| Per entities \| \| number \| precision \| recall \| f1 \| \|:---: \|:------: \|:---------: \|:--------: \|:--------: \| \| DAT \| 407 \| 0.844869 \| 0.869779 \| 0.857143 \| \| EVE \| 256 \| 0.948148 \| 1.000000 \| 0.973384 \| \| FAC \| 248 \| 0.957529 \| 1.000000 \| 0.978304 \| \| LOC \| 2884 \| 0.965422 \| 0.968100 \| 0.966759 \| \| MON \| 98 \| 0.937500 \| 0.918367 \| 0.927835 \| \| ORG \| 3216 \| 0.943662 \| 0.958333 \| 0.950941 \| \| PCT \| 94 \| 1.000000 \| 0.968085 \| 0.983784 \| \| PER \| 2646 \| 0.957030 \| 0.959562 \| 0.958294 \| \| PRO \| 318 \| 0.963636 \| 1.000000 \| 0.981481 \| \| TIM \| 43 \| 0.739130 \| 0.790698 \| 0.764045 \| ## How To Use You use this model with Transformers pipeline for NER. ### Installing requirements ```bash pip install transformers ``` ### How to predict using pipeline ```python from transformers import AutoTokenizer from transformers import AutoModelForTokenClassification # for pytorch from transformers import TFAutoModelForTokenClassification # for tensorflow from transformers import pipeline model_name_or_path = "HooshvareLab/roberta-fa-zwnj-base-ner" tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) model = AutoModelForTokenClassification.from_pretrained(model_name_or_path) # Pytorch # model = TFAutoModelForTokenClassification.from_pretrained(model_name_or_path) # Tensorflow nlp = pipeline("ner", model=model, tokenizer=tokenizer) example = "در سال ۲۰۱۳ درگذشت و آندرتیکر و کین برای او مراسم یادبود گرفتند." ner_results = nlp(example) print(ner_results) ``` ## Questions? Post a Github issue on the [ParsNER Issues](https://github.com/hooshvare/parsner/issues) repo.	{"language": "fa"}	token-classification	HooshvareLab/roberta-fa-zwnj-base-ner	[ "transformers", "pytorch", "tf", "jax", "roberta", "token-classification", "fa", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "fa" ]	TAGS #transformers #pytorch #tf #jax #roberta #token-classification #fa #autotrain_compatible #endpoints_compatible #region-us	RobertaNER ========== This model fine-tuned for the Named Entity Recognition (NER) task on a mixed NER dataset collected from ARMAN, PEYMA, and WikiANN that covered ten types of entities: * Date (DAT) * Event (EVE) * Facility (FAC) * Location (LOC) * Money (MON) * Organization (ORG) * Percent (PCT) * Person (PER) * Product (PRO) * Time (TIM) Dataset Information ------------------- Evaluation ---------- The following tables summarize the scores obtained by model overall and per each class. Overall Per entities How To Use ---------- You use this model with Transformers pipeline for NER. ### Installing requirements ### How to predict using pipeline Questions? ---------- Post a Github issue on the ParsNER Issues repo.	[ "### Installing requirements", "### How to predict using pipeline\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsNER Issues repo." ]	[ "TAGS\n#transformers #pytorch #tf #jax #roberta #token-classification #fa #autotrain_compatible #endpoints_compatible #region-us \n", "### Installing requirements", "### How to predict using pipeline\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsNER Issues repo." ]	[ 46, 5, 28 ]	[ "passage: TAGS\n#transformers #pytorch #tf #jax #roberta #token-classification #fa #autotrain_compatible #endpoints_compatible #region-us \n### Installing requirements### How to predict using pipeline\n\n\nQuestions?\n----------\n\n\nPost a Github issue on the ParsNER Issues repo." ]	[ -0.08314726501703262, 0.1157490536570549, -0.0024343165569007397, 0.027470091357827187, 0.15077988803386688, -0.055441174656152725, 0.009649896994233131, 0.05865620821714401, -0.004030258860439062, 0.07457054406404495, 0.23936396837234497, 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null	null	transformers	# Roberta This model can tackle the zero-width non-joiner character for Persian writing. Also, the model was trained on new multi-types corpora with a new set of vocabulary. ## Questions? Post a Github issue on the [ParsRoBERTa Issues](https://github.com/hooshvare/parsbert/issues) repo.	{"language": "fa", "license": "apache-2.0"}	fill-mask	HooshvareLab/roberta-fa-zwnj-base	[ "transformers", "pytorch", "tf", "jax", "roberta", "fill-mask", "fa", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "fa" ]	TAGS #transformers #pytorch #tf #jax #roberta #fill-mask #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	# Roberta This model can tackle the zero-width non-joiner character for Persian writing. Also, the model was trained on new multi-types corpora with a new set of vocabulary. ## Questions? Post a Github issue on the ParsRoBERTa Issues repo.	[ "# Roberta\nThis model can tackle the zero-width non-joiner character for Persian writing. Also, the model was trained on new multi-types corpora with a new set of vocabulary.", "## Questions?\nPost a Github issue on the ParsRoBERTa Issues repo." ]	[ "TAGS\n#transformers #pytorch #tf #jax #roberta #fill-mask #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# Roberta\nThis model can tackle the zero-width non-joiner character for Persian writing. Also, the model was trained on new multi-types corpora with a new set of vocabulary.", "## Questions?\nPost a Github issue on the ParsRoBERTa Issues repo." ]	[ 53, 44, 21 ]	[ "passage: TAGS\n#transformers #pytorch #tf #jax #roberta #fill-mask #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Roberta\nThis model can tackle the zero-width non-joiner character for Persian writing. Also, the model was trained on new multi-types corpora with a new set of vocabulary.## Questions?\nPost a Github issue on the ParsRoBERTa Issues repo." ]	[ 0.013316607102751732, 0.016602257266640663, -0.004560746252536774, 0.05762382596731186, 0.11933447420597076, 0.009296347387135029, 0.16469180583953857, 0.1337018758058548, 0.0056284754537045956, 0.000307341426378116, 0.19010688364505768, 0.011213063262403011, 0.04161045700311661, 0.08819986879825592, 0.05163419991731644, -0.29944539070129395, 0.03927408903837204, -0.03503430634737015, -0.01597783714532852, 0.10031022131443024, 0.15304860472679138, 0.028918324038386345, 0.04852067306637764, 0.01804042048752308, -0.1373795121908188, 0.03667570650577545, -0.05566154047846794, -0.11916959285736084, 0.0784757137298584, 0.05940894037485123, 0.07700962573289871, 0.07041047513484955, 0.010784598998725414, -0.07303524017333984, 0.05230426415801048, -0.0036070768255740404, -0.09988366812467575, -0.009097162634134293, 0.05003155767917633, 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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. --> # roberta-base-bne-finetuned-amazon_reviews_multi This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.2275 - Accuracy: 0.9335 ## 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 \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1909 \| 1.0 \| 1250 \| 0.1717 \| 0.9333 \| \| 0.0932 \| 2.0 \| 2500 \| 0.2275 \| 0.9335 \| ### Framework versions - Transformers 4.9.2 - Pytorch 1.9.0+cu102 - Datasets 1.11.0 - Tokenizers 0.10.3	{"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "metrics": ["accuracy"], "model_index": [{"name": "roberta-base-bne-finetuned-amazon_reviews_multi", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "amazon_reviews_multi", "type": "amazon_reviews_multi", "args": "es"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9335}}]}]}	text-classification	Hormigo/roberta-base-bne-finetuned-amazon_reviews_multi	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:amazon_reviews_multi", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us	roberta-base-bne-finetuned-amazon\_reviews\_multi ================================================= This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the amazon\_reviews\_multi dataset. It achieves the following results on the evaluation set: * Loss: 0.2275 * Accuracy: 0.9335 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 4.9.2 * Pytorch 1.9.0+cu102 * Datasets 1.11.0 * Tokenizers 0.10.3	[ "### 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 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3" ]	[ "TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-cc-by-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: 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 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3" ]	[ 68, 98, 4, 34 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-cc-by-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: 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 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3" ]	[ -0.08839257806539536, 0.07182613760232925, -0.0022761800792068243, 0.12354572117328644, 0.17461615800857544, 0.04664640501141548, 0.15001033246517181, 0.11295176297426224, -0.0911908969283104, 0.0025587626732885838, 0.11353598535060883, 0.16285838186740875, 0.006882305257022381, 0.11342660337686539, -0.06002412363886833, -0.25545111298561096, -0.014264321886003017, 0.04585016891360283, -0.039443038403987885, 0.1466878354549408, 0.10804580897092819, 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null	null	transformers	# SpongeBob DialoGPT Model	{"tags": ["conversational"]}	text-generation	Htenn/DialoGPT-small-spongebob	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# SpongeBob DialoGPT Model	[ "# SpongeBob DialoGPT Model" ]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# SpongeBob DialoGPT Model" ]	[ 51, 10 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# SpongeBob DialoGPT Model" ]	[ 0.04686988517642021, 0.01831582933664322, -0.006684387568384409, 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null	null	transformers	# SpongeBob DialoGPT Model	{"tags": ["conversational"]}	text-generation	Htenn/DialoGPT-small-spongebobv2	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# SpongeBob DialoGPT Model	[ "# SpongeBob DialoGPT Model" ]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# SpongeBob DialoGPT Model" ]	[ 51, 10 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# SpongeBob DialoGPT Model" ]	[ 0.04686988517642021, 0.01831582933664322, -0.006684387568384409, 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null	null	transformers	#Rick Sanchez DiaoloGPT Model	{"tags": ["conversational"]}	text-generation	HueJanus/DialoGPT-small-ricksanchez	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	#Rick Sanchez DiaoloGPT Model	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 51 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.009697278961539268, 0.03208012506365776, -0.007204889785498381, 0.004809224978089333, 0.16726240515708923, 0.014898733235895634, 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null	null	spacy	\| Feature \| Description \| \| --- \| --- \| \| Name \| `en_roberta_base_leetspeak_ner` \| \| Version \| `0.0.0` \| \| spaCy \| `>=3.2.1,<3.3.0` \| \| Default Pipeline \| `transformer`, `ner` \| \| Components \| `transformer`, `ner` \| \| Vectors \| 0 keys, 0 unique vectors (0 dimensions) \| \| Sources \| [roberta-base](https://huggingface.co/roberta-base) pre-trained model on English language using a masked language modeling (MLM) objective by Yinhan Liu et al. <br> [LeetSpeak-NER](https://huggingface.co/spaces/Huertas97/LeetSpeak-NER) app where this model is in production for countering information disorders\| \| License \| Apache 2.0 \| \| Author \| [Álvaro Huertas García](https://www.linkedin.com/in/alvaro-huertas-garcia/) at [AI+DA](http://aida.etsisi.upm.es/) \| ### Label Scheme <details> <summary>View label scheme (4 labels for 1 components)</summary> \| Component \| Labels \| \| --- \| --- \| \| `ner` \| `INV_CAMO`, `LEETSPEAK`, `MIX`, `PUNCT_CAMO` \| </details> ### Accuracy \| Type \| Score \| \| --- \| --- \| \| `ENTS_F` \| 82.80 \| \| `ENTS_P` \| 79.66 \| \| `ENTS_R` \| 86.20 \| \| `TRANSFORMER_LOSS` \| 177808.42 \| \| `NER_LOSS` \| 608427.31 \|	{"language": ["en"], "license": "apache-2.0", "tags": ["spacy", "token-classification"], "widget": [{"text": "But one other thing that we have to re;think is the way that we dy\u00a3 our #c!l.o\|th?\u00a3+s.", "example_title": "Word camouflage detection"}]}	token-classification	Huertas97/en_roberta_base_leetspeak_ner	[ "spacy", "token-classification", "en", "license:apache-2.0", "model-index", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "en" ]	TAGS #spacy #token-classification #en #license-apache-2.0 #model-index #region-us	### Label Scheme View label scheme (4 labels for 1 components) ### Accuracy	[ "### Label Scheme\n\n\n\nView label scheme (4 labels for 1 components)", "### Accuracy" ]	[ "TAGS\n#spacy #token-classification #en #license-apache-2.0 #model-index #region-us \n", "### Label Scheme\n\n\n\nView label scheme (4 labels for 1 components)", "### Accuracy" ]	[ 29, 16, 5 ]	[ "passage: TAGS\n#spacy #token-classification #en #license-apache-2.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (4 labels for 1 components)### Accuracy" ]	[ -0.05568309128284454, 0.17319437861442566, -0.0034472632687538862, 0.041638392955064774, 0.06083042547106743, 0.03757912665605545, 0.26408088207244873, 0.08819597214460373, 0.1715358942747116, 0.010658691637217999, 0.06291800737380981, 0.07930941879749298, 0.05281561613082886, 0.2113848477602005, -0.06922280043363571, -0.21227715909481049, 0.11571303755044937, -0.035667866468429565, 0.02719002589583397, 0.1196933165192604, 0.08958529680967331, -0.10638204962015152, 0.05836468189954758, -0.03823268041014671, -0.16291067004203796, 0.011090396903455257, 0.01565633900463581, -0.10898629575967789, 0.06445775181055069, -0.048063866794109344, 0.14021985232830048, 0.029567304998636246, 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null	null	spacy	\| Feature \| Description \| \| --- \| --- \| \| Name \| `es_roberta_base_bne_leetspeak_ner` \| \| Version \| `0.0.0` \| \| spaCy \| `>=3.2.1,<3.3.0` \| \| Default Pipeline \| `transformer`, `ner` \| \| Components \| `transformer`, `ner` \| \| Vectors \| 0 keys, 0 unique vectors (0 dimensions) \| \| Sources \| [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) model a transformer-based masked language model for the Spanish language pre-trained with a total of 570GB of clean and deduplicated text compiled from the web crawlings performed by the National Library of Spain (Biblioteca Nacional de España) <br> [LeetSpeak-NER](https://huggingface.co/spaces/Huertas97/LeetSpeak-NER) app where this model is in production for countering information disorders\| \| License \| Apache 2.0 \| \| Author \| [Álvaro Huertas García](https://www.linkedin.com/in/alvaro-huertas-garcia/) at [AI+DA](http://aida.etsisi.upm.es/) \| ### Label Scheme <details> <summary>View label scheme (4 labels for 1 components)</summary> \| Component \| Labels \| \| --- \| --- \| \| `ner` \| `INV_CAMO`, `LEETSPEAK`, `MIX`, `PUNCT_CAMO` \| </details> ### Accuracy \| Type \| Score \| \| --- \| --- \| \| `ENTS_F` \| 91.82 \| \| `ENTS_P` \| 89.79 \| \| `ENTS_R` \| 93.94 \| \| `TRANSFORMER_LOSS` \| 166484.92 \| \| `NER_LOSS` \| 318457.35 \|	{"language": ["es"], "license": "apache-2.0", "tags": ["spacy", "token-classification"], "widget": [{"text": "La C0v!d es un 3ng@\u00f1o de los G0b!3rno$", "example_title": "Word camouflage detection"}]}	token-classification	Huertas97/es_roberta_base_bne_leetspeak_ner	[ "spacy", "token-classification", "es", "license:apache-2.0", "model-index", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "es" ]	TAGS #spacy #token-classification #es #license-apache-2.0 #model-index #region-us	### Label Scheme View label scheme (4 labels for 1 components) ### Accuracy	[ "### Label Scheme\n\n\n\nView label scheme (4 labels for 1 components)", "### Accuracy" ]	[ "TAGS\n#spacy #token-classification #es #license-apache-2.0 #model-index #region-us \n", "### Label Scheme\n\n\n\nView label scheme (4 labels for 1 components)", "### Accuracy" ]	[ 29, 16, 5 ]	[ "passage: TAGS\n#spacy #token-classification #es #license-apache-2.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (4 labels for 1 components)### Accuracy" ]	[ -0.05436871945858002, 0.16220295429229736, -0.003784076776355505, 0.043531835079193115, 0.061964280903339386, 0.04371211305260658, 0.2640169858932495, 0.09127694368362427, 0.1790560632944107, 0.006836076267063618, 0.06730550527572632, 0.08496613800525665, 0.04982852190732956, 0.22175101935863495, -0.0681060403585434, -0.21301409602165222, 0.12032399326562881, -0.03481718897819519, 0.03391524404287338, 0.11355218291282654, 0.09467724710702896, -0.1033516451716423, 0.059108857065439224, -0.0404898077249527, -0.14982205629348755, 0.020420286804437637, 0.014769327826797962, 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null	null	transformers	# CamemBERT pretrained on french trade directories from the XIXth century This mdoel is part of the material of the paper > Abadie, N., Carlinet, E., Chazalon, J., Duménieu, B. (2022). A > Benchmark of Named Entity Recognition Approaches in Historical > Documents Application to 19𝑡ℎ Century French Directories. In: Uchida, > S., Barney, E., Eglin, V. (eds) Document Analysis Systems. DAS 2022. > Lecture Notes in Computer Science, vol 13237. Springer, Cham. > https://doi.org/10.1007/978-3-031-06555-2_30 The source code to train this model is available on the [GitHub repository](https://github.com/soduco/paper-ner-bench-das22) of the paper as a Jupyter notebook in `src/ner/10-camembert_pretraining.ipynb`. ## Model description This model pre-train the model [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on a set of ~845k entries from Paris trade directories from the XIXth century extracted with OCR. Trade directory entries are short and strongly structured texts that giving the name, activity and location of a person or business, e.g: ``` Peynaud, R. de la Vieille Bouclerie, 18. Richard, Joullain et comp., (commission- —Phéâtre Français. naire, (entrepôt), au port de la Rapée- ``` ## Intended uses & limitations This model is intended for reproducibility of the NER evaluation published in the DAS2022 paper. Several derived models trained for NER on trade directories are available on HuggingFace, each trained on a different dataset : - [das22-10-camembert_pretrained_finetuned_ref](): trained for NER on ~6000 directory entries manually corrected. - [das22-10-camembert_pretrained_finetuned_pero](): trained for NER on ~6000 directory entries extracted with PERO-OCR. - [das22-10-camembert_pretrained_finetuned_tess](): trained for NER on ~6000 directory entries extracted with Tesseract. ### 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 \| \|:-------------:\|:-----:\|:------:\|:---------------:\| \| 1.9603 \| 1.0 \| 100346 \| 1.8005 \| \| 1.7032 \| 2.0 \| 200692 \| 1.6460 \| \| 1.5879 \| 3.0 \| 301038 \| 1.5570 \| ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3	{"tags": ["generated_from_trainer"], "model-index": [{"name": "CamemBERT pretrained on french trade directories from the XIXth century", "results": []}]}	fill-mask	HueyNemud/das22-10-camembert_pretrained	[ "transformers", "pytorch", "camembert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #camembert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us	CamemBERT pretrained on french trade directories from the XIXth century ======================================================================= This mdoel is part of the material of the paper > > Abadie, N., Carlinet, E., Chazalon, J., Duménieu, B. (2022). A > Benchmark of Named Entity Recognition Approaches in Historical > Documents Application to 19𝑡ℎ Century French Directories. In: Uchida, > S., Barney, E., Eglin, V. (eds) Document Analysis Systems. DAS 2022. > Lecture Notes in Computer Science, vol 13237. Springer, Cham. > URL > > > The source code to train this model is available on the GitHub repository of the paper as a Jupyter notebook in 'src/ner/10-camembert\_pretraining.ipynb'. Model description ----------------- This model pre-train the model Jean-Baptiste/camembert-ner on a set of ~845k entries from Paris trade directories from the XIXth century extracted with OCR. Trade directory entries are short and strongly structured texts that giving the name, activity and location of a person or business, e.g: Intended uses & limitations --------------------------- This model is intended for reproducibility of the NER evaluation published in the DAS2022 paper. Several derived models trained for NER on trade directories are available on HuggingFace, each trained on a different dataset : * das22-10-camembert\_pretrained\_finetuned\_ref: trained for NER on ~6000 directory entries manually corrected. * das22-10-camembert\_pretrained\_finetuned\_pero: trained for NER on ~6000 directory entries extracted with PERO-OCR. * das22-10-camembert\_pretrained\_finetuned\_tess: trained for NER on ~6000 directory entries extracted with Tesseract. ### 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 4.16.0.dev0 * Pytorch 1.10.1+cu102 * Datasets 1.17.0 * Tokenizers 0.10.3	[ "### 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 4.16.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.17.0\n* Tokenizers 0.10.3" ]	[ "TAGS\n#transformers #pytorch #camembert #fill-mask #generated_from_trainer #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 4.16.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.17.0\n* Tokenizers 0.10.3" ]	[ 45, 98, 4, 38 ]	[ "passage: TAGS\n#transformers #pytorch #camembert #fill-mask #generated_from_trainer #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 4.16.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.17.0\n* Tokenizers 0.10.3" ]	[ -0.09896817058324814, 0.010975436307489872, -0.002216184511780739, 0.11319668591022491, 0.20645472407341003, 0.04067856818437576, 0.11228807270526886, 0.08891595155000687, -0.12411889433860779, 0.02546224184334278, 0.12731970846652985, 0.16430002450942993, -0.005618017166852951, 0.14664654433727264, -0.05268454551696777, -0.2486542910337448, -0.013169270940124989, 0.03372665494680405, -0.10152239352464676, 0.1261271983385086, 0.08708202838897705, 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null	null	sentence-transformers	# KLUE RoBERTa base model for Sentence Embeddings This is the `sentence-klue-roberta-base` model. The sentence-transformers repository allows to train and use Transformer models for generating sentence and text embeddings. The model is described in the paper [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084) ## Usage (Sentence-Transformers) Using this model becomes more convenient when you have [sentence-transformers](https://github.com/UKPLab/sentence-transformers) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python import torch from sentence_transformers import SentenceTransformer, util model = SentenceTransformer("Huffon/sentence-klue-roberta-base") docs = [ "1992년 7월 8일 손흥민은 강원도 춘천시 후평동에서 아버지 손웅정과 어머니 길은자의 차남으로 태어나 그곳에서 자랐다.", "형은 손흥윤이다.", "춘천 부안초등학교를 졸업했고, 춘천 후평중학교에 입학한 후 2학년때 원주 육민관중학교 축구부에 들어가기 위해 전학하여 졸업하였으며, 2008년 당시 FC 서울의 U-18팀이었던 동북고등학교 축구부에서 선수 활동 중 대한축구협회 우수선수 해외유학 프로젝트에 선발되어 2008년 8월 독일 분데스리가의 함부르크 유소년팀에 입단하였다.", "함부르크 유스팀 주전 공격수로 2008년 6월 네덜란드에서 열린 4개국 경기에서 4게임에 출전, 3골을 터뜨렸다.", "1년간의 유학 후 2009년 8월 한국으로 돌아온 후 10월에 개막한 FIFA U-17 월드컵에 출전하여 3골을 터트리며 한국을 8강으로 이끌었다.", "그해 11월 함부르크의 정식 유소년팀 선수 계약을 체결하였으며 독일 U-19 리그 4경기 2골을 넣고 2군 리그에 출전을 시작했다.", "독일 U-19 리그에서 손흥민은 11경기 6골, 2부 리그에서는 6경기 1골을 넣으며 재능을 인정받아 2010년 6월 17세의 나이로 함부르크의 1군 팀 훈련에 참가, 프리시즌 활약으로 함부르크와 정식 계약을 한 후 10월 18세에 함부르크 1군 소속으로 독일 분데스리가에 데뷔하였다.", ] document_embeddings = model.encode(docs) query = "손흥민은 어린 나이에 유럽에 진출하였다." query_embedding = model.encode(query) top_k = min(5, len(docs)) cos_scores = util.pytorch_cos_sim(query_embedding, document_embeddings)[0] top_results = torch.topk(cos_scores, k=top_k) print(f"입력 문장: {query}") print(f"<입력 문장과 유사한 {top_k} 개의 문장>") for i, (score, idx) in enumerate(zip(top_results[0], top_results[1])): print(f"{i+1}: {docs[idx]} {'(유사도: {:.4f})'.format(score)}") ```	{"language": "ko", "tags": ["roberta", "sentence-transformers"], "datasets": ["klue"]}	null	Huffon/sentence-klue-roberta-base	[ "sentence-transformers", "pytorch", "roberta", "ko", "dataset:klue", "arxiv:1908.10084", "has_space", "region:us" ]	2022-03-02T23:29:04+00:00	[ "1908.10084" ]	[ "ko" ]	TAGS #sentence-transformers #pytorch #roberta #ko #dataset-klue #arxiv-1908.10084 #has_space #region-us	# KLUE RoBERTa base model for Sentence Embeddings This is the 'sentence-klue-roberta-base' model. The sentence-transformers repository allows to train and use Transformer models for generating sentence and text embeddings. The model is described in the paper Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks ## Usage (Sentence-Transformers) Using this model becomes more convenient when you have sentence-transformers installed: Then you can use the model like this:	[ "# KLUE RoBERTa base model for Sentence Embeddings\n\nThis is the 'sentence-klue-roberta-base' model. The sentence-transformers repository allows to train and use Transformer models for generating sentence and text embeddings.\n\nThe model is described in the paper Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", "## Usage (Sentence-Transformers)\n\nUsing this model becomes more convenient when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:" ]	[ "TAGS\n#sentence-transformers #pytorch #roberta #ko #dataset-klue #arxiv-1908.10084 #has_space #region-us \n", "# KLUE RoBERTa base model for Sentence Embeddings\n\nThis is the 'sentence-klue-roberta-base' model. The sentence-transformers repository allows to train and use Transformer models for generating sentence and text embeddings.\n\nThe model is described in the paper Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", "## Usage (Sentence-Transformers)\n\nUsing this model becomes more convenient when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:" ]	[ 39, 88, 39 ]	[ "passage: TAGS\n#sentence-transformers #pytorch #roberta #ko #dataset-klue #arxiv-1908.10084 #has_space #region-us \n# KLUE RoBERTa base model for Sentence Embeddings\n\nThis is the 'sentence-klue-roberta-base' model. The sentence-transformers repository allows to train and use Transformer models for generating sentence and text embeddings.\n\nThe model is described in the paper Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks## Usage (Sentence-Transformers)\n\nUsing this model becomes more convenient when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:" ]	[ 0.0835380032658577, -0.037601057440042496, -0.004489980172365904, -0.04753978177905083, 0.06401754915714264, -0.009569678455591202, 0.13099096715450287, 0.0701083317399025, -0.0488557368516922, -0.04297046363353729, 0.10857958346605301, 0.10166940093040466, -0.027230791747570038, -0.03987205773591995, 0.05883629992604256, -0.43028950691223145, 0.09243132174015045, 0.01925746537744999, -0.06572236120700836, 0.1008298248052597, 0.1320512592792511, -0.0652090385556221, 0.029862577095627785, 0.0700588971376419, -0.08414604514837265, 0.0669446513056755, -0.054945822805166245, -0.04022688418626785, 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null	null	sentence-transformers	# Humair/all-mpnet-base-v2-finetuned-v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('Humair/all-mpnet-base-v2-finetuned-v2') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('Humair/all-mpnet-base-v2-finetuned-v2') model = AutoModel.from_pretrained('Humair/all-mpnet-base-v2-finetuned-v2') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=Humair/all-mpnet-base-v2-finetuned-v2) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 1 with parameters: ``` {'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 2, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 32, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->	{"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"}	sentence-similarity	Humair/all-mpnet-base-v2-finetuned-v2	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us	# Humair/all-mpnet-base-v2-finetuned-v2 This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: ## Usage (HuggingFace Transformers) Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ## Evaluation Results For an automated evaluation of this model, see the Sentence Embeddings Benchmark: URL ## Training The model was trained with the parameters: DataLoader: 'URL.dataloader.DataLoader' of length 1 with parameters: Loss: 'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters: Parameters of the fit()-Method: ## Full Model Architecture ## Citing & Authors	[ "# Humair/all-mpnet-base-v2-finetuned-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the Sentence Embeddings Benchmark: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 1 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]	[ "TAGS\n#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n", "# Humair/all-mpnet-base-v2-finetuned-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the Sentence Embeddings Benchmark: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 1 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]	[ 43, 62, 38, 64, 29, 85, 5, 6 ]	[ "passage: TAGS\n#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n# Humair/all-mpnet-base-v2-finetuned-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the Sentence Embeddings Benchmark: URL## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 1 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors" ]	[ -0.026652289554476738, 0.13101667165756226, -0.0068895574659109116, 0.04159511625766754, 0.11174267530441284, 0.020861923694610596, 0.13884101808071136, 0.08423162996768951, -0.03744105249643326, 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null	null	null	Model saved for Paraphrased Detection in English-Vietnamese cross-lingual based on XLM-R in MT-DNN MT-DNN: github.com/namisan/mt-dnn	{}	null	HungVo/mt-dnn-ev-mrpc	[ "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #region-us	Model saved for Paraphrased Detection in English-Vietnamese cross-lingual based on XLM-R in MT-DNN MT-DNN: URL	[]	[ "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, 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null	null	transformers	#DwightSchrute DialoGPT-Model #TheOffice	{"tags": ["conversational"]}	text-generation	HypNyx/DialoGPT-small-DwightBot	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	#DwightSchrute DialoGPT-Model #TheOffice	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 51 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.009697278961539268, 0.03208012506365776, -0.007204889785498381, 0.004809224978089333, 0.16726240515708923, 0.014898733235895634, 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null	null	transformers	#Thanos DialoGPT Model	{"tags": ["conversational"]}	text-generation	HypNyx/DialoGPT-small-Thanos	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	#Thanos DialoGPT Model	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 51 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.009697278961539268, 0.03208012506365776, -0.007204889785498381, 0.004809224978089333, 0.16726240515708923, 0.014898733235895634, 0.09765533357858658, 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null	null	transformers	# Peter from Your Boyfriend Game.	{"tags": ["conversational"]}	text-generation	HypedKid/PeterBot	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "has_space", "text-generation-inference", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us	# Peter from Your Boyfriend Game.	[ "# Peter from Your Boyfriend Game." ]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n", "# Peter from Your Boyfriend Game." ]	[ 55, 8 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n# Peter from Your Boyfriend Game." ]	[ 0.029336808249354362, 0.08473480492830276, -0.006291069556027651, 0.06550099700689316, 0.1508171707391739, -0.017133250832557678, 0.09925227612257004, 0.10033313184976578, -0.012911682017147541, 0.022072123363614082, 0.1630454957485199, 0.11513424664735794, -0.013527085073292255, 0.07097040116786957, 0.004938766825944185, -0.2923603355884552, 0.029714977368712425, 0.029295478016138077, -0.01047634519636631, 0.12709879875183105, 0.03987053781747818, -0.058486685156822205, 0.10198689997196198, 0.00495075574144721, 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null	null	transformers	# Erlangshen-MegatronBert-1.3B - Main Page:[Fengshenbang](https://fengshenbang-lm.com/) - Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM) ## 简介 Brief Introduction 2021登顶FewCLUE和ZeroCLUE，处理NLU任务，开源时最大的中文BERT模型 It topped FewCLUE and ZeroCLUE benchmarks in 2021, solving NLU tasks, was the largest BERT when publicly released. ## 模型分类 Model Taxonomy \| 需求 Demand \| 任务 Task \| 系列 Series \| 模型 Model \| 参数 Parameter \| 额外 Extra \| \| :----: \| :----: \| :----: \| :----: \| :----: \| :----: \| \| 通用 General \| 自然语言理解 NLU \| 二郎神 Erlangshen \| MegatronBERT \| 1.3B \| 中文 Chinese \| ## 模型信息 Model Information Encoder结构为主的双向语言模型，专注于解决各种自然语言理解任务。我们跟进了[Megatron-LM](https://github.com/NVIDIA/Megatron-LM)的工作，使用了32张A100，总共耗时14天在悟道语料库（180 GB版本）上训练了十亿级别参数量的BERT。同时，鉴于中文语法和大规模训练的难度，我们使用四种预训练策略来改进BERT：1) 整词掩码, 2) 知识动态遮掩, 3) 句子顺序预测, 4) 层前归一化. A bidirectional language model based on the Encoder structure, focusing on solving various NLU tasks. We follow [Megatron-LM](https://github.com/NVIDIA/Megatron-LM), using 32 A100s and spending 14 days training a billion-level BERT on WuDao Corpora (180 GB version). Given Chinese grammar and the difficulty of large-scale training, we use four pre-training procedures to improve BERT: 1) Whole Word Masking (WWM), 2) Knowledge-based Dynamic Masking (KDM), 3) Sentence Order Prediction (SOP), 4) Pre-layer Normalization (Pre-LN). ### 成就 Achievements 1.2021年11月10日，Erlangshen-MegatronBert-1.3B在FewCLUE上取得第一。其中，它在CHIDF(成语填空)和TNEWS(新闻分类)子任务中的表现优于人类表现。此外，它在CHIDF(成语填空), CSLDCP(学科文献分类), OCNLI(自然语言推理)任务中均名列前茅。 2.2022年1月24日，Erlangshen-MegatronBert-1.3B在CLUE基准测试中的ZeroCLUE中取得第一。具体到子任务，我们在CSLDCP(主题文献分类), TNEWS(新闻分类), IFLYTEK(应用描述分类), CSL(抽象关键字识别)和CLUEWSC(参考消歧)任务中取得第一。 3.在2022年7月10日，Erlangshen-MegatronBert-1.3B在CLUE基准的语义匹配任务中取得第一。 1.On November 10, 2021, Erlangshen-MegatronBert-1.3B topped the FewCLUE benchmark. Among them, our Erlangshen outperformed human performance in CHIDF (idiom fill-in-the-blank) and TNEWS (news classification) subtasks. In addition, our Erlangshen ranked the top in CHIDF (idiom fill-in-the-blank), CSLDCP (subject literature classification), and OCNLI (natural language inference) tasks. 2.On January 24, 2022, Erlangshen-MegatronBert-1.3B topped the ZeroCLUE benchmark. For each of these tasks, we rank the top ones in CSLDCP (Subject Literature Classification), TNEWS (News Classification), IFLYTEK (Application Description Classification), CSL (Abstract Keyword Recognition), and CLUEWSC (Referential Disambiguation) tasks. 3.Erlangshen-MegatronBert-1.3B topped the CLUE benchmark semantic matching task on July 10, 2022. ### 下游效果 Performance \| 模型 \| afqmc \| tnews \| iflytek \| ocnli \| cmnli \| wsc \| csl \| \| :--------: \| :-----: \| :----: \| :-----: \| :----: \| :----: \| :----: \| :----: \| \| roberta-wwm-ext-large \| 0.7514 \| 0.5872 \| 0.6152 \| 0.777 \| 0.814 \| 0.8914 \| 0.86 \| \| Erlangshen-MegatronBert-1.3B \| 0.7608 \| 0.5996 \| 0.6234 \| 0.7917 \| 0.81 \| 0.9243 \| 0.872 \| ## 使用 Usage ``` python from transformers import MegatronBertConfig, MegatronBertModel from transformers import BertTokenizer tokenizer = BertTokenizer.from_pretrained("IDEA-CCNL/Erlangshen-MegatronBert-1.3B") config = MegatronBertConfig.from_pretrained("IDEA-CCNL/Erlangshen-MegatronBert-1.3B") model = MegatronBertModel.from_pretrained("IDEA-CCNL/Erlangshen-MegatronBert-1.3B") ``` ## 引用 Citation 如果您在您的工作中使用了我们的模型，可以引用我们的[论文](https://arxiv.org/abs/2209.02970)： If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2209.02970): ```text @article{fengshenbang, author = {Jiaxing Zhang and Ruyi Gan and Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen}, title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence}, journal = {CoRR}, volume = {abs/2209.02970}, year = {2022} } ``` 也可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/): You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/): ```text @misc{Fengshenbang-LM, title={Fengshenbang-LM}, author={IDEA-CCNL}, year={2021}, howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}}, } ```	{"language": ["zh"], "license": "apache-2.0", "tags": ["bert", "NLU", "FewCLUE", "ZeroCLUE"], "inference": true}	null	IDEA-CCNL/Erlangshen-MegatronBert-1.3B	[ "transformers", "pytorch", "megatron-bert", "bert", "NLU", "FewCLUE", "ZeroCLUE", "zh", "arxiv:2209.02970", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[ "2209.02970" ]	[ "zh" ]	TAGS #transformers #pytorch #megatron-bert #bert #NLU #FewCLUE #ZeroCLUE #zh #arxiv-2209.02970 #license-apache-2.0 #endpoints_compatible #region-us	Erlangshen-MegatronBert-1.3B ============================ * Main Page:Fengshenbang * Github: Fengshenbang-LM 简介 Brief Introduction --------------------- 2021登顶FewCLUE和ZeroCLUE，处理NLU任务，开源时最大的中文BERT模型 It topped FewCLUE and ZeroCLUE benchmarks in 2021, solving NLU tasks, was the largest BERT when publicly released. 模型分类 Model Taxonomy ------------------- 模型信息 Model Information ---------------------- Encoder结构为主的双向语言模型，专注于解决各种自然语言理解任务。我们跟进了Megatron-LM的工作，使用了32张A100，总共耗时14天在悟道语料库（180 GB版本）上训练了十亿级别参数量的BERT。同时，鉴于中文语法和大规模训练的难度，我们使用四种预训练策略来改进BERT：1) 整词掩码, 2) 知识动态遮掩, 3) 句子顺序预测, 4) 层前归一化. A bidirectional language model based on the Encoder structure, focusing on solving various NLU tasks. We follow Megatron-LM, using 32 A100s and spending 14 days training a billion-level BERT on WuDao Corpora (180 GB version). Given Chinese grammar and the difficulty of large-scale training, we use four pre-training procedures to improve BERT: 1) Whole Word Masking (WWM), 2) Knowledge-based Dynamic Masking (KDM), 3) Sentence Order Prediction (SOP), 4) Pre-layer Normalization (Pre-LN). ### 成就 Achievements 1.2021年11月10日，Erlangshen-MegatronBert-1.3B在FewCLUE上取得第一。其中，它在CHIDF(成语填空)和TNEWS(新闻分类)子任务中的表现优于人类表现。此外，它在CHIDF(成语填空), CSLDCP(学科文献分类), OCNLI(自然语言推理)任务中均名列前茅。 2.2022年1月24日，Erlangshen-MegatronBert-1.3B在CLUE基准测试中的ZeroCLUE中取得第一。具体到子任务，我们在CSLDCP(主题文献分类), TNEWS(新闻分类), IFLYTEK(应用描述分类), CSL(抽象关键字识别)和CLUEWSC(参考消歧)任务中取得第一。 3.在2022年7月10日，Erlangshen-MegatronBert-1.3B在CLUE基准的语义匹配任务中取得第一。 1.On November 10, 2021, Erlangshen-MegatronBert-1.3B topped the FewCLUE benchmark. Among them, our Erlangshen outperformed human performance in CHIDF (idiom fill-in-the-blank) and TNEWS (news classification) subtasks. In addition, our Erlangshen ranked the top in CHIDF (idiom fill-in-the-blank), CSLDCP (subject literature classification), and OCNLI (natural language inference) tasks. 2.On January 24, 2022, Erlangshen-MegatronBert-1.3B topped the ZeroCLUE benchmark. For each of these tasks, we rank the top ones in CSLDCP (Subject Literature Classification), TNEWS (News Classification), IFLYTEK (Application Description Classification), CSL (Abstract Keyword Recognition), and CLUEWSC (Referential Disambiguation) tasks. 3.Erlangshen-MegatronBert-1.3B topped the CLUE benchmark semantic matching task on July 10, 2022. ### 下游效果 Performance 使用 Usage -------- 引用 Citation ----------- 如果您在您的工作中使用了我们的模型，可以引用我们的论文： If you are using the resource for your work, please cite the our paper: 也可以引用我们的网站: You can also cite our website:	[ "### 成就 Achievements\n\n\n1.2021年11月10日，Erlangshen-MegatronBert-1.3B在FewCLUE上取得第一。其中，它在CHIDF(成语填空)和TNEWS(新闻分类)子任务中的表现优于人类表现。此外，它在CHIDF(成语填空), CSLDCP(学科文献分类), OCNLI(自然语言推理)任务中均名列前茅。 \n\n2.2022年1月24日，Erlangshen-MegatronBert-1.3B在CLUE基准测试中的ZeroCLUE中取得第一。具体到子任务，我们在CSLDCP(主题文献分类), TNEWS(新闻分类), IFLYTEK(应用描述分类), CSL(抽象关键字识别)和CLUEWSC(参考消歧)任务中取得第一。 \n\n3.在2022年7月10日，Erlangshen-MegatronBert-1.3B在CLUE基准的语义匹配任务中取得第一。\n\n\n1.On November 10, 2021, Erlangshen-MegatronBert-1.3B topped the FewCLUE benchmark. Among them, our Erlangshen outperformed human performance in CHIDF (idiom fill-in-the-blank) and TNEWS (news classification) subtasks. In addition, our Erlangshen ranked the top in CHIDF (idiom fill-in-the-blank), CSLDCP (subject literature classification), and OCNLI (natural language inference) tasks. \n\n2.On January 24, 2022, Erlangshen-MegatronBert-1.3B topped the ZeroCLUE benchmark. For each of these tasks, we rank the top ones in CSLDCP (Subject Literature Classification), TNEWS (News Classification), IFLYTEK (Application Description Classification), CSL (Abstract Keyword Recognition), and CLUEWSC (Referential Disambiguation) tasks. \n\n3.Erlangshen-MegatronBert-1.3B topped the CLUE benchmark semantic matching task on July 10, 2022.", "### 下游效果 Performance\n\n\n\n使用 Usage\n--------\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n也可以引用我们的网站:\n\n\nYou can also cite our website:" ]	[ "TAGS\n#transformers #pytorch #megatron-bert #bert #NLU #FewCLUE #ZeroCLUE #zh #arxiv-2209.02970 #license-apache-2.0 #endpoints_compatible #region-us \n", "### 成就 Achievements\n\n\n1.2021年11月10日，Erlangshen-MegatronBert-1.3B在FewCLUE上取得第一。其中，它在CHIDF(成语填空)和TNEWS(新闻分类)子任务中的表现优于人类表现。此外，它在CHIDF(成语填空), CSLDCP(学科文献分类), OCNLI(自然语言推理)任务中均名列前茅。 \n\n2.2022年1月24日，Erlangshen-MegatronBert-1.3B在CLUE基准测试中的ZeroCLUE中取得第一。具体到子任务，我们在CSLDCP(主题文献分类), TNEWS(新闻分类), IFLYTEK(应用描述分类), CSL(抽象关键字识别)和CLUEWSC(参考消歧)任务中取得第一。 \n\n3.在2022年7月10日，Erlangshen-MegatronBert-1.3B在CLUE基准的语义匹配任务中取得第一。\n\n\n1.On November 10, 2021, Erlangshen-MegatronBert-1.3B topped the FewCLUE benchmark. Among them, our Erlangshen outperformed human performance in CHIDF (idiom fill-in-the-blank) and TNEWS (news classification) subtasks. In addition, our Erlangshen ranked the top in CHIDF (idiom fill-in-the-blank), CSLDCP (subject literature classification), and OCNLI (natural language inference) tasks. \n\n2.On January 24, 2022, Erlangshen-MegatronBert-1.3B topped the ZeroCLUE benchmark. For each of these tasks, we rank the top ones in CSLDCP (Subject Literature Classification), TNEWS (News Classification), IFLYTEK (Application Description Classification), CSL (Abstract Keyword Recognition), and CLUEWSC (Referential Disambiguation) tasks. \n\n3.Erlangshen-MegatronBert-1.3B topped the CLUE benchmark semantic matching task on July 10, 2022.", "### 下游效果 Performance\n\n\n\n使用 Usage\n--------\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n也可以引用我们的网站:\n\n\nYou can also cite our website:" ]	[ 59, 465, 63 ]	[ "passage: TAGS\n#transformers #pytorch #megatron-bert #bert #NLU #FewCLUE #ZeroCLUE #zh #arxiv-2209.02970 #license-apache-2.0 #endpoints_compatible #region-us \n" ]	[ -0.07020561397075653, 0.04604746028780937, -0.00557973375543952, 0.00908314436674118, 0.00874472688883543, -0.0005789474816992879, 0.1005411297082901, 0.09168349951505661, 0.017039312049746513, 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null	null	transformers	# Randeng-MegatronT5-770M - Main Page:[Fengshenbang](https://fengshenbang-lm.com/) - Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM) ## 简介 Brief Introduction 善于处理NLT任务，中文版的T5-large。 Good at solving NLT tasks, Chinese T5-large. ## 模型分类 Model Taxonomy \| 需求 Demand \| 任务 Task \| 系列 Series \| 模型 Model \| 参数 Parameter \| 额外 Extra \| \| :----: \| :----: \| :----: \| :----: \| :----: \| :----: \| \| 通用 General \| 自然语言转换 NLT \| 燃灯 Randeng \| MegatronT5 \| 770M \| 中文-Chinese \| ## 模型信息 Model Information 为了得到一个大规模的中文版的T5，我们使用了Megatron-LM的方法和悟道语料库(180G版本)用于预训练。具体地，我们在预训练阶段中使用了[Megatron-LM](https://github.com/NVIDIA/Megatron-LM) 大概花费了16张A100约14天。 To get a large-scale Chinese T5, we use of [Megatron-LM](https://github.com/NVIDIA/Megatron-LM) and WuDao Corpora (180 GB version) for pre-training. Specifically, in the pre-training phase which cost about 14 days with 16 A100 GPUs. ## 使用 Usage 因为[transformers](https://github.com/huggingface/transformers)库中是没有Randeng-MegatronT5-770M相关的模型结构的，所以你可以在我们的[Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)中找到并且运行代码。 Since there is no structure of Randeng-MegatronT5-770M in [transformers library](https://github.com/huggingface/transformers), you can find the structure of Randeng-MegatronT5-770M and run the codes in [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM). ```shell git clone https://github.com/IDEA-CCNL/Fengshenbang-LM.git ``` ### 加载模型 Loading Models ```python from fengshen import T5ForConditionalGeneration from fengshen import T5Config from fengshen import T5Tokenizer tokenizer = T5Tokenizer.from_pretrained('IDEA-CCNL/Randeng-MegatronT5-770M') config = T5Config.from_pretrained('IDEA-CCNL/Randeng-MegatronT5-770M') model = T5ForConditionalGeneration.from_pretrained('IDEA-CCNL/Randeng-MegatronT5-770M') ``` ## 引用 Citation 如果您在您的工作中使用了我们的模型，可以引用我们的[论文](https://arxiv.org/abs/2209.02970)： If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2209.02970): ```text @article{fengshenbang, author = {Jiaxing Zhang and Ruyi Gan and Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen}, title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence}, journal = {CoRR}, volume = {abs/2209.02970}, year = {2022} } ``` 也可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/): You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/): ```text @misc{Fengshenbang-LM, title={Fengshenbang-LM}, author={IDEA-CCNL}, year={2021}, howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}}, } ```	{"language": ["zh"], "license": "apache-2.0", "inference": false}	text2text-generation	IDEA-CCNL/Randeng-MegatronT5-770M	[ "transformers", "pytorch", "t5", "text2text-generation", "zh", "arxiv:2209.02970", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:04+00:00	[ "2209.02970" ]	[ "zh" ]	TAGS #transformers #pytorch #t5 #text2text-generation #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us	Randeng-MegatronT5-770M ======================= * Main Page:Fengshenbang * Github: Fengshenbang-LM 简介 Brief Introduction --------------------- 善于处理NLT任务，中文版的T5-large。 Good at solving NLT tasks, Chinese T5-large. 模型分类 Model Taxonomy ------------------- 模型信息 Model Information ---------------------- 为了得到一个大规模的中文版的T5，我们使用了Megatron-LM的方法和悟道语料库(180G版本)用于预训练。具体地，我们在预训练阶段中使用了Megatron-LM 大概花费了16张A100约14天。 To get a large-scale Chinese T5, we use of Megatron-LM and WuDao Corpora (180 GB version) for pre-training. Specifically, in the pre-training phase which cost about 14 days with 16 A100 GPUs. 使用 Usage -------- 因为transformers库中是没有Randeng-MegatronT5-770M相关的模型结构的，所以你可以在我们的Fengshenbang-LM中找到并且运行代码。 Since there is no structure of Randeng-MegatronT5-770M in transformers library, you can find the structure of Randeng-MegatronT5-770M and run the codes in Fengshenbang-LM. ### 加载模型 Loading Models 引用 Citation ----------- 如果您在您的工作中使用了我们的模型，可以引用我们的论文： If you are using the resource for your work, please cite the our paper: 也可以引用我们的网站: You can also cite our website:	[ "### 加载模型 Loading Models\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n也可以引用我们的网站:\n\n\nYou can also cite our website:" ]	[ "TAGS\n#transformers #pytorch #t5 #text2text-generation #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n", "### 加载模型 Loading Models\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n也可以引用我们的网站:\n\n\nYou can also cite our website:" ]	[ 58, 59 ]	[ "passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n### 加载模型 Loading Models\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n也可以引用我们的网站:\n\n\nYou can also cite our website:" ]	[ -0.03813690319657326, 0.08126543462276459, -0.0047030942514538765, 0.02359665371477604, 0.07687419652938843, 0.019748706370592117, 0.2221692055463791, 0.07526828348636627, 0.025652725249528885, -0.09931520372629166, 0.1695411503314972, 0.18001367151737213, 0.013550807721912861, 0.03685414418578148, 0.006438189186155796, -0.23167552053928375, 0.01716950163245201, 0.04641900956630707, 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null	null	transformers	# Wenzhong-GPT2-3.5B - Main Page:[Fengshenbang](https://fengshenbang-lm.com/) - Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM) ## 简介 Brief Introduction 善于处理NLG任务，目前最大的，中文版的GPT2 Focused on handling NLG tasks, the current largest, Chinese GPT2. ## 模型分类 Model Taxonomy \| 需求 Demand \| 任务 Task \| 系列 Series \| 模型 Model \| 参数 Parameter \| 额外 Extra \| \| :----: \| :----: \| :----: \| :----: \| :----: \| :----: \| \| 通用 General \| 自然语言生成 NLG\| 闻仲 Wenzhong \| GPT2 \| 3.5B \| 中文 Chinese \| ## 模型信息 Model Information 为了可以获得一个强大的单向语言模型，我们采用GPT模型结构，并且应用于中文语料上。具体地，这个模型拥有30层解码器和35亿参数，这比原本的GPT2-XL还要大。我们在100G的中文语料上预训练，这消耗了32个NVIDIA A100显卡大约28小时。据我们所知，它是目前最大的中文的GPT模型。 To obtain a robust unidirectional language model, we adopt the GPT model structure and apply it to the Chinese corpus. Specifically, this model has 30 decoder layers and 3.5 billion parameters, which is larger than the original GPT2-XL. We pre-train it on 100G of Chinese corpus, which consumes 32 NVIDIA A100 GPUs for about 28 hours. To the best of our knowledge, it is the largest Chinese GPT model currently available. ## 使用 Usage ### 加载模型 Loading Models ```python from transformers import GPT2Tokenizer, GPT2Model tokenizer = GPT2Tokenizer.from_pretrained('IDEA-CCNL/Wenzhong-GPT2-3.5B') model = GPT2Model.from_pretrained('IDEA-CCNL/Wenzhong-GPT2-3.5B') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` ### 使用示例 Usage Examples ```python from transformers import pipeline, set_seed set_seed(55) generator = pipeline('text-generation', model='IDEA-CCNL/Wenzhong-GPT2-3.5B') generator("北京位于", max_length=30, num_return_sequences=1) ``` ## 引用 Citation 如果您在您的工作中使用了我们的模型，可以引用我们的[论文](https://arxiv.org/abs/2209.02970)： If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2209.02970): ```text @article{fengshenbang, author = {Jiaxing Zhang and Ruyi Gan and Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen}, title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence}, journal = {CoRR}, volume = {abs/2209.02970}, year = {2022} } ``` 也可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/): You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/): ```text @misc{Fengshenbang-LM, title={Fengshenbang-LM}, author={IDEA-CCNL}, year={2021}, howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}}, } ```	{"language": ["zh"], "license": "apache-2.0", "inference": {"parameters": {"max_new_tokens": 128, "do_sample": true}}}	text-generation	IDEA-CCNL/Wenzhong-GPT2-3.5B	[ "transformers", "pytorch", "gpt2", "text-generation", "zh", "arxiv:2209.02970", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:04+00:00	[ "2209.02970" ]	[ "zh" ]	TAGS #transformers #pytorch #gpt2 #text-generation #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Wenzhong-GPT2-3.5B ================== * Main Page:Fengshenbang * Github: Fengshenbang-LM 简介 Brief Introduction --------------------- 善于处理NLG任务，目前最大的，中文版的GPT2 Focused on handling NLG tasks, the current largest, Chinese GPT2. 模型分类 Model Taxonomy ------------------- 模型信息 Model Information ---------------------- 为了可以获得一个强大的单向语言模型，我们采用GPT模型结构，并且应用于中文语料上。具体地，这个模型拥有30层解码器和35亿参数，这比原本的GPT2-XL还要大。我们在100G的中文语料上预训练，这消耗了32个NVIDIA A100显卡大约28小时。据我们所知，它是目前最大的中文的GPT模型。 To obtain a robust unidirectional language model, we adopt the GPT model structure and apply it to the Chinese corpus. Specifically, this model has 30 decoder layers and 3.5 billion parameters, which is larger than the original GPT2-XL. We pre-train it on 100G of Chinese corpus, which consumes 32 NVIDIA A100 GPUs for about 28 hours. To the best of our knowledge, it is the largest Chinese GPT model currently available. 使用 Usage -------- ### 加载模型 Loading Models ### 使用示例 Usage Examples 引用 Citation ----------- 如果您在您的工作中使用了我们的模型，可以引用我们的论文： If you are using the resource for your work, please cite the our paper: 也可以引用我们的网站: You can also cite our website:	[ "### 加载模型 Loading Models", "### 使用示例 Usage Examples\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n也可以引用我们的网站:\n\n\nYou can also cite our website:" ]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### 加载模型 Loading Models", "### 使用示例 Usage Examples\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n也可以引用我们的网站:\n\n\nYou can also cite our website:" ]	[ 65, 9, 60 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### 加载模型 Loading Models### 使用示例 Usage Examples\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n也可以引用我们的网站:\n\n\nYou can also cite our website:" ]	[ -0.04882131889462471, 0.012552875094115734, -0.0038968806620687246, 0.012530649080872536, 0.07627525180578232, 0.019277110695838928, 0.1598009169101715, 0.09122103452682495, -0.0007611630717292428, -0.09425868093967438, 0.20266227424144745, 0.20625998079776764, 0.0023251480888575315, -0.008545270189642906, -0.026633499190211296, -0.24291132390499115, 0.028121747076511383, 0.07625814527273178, -0.018084513023495674, 0.13177908957004547, 0.16650022566318512, -0.036235012114048004, 0.1053599864244461, 0.08635129034519196, 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null	null	transformers	# Yuyuan-GPT2-3.5B - Main Page:[Fengshenbang](https://fengshenbang-lm.com/) - Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM) ## 简介 Brief Introduction 目前最大的，医疗领域的生成语言模型GPT2。 The currently largest, generative language model GPT2 in the medical domain. ## 模型分类 Model Taxonomy \| 需求 Demand \| 任务 Task \| 系列 Series \| 模型 Model \| 参数 Parameter \| 额外 Extra \| \| :----: \| :----: \| :----: \| :----: \| :----: \| :----: \| \| 特殊 Special \| 领域 Domain \| 余元 Yuyuan \| GPT2 \| 3.5B \| - \| ## 模型信息 Model Information 我们采用与Wenzhong-GPT2-3.5B相同的架构，在50GB的医学(PubMed)语料库上进行预训练。我们使用了32个NVIDIA A100显卡大约7天。我们的Yuyuan-GPT2-3.5B是医疗领域最大的开源的GPT2模型。进一步地，模型可以通过计算困惑度（PPL）来判断事实。为了完成问答功能，我们将短语模式从疑问的形式转换为了陈述句。 We adopt the same architecture as Wenzhong-GPT2-3.5B to be pre-trained on 50 GB medical (PubMed) corpus. We use 32 NVIDIA A100 GPUs for about 7 days. Our Yuyuan-GPT2-3.5B is the largest open-source GPT2 model in the medical domain. We further allow the model to judge facts by computing perplexity (PPL). To accomplish question-and-answer functionality, we transform the phrase pattern from interrogative to declarative. ## 使用 Usage ### 加载模型 Loading Models ```python from transformers import GPT2Tokenizer, GPT2Model tokenizer = GPT2Tokenizer.from_pretrained('IDEA-CCNL/Yuyuan-GPT2-3.5B') model = GPT2Model.from_pretrained('IDEA-CCNL/Yuyuan-GPT2-3.5B') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` ### 使用示例 Usage Examples ```python from transformers import pipeline, set_seed set_seed(55) generator = pipeline('text-generation', model='IDEA-CCNL/Yuyuan-GPT2-3.5B') generator("Diabetics should not eat", max_length=30, num_return_sequences=1) ``` ## 引用 Citation 如果您在您的工作中使用了我们的模型，可以引用我们的[论文](https://arxiv.org/abs/2209.02970)： If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2209.02970): ```text @article{fengshenbang, author = {Jiaxing Zhang and Ruyi Gan and Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen}, title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence}, journal = {CoRR}, volume = {abs/2209.02970}, year = {2022} } ``` 也可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/): You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/): ```text @misc{Fengshenbang-LM, title={Fengshenbang-LM}, author={IDEA-CCNL}, year={2021}, howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}}, } ```	{"language": ["en"], "license": "apache-2.0", "inference": false}	text-generation	IDEA-CCNL/Yuyuan-GPT2-3.5B	[ "transformers", "pytorch", "gpt2", "text-generation", "en", "arxiv:2209.02970", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:04+00:00	[ "2209.02970" ]	[ "en" ]	TAGS #transformers #pytorch #gpt2 #text-generation #en #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us	Yuyuan-GPT2-3.5B ================ * Main Page:Fengshenbang * Github: Fengshenbang-LM 简介 Brief Introduction --------------------- 目前最大的，医疗领域的生成语言模型GPT2。 The currently largest, generative language model GPT2 in the medical domain. 模型分类 Model Taxonomy ------------------- 模型信息 Model Information ---------------------- 我们采用与Wenzhong-GPT2-3.5B相同的架构，在50GB的医学(PubMed)语料库上进行预训练。我们使用了32个NVIDIA A100显卡大约7天。我们的Yuyuan-GPT2-3.5B是医疗领域最大的开源的GPT2模型。进一步地，模型可以通过计算困惑度（PPL）来判断事实。为了完成问答功能，我们将短语模式从疑问的形式转换为了陈述句。 We adopt the same architecture as Wenzhong-GPT2-3.5B to be pre-trained on 50 GB medical (PubMed) corpus. We use 32 NVIDIA A100 GPUs for about 7 days. Our Yuyuan-GPT2-3.5B is the largest open-source GPT2 model in the medical domain. We further allow the model to judge facts by computing perplexity (PPL). To accomplish question-and-answer functionality, we transform the phrase pattern from interrogative to declarative. 使用 Usage -------- ### 加载模型 Loading Models ### 使用示例 Usage Examples 引用 Citation ----------- 如果您在您的工作中使用了我们的模型，可以引用我们的论文： If you are using the resource for your work, please cite the our paper: 也可以引用我们的网站: You can also cite our website:	[ "### 加载模型 Loading Models", "### 使用示例 Usage Examples\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n也可以引用我们的网站:\n\n\nYou can also cite our website:" ]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #en #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n", "### 加载模型 Loading Models", "### 使用示例 Usage Examples\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n也可以引用我们的网站:\n\n\nYou can also cite our website:" ]	[ 57, 9, 60 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #en #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n### 加载模型 Loading Models### 使用示例 Usage Examples\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n也可以引用我们的网站:\n\n\nYou can also cite our website:" ]	[ -0.022030368447303772, 0.09822552651166916, -0.0060209427028894424, 0.02618783712387085, 0.08629167824983597, 0.021036693826317787, 0.2228602170944214, 0.0805712342262268, -0.009432175196707249, -0.11066102236509323, 0.19998973608016968, 0.1824013739824295, 0.0020038033835589886, 0.02927674539387226, 0.011121860705316067, -0.24597713351249695, 0.023965638130903244, 0.03214946761727333, -0.0273252185434103, 0.14760832488536835, 0.13343125581741333, -0.030191199854016304, 0.07893471419811249, 0.07304202020168304, -0.08668408542871475, 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null	null	transformers	# Zhouwenwang-Unified-1.3B - Main Page:[Fengshenbang](https://fengshenbang-lm.com/) - Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM) ## 简介 Brief Introduction 与追一科技合作探索的中文统一模型，13亿参数的编码器结构模型。 The Chinese unified model explored in cooperation with Zhuiyi Technology, the encoder structure model with 1.3B parameters. ## 模型分类 Model Taxonomy \| 需求 Demand \| 任务 Task \| 系列 Series \| 模型 Model \| 参数 Parameter \| 额外 Extra \| \| :----: \| :----: \| :----: \| :----: \| :----: \| :----: \| \| 特殊 Special \| 探索 Exploration \| 周文王 Zhouwenwang \| 待定 TBD \| 1.3B \| 中文 Chinese \| ## 模型信息 Model Information IDEA研究院认知计算中心联合追一科技有限公司提出的具有新结构的大模型。该模型在预训练阶段时考虑统一LM和MLM的任务，这让其同时具备生成和理解的能力，并且增加了旋转位置编码技术。目前已有13亿参数的Zhouwenwang-Unified-1.3B大模型，是中文领域中可以同时做LM和MLM任务的最大的模型。我们后续会持续在模型规模、知识融入、监督辅助任务等方向不断优化。 A large-scale model (Zhouwenwang-Unified-1.3B) with a new structure proposed by IDEA CCNL and Zhuiyi Technology. The model considers the task of unifying LM (Language Modeling) and MLM (Masked Language Modeling) during the pre-training phase, which gives it both generative and comprehension capabilities, and applys rotational position encoding. At present, Zhouwenwang-Unified-1.3B with 13B parameters is the largest Chinese model that can do both LM and MLM tasks. In the future, we will continue to optimize it in the direction of model size, knowledge incorporation, and supervisory assistance tasks. ### 下游任务 Performance 下游中文任务的得分（没有做任何数据增强）。 Scores on downstream chinese tasks (without any data augmentation) \| 模型 Model \| afqmc \| tnews \| iflytek \| ocnli \| cmnli \| wsc \| csl \| \| :--------: \| :-----: \| :----: \| :-----: \| :----: \| :----: \| :----: \| :----: \| \| roberta-wwm-ext-large \| 0.7514 \| 0.5872 \| 0.6152 \| 0.7770 \| 0.8140 \| 0.8914 \| 0.8600 \| \| Zhouwenwang-Unified-1.3B \| 0.7463 \| 0.6036 \| 0.6288 \| 0.7654 \| 0.7741 \| 0.8849 \| 0. 8777 \| ## 使用 Usage 因为[transformers](https://github.com/huggingface/transformers)库中是没有 Zhouwenwang-Unified-1.3B相关的模型结构的，所以你可以在我们的[Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)中找到并且运行代码。 Since there is no structure of Zhouwenwang-Unified-1.3B in [transformers library](https://github.com/huggingface/transformers), you can find the structure of Zhouwenwang-Unified-1.3B and run the codes in [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM). ```shell git clone https://github.com/IDEA-CCNL/Fengshenbang-LM.git ``` ### 加载模型 Loading Models ```python from fengshen import RoFormerModel from fengshen import RoFormerConfig from transformers import BertTokenizer tokenizer = BertTokenizer.from_pretrained("IDEA-CCNL/Zhouwenwang-Unified-1.3B") config = RoFormerConfig.from_pretrained("IDEA-CCNL/Zhouwenwang-Unified-1.3B") model = RoFormerModel.from_pretrained("IDEA-CCNL/Zhouwenwang-Unified-1.3B") ``` ### 使用示例 Usage Examples 你可以使用该模型进行续写任务。 You can use the model for continuation writing tasks. ```python from fengshen import RoFormerModel from transformers import AutoTokenizer import torch import numpy as np sentence = '清华大学位于' max_length = 32 tokenizer = AutoTokenizer.from_pretrained("IDEA-CCNL/Zhouwenwang-Unified-1.3B") model = RoFormerModel.from_pretrained("IDEA-CCNL/Zhouwenwang-Unified-1.3B") for i in range(max_length): encode = torch.tensor( [[tokenizer.cls_token_id]+tokenizer.encode(sentence, add_special_tokens=False)]).long() logits = model(encode)[0] logits = torch.nn.functional.linear( logits, model.embeddings.word_embeddings.weight) logits = torch.nn.functional.softmax( logits, dim=-1).cpu().detach().numpy()[0] sentence = sentence + \ tokenizer.decode(int(np.random.choice(logits.shape[1], p=logits[-1]))) if sentence[-1] == '。': break print(sentence) ``` ## 引用 Citation 如果您在您的工作中使用了我们的模型，可以引用我们的[论文](https://arxiv.org/abs/2209.02970)： If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2209.02970): ```text @article{fengshenbang, author = {Jiaxing Zhang and Ruyi Gan and Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen}, title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence}, journal = {CoRR}, volume = {abs/2209.02970}, year = {2022} } ``` 也可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/): You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/): ```text @misc{Fengshenbang-LM, title={Fengshenbang-LM}, author={IDEA-CCNL}, year={2021}, howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}}, } ```	{"language": ["zh"], "license": "apache-2.0", "widget": [{"text": "\u751f\u6d3b\u7684\u771f\u8c1b\u662f[MASK]\u3002"}]}	null	IDEA-CCNL/Zhouwenwang-Unified-1.3B	[ "transformers", "pytorch", "megatron-bert", "zh", "arxiv:2209.02970", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[ "2209.02970" ]	[ "zh" ]	TAGS #transformers #pytorch #megatron-bert #zh #arxiv-2209.02970 #license-apache-2.0 #endpoints_compatible #region-us	Zhouwenwang-Unified-1.3B ======================== * Main Page:Fengshenbang * Github: Fengshenbang-LM 简介 Brief Introduction --------------------- 与追一科技合作探索的中文统一模型，13亿参数的编码器结构模型。 The Chinese unified model explored in cooperation with Zhuiyi Technology, the encoder structure model with 1.3B parameters. 模型分类 Model Taxonomy ------------------- 模型信息 Model Information ---------------------- IDEA研究院认知计算中心联合追一科技有限公司提出的具有新结构的大模型。该模型在预训练阶段时考虑统一LM和MLM的任务，这让其同时具备生成和理解的能力，并且增加了旋转位置编码技术。目前已有13亿参数的Zhouwenwang-Unified-1.3B大模型，是中文领域中可以同时做LM和MLM任务的最大的模型。我们后续会持续在模型规模、知识融入、监督辅助任务等方向不断优化。 A large-scale model (Zhouwenwang-Unified-1.3B) with a new structure proposed by IDEA CCNL and Zhuiyi Technology. The model considers the task of unifying LM (Language Modeling) and MLM (Masked Language Modeling) during the pre-training phase, which gives it both generative and comprehension capabilities, and applys rotational position encoding. At present, Zhouwenwang-Unified-1.3B with 13B parameters is the largest Chinese model that can do both LM and MLM tasks. In the future, we will continue to optimize it in the direction of model size, knowledge incorporation, and supervisory assistance tasks. ### 下游任务 Performance 下游中文任务的得分（没有做任何数据增强）。 Scores on downstream chinese tasks (without any data augmentation) 使用 Usage -------- 因为transformers库中是没有 Zhouwenwang-Unified-1.3B相关的模型结构的，所以你可以在我们的Fengshenbang-LM中找到并且运行代码。 Since there is no structure of Zhouwenwang-Unified-1.3B in transformers library, you can find the structure of Zhouwenwang-Unified-1.3B and run the codes in Fengshenbang-LM. ### 加载模型 Loading Models ### 使用示例 Usage Examples 你可以使用该模型进行续写任务。 You can use the model for continuation writing tasks. 引用 Citation ----------- 如果您在您的工作中使用了我们的模型，可以引用我们的论文： If you are using the resource for your work, please cite the our paper: 也可以引用我们的网站: You can also cite our website:	[ "### 下游任务 Performance\n\n\n下游中文任务的得分（没有做任何数据增强）。\n\n\nScores on downstream chinese tasks (without any data augmentation)\n\n\n\n使用 Usage\n--------\n\n\n因为transformers库中是没有 Zhouwenwang-Unified-1.3B相关的模型结构的，所以你可以在我们的Fengshenbang-LM中找到并且运行代码。\n\n\nSince there is no structure of Zhouwenwang-Unified-1.3B in transformers library, you can find the structure of Zhouwenwang-Unified-1.3B and run the codes in Fengshenbang-LM.", "### 加载模型 Loading Models", "### 使用示例 Usage Examples\n\n\n你可以使用该模型进行续写任务。\n\n\nYou can use the model for continuation writing tasks.\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n也可以引用我们的网站:\n\n\nYou can also cite our website:" ]	[ "TAGS\n#transformers #pytorch #megatron-bert #zh #arxiv-2209.02970 #license-apache-2.0 #endpoints_compatible #region-us \n", "### 下游任务 Performance\n\n\n下游中文任务的得分（没有做任何数据增强）。\n\n\nScores on downstream chinese tasks (without any data augmentation)\n\n\n\n使用 Usage\n--------\n\n\n因为transformers库中是没有 Zhouwenwang-Unified-1.3B相关的模型结构的，所以你可以在我们的Fengshenbang-LM中找到并且运行代码。\n\n\nSince there is no structure of Zhouwenwang-Unified-1.3B in transformers library, you can find the structure of Zhouwenwang-Unified-1.3B and run the codes in Fengshenbang-LM.", "### 加载模型 Loading Models", "### 使用示例 Usage Examples\n\n\n你可以使用该模型进行续写任务。\n\n\nYou can use the model for continuation writing tasks.\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n也可以引用我们的网站:\n\n\nYou can also cite our website:" ]	[ 44, 133, 9, 82 ]	[ "passage: TAGS\n#transformers #pytorch #megatron-bert #zh #arxiv-2209.02970 #license-apache-2.0 #endpoints_compatible #region-us \n### 下游任务 Performance\n\n\n下游中文任务的得分（没有做任何数据增强）。\n\n\nScores on downstream chinese tasks (without any data augmentation)\n\n\n\n使用 Usage\n--------\n\n\n因为transformers库中是没有 Zhouwenwang-Unified-1.3B相关的模型结构的，所以你可以在我们的Fengshenbang-LM中找到并且运行代码。\n\n\nSince there is no structure of Zhouwenwang-Unified-1.3B in transformers library, you can find the structure of Zhouwenwang-Unified-1.3B and run the codes in Fengshenbang-LM.### 加载模型 Loading Models### 使用示例 Usage Examples\n\n\n你可以使用该模型进行续写任务。\n\n\nYou can use the model for continuation writing tasks.\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n也可以引用我们的网站:\n\n\nYou can also cite our website:" ]	[ -0.039821524173021317, -0.006813276093453169, -0.005877257324755192, 0.026450103148818016, 0.09603244066238403, 0.018302246928215027, 0.14509651064872742, 0.035508278757333755, 0.05732133239507675, 0.006615886930376291, 0.029642857611179352, 0.06162279471755028, 0.04189788177609444, -0.04949153587222099, -0.025722282007336617, -0.2481021136045456, 0.024074293673038483, 0.09314857423305511, -0.09891664981842041, 0.045701030641794205, 0.11760399490594864, -0.07683704048395157, 0.09847921878099442, 0.010679499246180058, -0.011249455623328686, -0.01352844387292862, -0.05033384636044502, -0.019849618896842003, 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null	null	transformers	# Zhouwenwang-Unified-110M - Main Page:[Fengshenbang](https://fengshenbang-lm.com/) - Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM) ## 简介 Brief Introduction 与追一科技合作探索的中文统一模型，1.1亿参数的编码器结构模型。 The Chinese unified model explored in cooperation with Zhuiyi Technology, the encoder structure model with 110M parameters. ## 模型分类 Model Taxonomy \| 需求 Demand \| 任务 Task \| 系列 Series \| 模型 Model \| 参数 Parameter \| 额外 Extra \| \| :----: \| :----: \| :----: \| :----: \| :----: \| :----: \| \| 特殊 Special \| 探索 Exploration \| 周文王 Zhouwenwang \| 待定 TBD \| 110M \| 中文 Chinese \| ## 模型信息 Model Information IDEA研究院认知计算中心联合追一科技有限公司提出的具有新结构的大模型。该模型在预训练阶段时考虑统一LM和MLM的任务，这让其同时具备生成和理解的能力，并且增加了旋转位置编码技术。我们后续会持续在模型规模、知识融入、监督辅助任务等方向不断优化。 A large-scale model (Zhouwenwang-Unified-1.3B) with a new structure proposed by IDEA CCNL and Zhuiyi Technology. The model considers the task of unifying LM (Language Modeling) and MLM (Masked Language Modeling) during the pre-training phase, which gives it both generative and comprehension capabilities, and applys rotational position encoding. In the future, we will continue to optimize it in the direction of model size, knowledge incorporation, and supervisory assistance tasks. ## 使用 Usage 因为[transformers](https://github.com/huggingface/transformers)库中是没有 Zhouwenwang-Unified-110M相关的模型结构的，所以你可以在我们的[Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)中找到并且运行代码。 Since there is no structure of Zhouwenwang-Unified-110M in [transformers library](https://github.com/huggingface/transformers), you can find the structure of Zhouwenwang-Unified-110M and run the codes in [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM). ```shell git clone https://github.com/IDEA-CCNL/Fengshenbang-LM.git ``` ### 加载模型 Loading Models ```python from fengshen import RoFormerModel from fengshen import RoFormerConfig from transformers import BertTokenizer tokenizer = BertTokenizer.from_pretrained("IDEA-CCNL/Zhouwenwang-Unified-110M") config = RoFormerConfig.from_pretrained("IDEA-CCNL/Zhouwenwang-Unified-110M") model = RoFormerModel.from_pretrained("IDEA-CCNL/Zhouwenwang-Unified-110M") ``` ### 使用示例 Usage Examples 你可以使用该模型进行续写任务。 You can use the model for continuation writing tasks. ```python from fengshen import RoFormerModel from transformers import AutoTokenizer import torch import numpy as np sentence = '清华大学位于' max_length = 32 tokenizer = AutoTokenizer.from_pretrained("IDEA-CCNL/Zhouwenwang-Unified-110M") model = RoFormerModel.from_pretrained("IDEA-CCNL/Zhouwenwang-Unified-110M") for i in range(max_length): encode = torch.tensor( [[tokenizer.cls_token_id]+tokenizer.encode(sentence, add_special_tokens=False)]).long() logits = model(encode)[0] logits = torch.nn.functional.linear( logits, model.embeddings.word_embeddings.weight) logits = torch.nn.functional.softmax( logits, dim=-1).cpu().detach().numpy()[0] sentence = sentence + \ tokenizer.decode(int(np.random.choice(logits.shape[1], p=logits[-1]))) if sentence[-1] == '。': break print(sentence) ``` ## 引用 Citation 如果您在您的工作中使用了我们的模型，可以引用我们的[论文](https://arxiv.org/abs/2209.02970)： If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2209.02970): ```text @article{fengshenbang, author = {Jiaxing Zhang and Ruyi Gan and Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen}, title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence}, journal = {CoRR}, volume = {abs/2209.02970}, year = {2022} } ``` 也可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/): You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/): ```text @misc{Fengshenbang-LM, title={Fengshenbang-LM}, author={IDEA-CCNL}, year={2021}, howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}}, } ```	{"language": ["zh"], "license": "apache-2.0", "widget": [{"text": "\u751f\u6d3b\u7684\u771f\u8c1b\u662f[MASK]\u3002"}]}	null	IDEA-CCNL/Zhouwenwang-Unified-110M	[ "transformers", "pytorch", "megatron-bert", "zh", "arxiv:2209.02970", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[ "2209.02970" ]	[ "zh" ]	TAGS #transformers #pytorch #megatron-bert #zh #arxiv-2209.02970 #license-apache-2.0 #endpoints_compatible #region-us	Zhouwenwang-Unified-110M ======================== * Main Page:Fengshenbang * Github: Fengshenbang-LM 简介 Brief Introduction --------------------- 与追一科技合作探索的中文统一模型，1.1亿参数的编码器结构模型。 The Chinese unified model explored in cooperation with Zhuiyi Technology, the encoder structure model with 110M parameters. 模型分类 Model Taxonomy ------------------- 模型信息 Model Information ---------------------- IDEA研究院认知计算中心联合追一科技有限公司提出的具有新结构的大模型。该模型在预训练阶段时考虑统一LM和MLM的任务，这让其同时具备生成和理解的能力，并且增加了旋转位置编码技术。我们后续会持续在模型规模、知识融入、监督辅助任务等方向不断优化。 A large-scale model (Zhouwenwang-Unified-1.3B) with a new structure proposed by IDEA CCNL and Zhuiyi Technology. The model considers the task of unifying LM (Language Modeling) and MLM (Masked Language Modeling) during the pre-training phase, which gives it both generative and comprehension capabilities, and applys rotational position encoding. In the future, we will continue to optimize it in the direction of model size, knowledge incorporation, and supervisory assistance tasks. 使用 Usage -------- 因为transformers库中是没有 Zhouwenwang-Unified-110M相关的模型结构的，所以你可以在我们的Fengshenbang-LM中找到并且运行代码。 Since there is no structure of Zhouwenwang-Unified-110M in transformers library, you can find the structure of Zhouwenwang-Unified-110M and run the codes in Fengshenbang-LM. ### 加载模型 Loading Models ### 使用示例 Usage Examples 你可以使用该模型进行续写任务。 You can use the model for continuation writing tasks. 引用 Citation ----------- 如果您在您的工作中使用了我们的模型，可以引用我们的论文： If you are using the resource for your work, please cite the our paper: 也可以引用我们的网站: You can also cite our website:	[ "### 加载模型 Loading Models", "### 使用示例 Usage Examples\n\n\n你可以使用该模型进行续写任务。\n\n\nYou can use the model for continuation writing tasks.\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n也可以引用我们的网站:\n\n\nYou can also cite our website:" ]	[ "TAGS\n#transformers #pytorch #megatron-bert #zh #arxiv-2209.02970 #license-apache-2.0 #endpoints_compatible #region-us \n", "### 加载模型 Loading Models", "### 使用示例 Usage Examples\n\n\n你可以使用该模型进行续写任务。\n\n\nYou can use the model for continuation writing tasks.\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n也可以引用我们的网站:\n\n\nYou can also cite our website:" ]	[ 44, 9, 82 ]	[ "passage: TAGS\n#transformers #pytorch #megatron-bert #zh #arxiv-2209.02970 #license-apache-2.0 #endpoints_compatible #region-us \n### 加载模型 Loading Models### 使用示例 Usage Examples\n\n\n你可以使用该模型进行续写任务。\n\n\nYou can use the model for continuation writing tasks.\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n也可以引用我们的网站:\n\n\nYou can also cite our website:" ]	[ 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null	null	transformers	# Rick And Morty DialoGPT Model	{"tags": ["conversational"]}	text-generation	ILoveThatLady/DialoGPT-small-rickandmorty	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Rick And Morty DialoGPT Model	[ "# Rick And Morty DialoGPT Model" ]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Rick And Morty DialoGPT Model" ]	[ 51, 10 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Rick And Morty DialoGPT Model" ]	[ -0.019738588482141495, 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null	null	transformers	#Slovak RoBERTA Masked Language Model ###83Mil Parameters in small model Medium and Large models coming soon! RoBERTA pretrained tokenizer vocab and merges included. --- ##Training params: - Dataset: 8GB Slovak Monolingual dataset including ParaCrawl (monolingual), OSCAR, and several gigs of my own findings and cleaning. - Preprocessing: Tokenized with a pretrained ByteLevelBPETokenizer trained on the same dataset. Uncased, with s, pad, /s, unk, and mask special tokens. - Evaluation results: - Mnoho ľudí tu MASK - žije. - žijú. - je. - trpí. - Ako sa MASK - máte - máš - má - hovorí - Plážová sezóna pod Zoborom patrí medzi MASK obdobia. - ročné - najkrajšie - najobľúbenejšie - najnáročnejšie - Limitations: The current model is fairly small, although it works very well. This model is meant to be finetuned on downstream tasks e.g. Part-of-Speech tagging, Question Answering, anything in GLUE or SUPERGLUE. - Credit: If you use this or any of my models in research or professional work, please credit me - Christopher Brousseau in said work.	{}	fill-mask	IMJONEZZ/SlovenBERTcina	[ "transformers", "pytorch", "safetensors", "roberta", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #safetensors #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us	#Slovak RoBERTA Masked Language Model ###83Mil Parameters in small model Medium and Large models coming soon! RoBERTA pretrained tokenizer vocab and merges included. --- ##Training params: - Dataset: 8GB Slovak Monolingual dataset including ParaCrawl (monolingual), OSCAR, and several gigs of my own findings and cleaning. - Preprocessing: Tokenized with a pretrained ByteLevelBPETokenizer trained on the same dataset. Uncased, with s, pad, /s, unk, and mask special tokens. - Evaluation results: - Mnoho ľudí tu MASK - žije. - žijú. - je. - trpí. - Ako sa MASK - máte - máš - má - hovorí - Plážová sezóna pod Zoborom patrí medzi MASK obdobia. - ročné - najkrajšie - najobľúbenejšie - najnáročnejšie - Limitations: The current model is fairly small, although it works very well. This model is meant to be finetuned on downstream tasks e.g. Part-of-Speech tagging, Question Answering, anything in GLUE or SUPERGLUE. - Credit: If you use this or any of my models in research or professional work, please credit me - Christopher Brousseau in said work.	[]	[ "TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]	[ 42 ]	[ "passage: TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]	[ -0.0688251480460167, -0.0006219777278602123, -0.008977851830422878, 0.016062406823039055, 0.107199527323246, 0.009290448389947414, 0.1326877623796463, 0.06040089577436447, 0.05801795423030853, -0.0015195317100733519, 0.15865245461463928, 0.19646146893501282, -0.03707929328083992, 0.2128314971923828, -0.0636933371424675, -0.22736719250679016, 0.0831809863448143, 0.02399665117263794, -0.04596315324306488, 0.13289432227611542, 0.07743001729249954, -0.09078354388475418, 0.0527360662817955, -0.02064482681453228, -0.08989014476537704, 0.02641587145626545, 0.07978507876396179, -0.131717249751091, 0.13515058159828186, 0.003648138139396906, 0.23696967959403992, 0.0329892635345459, -0.0428297221660614, -0.08689593523740768, 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null	null	transformers	# Hate Speech Classifier for Social Media Content in English Language A monolingual model for hate speech classification of social media content in English language. The model was trained on 103190 YouTube comments and tested on an independent test set of 20554 YouTube comments. It is based on English BERT base pre-trained language model. ## Please cite: Kralj Novak, P., Scantamburlo, T., Pelicon, A., Cinelli, M., Mozetič, I., & Zollo, F. (2022, July). __Handling disagreement in hate speech modelling__. In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 681-695). Cham: Springer International Publishing. https://link.springer.com/chapter/10.1007/978-3-031-08974-9_54 ## Tokenizer During training the text was preprocessed using the original English BERT base tokenizer. We suggest the same tokenizer is used for inference. ## Model output The model classifies each input into one of four distinct classes: * 0 - acceptable * 1 - inappropriate * 2 - offensive * 3 - violent Details on data acquisition and labeling including the Annotation guidelines: http://imsypp.ijs.si/wp-content/uploads/2021/12/IMSyPP_D2.2_multilingual-dataset.pdf	{"language": ["en"], "license": "mit", "widget": [{"text": "My name is Mark and I live in London. I am a postgraduate student at Queen Mary University."}]}	text-classification	IMSyPP/hate_speech_en	[ "transformers", "pytorch", "bert", "text-classification", "en", "license:mit", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #bert #text-classification #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us	# Hate Speech Classifier for Social Media Content in English Language A monolingual model for hate speech classification of social media content in English language. The model was trained on 103190 YouTube comments and tested on an independent test set of 20554 YouTube comments. It is based on English BERT base pre-trained language model. ## Please cite: Kralj Novak, P., Scantamburlo, T., Pelicon, A., Cinelli, M., Mozetič, I., & Zollo, F. (2022, July). __Handling disagreement in hate speech modelling__. In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 681-695). Cham: Springer International Publishing. URL ## Tokenizer During training the text was preprocessed using the original English BERT base tokenizer. We suggest the same tokenizer is used for inference. ## Model output The model classifies each input into one of four distinct classes: * 0 - acceptable * 1 - inappropriate * 2 - offensive * 3 - violent Details on data acquisition and labeling including the Annotation guidelines: URL	[ "# Hate Speech Classifier for Social Media Content in English Language\n\nA monolingual model for hate speech classification of social media content in English language. The model was trained on 103190 YouTube comments and tested on an independent test set of 20554 YouTube comments. It is based on English BERT base pre-trained language model.", "## Please cite:\nKralj Novak, P., Scantamburlo, T., Pelicon, A., Cinelli, M., Mozetič, I., & Zollo, F. (2022, July). __Handling disagreement in hate speech modelling__. In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 681-695). Cham: Springer International Publishing.\nURL", "## Tokenizer\n\nDuring training the text was preprocessed using the original English BERT base tokenizer. We suggest the same tokenizer is used for inference.", "## Model output\n\nThe model classifies each input into one of four distinct classes:\n* 0 - acceptable\n* 1 - inappropriate\n* 2 - offensive\n* 3 - violent\n\n\nDetails on data acquisition and labeling including the Annotation guidelines: \nURL" ]	[ "TAGS\n#transformers #pytorch #bert #text-classification #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "# Hate Speech Classifier for Social Media Content in English Language\n\nA monolingual model for hate speech classification of social media content in English language. The model was trained on 103190 YouTube comments and tested on an independent test set of 20554 YouTube comments. It is based on English BERT base pre-trained language model.", "## Please cite:\nKralj Novak, P., Scantamburlo, T., Pelicon, A., Cinelli, M., Mozetič, I., & Zollo, F. (2022, July). __Handling disagreement in hate speech modelling__. In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 681-695). Cham: Springer International Publishing.\nURL", "## Tokenizer\n\nDuring training the text was preprocessed using the original English BERT base tokenizer. We suggest the same tokenizer is used for inference.", "## Model output\n\nThe model classifies each input into one of four distinct classes:\n* 0 - acceptable\n* 1 - inappropriate\n* 2 - offensive\n* 3 - violent\n\n\nDetails on data acquisition and labeling including the Annotation guidelines: \nURL" ]	[ 47, 70, 97, 36, 51 ]	[ "passage: TAGS\n#transformers #pytorch #bert #text-classification #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Hate Speech Classifier for Social Media Content in English Language\n\nA monolingual model for hate speech classification of social media content in English language. The model was trained on 103190 YouTube comments and tested on an independent test set of 20554 YouTube comments. It is based on English BERT base pre-trained language model.## Please cite:\nKralj Novak, P., Scantamburlo, T., Pelicon, A., Cinelli, M., Mozetič, I., & Zollo, F. (2022, July). __Handling disagreement in hate speech modelling__. In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 681-695). Cham: Springer International Publishing.\nURL## Tokenizer\n\nDuring training the text was preprocessed using the original English BERT base tokenizer. We suggest the same tokenizer is used for inference.## Model output\n\nThe model classifies each input into one of four distinct classes:\n* 0 - acceptable\n* 1 - inappropriate\n* 2 - offensive\n* 3 - violent\n\n\nDetails on data acquisition and labeling including the Annotation guidelines: \nURL" ]	[ -0.005059394519776106, 0.10795153677463531, -0.002367636188864708, 0.06571268290281296, 0.07175552099943161, -0.04325404763221741, 0.14472045004367828, 0.07002594321966171, -0.0483054555952549, 0.007850494235754013, 0.01546879205852747, 0.07732877880334854, 0.02796979621052742, 0.08634976297616959, -0.06710358709096909, -0.1928166151046753, 0.05765655264258385, 0.0010392038384452462, 0.07674198597669601, 0.1057484894990921, 0.19033409655094147, -0.09015105664730072, 0.03670365363359451, -0.007919960655272007, -0.0848778784275055, -0.009418487548828125, 0.01564774289727211, -0.07168752700090408, 0.09809176623821259, 0.005675151478499174, 0.11975900083780289, 0.06587672233581543, 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null	null	transformers	# Hate Speech Classifier for Social Media Content in Italian Language A monolingual model for hate speech classification of social media content in Italian language. The model was trained on 119,670 YouTube comments and tested on an independent test set of 21,072 YouTube comments. It is based on Italian ALBERTO pre-trained language model. ## Please cite: Kralj Novak, P., Scantamburlo, T., Pelicon, A., Cinelli, M., Mozetič, I., & Zollo, F. (2022, July). __Handling disagreement in hate speech modelling__. In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 681-695). Cham: Springer International Publishing. https://link.springer.com/chapter/10.1007/978-3-031-08974-9_54 ## Tokenizer During training the text was preprocessed using the original Italian ALBERTO tokenizer. We suggest the same tokenizer is used for inference. ## Model output The model classifies each input into one of four distinct classes: * 0 - acceptable * 1 - inappropriate * 2 - offensive * 3 - violent	{"language": ["it"], "license": "mit", "widget": [{"text": "Ciao, mi chiamo Marcantonio, sono di Roma. Studio informatica all'Universit\u00e0 di Roma."}]}	text-classification	IMSyPP/hate_speech_it	[ "transformers", "pytorch", "bert", "text-classification", "it", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "it" ]	TAGS #transformers #pytorch #bert #text-classification #it #license-mit #autotrain_compatible #endpoints_compatible #region-us	# Hate Speech Classifier for Social Media Content in Italian Language A monolingual model for hate speech classification of social media content in Italian language. The model was trained on 119,670 YouTube comments and tested on an independent test set of 21,072 YouTube comments. It is based on Italian ALBERTO pre-trained language model. ## Please cite: Kralj Novak, P., Scantamburlo, T., Pelicon, A., Cinelli, M., Mozetič, I., & Zollo, F. (2022, July). __Handling disagreement in hate speech modelling__. In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 681-695). Cham: Springer International Publishing. URL ## Tokenizer During training the text was preprocessed using the original Italian ALBERTO tokenizer. We suggest the same tokenizer is used for inference. ## Model output The model classifies each input into one of four distinct classes: * 0 - acceptable * 1 - inappropriate * 2 - offensive * 3 - violent	[ "# Hate Speech Classifier for Social Media Content in Italian Language\n\nA monolingual model for hate speech classification of social media content in Italian language. The model was trained on 119,670 YouTube comments and tested on an independent test set of 21,072 YouTube comments. It is based on Italian ALBERTO pre-trained language model.", "## Please cite:\nKralj Novak, P., Scantamburlo, T., Pelicon, A., Cinelli, M., Mozetič, I., & Zollo, F. (2022, July). __Handling disagreement in hate speech modelling__. In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 681-695). Cham: Springer International Publishing.\nURL", "## Tokenizer\n\nDuring training the text was preprocessed using the original Italian ALBERTO tokenizer. We suggest the same tokenizer is used for inference.", "## Model output\n\nThe model classifies each input into one of four distinct classes:\n\n* 0 - acceptable\n\n* 1 - inappropriate\n\n* 2 - offensive\n\n* 3 - violent" ]	[ "TAGS\n#transformers #pytorch #bert #text-classification #it #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "# Hate Speech Classifier for Social Media Content in Italian Language\n\nA monolingual model for hate speech classification of social media content in Italian language. The model was trained on 119,670 YouTube comments and tested on an independent test set of 21,072 YouTube comments. It is based on Italian ALBERTO pre-trained language model.", "## Please cite:\nKralj Novak, P., Scantamburlo, T., Pelicon, A., Cinelli, M., Mozetič, I., & Zollo, F. (2022, July). __Handling disagreement in hate speech modelling__. In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 681-695). Cham: Springer International Publishing.\nURL", "## Tokenizer\n\nDuring training the text was preprocessed using the original Italian ALBERTO tokenizer. We suggest the same tokenizer is used for inference.", "## Model output\n\nThe model classifies each input into one of four distinct classes:\n\n* 0 - acceptable\n\n* 1 - inappropriate\n\n* 2 - offensive\n\n* 3 - violent" ]	[ 43, 72, 97, 36, 36 ]	[ "passage: TAGS\n#transformers #pytorch #bert #text-classification #it #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# Hate Speech Classifier for Social Media Content in Italian Language\n\nA monolingual model for hate speech classification of social media content in Italian language. The model was trained on 119,670 YouTube comments and tested on an independent test set of 21,072 YouTube comments. It is based on Italian ALBERTO pre-trained language model.## Please cite:\nKralj Novak, P., Scantamburlo, T., Pelicon, A., Cinelli, M., Mozetič, I., & Zollo, F. (2022, July). __Handling disagreement in hate speech modelling__. In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 681-695). Cham: Springer International Publishing.\nURL## Tokenizer\n\nDuring training the text was preprocessed using the original Italian ALBERTO tokenizer. We suggest the same tokenizer is used for inference.## Model output\n\nThe model classifies each input into one of four distinct classes:\n\n* 0 - acceptable\n\n* 1 - inappropriate\n\n* 2 - offensive\n\n* 3 - violent" ]	[ -0.018123846501111984, 0.07766526937484741, -0.002761959098279476, 0.04723672941327095, 0.07504807412624359, -0.04187069088220596, 0.188335582613945, 0.10686273872852325, 0.030291743576526642, 0.020441874861717224, 0.0013785025803372264, 0.07990284264087677, 0.025085540488362312, 0.05828462168574333, -0.09112200886011124, -0.18484441936016083, 0.06363549083471298, -0.007546508219093084, 0.10911611467599869, 0.10932210832834244, 0.18435059487819672, -0.06022253260016441, 0.02778560295701027, -0.008972566574811935, -0.10154209285974503, 0.019695062190294266, 0.03973384201526642, -0.07800368219614029, 0.10022787749767303, 0.034433476626873016, 0.11195604503154755, 0.08804546296596527, 0.03183498606085777, -0.1413683444261551, 0.032392967492341995, 0.06914258748292923, 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null	null	transformers	# Hate Speech Classifier for Social Media Content in Dutch A monolingual model for hate speech classification of social media content in Dutch. The model was trained on 20000 social media posts (youtube, twitter, facebook) and tested on an independent test set of 2000 posts. It is based on thepre-trained language model [BERTje](https://huggingface.co/wietsedv/bert-base-dutch-cased). ## Tokenizer During training the text was preprocessed using the BERTje tokenizer. We suggest the same tokenizer is used for inference. ## Model output The model classifies each input into one of four distinct classes: * 0 - acceptable * 1 - inappropriate * 2 - offensive * 3 - violent	{"language": ["nl"], "license": "mit"}	text-classification	IMSyPP/hate_speech_nl	[ "transformers", "pytorch", "bert", "text-classification", "nl", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "nl" ]	TAGS #transformers #pytorch #bert #text-classification #nl #license-mit #autotrain_compatible #endpoints_compatible #region-us	# Hate Speech Classifier for Social Media Content in Dutch A monolingual model for hate speech classification of social media content in Dutch. The model was trained on 20000 social media posts (youtube, twitter, facebook) and tested on an independent test set of 2000 posts. It is based on thepre-trained language model BERTje. ## Tokenizer During training the text was preprocessed using the BERTje tokenizer. We suggest the same tokenizer is used for inference. ## Model output The model classifies each input into one of four distinct classes: * 0 - acceptable * 1 - inappropriate * 2 - offensive * 3 - violent	[ "# Hate Speech Classifier for Social Media Content in Dutch\n\nA monolingual model for hate speech classification of social media content in Dutch. The model was trained on 20000 social media posts (youtube, twitter, facebook) and tested on an independent test set of 2000 posts. It is based on thepre-trained language model BERTje.", "## Tokenizer\n\nDuring training the text was preprocessed using the BERTje tokenizer. We suggest the same tokenizer is used for inference.", "## Model output\n\nThe model classifies each input into one of four distinct classes:\n* 0 - acceptable\n* 1 - inappropriate\n* 2 - offensive\n* 3 - violent" ]	[ "TAGS\n#transformers #pytorch #bert #text-classification #nl #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "# Hate Speech Classifier for Social Media Content in Dutch\n\nA monolingual model for hate speech classification of social media content in Dutch. The model was trained on 20000 social media posts (youtube, twitter, facebook) and tested on an independent test set of 2000 posts. It is based on thepre-trained language model BERTje.", "## Tokenizer\n\nDuring training the text was preprocessed using the BERTje tokenizer. We suggest the same tokenizer is used for inference.", "## Model output\n\nThe model classifies each input into one of four distinct classes:\n* 0 - acceptable\n* 1 - inappropriate\n* 2 - offensive\n* 3 - violent" ]	[ 43, 74, 34, 36 ]	[ "passage: TAGS\n#transformers #pytorch #bert #text-classification #nl #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# Hate Speech Classifier for Social Media Content in Dutch\n\nA monolingual model for hate speech classification of social media content in Dutch. The model was trained on 20000 social media posts (youtube, twitter, facebook) and tested on an independent test set of 2000 posts. It is based on thepre-trained language model BERTje.## Tokenizer\n\nDuring training the text was preprocessed using the BERTje tokenizer. We suggest the same tokenizer is used for inference.## Model output\n\nThe model classifies each input into one of four distinct classes:\n* 0 - acceptable\n* 1 - inappropriate\n* 2 - offensive\n* 3 - violent" ]	[ -0.018114499747753143, -0.04588284343481064, -0.002575149293988943, 0.06062249094247818, 0.04953495040535927, -0.07350874692201614, 0.18993869423866272, 0.05826412886381149, 0.09239859133958817, -0.06884898245334625, 0.13915954530239105, 0.06428954005241394, -0.03238203376531601, 0.09523464739322662, -0.05471052601933479, -0.22522766888141632, 0.10116815567016602, 0.021552199497818947, 0.07747313380241394, 0.14290593564510345, 0.15818899869918823, -0.053972695022821426, 0.054365374147892, 0.07623246312141418, -0.14273425936698914, 0.05186126008629799, 0.0377117283642292, -0.10186181217432022, 0.10391651093959808, -0.012434126809239388, 0.07092180103063583, 0.08327081054449081, 0.04032500833272934, -0.005920943804085255, 0.027017904445528984, -0.01599382422864437, 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null	null	transformers	# Hate Speech Classifier for Social Media Content in Slovenian Language A monolingual model for hate speech classification of social media content in Slovenian language. The model was trained on 50,000 Twitter comments and tested on an independent test set of 10,000 Twitter comments. It is based on multilingual CroSloEngual BERT pre-trained language model. ## Please cite: Kralj Novak, P., Scantamburlo, T., Pelicon, A., Cinelli, M., Mozetič, I., & Zollo, F. (2022, July). __Handling disagreement in hate speech modelling__. In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 681-695). Cham: Springer International Publishing. https://link.springer.com/chapter/10.1007/978-3-031-08974-9_54 ## Tokenizer During training the text was preprocessed using the original CroSloEngual BERT tokenizer. We suggest the same tokenizer is used for inference. ## Model output The model classifies each input into one of four distinct classes: * 0 - acceptable * 1 - inappropriate * 2 - offensive * 3 - violent	{"language": ["sl"], "license": "mit", "pipeline_tag": "text-classification", "inference": true, "widget": [{"text": "Sem Mark in \u017eivim v Ljubljani. Sem doktorski \u0161tudent na Mednarodni podiplomski \u0161oli Jo\u017eefa Stefana."}]}	text-classification	IMSyPP/hate_speech_slo	[ "transformers", "pytorch", "bert", "text-classification", "sl", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "sl" ]	TAGS #transformers #pytorch #bert #text-classification #sl #license-mit #autotrain_compatible #endpoints_compatible #region-us	# Hate Speech Classifier for Social Media Content in Slovenian Language A monolingual model for hate speech classification of social media content in Slovenian language. The model was trained on 50,000 Twitter comments and tested on an independent test set of 10,000 Twitter comments. It is based on multilingual CroSloEngual BERT pre-trained language model. ## Please cite: Kralj Novak, P., Scantamburlo, T., Pelicon, A., Cinelli, M., Mozetič, I., & Zollo, F. (2022, July). __Handling disagreement in hate speech modelling__. In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 681-695). Cham: Springer International Publishing. URL ## Tokenizer During training the text was preprocessed using the original CroSloEngual BERT tokenizer. We suggest the same tokenizer is used for inference. ## Model output The model classifies each input into one of four distinct classes: * 0 - acceptable * 1 - inappropriate * 2 - offensive * 3 - violent	[ "# Hate Speech Classifier for Social Media Content in Slovenian Language\n\nA monolingual model for hate speech classification of social media content in Slovenian language. The model was trained on 50,000 Twitter comments and tested on an independent test set of 10,000 Twitter comments. It is based on multilingual CroSloEngual BERT pre-trained language model.", "## Please cite:\nKralj Novak, P., Scantamburlo, T., Pelicon, A., Cinelli, M., Mozetič, I., & Zollo, F. (2022, July). __Handling disagreement in hate speech modelling__. In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 681-695). Cham: Springer International Publishing.\nURL", "## Tokenizer\n\nDuring training the text was preprocessed using the original CroSloEngual BERT tokenizer. We suggest the same tokenizer is used for inference.", "## Model output\n\nThe model classifies each input into one of four distinct classes:\n* 0 - acceptable\n* 1 - inappropriate\n* 2 - offensive\n* 3 - violent" ]	[ "TAGS\n#transformers #pytorch #bert #text-classification #sl #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "# Hate Speech Classifier for Social Media Content in Slovenian Language\n\nA monolingual model for hate speech classification of social media content in Slovenian language. The model was trained on 50,000 Twitter comments and tested on an independent test set of 10,000 Twitter comments. It is based on multilingual CroSloEngual BERT pre-trained language model.", "## Please cite:\nKralj Novak, P., Scantamburlo, T., Pelicon, A., Cinelli, M., Mozetič, I., & Zollo, F. (2022, July). __Handling disagreement in hate speech modelling__. In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 681-695). Cham: Springer International Publishing.\nURL", "## Tokenizer\n\nDuring training the text was preprocessed using the original CroSloEngual BERT tokenizer. We suggest the same tokenizer is used for inference.", "## Model output\n\nThe model classifies each input into one of four distinct classes:\n* 0 - acceptable\n* 1 - inappropriate\n* 2 - offensive\n* 3 - violent" ]	[ 43, 77, 97, 39, 36 ]	[ "passage: TAGS\n#transformers #pytorch #bert #text-classification #sl #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# Hate Speech Classifier for Social Media Content in Slovenian Language\n\nA monolingual model for hate speech classification of social media content in Slovenian language. The model was trained on 50,000 Twitter comments and tested on an independent test set of 10,000 Twitter comments. It is based on multilingual CroSloEngual BERT pre-trained language model.## Please cite:\nKralj Novak, P., Scantamburlo, T., Pelicon, A., Cinelli, M., Mozetič, I., & Zollo, F. (2022, July). __Handling disagreement in hate speech modelling__. In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 681-695). Cham: Springer International Publishing.\nURL## Tokenizer\n\nDuring training the text was preprocessed using the original CroSloEngual BERT tokenizer. We suggest the same tokenizer is used for inference.## Model output\n\nThe model classifies each input into one of four distinct classes:\n* 0 - acceptable\n* 1 - inappropriate\n* 2 - offensive\n* 3 - violent" ]	[ -0.010991141200065613, 0.013532072305679321, -0.003011934692040086, 0.07234567403793335, 0.03945344313979149, -0.028442753478884697, 0.23166023194789886, 0.07191477715969086, 0.0415385365486145, 0.008136129938066006, 0.03703615441918373, 0.10445785522460938, -0.010330617427825928, 0.04511367529630661, -0.059045083820819855, -0.1917646825313568, 0.08447372913360596, -0.012878820300102234, 0.08181323856115341, 0.09343104809522629, 0.18177148699760437, -0.060829054564237595, 0.02981627732515335, -0.00023069190501701087, -0.05544249713420868, 0.012747135013341904, 0.010900923050940037, -0.08992414176464081, 0.08740425854921341, -0.004389022011309862, 0.08924639225006104, 0.10225780308246613, 0.04851158708333969, -0.11542762070894241, 0.02440452203154564, 0.024054143577814102, 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null	null	transformers	# Cyber Bones DialoGPT Model	{"tags": ["conversational"]}	text-generation	ITNODove/DialoGPT-medium-cyberbones	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Cyber Bones DialoGPT Model	[ "# Cyber Bones DialoGPT Model" ]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Cyber Bones DialoGPT Model" ]	[ 51, 9 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Cyber Bones DialoGPT Model" ]	[ 0.022881697863340378, 0.11512941867113113, 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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. --> # output This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on a dataset of Shakespeare's plays. ## Model description The model is the original gpt-2 model fine-tuned on a custom dataset. ## Intended uses & limitations The model can be used to generate Shakespearean-like text. Consider that because it comes from plays, such a typographical structure might be reproduced. ## Training and evaluation data Trained with Shakespeare's plays corpus. ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.0 - Tokenizers 0.11.0	{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "output", "results": []}]}	text-generation	Iacopo/Shakespear-GPT2	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# output This model is a fine-tuned version of gpt2 on a dataset of Shakespeare's plays. ## Model description The model is the original gpt-2 model fine-tuned on a custom dataset. ## Intended uses & limitations The model can be used to generate Shakespearean-like text. Consider that because it comes from plays, such a typographical structure might be reproduced. ## Training and evaluation data Trained with Shakespeare's plays corpus. ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.0 - Tokenizers 0.11.0	[ "# output\n\nThis model is a fine-tuned version of gpt2 on a dataset of Shakespeare's plays.", "## Model description\n\nThe model is the original gpt-2 model fine-tuned on a custom dataset.", "## Intended uses & limitations\n\nThe model can be used to generate Shakespearean-like text. Consider that because it comes from plays, such a typographical structure might be reproduced.", "## Training and evaluation data\n\nTrained with Shakespeare's plays corpus.", "## 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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2.0", "### Training results", "### Framework versions\n\n- Transformers 4.16.0.dev0\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.0\n- Tokenizers 0.11.0" ]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# output\n\nThis model is a fine-tuned version of gpt2 on a dataset of Shakespeare's plays.", "## Model description\n\nThe model is the original gpt-2 model fine-tuned on a custom dataset.", "## Intended uses & limitations\n\nThe model can be used to generate Shakespearean-like text. Consider that because it comes from plays, such a typographical structure might be reproduced.", "## Training and evaluation data\n\nTrained with Shakespeare's plays corpus.", "## 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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2.0", "### Training results", "### Framework versions\n\n- Transformers 4.16.0.dev0\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.0\n- Tokenizers 0.11.0" ]	[ 59, 26, 22, 43, 15, 3, 90, 4, 40 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# output\n\nThis model is a fine-tuned version of gpt2 on a dataset of Shakespeare's plays.## Model description\n\nThe model is the original gpt-2 model fine-tuned on a custom dataset.## Intended uses & limitations\n\nThe model can be used to generate Shakespearean-like text. Consider that because it comes from plays, such a typographical structure might be reproduced.## Training and evaluation data\n\nTrained with Shakespeare's plays corpus.## 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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2.0### Training results### Framework versions\n\n- Transformers 4.16.0.dev0\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.0\n- Tokenizers 0.11.0" ]	[ -0.1006804034113884, 0.15855950117111206, -0.003161470405757427, 0.13156741857528687, 0.1230502650141716, -0.004605889320373535, 0.17130474746227264, 0.14262211322784424, -0.015744544565677643, 0.04345357045531273, 0.11201438307762146, 0.058831553906202316, -0.013258079998195171, 0.10647077113389969, 0.05808276683092117, -0.3027428388595581, -0.015878230333328247, 0.0022772205993533134, 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null	null	transformers	# Hank Hill DialoGPT Model	{"tags": ["conversational"]}	text-generation	Icemiser/chat-test	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Hank Hill DialoGPT Model	[ "# Hank Hill DialoGPT Model" ]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Hank Hill DialoGPT Model" ]	[ 51, 9 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Hank Hill DialoGPT Model" ]	[ -0.06167367845773697, 0.12431930005550385, -0.005004893988370895, 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null	null	transformers	@inproceedings{adebara-abdul-mageed-2021-improving, title = "Improving Similar Language Translation With Transfer Learning", author = "Adebara, Ife and Abdul-Mageed, Muhammad", booktitle = "Proceedings of the Sixth Conference on Machine Translation", month = nov, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.wmt-1.27", pages = "273--278", abstract = "We investigate transfer learning based on pre-trained neural machine translation models to translate between (low-resource) similar languages. This work is part of our contribution to the WMT 2021 Similar Languages Translation Shared Task where we submitted models for different language pairs, including French-Bambara, Spanish-Catalan, and Spanish-Portuguese in both directions. Our models for Catalan-Spanish (82.79 BLEU)and Portuguese-Spanish (87.11 BLEU) rank top 1 in the official shared task evaluation, and we are the only team to submit models for the French-Bambara pairs.", }	{"language": ["bm", "fr"]}	text2text-generation	Ife/BM-FR	[ "transformers", "pytorch", "marian", "text2text-generation", "bm", "fr", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "bm", "fr" ]	TAGS #transformers #pytorch #marian #text2text-generation #bm #fr #autotrain_compatible #endpoints_compatible #region-us	@inproceedings{adebara-abdul-mageed-2021-improving, title = "Improving Similar Language Translation With Transfer Learning", author = "Adebara, Ife and Abdul-Mageed, Muhammad", booktitle = "Proceedings of the Sixth Conference on Machine Translation", month = nov, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "URL pages = "273--278", abstract = "We investigate transfer learning based on pre-trained neural machine translation models to translate between (low-resource) similar languages. This work is part of our contribution to the WMT 2021 Similar Languages Translation Shared Task where we submitted models for different language pairs, including French-Bambara, Spanish-Catalan, and Spanish-Portuguese in both directions. Our models for Catalan-Spanish (82.79 BLEU)and Portuguese-Spanish (87.11 BLEU) rank top 1 in the official shared task evaluation, and we are the only team to submit models for the French-Bambara pairs.", }	[]	[ "TAGS\n#transformers #pytorch #marian #text2text-generation #bm #fr #autotrain_compatible #endpoints_compatible #region-us \n" ]	[ 44 ]	[ "passage: TAGS\n#transformers #pytorch #marian #text2text-generation #bm #fr #autotrain_compatible #endpoints_compatible #region-us \n" ]	[ -0.04999151825904846, -0.023952051997184753, -0.008309788070619106, 0.020969443023204803, 0.13934160768985748, 0.006741134915500879, 0.08761423826217651, 0.09994586557149887, -0.0010515438625589013, -0.019520321860909462, 0.15361110866069794, 0.2023184448480606, -0.04622964933514595, 0.13466285169124603, -0.10785374790430069, -0.2532006800174713, 0.027011768892407417, 0.0859806165099144, -0.02445891499519348, 0.11994960904121399, 0.08780743926763535, -0.07075979560613632, 0.09327659755945206, 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null	null	transformers	# Similar-Languages-MT	{}	text2text-generation	Ife/CA-ES	[ "transformers", "pytorch", "marian", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #marian #text2text-generation #autotrain_compatible #endpoints_compatible #region-us	# Similar-Languages-MT	[ "# Similar-Languages-MT" ]	[ "TAGS\n#transformers #pytorch #marian #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n", "# Similar-Languages-MT" ]	[ 39, 8 ]	[ "passage: TAGS\n#transformers #pytorch #marian #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n# Similar-Languages-MT" ]	[ -0.073247991502285, 0.011782562360167503, -0.00754973292350769, 0.043536845594644547, 0.16223813593387604, 0.016145052388310432, 0.09402257949113846, 0.13919037580490112, 0.0021659049671143293, -0.04813771694898605, 0.08614983409643173, 0.20872557163238525, -0.022190215066075325, 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null	null	transformers	A distilbert model fine-tuned for question answering.	{"language": ["en"], "datasets": ["squad_v2", "wiki_qa"], "metrics": ["accuracy"], "pipeline_tag": "question-answering"}	question-answering	Ifenna/dbert-3epoch	[ "transformers", "pytorch", "safetensors", "distilbert", "question-answering", "en", "dataset:squad_v2", "dataset:wiki_qa", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #safetensors #distilbert #question-answering #en #dataset-squad_v2 #dataset-wiki_qa #endpoints_compatible #region-us	A distilbert model fine-tuned for question answering.	[]	[ "TAGS\n#transformers #pytorch #safetensors #distilbert #question-answering #en #dataset-squad_v2 #dataset-wiki_qa #endpoints_compatible #region-us \n" ]	[ 54 ]	[ "passage: TAGS\n#transformers #pytorch #safetensors #distilbert #question-answering #en #dataset-squad_v2 #dataset-wiki_qa #endpoints_compatible #region-us \n" ]	[ -0.12236440926790237, 0.0662885382771492, -0.007256162352859974, -0.00013928886619396508, 0.0720784142613411, 0.04070528596639633, 0.04394685849547386, 0.09999126195907593, 0.09638366848230362, 0.0013243185821920633, 0.14885686337947845, 0.19087408483028412, 0.0009736591018736362, 0.018146997317671776, -0.11471309512853622, -0.1267337054014206, 0.08268019556999207, 0.07216952741146088, -0.08645731955766678, 0.1268284022808075, 0.0844498798251152, -0.131265327334404, 0.04881974309682846, -0.03603867068886757, -0.08133601397275925, 0.024591192603111267, 0.031169481575489044, -0.06986793875694275, 0.14075714349746704, 0.017688293009996414, 0.16336612403392792, 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null	null	transformers	Забавное для дискордика))00)) https://discord.gg/HpeadKH Offers [email protected]	{"tags": ["ru", "4ulan"]}	text-generation	Ifromspace/GRIEFSOFT-walr	[ "transformers", "pytorch", "gpt2", "text-generation", "ru", "4ulan", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #ru #4ulan #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Забавное для дискордика))00)) URL Offers work@URL	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #ru #4ulan #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 52 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #ru #4ulan #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.01868395321071148, 0.012232674285769463, -0.007092975080013275, 0.011343655176460743, 0.16620121896266937, 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null	null	transformers	Fork of https://huggingface.co/sberbank-ai/rugpt3large_based_on_gpt2 Забавное для дискордика))00)) ROADMAP: - Собираю датасетик из книжек про попаданцев. <------------------------- Сейчас тут. - Дообучаю. - Выбрасываю в дискордик. https://discord.gg/HpeadKH	{"language": ["ru"], "tags": ["PyTorch", "Transformers", "4ulan"]}	text-generation	Ifromspace/GRIEFSOFT	[ "transformers", "pytorch", "gpt2", "text-generation", "PyTorch", "Transformers", "4ulan", "ru", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "ru" ]	TAGS #transformers #pytorch #gpt2 #text-generation #PyTorch #Transformers #4ulan #ru #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Fork of URL Забавное для дискордика))00)) ROADMAP: - Собираю датасетик из книжек про попаданцев. <------------------------- Сейчас тут. - Дообучаю. - Выбрасываю в дискордик. 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null	null	transformers	# MBARTRuSumGazeta ## Model description This is a ported version of [fairseq model](https://www.dropbox.com/s/fijtntnifbt9h0k/gazeta_mbart_v2_fairseq.tar.gz). For more details, please see [Dataset for Automatic Summarization of Russian News](https://arxiv.org/abs/2006.11063). ## Intended uses & limitations #### How to use Colab: [link](https://colab.research.google.com/drive/1wdo_nPZPk6dWAn1J8nGx4Z5Ef82jCCob) ```python from transformers import MBartTokenizer, MBartForConditionalGeneration model_name = "IlyaGusev/mbart_ru_sum_gazeta" tokenizer = MBartTokenizer.from_pretrained(model_name) model = MBartForConditionalGeneration.from_pretrained(model_name) article_text = "..." input_ids = tokenizer( [article_text], max_length=600, padding="max_length", truncation=True, return_tensors="pt", )["input_ids"] output_ids = model.generate( input_ids=input_ids, no_repeat_ngram_size=4 )[0] summary = tokenizer.decode(output_ids, skip_special_tokens=True) print(summary) ``` #### Limitations and bias - The model should work well with Gazeta.ru articles, but for any other agencies it can suffer from domain shift ## Training data - Dataset: [Gazeta](https://huggingface.co/datasets/IlyaGusev/gazeta) ## Training procedure - Fairseq training script: [train.sh](https://github.com/IlyaGusev/summarus/blob/master/external/bart_scripts/train.sh) - Porting: [Colab link](https://colab.research.google.com/drive/13jXOlCpArV-lm4jZQ0VgOpj6nFBYrLAr) ## Eval results * Train dataset: Gazeta v1 train * Test dataset: Gazeta v1 test * Source max_length: 600 * Target max_length: 200 * no_repeat_ngram_size: 4 * num_beams: 5 \| Model \| R-1-f \| R-2-f \| R-L-f \| chrF \| METEOR \| BLEU \| Avg char length \| \|:--------------------------\|:------\|:------\|:------\|:-------\|:-------\|:-----\|:-----\| \| [mbart_ru_sum_gazeta](https://huggingface.co/IlyaGusev/mbart_ru_sum_gazeta) \| 32.4 \| 14.3 \| 28.0 \| 39.7 \| 26.4 \| 12.1 \| 371 \| \| [rut5_base_sum_gazeta](https://huggingface.co/IlyaGusev/rut5_base_sum_gazeta) \| 32.2 \| 14.4 \| 28.1 \| 39.8 \| 25.7 \| 12.3 \| 330 \| \| [rugpt3medium_sum_gazeta](https://huggingface.co/IlyaGusev/rugpt3medium_sum_gazeta) \| 26.2 \| 7.7 \| 21.7 \| 33.8 \| 18.2 \| 4.3 \| 244 \| * Train dataset: Gazeta v1 train * Test dataset: Gazeta v2 test * Source max_length: 600 * Target max_length: 200 * no_repeat_ngram_size: 4 * num_beams: 5 \| Model \| R-1-f \| R-2-f \| R-L-f \| chrF \| METEOR \| BLEU \| Avg char length \| \|:--------------------------\|:------\|:------\|:------\|:-------\|:-------\|:-----\|:-----\| \| [mbart_ru_sum_gazeta](https://huggingface.co/IlyaGusev/mbart_ru_sum_gazeta) \| 28.7 \| 11.1 \| 24.4 \| 37.3 \| 22.7 \| 9.4 \| 373 \| \| [rut5_base_sum_gazeta](https://huggingface.co/IlyaGusev/rut5_base_sum_gazeta) \| 28.6 \| 11.1 \| 24.5 \| 37.2 \| 22.0 \| 9.4 \| 331 \| \| [rugpt3medium_sum_gazeta](https://huggingface.co/IlyaGusev/rugpt3medium_sum_gazeta) \| 24.1 \| 6.5 \| 19.8 \| 32.1 \| 16.3 \| 3.6 \| 242 \| Predicting all summaries: ```python import json import torch from transformers import MBartTokenizer, MBartForConditionalGeneration from datasets import load_dataset def gen_batch(inputs, batch_size): batch_start = 0 while batch_start < len(inputs): yield inputs[batch_start: batch_start + batch_size] batch_start += batch_size def predict( model_name, input_records, output_file, max_source_tokens_count=600, batch_size=4 ): device = "cuda" if torch.cuda.is_available() else "cpu" tokenizer = MBartTokenizer.from_pretrained(model_name) model = MBartForConditionalGeneration.from_pretrained(model_name).to(device) predictions = [] for batch in gen_batch(inputs, batch_size): texts = [r["text"] for r in batch] input_ids = tokenizer( batch, return_tensors="pt", padding="max_length", truncation=True, max_length=max_source_tokens_count )["input_ids"].to(device) output_ids = model.generate( input_ids=input_ids, no_repeat_ngram_size=4 ) summaries = tokenizer.batch_decode(output_ids, skip_special_tokens=True) for s in summaries: print(s) predictions.extend(summaries) with open(output_file, "w") as w: for p in predictions: w.write(p.strip().replace("\n", " ") + "\n") gazeta_test = load_dataset('IlyaGusev/gazeta', script_version="v1.0")["test"] predict("IlyaGusev/mbart_ru_sum_gazeta", list(gazeta_test), "mbart_predictions.txt") ``` Evaluation: https://github.com/IlyaGusev/summarus/blob/master/evaluate.py Flags: --language ru --tokenize-after --lower ### BibTeX entry and citation info ```bibtex @InProceedings{10.1007/978-3-030-59082-6_9, author="Gusev, Ilya", editor="Filchenkov, Andrey and Kauttonen, Janne and Pivovarova, Lidia", title="Dataset for Automatic Summarization of Russian News", booktitle="Artificial Intelligence and Natural Language", year="2020", publisher="Springer International Publishing", address="Cham", pages="122--134", isbn="978-3-030-59082-6" } ```	{"language": ["ru"], "license": "apache-2.0", "tags": ["summarization", "mbart"], "datasets": ["IlyaGusev/gazeta"], "inference": {"parameters": 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"example_title": "\u041d\u043e\u0432\u043e\u0441\u0442\u0438"}, {"text": "\u0410\u043a\u0442\u0443\u0430\u043b\u044c\u043d\u043e\u0441\u0442\u044c \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u044b. \u042d\u043b\u0435\u043a\u0442\u0440\u043e\u043d\u043d\u0430\u044f \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044f \u0438\u0433\u0440\u0430\u0435\u0442 \u0432\u0441\u0435 \u0431\u043e\u043b\u044c\u0448\u0443\u044e \u0440\u043e\u043b\u044c \u0432\u043e \u0432\u0441\u0435\u0445 \u0441\u0444\u0435\u0440\u0430\u0445 \u0436\u0438\u0437\u043d\u0438 \u0441\u043e\u0432\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0449\u0435\u0441\u0442\u0432\u0430. \u0412 \u043f\u043e\u0441\u043b\u0435\u0434\u043d\u0438\u0435 \u0433\u043e\u0434\u044b \u043e\u0431\u044a\u0435\u043c \u043d\u0430\u0443\u0447\u043d\u043e-\u0442\u0435\u0445\u043d\u0438\u0447\u0435\u0441\u043a\u043e\u0439 \u0442\u0435\u043a\u0441\u0442\u043e\u0432\u043e\u0439 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For more details, please see Dataset for Automatic Summarization of Russian News. Intended uses & limitations --------------------------- #### How to use Colab: link #### Limitations and bias * The model should work well with URL articles, but for any other agencies it can suffer from domain shift Training data ------------- * Dataset: Gazeta Training procedure ------------------ * Fairseq training script: URL * Porting: Colab link Eval results ------------ * Train dataset: Gazeta v1 train * Test dataset: Gazeta v1 test * Source max\_length: 600 * Target max\_length: 200 * no\_repeat\_ngram\_size: 4 * num\_beams: 5 * Train dataset: Gazeta v1 train * Test dataset: Gazeta v2 test * Source max\_length: 600 * Target max\_length: 200 * no\_repeat\_ngram\_size: 4 * num\_beams: 5 Predicting all summaries: Evaluation: URL Flags: --language ru --tokenize-after --lower ### BibTeX entry and citation info	[ "#### How to use\n\n\nColab: link", "#### Limitations and bias\n\n\n* The model should work well with URL articles, but for any other agencies it can suffer from domain shift\n\n\nTraining data\n-------------\n\n\n* Dataset: Gazeta\n\n\nTraining procedure\n------------------\n\n\n* Fairseq training script: URL\n* Porting: Colab link\n\n\nEval results\n------------\n\n\n* Train dataset: Gazeta v1 train\n* Test dataset: Gazeta v1 test\n* Source max\\_length: 600\n* Target max\\_length: 200\n* no\\_repeat\\_ngram\\_size: 4\n* num\\_beams: 5\n\n\n\n* Train dataset: Gazeta v1 train\n* Test dataset: Gazeta v2 test\n* Source max\\_length: 600\n* Target max\\_length: 200\n* no\\_repeat\\_ngram\\_size: 4\n* num\\_beams: 5\n\n\n\nPredicting all summaries:\n\n\nEvaluation: URL\n\n\nFlags: --language ru --tokenize-after --lower", "### BibTeX entry and citation info" ]	[ "TAGS\n#transformers #pytorch #safetensors #mbart #text2text-generation #summarization #ru #dataset-IlyaGusev/gazeta #arxiv-2006.11063 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "#### How to use\n\n\nColab: link", "#### Limitations and bias\n\n\n* The model should work well with URL articles, but for any other agencies it can suffer from domain shift\n\n\nTraining data\n-------------\n\n\n* Dataset: Gazeta\n\n\nTraining procedure\n------------------\n\n\n* Fairseq training script: URL\n* Porting: Colab link\n\n\nEval results\n------------\n\n\n* Train dataset: Gazeta v1 train\n* Test dataset: Gazeta v1 test\n* Source max\\_length: 600\n* Target max\\_length: 200\n* no\\_repeat\\_ngram\\_size: 4\n* num\\_beams: 5\n\n\n\n* Train dataset: Gazeta v1 train\n* Test dataset: Gazeta v2 test\n* Source max\\_length: 600\n* Target max\\_length: 200\n* no\\_repeat\\_ngram\\_size: 4\n* num\\_beams: 5\n\n\n\nPredicting all summaries:\n\n\nEvaluation: URL\n\n\nFlags: --language ru --tokenize-after --lower", "### BibTeX entry and citation info" ]	[ 82, 9, 208, 11 ]	[ "passage: TAGS\n#transformers #pytorch #safetensors #mbart #text2text-generation #summarization #ru #dataset-IlyaGusev/gazeta #arxiv-2006.11063 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n#### How to use\n\n\nColab: link#### Limitations and bias\n\n\n* The model should work well with URL articles, but for any other agencies it can suffer from domain shift\n\n\nTraining data\n-------------\n\n\n* Dataset: Gazeta\n\n\nTraining procedure\n------------------\n\n\n* Fairseq training script: URL\n* Porting: Colab link\n\n\nEval results\n------------\n\n\n* Train dataset: Gazeta v1 train\n* Test dataset: Gazeta v1 test\n* Source max\\_length: 600\n* Target max\\_length: 200\n* no\\_repeat\\_ngram\\_size: 4\n* num\\_beams: 5\n\n\n\n* Train dataset: Gazeta v1 train\n* Test dataset: Gazeta v2 test\n* Source max\\_length: 600\n* Target max\\_length: 200\n* no\\_repeat\\_ngram\\_size: 4\n* num\\_beams: 5\n\n\n\nPredicting all summaries:\n\n\nEvaluation: URL\n\n\nFlags: --language ru --tokenize-after --lower### BibTeX entry and citation info" ]	[ -0.10598663240671158, 0.17051684856414795, -0.0037427390925586224, 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null	null	transformers	# NewsTgRuBERT Training script: https://github.com/dialogue-evaluation/Russian-News-Clustering-and-Headline-Generation/blob/main/train_mlm.py	{"language": ["ru"], "license": "apache-2.0"}	null	IlyaGusev/news_tg_rubert	[ "transformers", "pytorch", "ru", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "ru" ]	TAGS #transformers #pytorch #ru #license-apache-2.0 #endpoints_compatible #region-us	# NewsTgRuBERT Training script: URL	[ "# NewsTgRuBERT\n\nTraining script: URL" ]	[ "TAGS\n#transformers #pytorch #ru #license-apache-2.0 #endpoints_compatible #region-us \n", "# NewsTgRuBERT\n\nTraining script: URL" ]	[ 31, 11 ]	[ "passage: TAGS\n#transformers #pytorch #ru #license-apache-2.0 #endpoints_compatible #region-us \n# NewsTgRuBERT\n\nTraining script: URL" ]	[ -0.051444925367832184, 0.09465666115283966, -0.0044098407961428165, 0.011484785936772823, 0.09885992109775543, 0.028867140412330627, 0.11195389926433563, 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null	null	transformers	# RuBERTExtSumGazeta ## Model description Model for extractive summarization based on [rubert-base-cased](DeepPavlov/rubert-base-cased) ## Intended uses & limitations #### How to use Colab: [link](https://colab.research.google.com/drive/1Q8_v3H-kxdJhZIiyLYat7Kj02qDq7M1L) ```python import razdel from transformers import AutoTokenizer, BertForTokenClassification model_name = "IlyaGusev/rubert_ext_sum_gazeta" tokenizer = AutoTokenizer.from_pretrained(model_name) sep_token = tokenizer.sep_token sep_token_id = tokenizer.sep_token_id model = BertForTokenClassification.from_pretrained(model_name) article_text = "..." sentences = [s.text for s in razdel.sentenize(article_text)] article_text = sep_token.join(sentences) inputs = tokenizer( [article_text], max_length=500, padding="max_length", truncation=True, return_tensors="pt", ) sep_mask = inputs["input_ids"][0] == sep_token_id # Fix token_type_ids current_token_type_id = 0 for pos, input_id in enumerate(inputs["input_ids"][0]): inputs["token_type_ids"][0][pos] = current_token_type_id if input_id == sep_token_id: current_token_type_id = 1 - current_token_type_id # Infer model with torch.no_grad(): outputs = model(**inputs) logits = outputs.logits[0, :, 1] # Choose sentences logits = logits[sep_mask] logits, indices = logits.sort(descending=True) logits, indices = logits.cpu().tolist(), indices.cpu().tolist() pairs = list(zip(logits, indices)) pairs = pairs[:3] indices = list(sorted([idx for _, idx in pairs])) summary = " ".join([sentences[idx] for idx in indices]) print(summary) ``` #### Limitations and bias - The model should work well with Gazeta.ru articles, but for any other agencies it can suffer from domain shift ## Training data - Dataset: [Gazeta](https://huggingface.co/datasets/IlyaGusev/gazeta) ## Training procedure TBD ## Eval results TBD Evaluation: https://github.com/IlyaGusev/summarus/blob/master/evaluate.py Flags: --language ru --tokenize-after 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"dataset:IlyaGusev/gazeta", "license:apache-2.0", "autotrain_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "ru" ]	TAGS #transformers #pytorch #bert #token-classification #summarization #t5 #ru #dataset-IlyaGusev/gazeta #license-apache-2.0 #autotrain_compatible #region-us	# RuBERTExtSumGazeta ## Model description Model for extractive summarization based on rubert-base-cased ## Intended uses & limitations #### How to use Colab: link #### Limitations and bias - The model should work well with URL articles, but for any other agencies it can suffer from domain shift ## Training data - Dataset: Gazeta ## Training procedure TBD ## Eval results TBD Evaluation: URL Flags: --language ru --tokenize-after --lower	[ "# RuBERTExtSumGazeta", "## Model description\n\nModel for extractive summarization based on rubert-base-cased", "## Intended uses & limitations", "#### How to use\n\nColab: link", "#### Limitations and bias\n\n- The model should work well with URL articles, but for any other agencies it can suffer from domain shift", "## Training data\n\n- Dataset: Gazeta", "## Training procedure\n\nTBD", "## Eval results\n\nTBD\n\nEvaluation: URL\n\nFlags: --language ru --tokenize-after --lower" ]	[ "TAGS\n#transformers #pytorch #bert #token-classification #summarization #t5 #ru #dataset-IlyaGusev/gazeta #license-apache-2.0 #autotrain_compatible #region-us \n", "# RuBERTExtSumGazeta", "## Model description\n\nModel for extractive summarization based on rubert-base-cased", "## Intended uses & limitations", "#### How to use\n\nColab: link", "#### Limitations and bias\n\n- The model should work well with URL articles, but for any other agencies it can suffer from domain shift", "## Training data\n\n- Dataset: Gazeta", "## Training procedure\n\nTBD", "## Eval results\n\nTBD\n\nEvaluation: URL\n\nFlags: --language ru --tokenize-after --lower" ]	[ 57, 10, 19, 9, 9, 28, 8, 5, 26 ]	[ "passage: TAGS\n#transformers #pytorch #bert #token-classification #summarization #t5 #ru #dataset-IlyaGusev/gazeta #license-apache-2.0 #autotrain_compatible #region-us \n# RuBERTExtSumGazeta## Model description\n\nModel for extractive summarization based on rubert-base-cased## Intended uses & limitations#### How to use\n\nColab: link#### Limitations and bias\n\n- The model should work well with URL articles, but for any other agencies it can suffer from domain shift## Training data\n\n- Dataset: Gazeta## Training procedure\n\nTBD## Eval results\n\nTBD\n\nEvaluation: URL\n\nFlags: --language ru --tokenize-after --lower" ]	[ -0.006325070746243, 0.19307652115821838, -0.0023815641179680824, 0.07151832431554794, 0.1124335378408432, 0.018662821501493454, 0.16449032723903656, 0.05471857264637947, -0.009927099570631981, -0.06162893399596214, 0.07850902527570724, 0.03059552051126957, 0.012886728160083294, 0.1357753574848175, -0.0935148373246193, -0.23223179578781128, 0.03489863872528076, -0.024988558143377304, -0.06203655153512955, 0.10616704821586609, 0.15342392027378082, 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null	null	transformers	# RuBertTelegramHeadlines ## Model description Example model for [Headline generation competition](https://competitions.codalab.org/competitions/29905) Based on [RuBERT](http://docs.deeppavlov.ai/en/master/features/models/bert.html) model ## Intended uses & limitations #### How to use ```python from transformers import AutoTokenizer, EncoderDecoderModel model_name = "IlyaGusev/rubert_telegram_headlines" tokenizer = AutoTokenizer.from_pretrained(model_name, do_lower_case=False, do_basic_tokenize=False, strip_accents=False) model = EncoderDecoderModel.from_pretrained(model_name) article_text = "..." input_ids = tokenizer( [article_text], add_special_tokens=True, max_length=256, padding="max_length", truncation=True, return_tensors="pt", )["input_ids"] output_ids = model.generate( input_ids=input_ids, max_length=64, no_repeat_ngram_size=3, num_beams=10, top_p=0.95 )[0] headline = tokenizer.decode(output_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True) print(headline) ``` ## Training data - Dataset: [ru_all_split.tar.gz](https://www.dropbox.com/s/ykqk49a8avlmnaf/ru_all_split.tar.gz) ## Training procedure ```python import random import torch from torch.utils.data import Dataset from tqdm.notebook import tqdm from transformers import BertTokenizer, EncoderDecoderModel, Trainer, TrainingArguments, logging def convert_to_tensors( tokenizer, text, max_text_tokens_count, max_title_tokens_count = None, title = None ): inputs = tokenizer( text, add_special_tokens=True, max_length=max_text_tokens_count, padding="max_length", truncation=True ) result = { "input_ids": torch.tensor(inputs["input_ids"]), "attention_mask": torch.tensor(inputs["attention_mask"]), } if title is not None: outputs = tokenizer( title, add_special_tokens=True, max_length=max_title_tokens_count, padding="max_length", truncation=True ) decoder_input_ids = torch.tensor(outputs["input_ids"]) decoder_attention_mask = torch.tensor(outputs["attention_mask"]) labels = decoder_input_ids.clone() labels[decoder_attention_mask == 0] = -100 result.update({ "labels": labels, "decoder_input_ids": decoder_input_ids, "decoder_attention_mask": decoder_attention_mask }) return result class GetTitleDataset(Dataset): def __init__( self, original_records, sample_rate, tokenizer, max_text_tokens_count, max_title_tokens_count ): self.original_records = original_records self.sample_rate = sample_rate self.tokenizer = tokenizer self.max_text_tokens_count = max_text_tokens_count self.max_title_tokens_count = max_title_tokens_count self.records = [] for record in tqdm(original_records): if random.random() > self.sample_rate: continue tensors = convert_to_tensors( tokenizer=tokenizer, title=record["title"], text=record["text"], max_title_tokens_count=self.max_title_tokens_count, max_text_tokens_count=self.max_text_tokens_count ) self.records.append(tensors) def __len__(self): return len(self.records) def __getitem__(self, index): return self.records[index] def train( train_records, val_records, pretrained_model_path, train_sample_rate=1.0, val_sample_rate=1.0, output_model_path="models", checkpoint=None, max_text_tokens_count=256, max_title_tokens_count=64, batch_size=8, logging_steps=1000, eval_steps=10000, save_steps=10000, learning_rate=0.00003, warmup_steps=2000, num_train_epochs=3 ): logging.set_verbosity_info() tokenizer = BertTokenizer.from_pretrained( pretrained_model_path, do_lower_case=False, do_basic_tokenize=False, strip_accents=False ) train_dataset = GetTitleDataset( train_records, train_sample_rate, tokenizer, max_text_tokens_count=max_text_tokens_count, max_title_tokens_count=max_title_tokens_count ) val_dataset = GetTitleDataset( val_records, val_sample_rate, tokenizer, max_text_tokens_count=max_text_tokens_count, max_title_tokens_count=max_title_tokens_count ) model = EncoderDecoderModel.from_encoder_decoder_pretrained(pretrained_model_path, pretrained_model_path) training_args = TrainingArguments( output_dir=output_model_path, per_device_train_batch_size=batch_size, per_device_eval_batch_size=batch_size, do_train=True, do_eval=True, overwrite_output_dir=False, logging_steps=logging_steps, eval_steps=eval_steps, evaluation_strategy="steps", save_steps=save_steps, learning_rate=learning_rate, warmup_steps=warmup_steps, num_train_epochs=num_train_epochs, max_steps=-1, save_total_limit=1, ) trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset, eval_dataset=val_dataset ) trainer.train(checkpoint) model.save_pretrained(output_model_path) ```	{"language": ["ru"], "license": "apache-2.0", "tags": ["summarization"], "inference": {"parameters": {"no_repeat_ngram_size": 4}}}	summarization	IlyaGusev/rubert_telegram_headlines	[ "transformers", "pytorch", "encoder-decoder", "text2text-generation", "summarization", "ru", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "ru" ]	TAGS #transformers #pytorch #encoder-decoder #text2text-generation #summarization #ru #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us	# RuBertTelegramHeadlines ## Model description Example model for Headline generation competition Based on RuBERT model ## Intended uses & limitations #### How to use ## Training data - Dataset: ru_all_split.URL ## Training procedure	[ "# RuBertTelegramHeadlines", "## Model description\n\nExample model for Headline generation competition\n\nBased on RuBERT model", "## Intended uses & limitations", "#### How to use", "## Training data\n\n- Dataset: ru_all_split.URL", "## Training procedure" ]	[ "TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #summarization #ru #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "# RuBertTelegramHeadlines", "## Model description\n\nExample model for Headline generation competition\n\nBased on RuBERT model", "## Intended uses & limitations", "#### How to use", "## Training data\n\n- Dataset: ru_all_split.URL", "## Training procedure" ]	[ 60, 9, 18, 9, 5, 15, 3 ]	[ "passage: TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #summarization #ru #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# RuBertTelegramHeadlines## Model description\n\nExample model for Headline generation competition\n\nBased on RuBERT model## Intended uses & limitations#### How to use## Training data\n\n- Dataset: ru_all_split.URL## Training procedure" ]	[ -0.0012075999984517694, 0.12460914254188538, -0.001365180709399283, 0.08217024803161621, 0.11121687293052673, -0.002667743247002363, 0.1060275062918663, 0.09833584725856781, -0.1021660789847374, -0.024516403675079346, 0.168054461479187, 0.05795234441757202, 0.014898933470249176, 0.09659691154956818, -0.07085184007883072, -0.2844957113265991, 0.0256298016756773, 0.024356013163924217, 0.02592228353023529, 0.11728625744581223, 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null	null	transformers	# RuBERTConv Toxic Classifier ## Model description Based on [rubert-base-cased-conversational](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational) model ## Intended uses & limitations #### How to use Colab: [link](https://colab.research.google.com/drive/1veKO9hke7myxKigZtZho_F-UM2fD9kp8) ```python from transformers import pipeline model_name = "IlyaGusev/rubertconv_toxic_clf" pipe = pipeline("text-classification", model=model_name, tokenizer=model_name, framework="pt") text = "Ты придурок из интернета" pipe([text]) ``` ## Training data Datasets: - [2ch]( https://www.kaggle.com/blackmoon/russian-language-toxic-comments) - [Odnoklassniki](https://www.kaggle.com/alexandersemiletov/toxic-russian-comments) - [Toloka Persona Chat Rus](https://toloka.ai/ru/datasets) - [Koziev's Conversations](https://github.com/Koziev/NLP_Datasets/blob/master/Conversations/Data) with [toxic words vocabulary](https://www.dropbox.com/s/ou6lx03b10yhrfl/bad_vocab.txt.tar.gz) Augmentations: - ё -> е - Remove or add "?" or "!" - Fix CAPS - Concatenate toxic and non-toxic texts - Concatenate two non-toxic texts - Add toxic words from vocabulary - Add typos - Mask toxic words with "*", "@", "$" ## Training procedure TBA	{"language": ["ru"], "license": "apache-2.0", "tags": ["text-classification"]}	text-classification	IlyaGusev/rubertconv_toxic_clf	[ "transformers", "pytorch", "bert", "text-classification", "ru", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "ru" ]	TAGS #transformers #pytorch #bert #text-classification #ru #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	# RuBERTConv Toxic Classifier ## Model description Based on rubert-base-cased-conversational model ## Intended uses & limitations #### How to use Colab: link ## Training data Datasets: - 2ch - Odnoklassniki - Toloka Persona Chat Rus - Koziev's Conversations with toxic words vocabulary Augmentations: - ё -> е - Remove or add "?" or "!" - Fix CAPS - Concatenate toxic and non-toxic texts - Concatenate two non-toxic texts - Add toxic words from vocabulary - Add typos - Mask toxic words with "*", "@", "$" ## Training procedure TBA	[ "# RuBERTConv Toxic Classifier", "## Model description\n\nBased on rubert-base-cased-conversational model", "## Intended uses & limitations", "#### How to use\n\nColab: link", "## Training data\n\nDatasets:\n- 2ch\n- Odnoklassniki\n- Toloka Persona Chat Rus\n- Koziev's Conversations with toxic words vocabulary\n\nAugmentations:\n- ё -> е\n- Remove or add \"?\" or \"!\"\n- Fix CAPS\n- Concatenate toxic and non-toxic texts\n- Concatenate two non-toxic texts\n- Add toxic words from vocabulary\n- Add typos\n- Mask toxic words with \"*\", \"@\", \"$\"", "## Training procedure\n\nTBA" ]	[ "TAGS\n#transformers #pytorch #bert #text-classification #ru #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# RuBERTConv Toxic Classifier", "## Model description\n\nBased on rubert-base-cased-conversational model", "## Intended uses & limitations", "#### How to use\n\nColab: link", "## Training data\n\nDatasets:\n- 2ch\n- Odnoklassniki\n- Toloka Persona Chat Rus\n- Koziev's Conversations with toxic words vocabulary\n\nAugmentations:\n- ё -> е\n- Remove or add \"?\" or \"!\"\n- Fix CAPS\n- Concatenate toxic and non-toxic texts\n- Concatenate two non-toxic texts\n- Add toxic words from vocabulary\n- Add typos\n- Mask toxic words with \"*\", \"@\", \"$\"", "## Training procedure\n\nTBA" ]	[ 46, 11, 18, 9, 9, 109, 5 ]	[ "passage: TAGS\n#transformers #pytorch #bert #text-classification #ru #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# RuBERTConv Toxic Classifier## Model description\n\nBased on rubert-base-cased-conversational model## Intended uses & limitations#### How to use\n\nColab: link## Training data\n\nDatasets:\n- 2ch\n- Odnoklassniki\n- Toloka Persona Chat Rus\n- Koziev's Conversations with toxic words vocabulary\n\nAugmentations:\n- ё -> е\n- Remove or add \"?\" or \"!\"\n- Fix CAPS\n- Concatenate toxic and non-toxic texts\n- Concatenate two non-toxic texts\n- Add toxic words from vocabulary\n- Add typos\n- Mask toxic words with \"*\", \"@\", \"$\"## Training procedure\n\nTBA" ]	[ -0.018180394545197487, 0.018008600920438766, -0.00531100993975997, 0.027098236605525017, 0.12404359877109528, 0.016501372680068016, 0.1477121114730835, 0.135733962059021, 0.06885620206594467, 0.029844140633940697, 0.05238344147801399, 0.15655536949634552, 0.02407229132950306, 0.14541855454444885, -0.0797123834490776, -0.21166761219501495, 0.056910764425992966, -0.012761307880282402, -0.07386906445026398, 0.12613242864608765, 0.1564866155385971, -0.03829372674226761, 0.0609382800757885, 0.007022075355052948, -0.03765714913606644, 0.00803240854293108, 0.06058827042579651, -0.13667429983615875, 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null	null	transformers	# RuBERTConv Toxic Editor ## Model description Tagging model for detoxification based on [rubert-base-cased-conversational](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational). 4 possible classes: - Equal = save tokens - Replace = replace tokens with mask - Delete = remove tokens - Insert = insert mask before tokens Use in pair with [mask filler](https://huggingface.co/IlyaGusev/sber_rut5_filler). ## Intended uses & limitations #### How to use Colab: [link](https://colab.research.google.com/drive/1NUSO1QGlDgD-IWXa2SpeND089eVxrCJW) ```python import torch from transformers import AutoTokenizer, pipeline tagger_model_name = "IlyaGusev/rubertconv_toxic_editor" device = "cuda" if torch.cuda.is_available() else "cpu" device_num = 0 if device == "cuda" else -1 tagger_pipe = pipeline( "token-classification", model=tagger_model_name, tokenizer=tagger_model_name, framework="pt", device=device_num, aggregation_strategy="max" ) text = "..." tagger_predictions = tagger_pipe([text], batch_size=1) sample_predictions = tagger_predictions[0] print(sample_predictions) ``` ## Training data - Dataset: [russe_detox_2022](https://github.com/skoltech-nlp/russe_detox_2022/tree/main/data) ## Training procedure - Parallel corpus convertion: [compute_tags.py](https://github.com/IlyaGusev/rudetox/blob/main/rudetox/marker/compute_tags.py) - Training script: [train.py](https://github.com/IlyaGusev/rudetox/blob/main/rudetox/marker/train.py) - Pipeline step: [dvc.yaml, train_marker](https://github.com/IlyaGusev/rudetox/blob/main/dvc.yaml#L367) ## Eval results TBA	{"language": ["ru"], "license": "apache-2.0", "tags": ["token-classification"], "widget": [{"text": "\u0401\u043f\u0442\u0430, \u043c\u0435\u043d\u044f \u0437\u043e\u0432\u0443\u0442 \u043f\u0440\u0438\u0434\u0443\u0440\u043e\u043a \u0438 \u044f \u0436\u0438\u0432\u0443 \u0432 \u0436\u043e\u043f\u0435"}]}	token-classification	IlyaGusev/rubertconv_toxic_editor	[ "transformers", "pytorch", "bert", "token-classification", "ru", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "ru" ]	TAGS #transformers #pytorch #bert #token-classification #ru #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	# RuBERTConv Toxic Editor ## Model description Tagging model for detoxification based on rubert-base-cased-conversational. 4 possible classes: - Equal = save tokens - Replace = replace tokens with mask - Delete = remove tokens - Insert = insert mask before tokens Use in pair with mask filler. ## Intended uses & limitations #### How to use Colab: link ## Training data - Dataset: russe_detox_2022 ## Training procedure - Parallel corpus convertion: compute_tags.py - Training script: URL - Pipeline step: URL, train_marker ## Eval results TBA	[ "# RuBERTConv Toxic Editor", "## Model description\n\nTagging model for detoxification based on rubert-base-cased-conversational.\n\n4 possible classes:\n- Equal = save tokens\n- Replace = replace tokens with mask\n- Delete = remove tokens\n- Insert = insert mask before tokens\n\nUse in pair with mask filler.", "## Intended uses & limitations", "#### How to use\n\nColab: link", "## Training data\n\n- Dataset: russe_detox_2022", "## Training procedure\n\n- Parallel corpus convertion: compute_tags.py\n- Training script: URL\n- Pipeline step: URL, train_marker", "## Eval results\n\nTBA" ]	[ "TAGS\n#transformers #pytorch #bert #token-classification #ru #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# RuBERTConv Toxic Editor", "## Model description\n\nTagging model for detoxification based on rubert-base-cased-conversational.\n\n4 possible classes:\n- Equal = save tokens\n- Replace = replace tokens with mask\n- Delete = remove tokens\n- Insert = insert mask before tokens\n\nUse in pair with mask filler.", "## Intended uses & limitations", "#### How to use\n\nColab: link", "## Training data\n\n- Dataset: russe_detox_2022", "## Training procedure\n\n- Parallel corpus convertion: compute_tags.py\n- Training script: URL\n- Pipeline step: URL, train_marker", "## Eval results\n\nTBA" ]	[ 47, 10, 67, 9, 9, 13, 31, 6 ]	[ "passage: TAGS\n#transformers #pytorch #bert #token-classification #ru #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# RuBERTConv Toxic Editor## Model description\n\nTagging model for detoxification based on rubert-base-cased-conversational.\n\n4 possible classes:\n- Equal = save tokens\n- Replace = replace tokens with mask\n- Delete = remove tokens\n- Insert = insert mask before tokens\n\nUse in pair with mask filler.## Intended uses & limitations#### How to use\n\nColab: link## Training data\n\n- Dataset: russe_detox_2022## Training procedure\n\n- Parallel corpus convertion: compute_tags.py\n- Training script: URL\n- Pipeline step: URL, train_marker## Eval results\n\nTBA" ]	[ -0.09532883018255234, 0.14129488170146942, 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null	null	transformers	# RuGPT3MediumSumGazeta ## Model description This is the model for abstractive summarization for Russian based on [rugpt3medium_based_on_gpt2](https://huggingface.co/sberbank-ai/rugpt3medium_based_on_gpt2). ## Intended uses & limitations #### How to use Colab: [link](https://colab.research.google.com/drive/1eR-ev0Y5ISWIwGnzYYoHyGMaSIUz8GTN) ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_name = "IlyaGusev/rugpt3medium_sum_gazeta" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) article_text = "..." text_tokens = tokenizer( article_text, max_length=600, add_special_tokens=False, padding=False, truncation=True )["input_ids"] input_ids = text_tokens + [tokenizer.sep_token_id] input_ids = torch.LongTensor([input_ids]) output_ids = model.generate( input_ids=input_ids, no_repeat_ngram_size=4 ) summary = tokenizer.decode(output_ids[0], skip_special_tokens=False) summary = summary.split(tokenizer.sep_token)[1] summary = summary.split(tokenizer.eos_token)[0] print(summary) ``` ## Training data - Dataset: [Gazeta](https://huggingface.co/datasets/IlyaGusev/gazeta) ## Training procedure - Training script: [train.py](https://github.com/IlyaGusev/summarus/blob/master/external/hf_scripts/train.py) - Config: [gpt_training_config.json](https://github.com/IlyaGusev/summarus/blob/master/external/hf_scripts/configs/gpt_training_config.json) ## Eval results * Train dataset: Gazeta v1 train * Test dataset: Gazeta v1 test * Source max_length: 600 * Target max_length: 200 * no_repeat_ngram_size: 4 * num_beams: 5 \| Model \| R-1-f \| R-2-f \| R-L-f \| chrF \| METEOR \| BLEU \| Avg char length \| \|:--------------------------\|:------\|:------\|:------\|:-------\|:-------\|:-----\|:-----\| \| [mbart_ru_sum_gazeta](https://huggingface.co/IlyaGusev/mbart_ru_sum_gazeta) \| 32.4 \| 14.3 \| 28.0 \| 39.7 \| 26.4 \| 12.1 \| 371 \| \| [rut5_base_sum_gazeta](https://huggingface.co/IlyaGusev/rut5_base_sum_gazeta) \| 32.2 \| 14.4 \| 28.1 \| 39.8 \| 25.7 \| 12.3 \| 330 \| \| [rugpt3medium_sum_gazeta](https://huggingface.co/IlyaGusev/rugpt3medium_sum_gazeta) \| 26.2 \| 7.7 \| 21.7 \| 33.8 \| 18.2 \| 4.3 \| 244 \| * Train dataset: Gazeta v1 train * Test dataset: Gazeta v2 test * Source max_length: 600 * Target max_length: 200 * no_repeat_ngram_size: 4 * num_beams: 5 \| Model \| R-1-f \| R-2-f \| R-L-f \| chrF \| METEOR \| BLEU \| Avg char length \| \|:--------------------------\|:------\|:------\|:------\|:-------\|:-------\|:-----\|:-----\| \| [mbart_ru_sum_gazeta](https://huggingface.co/IlyaGusev/mbart_ru_sum_gazeta) \| 28.7 \| 11.1 \| 24.4 \| 37.3 \| 22.7 \| 9.4 \| 373 \| \| [rut5_base_sum_gazeta](https://huggingface.co/IlyaGusev/rut5_base_sum_gazeta) \| 28.6 \| 11.1 \| 24.5 \| 37.2 \| 22.0 \| 9.4 \| 331 \| \| [rugpt3medium_sum_gazeta](https://huggingface.co/IlyaGusev/rugpt3medium_sum_gazeta) \| 24.1 \| 6.5 \| 19.8 \| 32.1 \| 16.3 \| 3.6 \| 242 \| Evaluation script: [evaluate.py](https://github.com/IlyaGusev/summarus/blob/master/evaluate.py) Flags: --language ru --tokenize-after --lower	{"language": ["ru"], "license": ["apache-2.0"], "tags": ["causal-lm", "summarization"], "datasets": ["IlyaGusev/gazeta"], "inference": false, "widget": [{"text": "\u0412\u044b\u0441\u043e\u0442\u0430 \u0431\u0430\u0448\u043d\u0438 \u0441\u043e\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u0442 324 \u043c\u0435\u0442\u0440\u0430 (1063 \u0444\u0443\u0442\u0430), \u043f\u0440\u0438\u043c\u0435\u0440\u043d\u043e \u0442\u0430\u043a\u0430\u044f \u0436\u0435 \u0432\u044b\u0441\u043e\u0442\u0430, \u043a\u0430\u043a \u0443 81-\u044d\u0442\u0430\u0436\u043d\u043e\u0433\u043e \u0437\u0434\u0430\u043d\u0438\u044f, \u0438 \u0441\u0430\u043c\u043e\u0435 \u0432\u044b\u0441\u043e\u043a\u043e\u0435 \u0441\u043e\u043e\u0440\u0443\u0436\u0435\u043d\u0438\u0435 \u0432 \u041f\u0430\u0440\u0438\u0436\u0435. \u0415\u0433\u043e \u043e\u0441\u043d\u043e\u0432\u0430\u043d\u0438\u0435 \u043a\u0432\u0430\u0434\u0440\u0430\u0442\u043d\u043e, \u0440\u0430\u0437\u043c\u0435\u0440\u043e\u043c 125 \u043c\u0435\u0442\u0440\u043e\u0432 (410 \u0444\u0443\u0442\u043e\u0432) \u0441 \u043b\u044e\u0431\u043e\u0439 \u0441\u0442\u043e\u0440\u043e\u043d\u044b. \u0412\u043e \u0432\u0440\u0435\u043c\u044f \u0441\u0442\u0440\u043e\u0438\u0442\u0435\u043b\u044c\u0441\u0442\u0432\u0430 \u042d\u0439\u0444\u0435\u043b\u0435\u0432\u0430 \u0431\u0430\u0448\u043d\u044f \u043f\u0440\u0435\u0432\u0437\u043e\u0448\u043b\u0430 \u043c\u043e\u043d\u0443\u043c\u0435\u043d\u0442 \u0412\u0430\u0448\u0438\u043d\u0433\u0442\u043e\u043d\u0430, \u0441\u0442\u0430\u0432 \u0441\u0430\u043c\u044b\u043c \u0432\u044b\u0441\u043e\u043a\u0438\u043c \u0438\u0441\u043a\u0443\u0441\u0441\u0442\u0432\u0435\u043d\u043d\u044b\u043c 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\u0434\u043e\u0431\u0430\u0432\u043b\u0435\u043d\u0438\u044f \u0432\u0435\u0449\u0430\u0442\u0435\u043b\u044c\u043d\u043e\u0439 \u0430\u043d\u0442\u0435\u043d\u043d\u044b \u043d\u0430 \u0432\u0435\u0440\u0448\u0438\u043d\u0435 \u0431\u0430\u0448\u043d\u0438 \u0432 1957 \u0433\u043e\u0434\u0443 \u043e\u043d\u0430 \u0441\u0435\u0439\u0447\u0430\u0441 \u0432\u044b\u0448\u0435 \u0437\u0434\u0430\u043d\u0438\u044f \u041a\u0440\u0430\u0439\u0441\u043b\u0435\u0440 \u043d\u0430 5,2 \u043c\u0435\u0442\u0440\u0430 (17 \u0444\u0443\u0442\u043e\u0432). \u0417\u0430 \u0438\u0441\u043a\u043b\u044e\u0447\u0435\u043d\u0438\u0435\u043c \u043f\u0435\u0440\u0435\u0434\u0430\u0442\u0447\u0438\u043a\u043e\u0432, \u042d\u0439\u0444\u0435\u043b\u0435\u0432\u0430 \u0431\u0430\u0448\u043d\u044f \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u0432\u0442\u043e\u0440\u043e\u0439 \u0441\u0430\u043c\u043e\u0439 \u0432\u044b\u0441\u043e\u043a\u043e\u0439 \u043e\u0442\u0434\u0435\u043b\u044c\u043d\u043e \u0441\u0442\u043e\u044f\u0449\u0435\u0439 \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u043e\u0439 \u0432\u043e \u0424\u0440\u0430\u043d\u0446\u0438\u0438 \u043f\u043e\u0441\u043b\u0435 \u0432\u0438\u0430\u0434\u0443\u043a\u0430 \u041c\u0438\u0439\u043e.<s>", "example_title": "\u0412\u0438\u043a\u0438\u043f\u0435\u0434\u0438\u044f"}]}	summarization	IlyaGusev/rugpt3medium_sum_gazeta	[ "transformers", "pytorch", "gpt2", "text-generation", "causal-lm", "summarization", "ru", "dataset:IlyaGusev/gazeta", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "ru" ]	TAGS #transformers #pytorch #gpt2 #text-generation #causal-lm #summarization #ru #dataset-IlyaGusev/gazeta #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us	RuGPT3MediumSumGazeta ===================== Model description ----------------- This is the model for abstractive summarization for Russian based on rugpt3medium\_based\_on\_gpt2. Intended uses & limitations --------------------------- #### How to use Colab: link Training data ------------- * Dataset: Gazeta Training procedure ------------------ * Training script: URL * Config: gpt\_training\_config.json Eval results ------------ * Train dataset: Gazeta v1 train * Test dataset: Gazeta v1 test * Source max\_length: 600 * Target max\_length: 200 * no\_repeat\_ngram\_size: 4 * num\_beams: 5 * Train dataset: Gazeta v1 train * Test dataset: Gazeta v2 test * Source max\_length: 600 * Target max\_length: 200 * no\_repeat\_ngram\_size: 4 * num\_beams: 5 Evaluation script: URL Flags: --language ru --tokenize-after --lower	[ "#### How to use\n\n\nColab: link\n\n\nTraining data\n-------------\n\n\n* Dataset: Gazeta\n\n\nTraining procedure\n------------------\n\n\n* Training script: URL\n* Config: gpt\\_training\\_config.json\n\n\nEval results\n------------\n\n\n* Train dataset: Gazeta v1 train\n* Test dataset: Gazeta v1 test\n* Source max\\_length: 600\n* Target max\\_length: 200\n* no\\_repeat\\_ngram\\_size: 4\n* num\\_beams: 5\n\n\n\n* Train dataset: Gazeta v1 train\n* Test dataset: Gazeta v2 test\n* Source max\\_length: 600\n* Target max\\_length: 200\n* no\\_repeat\\_ngram\\_size: 4\n* num\\_beams: 5\n\n\n\nEvaluation script: URL\n\n\nFlags: --language ru --tokenize-after --lower" ]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #causal-lm #summarization #ru #dataset-IlyaGusev/gazeta #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n", "#### How to use\n\n\nColab: link\n\n\nTraining data\n-------------\n\n\n* Dataset: Gazeta\n\n\nTraining procedure\n------------------\n\n\n* Training script: URL\n* Config: gpt\\_training\\_config.json\n\n\nEval results\n------------\n\n\n* Train dataset: Gazeta v1 train\n* Test dataset: Gazeta v1 test\n* Source max\\_length: 600\n* Target max\\_length: 200\n* no\\_repeat\\_ngram\\_size: 4\n* num\\_beams: 5\n\n\n\n* Train dataset: Gazeta v1 train\n* Test dataset: Gazeta v2 test\n* Source max\\_length: 600\n* Target max\\_length: 200\n* no\\_repeat\\_ngram\\_size: 4\n* num\\_beams: 5\n\n\n\nEvaluation script: URL\n\n\nFlags: --language ru --tokenize-after --lower" ]	[ 70, 188 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #causal-lm #summarization #ru #dataset-IlyaGusev/gazeta #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n#### How to use\n\n\nColab: link\n\n\nTraining data\n-------------\n\n\n* Dataset: Gazeta\n\n\nTraining procedure\n------------------\n\n\n* Training script: URL\n* Config: gpt\\_training\\_config.json\n\n\nEval results\n------------\n\n\n* Train dataset: Gazeta v1 train\n* Test dataset: Gazeta v1 test\n* Source max\\_length: 600\n* Target max\\_length: 200\n* no\\_repeat\\_ngram\\_size: 4\n* num\\_beams: 5\n\n\n\n* Train dataset: Gazeta v1 train\n* Test dataset: Gazeta v2 test\n* Source max\\_length: 600\n* Target max\\_length: 200\n* no\\_repeat\\_ngram\\_size: 4\n* num\\_beams: 5\n\n\n\nEvaluation script: URL\n\n\nFlags: --language ru --tokenize-after --lower" ]	[ -0.09073565155267715, 0.11748497188091278, -0.004591652192175388, 0.046358898282051086, 0.08858153969049454, 0.05195655673742294, 0.09708724170923233, 0.16622458398342133, -0.01792297326028347, 0.08958771079778671, 0.12663333117961884, 0.002123309066519141, 0.07569772005081177, 0.22217415273189545, 0.0025088260881602764, -0.22042234241962433, -0.02397025004029274, -0.02582642249763012, -0.12266125530004501, 0.1380469799041748, 0.1138479933142662, -0.09383220970630646, 0.05626779794692993, -0.05037666857242584, -0.07428739964962006, 0.027927281334996223, -0.008816358633339405, -0.0883118137717247, 0.08885867893695831, 0.03776775300502777, 0.04249725118279457, 0.02322465553879738, 0.024774907156825066, -0.2338094860315323, 0.0031922946218401194, 0.028906239196658134, 0.008917692117393017, 0.05236683785915375, 0.10830160975456238, 0.011669950559735298, 0.22147811949253082, 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null	null	transformers	# RuT5TelegramHeadlines ## Model description Based on [rut5-base](https://huggingface.co/cointegrated/rut5-base) model ## Intended uses & limitations #### How to use ```python from transformers import AutoTokenizer, T5ForConditionalGeneration model_name = "IlyaGusev/rut5_base_headline_gen_telegram" tokenizer = AutoTokenizer.from_pretrained(model_name) model = T5ForConditionalGeneration.from_pretrained(model_name) article_text = "..." input_ids = tokenizer( [article_text], max_length=600, add_special_tokens=True, padding="max_length", truncation=True, return_tensors="pt" )["input_ids"] output_ids = model.generate( input_ids=input_ids )[0] headline = tokenizer.decode(output_ids, skip_special_tokens=True) print(headline) ``` ## Training data - Dataset: [ru_all_split.tar.gz](https://www.dropbox.com/s/ykqk49a8avlmnaf/ru_all_split.tar.gz) ## Training procedure - Training script: [train.py](https://github.com/IlyaGusev/summarus/blob/master/external/hf_scripts/train.py)	{"language": ["ru"], "license": "apache-2.0", "tags": ["summarization"], "widget": [{"text": "\u041a\u043e\u043c\u0438\u0441\u0441\u0438\u044f \u0421\u043e\u0432\u0435\u0442\u0430 \u0424\u0435\u0434\u0435\u0440\u0430\u0446\u0438\u0438 \u043f\u043e \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u043e\u043d\u043d\u043e\u0439 \u043f\u043e\u043b\u0438\u0442\u0438\u043a\u0435 \u0438 \u0432\u0437\u0430\u0438\u043c\u043e\u0434\u0435\u0439\u0441\u0442\u0432\u0438\u044e \u0441\u043e \u0421\u041c\u0418 \u0441\u043e\u0432\u043c\u0435\u0441\u0442\u043d\u043e \u0441 \u0437\u0430\u0438\u043d\u0442\u0435\u0440\u0435\u0441\u043e\u0432\u0430\u043d\u043d\u044b\u043c\u0438 \u0432\u0435\u0434\u043e\u043c\u0441\u0442\u0432\u0430\u043c\u0438 \u0434\u0443\u043c\u0430\u0435\u0442 \u043d\u0430\u0434 \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u043a\u043e\u0439 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\u043a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u043d\u043e\u0433, \u0443\u0447\u0435\u043d\u044b\u043c \u043f\u0440\u0438\u0448\u043b\u043e\u0441\u044c \u0441\u043d\u0430\u0447\u0430\u043b\u0430 \u0441\u043d\u044f\u0442\u044c \u043c\u043d\u043e\u0433\u043e\u043d\u043e\u0436\u043a\u0443 \u0432 \u0432\u044b\u0441\u043e\u043a\u043e\u043c \u0440\u0430\u0437\u0440\u0435\u0448\u0435\u043d\u0438\u0438, \u0430 \u0437\u0430\u0442\u0435\u043c \u0437\u0430\u043a\u0440\u0430\u0448\u0438\u0432\u0430\u0442\u044c \u043d\u0430 \u0444\u043e\u0442\u043e \u043a\u0430\u0436\u0434\u044b\u0439 \u0434\u0435\u0441\u044f\u0442\u043e\u043a \u043d\u043e\u0433 \u0434\u0440\u0443\u0433\u0438\u043c \u0446\u0432\u0435\u0442\u043e\u043c. (https://www.gazeta.ru/science/2021/12/17_a_14325355.shtml)", "example_title": "\u041d\u043e\u0432\u043e\u0441\u0442\u044c \u043f\u0440\u043e \u043c\u043d\u043e\u0433\u043e\u043d\u043e\u0436\u043a\u0443"}, {"text": "\u0412\u044b\u0441\u043e\u0442\u0430 \u0431\u0430\u0448\u043d\u0438 \u0441\u043e\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u0442 324 \u043c\u0435\u0442\u0440\u0430 (1063 \u0444\u0443\u0442\u0430), \u043f\u0440\u0438\u043c\u0435\u0440\u043d\u043e \u0442\u0430\u043a\u0430\u044f \u0436\u0435 \u0432\u044b\u0441\u043e\u0442\u0430, \u043a\u0430\u043a \u0443 81-\u044d\u0442\u0430\u0436\u043d\u043e\u0433\u043e \u0437\u0434\u0430\u043d\u0438\u044f, \u0438 \u0441\u0430\u043c\u043e\u0435 \u0432\u044b\u0441\u043e\u043a\u043e\u0435 \u0441\u043e\u043e\u0440\u0443\u0436\u0435\u043d\u0438\u0435 \u0432 \u041f\u0430\u0440\u0438\u0436\u0435. \u0415\u0433\u043e \u043e\u0441\u043d\u043e\u0432\u0430\u043d\u0438\u0435 \u043a\u0432\u0430\u0434\u0440\u0430\u0442\u043d\u043e, 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\u0433\u043e\u0434\u0443 \u043e\u043d\u0430 \u0441\u0435\u0439\u0447\u0430\u0441 \u0432\u044b\u0448\u0435 \u0437\u0434\u0430\u043d\u0438\u044f \u041a\u0440\u0430\u0439\u0441\u043b\u0435\u0440 \u043d\u0430 5,2 \u043c\u0435\u0442\u0440\u0430 (17 \u0444\u0443\u0442\u043e\u0432). \u0417\u0430 \u0438\u0441\u043a\u043b\u044e\u0447\u0435\u043d\u0438\u0435\u043c \u043f\u0435\u0440\u0435\u0434\u0430\u0442\u0447\u0438\u043a\u043e\u0432, \u042d\u0439\u0444\u0435\u043b\u0435\u0432\u0430 \u0431\u0430\u0448\u043d\u044f \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u0432\u0442\u043e\u0440\u043e\u0439 \u0441\u0430\u043c\u043e\u0439 \u0432\u044b\u0441\u043e\u043a\u043e\u0439 \u043e\u0442\u0434\u0435\u043b\u044c\u043d\u043e \u0441\u0442\u043e\u044f\u0449\u0435\u0439 \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u043e\u0439 \u0432\u043e \u0424\u0440\u0430\u043d\u0446\u0438\u0438 \u043f\u043e\u0441\u043b\u0435 \u0432\u0438\u0430\u0434\u0443\u043a\u0430 \u041c\u0438\u0439\u043e.", "example_title": "\u0412\u0438\u043a\u0438\u043f\u0435\u0434\u0438\u044f"}]}	summarization	IlyaGusev/rut5_base_headline_gen_telegram	[ "transformers", "pytorch", "t5", "text2text-generation", "summarization", "ru", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "ru" ]	TAGS #transformers #pytorch #t5 #text2text-generation #summarization #ru #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# RuT5TelegramHeadlines ## Model description Based on rut5-base model ## Intended uses & limitations #### How to use ## Training data - Dataset: ru_all_split.URL ## Training procedure - Training script: URL	[ "# RuT5TelegramHeadlines", "## Model description\n\nBased on rut5-base model", "## Intended uses & limitations", "#### How to use", "## Training data\n\n- Dataset: ru_all_split.URL", "## Training procedure\n\n- Training script: URL" ]	[ "TAGS\n#transformers #pytorch #t5 #text2text-generation #summarization #ru #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# RuT5TelegramHeadlines", "## Model description\n\nBased on rut5-base model", "## Intended uses & limitations", "#### How to use", "## Training data\n\n- Dataset: ru_all_split.URL", "## Training procedure\n\n- Training script: URL" ]	[ 62, 9, 11, 9, 5, 15, 8 ]	[ "passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #summarization #ru #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# RuT5TelegramHeadlines## Model description\n\nBased on rut5-base model## Intended uses & limitations#### How to use## Training data\n\n- Dataset: ru_all_split.URL## Training procedure\n\n- Training script: URL" ]	[ -0.02060420624911785, 0.04053904861211777, -0.0017695092828944325, 0.07286142557859421, 0.15548820793628693, 0.03976593166589737, 0.09921891987323761, 0.11977134644985199, 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null	null	transformers	# RuT5SumGazeta ## Model description This is the model for abstractive summarization for Russian based on [rut5-base](https://huggingface.co/cointegrated/rut5-base). ## Intended uses & limitations #### How to use Colab: [link](https://colab.research.google.com/drive/1re5E26ZIDUpAx1gOCZkbF3hcwjozmgG0) ```python from transformers import AutoTokenizer, T5ForConditionalGeneration model_name = "IlyaGusev/rut5_base_sum_gazeta" tokenizer = AutoTokenizer.from_pretrained(model_name) model = T5ForConditionalGeneration.from_pretrained(model_name) article_text = "..." input_ids = tokenizer( [article_text], max_length=600, add_special_tokens=True, padding="max_length", truncation=True, return_tensors="pt" )["input_ids"] output_ids = model.generate( input_ids=input_ids, no_repeat_ngram_size=4 )[0] summary = tokenizer.decode(output_ids, skip_special_tokens=True) print(summary) ``` ## Training data - Dataset: [Gazeta](https://huggingface.co/datasets/IlyaGusev/gazeta) ## Training procedure - Training script: [train.py](https://github.com/IlyaGusev/summarus/blob/master/external/hf_scripts/train.py) - Config: [t5_training_config.json](https://github.com/IlyaGusev/summarus/blob/master/external/hf_scripts/configs/t5_training_config.json) ## Eval results * Train dataset: Gazeta v1 train * Test dataset: Gazeta v1 test * Source max_length: 600 * Target max_length: 200 * no_repeat_ngram_size: 4 * num_beams: 5 \| Model \| R-1-f \| R-2-f \| R-L-f \| chrF \| METEOR \| BLEU \| Avg char length \| \|:--------------------------\|:------\|:------\|:------\|:-------\|:-------\|:-----\|:-----\| \| [mbart_ru_sum_gazeta](https://huggingface.co/IlyaGusev/mbart_ru_sum_gazeta) \| 32.4 \| 14.3 \| 28.0 \| 39.7 \| 26.4 \| 12.1 \| 371 \| \| [rut5_base_sum_gazeta](https://huggingface.co/IlyaGusev/rut5_base_sum_gazeta) \| 32.2 \| 14.4 \| 28.1 \| 39.8 \| 25.7 \| 12.3 \| 330 \| \| [rugpt3medium_sum_gazeta](https://huggingface.co/IlyaGusev/rugpt3medium_sum_gazeta) \| 26.2 \| 7.7 \| 21.7 \| 33.8 \| 18.2 \| 4.3 \| 244 \| * Train dataset: Gazeta v1 train * Test dataset: Gazeta v2 test * Source max_length: 600 * Target max_length: 200 * no_repeat_ngram_size: 4 * num_beams: 5 \| Model \| R-1-f \| R-2-f \| R-L-f \| chrF \| METEOR \| BLEU \| Avg char length \| \|:--------------------------\|:------\|:------\|:------\|:-------\|:-------\|:-----\|:-----\| \| [mbart_ru_sum_gazeta](https://huggingface.co/IlyaGusev/mbart_ru_sum_gazeta) \| 28.7 \| 11.1 \| 24.4 \| 37.3 \| 22.7 \| 9.4 \| 373 \| \| [rut5_base_sum_gazeta](https://huggingface.co/IlyaGusev/rut5_base_sum_gazeta) \| 28.6 \| 11.1 \| 24.5 \| 37.2 \| 22.0 \| 9.4 \| 331 \| \| [rugpt3medium_sum_gazeta](https://huggingface.co/IlyaGusev/rugpt3medium_sum_gazeta) \| 24.1 \| 6.5 \| 19.8 \| 32.1 \| 16.3 \| 3.6 \| 242 \| Predicting all summaries: ```python import json import torch from transformers import AutoTokenizer, T5ForConditionalGeneration from datasets import load_dataset def gen_batch(inputs, batch_size): batch_start = 0 while batch_start < len(inputs): yield inputs[batch_start: batch_start + batch_size] batch_start += batch_size def predict( model_name, input_records, output_file, max_source_tokens_count=600, batch_size=8 ): device = "cuda" if torch.cuda.is_available() else "cpu" tokenizer = AutoTokenizer.from_pretrained(model_name) model = T5ForConditionalGeneration.from_pretrained(model_name).to(device) predictions = [] for batch in gen_batch(input_records, batch_size): texts = [r["text"] for r in batch] input_ids = tokenizer( texts, add_special_tokens=True, max_length=max_source_tokens_count, padding="max_length", truncation=True, return_tensors="pt" )["input_ids"].to(device) output_ids = model.generate( input_ids=input_ids, no_repeat_ngram_size=4 ) summaries = tokenizer.batch_decode(output_ids, skip_special_tokens=True) for s in summaries: print(s) predictions.extend(summaries) with open(output_file, "w") as w: for p in predictions: w.write(p.strip().replace("\n", " ") + "\n") gazeta_test = load_dataset('IlyaGusev/gazeta', script_version="v1.0")["test"] predict("IlyaGusev/rut5_base_sum_gazeta", list(gazeta_test), "t5_predictions.txt") ``` Evaluation script: [evaluate.py](https://github.com/IlyaGusev/summarus/blob/master/evaluate.py) Flags: --language ru --tokenize-after --lower	{"language": ["ru"], "license": ["apache-2.0"], "tags": ["summarization", "t5"], "datasets": ["IlyaGusev/gazeta"], "inference": {"parameters": {"no_repeat_ngram_size": 4}}, "widget": [{"text": "\u0412\u044b\u0441\u043e\u0442\u0430 \u0431\u0430\u0448\u043d\u0438 \u0441\u043e\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u0442 324 \u043c\u0435\u0442\u0440\u0430 (1063 \u0444\u0443\u0442\u0430), \u043f\u0440\u0438\u043c\u0435\u0440\u043d\u043e \u0442\u0430\u043a\u0430\u044f \u0436\u0435 \u0432\u044b\u0441\u043e\u0442\u0430, \u043a\u0430\u043a \u0443 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Intended uses & limitations --------------------------- #### How to use Colab: link Training data ------------- * Dataset: Gazeta Training procedure ------------------ * Training script: URL * Config: t5\_training\_config.json Eval results ------------ * Train dataset: Gazeta v1 train * Test dataset: Gazeta v1 test * Source max\_length: 600 * Target max\_length: 200 * no\_repeat\_ngram\_size: 4 * num\_beams: 5 * Train dataset: Gazeta v1 train * Test dataset: Gazeta v2 test * Source max\_length: 600 * Target max\_length: 200 * no\_repeat\_ngram\_size: 4 * num\_beams: 5 Predicting all summaries: Evaluation script: URL Flags: --language ru --tokenize-after --lower	[ "#### How to use\n\n\nColab: link\n\n\nTraining data\n-------------\n\n\n* Dataset: Gazeta\n\n\nTraining procedure\n------------------\n\n\n* Training script: URL\n* Config: t5\\_training\\_config.json\n\n\nEval results\n------------\n\n\n* Train dataset: Gazeta v1 train\n* Test dataset: Gazeta v1 test\n* Source max\\_length: 600\n* Target max\\_length: 200\n* no\\_repeat\\_ngram\\_size: 4\n* num\\_beams: 5\n\n\n\n* Train dataset: Gazeta v1 train\n* Test dataset: Gazeta v2 test\n* Source max\\_length: 600\n* Target max\\_length: 200\n* no\\_repeat\\_ngram\\_size: 4\n* num\\_beams: 5\n\n\n\nPredicting all summaries:\n\n\nEvaluation script: URL\n\n\nFlags: --language ru --tokenize-after --lower" ]	[ "TAGS\n#transformers #pytorch #t5 #text2text-generation #summarization #ru #dataset-IlyaGusev/gazeta #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n", "#### How to use\n\n\nColab: link\n\n\nTraining data\n-------------\n\n\n* Dataset: Gazeta\n\n\nTraining procedure\n------------------\n\n\n* Training script: URL\n* Config: t5\\_training\\_config.json\n\n\nEval results\n------------\n\n\n* Train dataset: Gazeta v1 train\n* Test dataset: Gazeta v1 test\n* Source max\\_length: 600\n* Target max\\_length: 200\n* no\\_repeat\\_ngram\\_size: 4\n* num\\_beams: 5\n\n\n\n* Train dataset: Gazeta v1 train\n* Test dataset: Gazeta v2 test\n* Source max\\_length: 600\n* Target max\\_length: 200\n* no\\_repeat\\_ngram\\_size: 4\n* num\\_beams: 5\n\n\n\nPredicting all summaries:\n\n\nEvaluation script: URL\n\n\nFlags: --language ru --tokenize-after --lower" ]	[ 77, 195 ]	[ "passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #summarization #ru #dataset-IlyaGusev/gazeta #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n#### How to use\n\n\nColab: link\n\n\nTraining data\n-------------\n\n\n* Dataset: Gazeta\n\n\nTraining procedure\n------------------\n\n\n* Training script: URL\n* Config: t5\\_training\\_config.json\n\n\nEval results\n------------\n\n\n* Train dataset: Gazeta v1 train\n* Test dataset: Gazeta v1 test\n* Source max\\_length: 600\n* Target max\\_length: 200\n* no\\_repeat\\_ngram\\_size: 4\n* num\\_beams: 5\n\n\n\n* Train dataset: Gazeta v1 train\n* Test dataset: Gazeta v2 test\n* Source max\\_length: 600\n* Target max\\_length: 200\n* no\\_repeat\\_ngram\\_size: 4\n* num\\_beams: 5\n\n\n\nPredicting all summaries:\n\n\nEvaluation script: URL\n\n\nFlags: --language ru --tokenize-after --lower" ]	[ -0.0946706011891365, 0.11109413206577301, -0.00425725756213069, 0.03741257265210152, 0.09082405269145966, 0.04817720875144005, 0.08288158476352692, 0.18134431540966034, -0.012352474965155125, 0.08692993968725204, 0.10921500623226166, -0.003729622345417738, 0.06824909895658493, 0.22352205216884613, -0.002467620884999633, -0.22702285647392273, -0.02732270024716854, -0.035099178552627563, -0.13793694972991943, 0.13048721849918365, 0.13682231307029724, -0.09324663132429123, 0.04926297068595886, -0.046657830476760864, -0.05493786185979843, 0.035745542496442795, -0.009582050144672394, -0.1047007218003273, 0.08583798259496689, 0.03356268256902695, 0.04307173565030098, 0.025806980207562447, 0.01886092685163021, -0.19933894276618958, 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null	null	transformers	# XLM-RoBERTa HeadlineCause Full ## Model description This model was trained to predict the presence of causal relations between two headlines. This model is for the Full task with 7 possible labels: titles are almost the same, A causes B, B causes A, A refutes B, B refutes A, A linked with B in another way, A is not linked to B. English and Russian languages are supported. You can use hosted inference API to infer a label for a headline pair. To do this, you shoud seperate headlines with ```</s>``` token. For example: ``` Песков опроверг свой перевод на удаленку</s>Дмитрий Песков перешел на удаленку ``` ## Intended uses & limitations #### How to use ```python from tqdm.notebook import tqdm from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline def get_batch(data, batch_size): start_index = 0 while start_index < len(data): end_index = start_index + batch_size batch = data[start_index:end_index] yield batch start_index = end_index def pipe_predict(data, pipe, batch_size=64): raw_preds = [] for batch in tqdm(get_batch(data, batch_size)): raw_preds += pipe(batch) return raw_preds MODEL_NAME = TOKENIZER_NAME = "IlyaGusev/xlm_roberta_large_headline_cause_full" tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_NAME, do_lower_case=False) model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME) model.eval() pipe = pipeline("text-classification", model=model, tokenizer=tokenizer, framework="pt", return_all_scores=True) texts = [ ( "Judge issues order to allow indoor worship in NC churches", "Some local churches resume indoor services after judge lifted NC governor’s restriction" ), ( "Gov. Kevin Stitt defends $2 million purchase of malaria drug touted by Trump", "Oklahoma spent $2 million on malaria drug touted by Trump" ), ( "Песков опроверг свой перевод на удаленку", "Дмитрий Песков перешел на удаленку" ) ] pipe_predict(texts, pipe) ``` #### Limitations and bias The models are intended to be used on news headlines. No other limitations are known. ## Training data * HuggingFace dataset: [IlyaGusev/headline_cause](https://huggingface.co/datasets/IlyaGusev/headline_cause) * GitHub: [IlyaGusev/HeadlineCause](https://github.com/IlyaGusev/HeadlineCause) ## Training procedure * Notebook: [HeadlineCause](https://colab.research.google.com/drive/1NAnD0OJ0TnYCJRsHpYUyYkjr_yi8ObcA) * Stand-alone script: [train.py](https://github.com/IlyaGusev/HeadlineCause/blob/main/headline_cause/train.py) ## Eval results Evaluation results can be found in the [arxiv paper](https://arxiv.org/pdf/2108.12626.pdf). ### BibTeX entry and citation info ```bibtex @misc{gusev2021headlinecause, title={HeadlineCause: A Dataset of News Headlines for Detecting Causalities}, author={Ilya Gusev and Alexey Tikhonov}, year={2021}, eprint={2108.12626}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```	{"language": ["ru", "en"], "license": "apache-2.0", "tags": ["xlm-roberta-large"], "datasets": ["IlyaGusev/headline_cause"], "widget": [{"text": "\u041f\u0435\u0441\u043a\u043e\u0432 \u043e\u043f\u0440\u043e\u0432\u0435\u0440\u0433 \u0441\u0432\u043e\u0439 \u043f\u0435\u0440\u0435\u0432\u043e\u0434 \u043d\u0430 \u0443\u0434\u0430\u043b\u0435\u043d\u043a\u0443</s>\u0414\u043c\u0438\u0442\u0440\u0438\u0439 \u041f\u0435\u0441\u043a\u043e\u0432 \u043f\u0435\u0440\u0435\u0448\u0435\u043b \u043d\u0430 \u0443\u0434\u0430\u043b\u0435\u043d\u043a\u0443"}]}	text-classification	IlyaGusev/xlm_roberta_large_headline_cause_full	[ "transformers", "pytorch", "xlm-roberta", "text-classification", "xlm-roberta-large", "ru", "en", "dataset:IlyaGusev/headline_cause", "arxiv:2108.12626", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[ "2108.12626" ]	[ "ru", "en" ]	TAGS #transformers #pytorch #xlm-roberta #text-classification #xlm-roberta-large #ru #en #dataset-IlyaGusev/headline_cause #arxiv-2108.12626 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	# XLM-RoBERTa HeadlineCause Full ## Model description This model was trained to predict the presence of causal relations between two headlines. This model is for the Full task with 7 possible labels: titles are almost the same, A causes B, B causes A, A refutes B, B refutes A, A linked with B in another way, A is not linked to B. English and Russian languages are supported. You can use hosted inference API to infer a label for a headline pair. To do this, you shoud seperate headlines with token. For example: ## Intended uses & limitations #### How to use #### Limitations and bias The models are intended to be used on news headlines. No other limitations are known. ## Training data * HuggingFace dataset: IlyaGusev/headline_cause * GitHub: IlyaGusev/HeadlineCause ## Training procedure * Notebook: HeadlineCause * Stand-alone script: URL ## Eval results Evaluation results can be found in the arxiv paper. ### BibTeX entry and citation info	[ "# XLM-RoBERTa HeadlineCause Full", "## Model description\n\nThis model was trained to predict the presence of causal relations between two headlines. This model is for the Full task with 7 possible labels: titles are almost the same, A causes B, B causes A, A refutes B, B refutes A, A linked with B in another way, A is not linked to B. English and Russian languages are supported.\n\nYou can use hosted inference API to infer a label for a headline pair. To do this, you shoud seperate headlines with token.\nFor example:", "## Intended uses & limitations", "#### How to use", "#### Limitations and bias\n\nThe models are intended to be used on news headlines. No other limitations are known.", "## Training data\n\n* HuggingFace dataset: IlyaGusev/headline_cause\n* GitHub: IlyaGusev/HeadlineCause", "## Training procedure\n\n* Notebook: HeadlineCause\n* Stand-alone script: URL", "## Eval results\n\nEvaluation results can be found in the arxiv paper.", "### BibTeX entry and citation info" ]	[ "TAGS\n#transformers #pytorch #xlm-roberta #text-classification #xlm-roberta-large #ru #en #dataset-IlyaGusev/headline_cause #arxiv-2108.12626 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# XLM-RoBERTa HeadlineCause Full", "## Model description\n\nThis model was trained to predict the presence of causal relations between two headlines. This model is for the Full task with 7 possible labels: titles are almost the same, A causes B, B causes A, A refutes B, B refutes A, A linked with B in another way, A is not linked to B. English and Russian languages are supported.\n\nYou can use hosted inference API to infer a label for a headline pair. To do this, you shoud seperate headlines with token.\nFor example:", "## Intended uses & limitations", "#### How to use", "#### Limitations and bias\n\nThe models are intended to be used on news headlines. No other limitations are known.", "## Training data\n\n* HuggingFace dataset: IlyaGusev/headline_cause\n* GitHub: IlyaGusev/HeadlineCause", "## Training procedure\n\n* Notebook: HeadlineCause\n* Stand-alone script: URL", "## Eval results\n\nEvaluation results can be found in the arxiv paper.", "### BibTeX entry and citation info" ]	[ 83, 12, 122, 9, 5, 26, 36, 18, 15, 11 ]	[ "passage: TAGS\n#transformers #pytorch #xlm-roberta #text-classification #xlm-roberta-large #ru #en #dataset-IlyaGusev/headline_cause #arxiv-2108.12626 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# XLM-RoBERTa HeadlineCause Full## Model description\n\nThis model was trained to predict the presence of causal relations between two headlines. This model is for the Full task with 7 possible labels: titles are almost the same, A causes B, B causes A, A refutes B, B refutes A, A linked with B in another way, A is not linked to B. English and Russian languages are supported.\n\nYou can use hosted inference API to infer a label for a headline pair. To do this, you shoud seperate headlines with token.\nFor example:## Intended uses & limitations#### How to use#### Limitations and bias\n\nThe models are intended to be used on news headlines. No other limitations are known.## Training data\n\n* HuggingFace dataset: IlyaGusev/headline_cause\n* GitHub: IlyaGusev/HeadlineCause## Training procedure\n\n* Notebook: HeadlineCause\n* Stand-alone script: URL## Eval results\n\nEvaluation results can be found in the arxiv paper.### BibTeX entry and citation info" ]	[ -0.050686489790678024, 0.11470957100391388, -0.007132134400308132, 0.031015435233712196, 0.08970755338668823, -0.00131335633341223, 0.24930578470230103, 0.10615971684455872, 0.134908065199852, 0.11446116119623184, 0.03279201686382294, -0.004193321336060762, 0.08824863284826279, 0.06538619101047516, 0.028922436758875847, -0.18115010857582092, 0.07333693653345108, -0.08538402616977692, 0.08811787515878677, 0.0544368177652359, 0.1242738589644432, -0.05677224323153496, 0.04439171403646469, 0.016184795647859573, -0.10523228347301483, 0.07205270975828171, 0.013088983483612537, -0.024909188970923424, 0.058865200728178024, 0.08484815806150436, 0.05726538226008415, 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null	null	transformers	# XLM-RoBERTa HeadlineCause Simple ## Model description This model was trained to predict the presence of causal relations between two headlines. This model is for the Simple task with 3 possible labels: A causes B, B causes A, no causal relation. English and Russian languages are supported. You can use hosted inference API to infer a label for a headline pair. To do this, you shoud seperate headlines with ```</s>``` token. For example: ``` Песков опроверг свой перевод на удаленку</s>Дмитрий Песков перешел на удаленку ``` ## Intended uses & limitations #### How to use ```python from tqdm.notebook import tqdm from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline def get_batch(data, batch_size): start_index = 0 while start_index < len(data): end_index = start_index + batch_size batch = data[start_index:end_index] yield batch start_index = end_index def pipe_predict(data, pipe, batch_size=64): raw_preds = [] for batch in tqdm(get_batch(data, batch_size)): raw_preds += pipe(batch) return raw_preds MODEL_NAME = TOKENIZER_NAME = "IlyaGusev/xlm_roberta_large_headline_cause_simple" tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_NAME, do_lower_case=False) model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME) model.eval() pipe = pipeline("text-classification", model=model, tokenizer=tokenizer, framework="pt", return_all_scores=True) texts = [ ( "Judge issues order to allow indoor worship in NC churches", "Some local churches resume indoor services after judge lifted NC governor’s restriction" ), ( "Gov. Kevin Stitt defends $2 million purchase of malaria drug touted by Trump", "Oklahoma spent $2 million on malaria drug touted by Trump" ), ( "Песков опроверг свой перевод на удаленку", "Дмитрий Песков перешел на удаленку" ) ] pipe_predict(texts, pipe) ``` #### Limitations and bias The models are intended to be used on news headlines. No other limitations are known. ## Training data * HuggingFace dataset: [IlyaGusev/headline_cause](https://huggingface.co/datasets/IlyaGusev/headline_cause) * GitHub: [IlyaGusev/HeadlineCause](https://github.com/IlyaGusev/HeadlineCause) ## Training procedure * Notebook: [HeadlineCause](https://colab.research.google.com/drive/1NAnD0OJ0TnYCJRsHpYUyYkjr_yi8ObcA) * Stand-alone script: [train.py](https://github.com/IlyaGusev/HeadlineCause/blob/main/headline_cause/train.py) ## Eval results Evaluation results can be found in the [arxiv paper](https://arxiv.org/pdf/2108.12626.pdf). ### BibTeX entry and citation info ```bibtex @misc{gusev2021headlinecause, title={HeadlineCause: A Dataset of News Headlines for Detecting Causalities}, author={Ilya Gusev and Alexey Tikhonov}, year={2021}, eprint={2108.12626}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```	{"language": ["ru", "en"], "license": "apache-2.0", "tags": ["xlm-roberta-large"], "datasets": ["IlyaGusev/headline_cause"], "widget": [{"text": "\u041f\u0435\u0441\u043a\u043e\u0432 \u043e\u043f\u0440\u043e\u0432\u0435\u0440\u0433 \u0441\u0432\u043e\u0439 \u043f\u0435\u0440\u0435\u0432\u043e\u0434 \u043d\u0430 \u0443\u0434\u0430\u043b\u0435\u043d\u043a\u0443</s>\u0414\u043c\u0438\u0442\u0440\u0438\u0439 \u041f\u0435\u0441\u043a\u043e\u0432 \u043f\u0435\u0440\u0435\u0448\u0435\u043b \u043d\u0430 \u0443\u0434\u0430\u043b\u0435\u043d\u043a\u0443"}]}	text-classification	IlyaGusev/xlm_roberta_large_headline_cause_simple	[ "transformers", "pytorch", "xlm-roberta", "text-classification", "xlm-roberta-large", "ru", "en", "dataset:IlyaGusev/headline_cause", "arxiv:2108.12626", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[ "2108.12626" ]	[ "ru", "en" ]	TAGS #transformers #pytorch #xlm-roberta #text-classification #xlm-roberta-large #ru #en #dataset-IlyaGusev/headline_cause #arxiv-2108.12626 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	# XLM-RoBERTa HeadlineCause Simple ## Model description This model was trained to predict the presence of causal relations between two headlines. This model is for the Simple task with 3 possible labels: A causes B, B causes A, no causal relation. English and Russian languages are supported. You can use hosted inference API to infer a label for a headline pair. To do this, you shoud seperate headlines with token. For example: ## Intended uses & limitations #### How to use #### Limitations and bias The models are intended to be used on news headlines. No other limitations are known. ## Training data * HuggingFace dataset: IlyaGusev/headline_cause * GitHub: IlyaGusev/HeadlineCause ## Training procedure * Notebook: HeadlineCause * Stand-alone script: URL ## Eval results Evaluation results can be found in the arxiv paper. ### BibTeX entry and citation info	[ "# XLM-RoBERTa HeadlineCause Simple", "## Model description\n\nThis model was trained to predict the presence of causal relations between two headlines. This model is for the Simple task with 3 possible labels: A causes B, B causes A, no causal relation. English and Russian languages are supported.\n\nYou can use hosted inference API to infer a label for a headline pair. To do this, you shoud seperate headlines with token.\nFor example:", "## Intended uses & limitations", "#### How to use", "#### Limitations and bias\n\nThe models are intended to be used on news headlines. No other limitations are known.", "## Training data\n\n* HuggingFace dataset: IlyaGusev/headline_cause\n* GitHub: IlyaGusev/HeadlineCause", "## Training procedure\n\n* Notebook: HeadlineCause\n* Stand-alone script: URL", "## Eval results\n\nEvaluation results can be found in the arxiv paper.", "### BibTeX entry and citation info" ]	[ "TAGS\n#transformers #pytorch #xlm-roberta #text-classification #xlm-roberta-large #ru #en #dataset-IlyaGusev/headline_cause #arxiv-2108.12626 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# XLM-RoBERTa HeadlineCause Simple", "## Model description\n\nThis model was trained to predict the presence of causal relations between two headlines. This model is for the Simple task with 3 possible labels: A causes B, B causes A, no causal relation. English and Russian languages are supported.\n\nYou can use hosted inference API to infer a label for a headline pair. To do this, you shoud seperate headlines with token.\nFor example:", "## Intended uses & limitations", "#### How to use", "#### Limitations and bias\n\nThe models are intended to be used on news headlines. No other limitations are known.", "## Training data\n\n* HuggingFace dataset: IlyaGusev/headline_cause\n* GitHub: IlyaGusev/HeadlineCause", "## Training procedure\n\n* Notebook: HeadlineCause\n* Stand-alone script: URL", "## Eval results\n\nEvaluation results can be found in the arxiv paper.", "### BibTeX entry and citation info" ]	[ 83, 12, 93, 9, 5, 26, 36, 18, 15, 11 ]	[ "passage: TAGS\n#transformers #pytorch #xlm-roberta #text-classification #xlm-roberta-large #ru #en #dataset-IlyaGusev/headline_cause #arxiv-2108.12626 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# XLM-RoBERTa HeadlineCause Simple## Model description\n\nThis model was trained to predict the presence of causal relations between two headlines. This model is for the Simple task with 3 possible labels: A causes B, B causes A, no causal relation. English and Russian languages are supported.\n\nYou can use hosted inference API to infer a label for a headline pair. To do this, you shoud seperate headlines with token.\nFor example:## Intended uses & limitations#### How to use#### Limitations and bias\n\nThe models are intended to be used on news headlines. No other limitations are known.## Training data\n\n* HuggingFace dataset: IlyaGusev/headline_cause\n* GitHub: IlyaGusev/HeadlineCause## Training procedure\n\n* Notebook: HeadlineCause\n* Stand-alone script: URL## Eval results\n\nEvaluation results can be found in the arxiv paper.### BibTeX entry and citation info" ]	[ -0.06559142470359802, 0.12409330904483795, -0.004736669361591339, 0.01258858386427164, 0.1024930477142334, -0.009730619378387928, 0.2541933059692383, 0.10473453998565674, 0.09829641133546829, 0.11251704394817352, 0.019058819860219955, 0.04459993913769722, 0.059718601405620575, 0.1207197904586792, 0.027326617389917374, -0.1685541421175003, 0.04621453210711479, -0.0320458859205246, 0.12373984605073929, 0.06152605637907982, 0.12417816370725632, -0.03459414094686508, 0.017005467787384987, 0.03080069273710251, -0.08371367305517197, 0.10237113386392593, -0.0028406018391251564, -0.03710825741291046, 0.05529459938406944, 0.09118533134460449, 0.050086989998817444, 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null	null	transformers	# Harry Botter Model	{"tags": ["conversational"]}	text-generation	Ilyabarigou/Genesis-harrybotter	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Harry Botter Model	[ "# Harry Botter Model" ]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Harry Botter Model" ]	[ 51, 5 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Harry Botter Model" ]	[ -0.011924241669476032, 0.07558048516511917, -0.006045501213520765, 0.02533215656876564, 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null	null	transformers	## Evaluation on Common Voice FR Test The script used for training and evaluation can be found here: https://github.com/irebai/wav2vec2 ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import ( Wav2Vec2ForCTC, Wav2Vec2Processor, ) import re model_name = "Ilyes/wav2vec2-large-xlsr-53-french" device = "cpu" # "cuda" model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device) processor = Wav2Vec2Processor.from_pretrained(model_name) ds = load_dataset("common_voice", "fr", split="test", cache_dir="./data/fr") chars_to_ignore_regex = '[\,\?\.\!\;\:\"\“\%\‘\”\�\‘\’\’\’\‘\…\·\!\ǃ\?\«\‹\»\›“\”\\ʿ\ʾ\„\∞\\\|\.\,\;\:\*\—\–\─\―\_\/\:\ː\;\,\=\«\»\→]' def map_to_array(batch): speech, _ = torchaudio.load(batch["path"]) batch["speech"] = resampler.forward(speech.squeeze(0)).numpy() batch["sampling_rate"] = resampler.new_freq batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'") return batch resampler = torchaudio.transforms.Resample(48_000, 16_000) ds = ds.map(map_to_array) def map_to_pred(batch): features = processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt") input_values = features.input_values.to(device) attention_mask = features.attention_mask.to(device) with torch.no_grad(): logits = model(input_values, attention_mask=attention_mask).logits pred_ids = torch.argmax(logits, dim=-1) batch["predicted"] = processor.batch_decode(pred_ids) batch["target"] = batch["sentence"] return batch result = ds.map(map_to_pred, batched=True, batch_size=16, remove_columns=list(ds.features.keys())) wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` ## Results WER=12.82% CER=4.40%	{"language": "fr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-53-French by Ilyes Rebai", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice fr", "type": "common_voice", "args": "fr"}, "metrics": [{"type": "wer", "value": 12.82, "name": "Test WER"}]}]}]}	automatic-speech-recognition	Ilyes/wav2vec2-large-xlsr-53-french	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "xlsr-fine-tuning-week", "fr", "dataset:common_voice", "license:apache-2.0", "model-index", "endpoints_compatible", "has_space", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "fr" ]	TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #fr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us	## Evaluation on Common Voice FR Test The script used for training and evaluation can be found here: URL ## Results WER=12.82% CER=4.40%	[ "## Evaluation on Common Voice FR Test\nThe script used for training and evaluation can be found here: URL", "## Results\n\nWER=12.82%\n\nCER=4.40%" ]	[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #fr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n", "## Evaluation on Common Voice FR Test\nThe script used for training and evaluation can be found here: URL", "## Results\n\nWER=12.82%\n\nCER=4.40%" ]	[ 81, 21, 11 ]	[ "passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #fr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n## Evaluation on Common Voice FR Test\nThe script used for training and evaluation can be found here: URL## Results\n\nWER=12.82%\n\nCER=4.40%" ]	[ -0.20887020230293274, 0.05255004018545151, -0.001490260474383831, -0.031715139746665955, 0.05659177899360657, 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null	null	transformers	## Evaluation on Common Voice FR Test ```python import re import torch import torchaudio from datasets import load_dataset, load_metric from transformers import ( Wav2Vec2ForCTC, Wav2Vec2Processor, ) model_name = "Ilyes/wav2vec2-large-xlsr-53-french_punctuation" model = Wav2Vec2ForCTC.from_pretrained(model_name).to('cuda') processor = Wav2Vec2Processor.from_pretrained(model_name) ds = load_dataset("common_voice", "fr", split="test") chars_to_ignore_regex = '[\;\:\"\“\%\‘\”\�\‘\’\’\’\‘\…\·\ǃ\«\‹\»\›“\”\\ʿ\ʾ\„\∞\\\|\;\:\*\—\–\─\―\_\/\:\ː\;\=\«\»\→]' def normalize_text(text): text = text.lower().strip() text = re.sub('œ', 'oe', text) text = re.sub('æ', 'ae', text) text = re.sub("’\|´\|′\|ʼ\|‘\|ʻ\|`", "'", text) text = re.sub("'+ ", " ", text) text = re.sub(" '+", " ", text) text = re.sub("'$", " ", text) text = re.sub("' ", " ", text) text = re.sub("−\|‐", "-", text) text = re.sub(" -", "", text) text = re.sub("- ", "", text) text = re.sub(chars_to_ignore_regex, '', text) return text def map_to_array(batch): speech, _ = torchaudio.load(batch["path"]) batch["speech"] = resampler.forward(speech.squeeze(0)).numpy() batch["sampling_rate"] = resampler.new_freq batch["sentence"] = normalize_text(batch["sentence"]) return batch ds = ds.map(map_to_array) resampler = torchaudio.transforms.Resample(48_000, 16_000) def map_to_pred(batch): features = processor(batch["speech"], sampling_rate=batch["sampling_rate"][0], padding=True, return_tensors="pt") input_values = features.input_values.to(device) attention_mask = features.attention_mask.to(device) with torch.no_grad(): logits = model(input_values, attention_mask=attention_mask).logits pred_ids = torch.argmax(logits, dim=-1) batch["predicted"] = processor.batch_decode(pred_ids) batch["target"] = batch["sentence"] # remove duplicates batch["target"] = re.sub('\.+', '.', batch["target"]) batch["target"] = re.sub('\?+', '?', batch["target"]) batch["target"] = re.sub('!+', '!', batch["target"]) batch["target"] = re.sub(',+', ',', batch["target"]) return batch result = ds.map(map_to_pred, batched=True, batch_size=16, remove_columns=list(ds.features.keys())) wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` ## Some results \| Reference \| Prediction \| \| ------------- \| ------------- \| \| il vécut à new york et y enseigna une grande partie de sa vie. \| il a vécu à new york et y enseigna une grande partie de sa vie. \| \| au classement par nations, l'allemagne est la tenante du titre. \| au classement der nation l'allemagne est la tenante du titre. \| \| voici un petit calcul pour fixer les idées. \| voici un petit calcul pour fixer les idées. \| \| oh! tu dois être beau avec \| oh! tu dois être beau avec. \| \| babochet vous le voulez? \| baboche, vous le voulez? \| \| la commission est, par conséquent, défavorable à cet amendement. \| la commission est, par conséquent, défavorable à cet amendement. \| All the references and predictions of the test corpus are already available in this repository. ## Results text + punctuation WER=21.47% CER=7.21% text (without punctuation) WER=19.71% CER=6.91%	{"language": "fr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning"], "datasets": ["common_voice"]}	automatic-speech-recognition	Ilyes/wav2vec2-large-xlsr-53-french_punctuation	[ "transformers", "pytorch", "jax", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "xlsr-fine-tuning", "fr", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "fr" ]	TAGS #transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning #fr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us	Evaluation on Common Voice FR Test ---------------------------------- Some results ------------ All the references and predictions of the test corpus are already available in this repository. Results ------- text + punctuation WER=21.47% CER=7.21% text (without punctuation) WER=19.71% CER=6.91%	[]	[ "TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning #fr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n" ]	[ 74 ]	[ "passage: TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning #fr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n" ]	[ -0.13115404546260834, 0.11908461153507233, -0.005509228445589542, -0.048635777086019516, 0.06382139772176743, -0.051045291125774384, 0.08599433302879333, 0.12311245501041412, 0.04710189625620842, -0.00429736590012908, 0.06549762189388275, 0.19077834486961365, 0.03276311233639717, 0.017424987629055977, -0.07865917682647705, -0.17715871334075928, 0.08203429728746414, 0.04231003671884537, 0.08601967990398407, 0.07985451817512512, 0.10834106802940369, -0.04531025141477585, 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null	null	transformers	# Albert DialoGPT Model	{"tags": ["conversational"]}	text-generation	ImAPizza/DialoGPT-medium-albert	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Albert DialoGPT Model	[ "# Albert DialoGPT Model" ]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Albert DialoGPT Model" ]	[ 51, 7 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Albert DialoGPT Model" ]	[ -0.03233298659324646, 0.04510757699608803, -0.005759505555033684, 0.01878993958234787, 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null	null	transformers	# Alberttwo DialoGPT Model	{"tags": ["conversational"]}	text-generation	ImAPizza/DialoGPT-medium-alberttwo	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Alberttwo DialoGPT Model	[ "# Alberttwo DialoGPT Model" ]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Alberttwo DialoGPT Model" ]	[ 51, 8 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Alberttwo DialoGPT Model" ]	[ -0.019697828218340874, 0.03649180382490158, -0.006464163772761822, 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null	null	transformers	## Usage: ``` from sentence_transformers import models from sentence_transformers import SentenceTransformer word_embedding_model = models.Transformer('Cro-CoV-cseBERT') pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(), pooling_mode_mean_tokens=True, pooling_mode_cls_token=False, pooling_mode_max_tokens=False) model = SentenceTransformer(modules=[word_embedding_model, pooling_model], device='') ## device = 'gpu' or 'cpu' texts_emb = model.encode(texts) ``` ## Datasets: https://github.com/InfoCoV/InfoCoV ## Paper: Please cite https://www.mdpi.com/2076-3417/11/21/10442	{}	fill-mask	InfoCoV/Cro-CoV-cseBERT	[ "transformers", "pytorch", "bert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us	## Usage: ## Datasets: URL ## Paper: Please cite URL	[ "## Usage:", "## Datasets:\nURL", 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null	null	transformers	# Inkdrop/gpt2-property-classifier	{"language": ["de"], "license": "mit", "tags": ["text-generation"], "widget": [{"text": "\"Ideal als kleine Aufmerksamkeit mit emotionalem Wert Neue Tuchmasken-Referenz \"Verw\u00f6hnmoment\u00bb exklusiv im Set Langanhaltende Feuchtigkeit und Erholung Mit strahlendem Teint Sofort-Effekt Naturnahe Kosmetik Inhalt: Badekristalle Kleiner Gruss von Herzen 60 g, Tuchmaske Verw\u00f6hnmoment 1x\" is a", "example_title": "Bullet point classification"}]}	text-generation	Inkdrop/gpt2-property-classifier	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "de", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "de" ]	TAGS #transformers #pytorch #tensorboard #gpt2 #text-generation #de #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Inkdrop/gpt2-property-classifier	[ "# Inkdrop/gpt2-property-classifier" ]	[ "TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #de #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Inkdrop/gpt2-property-classifier" ]	[ 58, 14 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #de #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Inkdrop/gpt2-property-classifier" ]	[ -0.0219278484582901, 0.09430024772882462, -0.005958787631243467, 0.06647833436727524, 0.12982290983200073, 0.009877794422209263, 0.15619824826717377, 0.15229666233062744, 0.005477330181747675, 0.006029786076396704, 0.15950393676757812, 0.28233984112739563, 0.029215160757303238, 0.06004898622632027, -0.060488510876894, -0.2724311947822571, 0.028451869264245033, 0.0916321724653244, -0.030881380662322044, 0.09238845109939575, 0.08892469853162766, -0.04780067875981331, 0.09859927743673325, 0.01804696023464203, 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null	null	null	# Welcome to my model	{"tags": ["chemistry", "climate"]}	null	Intae/mymodel	[ "chemistry", "climate", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #chemistry #climate #region-us	# Welcome to my model	[ "# Welcome to my model" ]	[ "TAGS\n#chemistry #climate #region-us \n", "# Welcome to my model" ]	[ 14, 5 ]	[ "passage: TAGS\n#chemistry #climate #region-us \n# Welcome to my model" ]	[ -0.03700297698378563, 0.04386766999959946, -0.00647616246715188, -0.08395387977361679, -0.031543705612421036, 0.018275421112775803, 0.053873009979724884, 0.021560514345765114, 0.3672979474067688, -0.03135111927986145, 0.16301926970481873, 0.03857945278286934, -0.12305682897567749, 0.1535329967737198, 0.025454329326748848, -0.3441774249076843, 0.13370010256767273, -0.1235765591263771, -0.08541221916675568, 0.04939960315823555, 0.01901412382721901, -0.12814602255821228, 0.06480381637811661, -0.07301826775074005, -0.008590108714997768, -0.010883228853344917, -0.04977588728070259, 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null	null	transformers	# Sparse BERT base model fine tuned to MNLI without classifier layer (uncased) Fine tuned sparse BERT base to MNLI (GLUE Benchmark) task from [bert-base-uncased-sparse-70-unstructured](https://huggingface.co/Intel/bert-base-uncased-sparse-70-unstructured). <br> This model doesn't have a classifier layer to enable easier loading of the model for training to other downstream tasks. In all the other layers this model is similar to [bert-base-uncased-mnli-sparse-70-unstructured](https://huggingface.co/Intel/bert-base-uncased-mnli-sparse-70-unstructured). <br><br> Note: This model requires `transformers==2.10.0` ## Evaluation Results Matched: 82.5% Mismatched: 83.3% This model can be further fine-tuned to other tasks and achieve the following evaluation results: \| Task \| QQP (Acc/F1) \| QNLI (Acc) \| SST-2 (Acc) \| STS-B (Pears/Spear) \| SQuADv1.1 (Acc/F1) \| \|------\|--------------\|------------\|-------------\|---------------------\|--------------------\| \| \| 90.2/86.7 \| 90.3 \| 91.5 \| 88.9/88.6 \| 80.5/88.2 \|	{"language": "en"}	fill-mask	Intel/bert-base-uncased-mnli-sparse-70-unstructured-no-classifier	[ "transformers", "pytorch", "bert", "fill-mask", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #bert #fill-mask #en #autotrain_compatible #endpoints_compatible #region-us	Sparse BERT base model fine tuned to MNLI without classifier layer (uncased) ============================================================================ Fine tuned sparse BERT base to MNLI (GLUE Benchmark) task from bert-base-uncased-sparse-70-unstructured. This model doesn't have a classifier layer to enable easier loading of the model for training to other downstream tasks. In all the other layers this model is similar to bert-base-uncased-mnli-sparse-70-unstructured. Note: This model requires 'transformers==2.10.0' Evaluation Results ------------------ ``` Matched: 82.5% Mismatched: 83.3% ``` This model can be further fine-tuned to other tasks and achieve the following evaluation results:	[]	[ "TAGS\n#transformers #pytorch #bert #fill-mask #en #autotrain_compatible #endpoints_compatible #region-us \n" ]	[ 38 ]	[ "passage: TAGS\n#transformers #pytorch #bert #fill-mask #en #autotrain_compatible #endpoints_compatible #region-us \n" ]	[ -0.06456993520259857, 0.012435728684067726, -0.00861880462616682, 0.026134897023439407, 0.12451299279928207, 0.029568884521722794, 0.09293641149997711, 0.07551068067550659, 0.09861491620540619, -0.00676476676017046, 0.17096230387687683, 0.20306405425071716, -0.03400938957929611, 0.17418460547924042, -0.06299720704555511, -0.2666623294353485, 0.0597798191010952, 0.06285830587148666, -0.06586705148220062, 0.11906897276639938, 0.058261655271053314, -0.08754947036504745, 0.07357781380414963, -0.02002127654850483, 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null	null	transformers	# Sparse BERT base model fine tuned to MNLI (uncased) Fine tuned sparse BERT base to MNLI (GLUE Benchmark) task from [bert-base-uncased-sparse-70-unstructured](https://huggingface.co/Intel/bert-base-uncased-sparse-70-unstructured). <br><br> Note: This model requires `transformers==2.10.0` ## Evaluation Results Matched: 82.5% Mismatched: 83.3% This model can be further fine-tuned to other tasks and achieve the following evaluation results: \| Task \| QQP (Acc/F1) \| QNLI (Acc) \| SST-2 (Acc) \| STS-B (Pears/Spear) \| SQuADv1.1 (Acc/F1) \| \|------\|--------------\|------------\|-------------\|---------------------\|--------------------\| \| \| 90.2/86.7 \| 90.3 \| 91.5 \| 88.9/88.6 \| 80.5/88.2 \|	{"language": "en"}	text-classification	Intel/bert-base-uncased-mnli-sparse-70-unstructured	[ "transformers", "pytorch", "bert", "text-classification", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #bert #text-classification #en #autotrain_compatible #endpoints_compatible #region-us	Sparse BERT base model fine tuned to MNLI (uncased) =================================================== Fine tuned sparse BERT base to MNLI (GLUE Benchmark) task from bert-base-uncased-sparse-70-unstructured. Note: This model requires 'transformers==2.10.0' Evaluation Results ------------------ ``` Matched: 82.5% Mismatched: 83.3% ``` This model can be further fine-tuned to other tasks and achieve the following evaluation results:	[]	[ "TAGS\n#transformers #pytorch #bert #text-classification #en #autotrain_compatible #endpoints_compatible #region-us \n" ]	[ 38 ]	[ "passage: TAGS\n#transformers #pytorch #bert #text-classification #en #autotrain_compatible #endpoints_compatible #region-us \n" ]	[ -0.025796223431825638, 0.05117085576057434, -0.007793994154781103, 0.02724592387676239, 0.20128965377807617, 0.03925756737589836, 0.06982962787151337, 0.10690335184335709, 0.05743226408958435, -0.029553305357694626, 0.11680170148611069, 0.22629117965698242, -0.03616909310221672, 0.1057952344417572, -0.1196640133857727, -0.3019154667854309, 0.05957525223493576, 0.07097269594669342, 0.00040068230009637773, 0.11871974915266037, 0.086296446621418, -0.09110052138566971, 0.07162238657474518, 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null	null	transformers	# Sparse BERT base model (uncased) Pretrained model pruned to 1:2 structured sparsity. The model is a pruned version of the [BERT base model](https://huggingface.co/bert-base-uncased). ## Intended Use The model can be used for fine-tuning to downstream tasks with sparsity already embeded to the model. To keep the sparsity a mask should be added to each sparse weight blocking the optimizer from updating the zeros. ## Evaluation Results We get the following results on the tasks development set, all results are mean of 5 different seeded models: \| Task \| MNLI-m (Acc) \| MNLI-mm (Acc) \| QQP (Acc/F1) \| QNLI (Acc) \| SST-2 (Acc) \| STS-B (Pears/Spear) \| SQuADv1.1 (Acc/F1) \| \|------\|--------------\|---------------\|--------------\|------------\|-------------\|---------------------\|--------------------\| \| \| 83.3 \| 83.9 \| 90.8/87.6 \| 90.4 \| 91.3 \| 88.8/88.3 \| 80.5/88.2 \|	{"language": "en"}	null	Intel/bert-base-uncased-sparse-1_2	[ "transformers", "pytorch", "bert", "pretraining", "en", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #bert #pretraining #en #endpoints_compatible #region-us	Sparse BERT base model (uncased) ================================ Pretrained model pruned to 1:2 structured sparsity. The model is a pruned version of the BERT base model. Intended Use ------------ The model can be used for fine-tuning to downstream tasks with sparsity already embeded to the model. To keep the sparsity a mask should be added to each sparse weight blocking the optimizer from updating the zeros. Evaluation Results ------------------ We get the following results on the tasks development set, all results are mean of 5 different seeded models:	[]	[ "TAGS\n#transformers #pytorch #bert #pretraining #en #endpoints_compatible #region-us \n" ]	[ 28 ]	[ "passage: TAGS\n#transformers #pytorch #bert #pretraining #en #endpoints_compatible #region-us \n" ]	[ -0.06031027436256409, 0.01853039488196373, -0.010262301191687584, -0.031429290771484375, 0.13131536543369293, 0.03042694926261902, 0.047801099717617035, 0.0511772483587265, 0.0705023929476738, -0.014373849146068096, 0.16220232844352722, 0.17742913961410522, -0.0544629767537117, -0.005108308047056198, -0.04390354081988335, -0.2567123472690582, 0.05760765075683594, 0.0940965861082077, -0.07264886796474457, 0.11598016321659088, 0.05436844751238823, -0.11775700747966766, 0.05320095270872116, -0.03181854635477066, -0.11234714090824127, 0.02225741185247898, -0.0074081458151340485, -0.061159033328294754, 0.13941732048988342, 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null	null	transformers	# Sparse BERT base model (uncased) Pretrained model pruned to 70% sparsity. The model is a pruned version of the [BERT base model](https://huggingface.co/bert-base-uncased). ## Intended Use The model can be used for fine-tuning to downstream tasks with sparsity already embeded to the model. To keep the sparsity a mask should be added to each sparse weight blocking the optimizer from updating the zeros.	{"language": "en"}	fill-mask	Intel/bert-base-uncased-sparse-70-unstructured	[ "transformers", "pytorch", "bert", "fill-mask", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #bert #fill-mask #en #autotrain_compatible #endpoints_compatible #region-us	# Sparse BERT base model (uncased) Pretrained model pruned to 70% sparsity. The model is a pruned version of the BERT base model. ## Intended Use The model can be used for fine-tuning to downstream tasks with sparsity already embeded to the model. To keep the sparsity a mask should be added to each sparse weight blocking the optimizer from updating the zeros.	[ "# Sparse BERT base model (uncased)\n\nPretrained model pruned to 70% sparsity.\nThe model is a pruned version of the BERT base model.", "## Intended Use\n\nThe model can be used for fine-tuning to downstream tasks with sparsity already embeded to the model.\nTo keep the sparsity a mask should be added to each sparse weight blocking the optimizer from updating the zeros." ]	[ "TAGS\n#transformers #pytorch #bert #fill-mask #en #autotrain_compatible #endpoints_compatible #region-us \n", "# Sparse BERT base model (uncased)\n\nPretrained model pruned to 70% sparsity.\nThe model is a pruned version of the BERT base model.", "## Intended Use\n\nThe model can be used for fine-tuning to downstream tasks with sparsity already embeded to the model.\nTo keep the sparsity a mask should be added to each sparse weight blocking the optimizer from updating the zeros." ]	[ 38, 38, 60 ]	[ "passage: TAGS\n#transformers #pytorch #bert #fill-mask #en #autotrain_compatible #endpoints_compatible #region-us \n# Sparse BERT base model (uncased)\n\nPretrained model pruned to 70% sparsity.\nThe model is a pruned version of the BERT base model.## Intended Use\n\nThe model can be used for fine-tuning to downstream tasks with sparsity already embeded to the model.\nTo keep the sparsity a mask should be added to each sparse weight blocking the optimizer from updating the zeros." ]	[ -0.06153089925646782, -0.07240350544452667, -0.0021561074536293745, 0.05465533211827278, 0.07097543776035309, -0.03300677612423897, 0.049230121076107025, 0.018746841698884964, -0.07452163845300674, 0.00983873289078474, 0.164375901222229, 0.06690197438001633, -0.040779583156108856, 0.15818466246128082, -0.059995319694280624, -0.30859893560409546, 0.1183779388666153, 0.08930886536836624, -0.03029904142022133, 0.049104850739240646, 0.0438370481133461, -0.08163667470216751, 0.09979340434074402, 0.0435592383146286, -0.12651868164539337, 0.007330766879022121, 0.05962393432855606, 0.014884712174534798, 0.05120008438825607, 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null	null	transformers	## Model Details: 85% Sparse BERT-Base (uncased) Prune Once for All This model is a sparse pre-trained model that can be fine-tuned for a wide range of language tasks. The process of weight pruning is forcing some of the weights of the neural network to zero. Setting some of the weights to zero results in sparser matrices. Updating neural network weights does involve matrix multiplication, and if we can keep the matrices sparse while retaining enough important information, we can reduce the overall computational overhead. The term "sparse" in the title of the model indicates a ratio of sparsity in the weights; for more details, you can read [Zafrir et al. (2021)](https://arxiv.org/abs/2111.05754). Visualization of Prunce Once for All method from [Zafrir et al. (2021)](https://arxiv.org/abs/2111.05754): ![Zafrir2021_Fig1.png](https://s3.amazonaws.com/moonup/production/uploads/6297f0e30bd2f58c647abb1d/nSDP62H9NHC1FA0C429Xo.png) \| Model Detail \| Description \| \| ----------- \| ----------- \| \| Model Authors - Company \| Intel \| \| Date \| September 30, 2021 \| \| Version \| 1 \| \| Type \| NLP - General sparse language model \| \| Architecture \| "The method consists of two steps, teacher preparation and student pruning. The sparse pre-trained model we trained is the model we use for transfer learning while maintaining its sparsity pattern. We call the method Prune Once for All since we show how to fine-tune the sparse pre-trained models for several language tasks while we prune the pre-trained model only once." [(Zafrir et al., 2021)](https://arxiv.org/abs/2111.05754) \| \| Paper or Other Resources \| [Zafrir et al. (2021)](https://arxiv.org/abs/2111.05754); [GitHub Repo](https://github.com/IntelLabs/Model-Compression-Research-Package/tree/main/research/prune-once-for-all) \| \| License \| Apache 2.0 \| \| Questions or Comments \| [Community Tab](https://huggingface.co/Intel/bert-base-uncased-sparse-85-unstructured-pruneofa/discussions) and [Intel Developers Discord](https://discord.gg/rv2Gp55UJQ)\| \| Intended Use \| Description \| \| ----------- \| ----------- \| \| Primary intended uses \| This is a general sparse language model; in its current form, it is not ready for downstream prediction tasks, but it can be fine-tuned for several language tasks including (but not limited to) question-answering, genre natural language inference, and sentiment classification. \| \| Primary intended users \| Anyone who needs an efficient general language model for other downstream tasks. \| \| Out-of-scope uses \| The model should not be used to intentionally create hostile or alienating environments for people.\| ### How to use Here is an example of how to import this model in Python: ```python import transformers model = transformers.AutoModelForQuestionAnswering.from_pretrained('Intel/bert-base-uncased-sparse-85-unstructured-pruneofa') ``` For more code examples, refer to the [GitHub Repo](https://github.com/IntelLabs/Model-Compression-Research-Package/tree/main/research/prune-once-for-all). ### Metrics (Model Performance): \| Model \| Model Size \| SQuADv1.1 (EM/F1) \| MNLI-m (Acc) \| MNLI-mm (Acc) \| QQP (Acc/F1) \| QNLI (Acc) \| SST-2 (Acc) \| \|-------------------------------\|:----------:\|:-----------------:\|:------------:\|:-------------:\|:------------:\|:----------:\|:-----------:\| \| [80% Sparse BERT-Base uncased fine-tuned on SQuAD1.1](https://huggingface.co/Intel/bert-base-uncased-squadv1.1-sparse-80-1x4-block-pruneofa) \| - \| 81.29/88.47 \| - \| - \| - \| - \| - \| \| [85% Sparse BERT-Base uncased](https://huggingface.co/Intel/bert-base-uncased-sparse-85-unstructured-pruneofa) \| Medium \| 81.10/88.42 \| 82.71 \| 83.67 \| 91.15/88.00 \| 90.34 \| 91.46 \| \| [90% Sparse BERT-Base uncased](https://huggingface.co/Intel/bert-base-uncased-sparse-90-unstructured-pruneofa) \| Medium \| 79.83/87.25 \| 81.45 \| 82.43 \| 90.93/87.72 \| 89.07 \| 90.88 \| \| [90% Sparse BERT-Large uncased](https://huggingface.co/Intel/bert-large-uncased-sparse-90-unstructured-pruneofa) \| Large \| 83.35/90.20 \| 83.74 \| 84.20 \| 91.48/88.43 \| 91.39 \| 92.95 \| \| [85% Sparse DistilBERT uncased](https://huggingface.co/Intel/distilbert-base-uncased-sparse-85-unstructured-pruneofa) \| Small \| 78.10/85.82 \| 81.35 \| 82.03 \| 90.29/86.97 \| 88.31 \| 90.60 \| \| [90% Sparse DistilBERT uncased](https://huggingface.co/Intel/distilbert-base-uncased-sparse-90-unstructured-pruneofa) \| Small \| 76.91/84.82 \| 80.68 \| 81.47 \| 90.05/86.67 \| 87.66 \| 90.02 \| All the results are the mean of two seperate experiments with the same hyper-parameters and different seeds. \| Training and Evaluation Data \| Description \| \| ----------- \| ----------- \| \| Datasets \| [English Wikipedia Dataset](https://huggingface.co/datasets/wikipedia) (2500M words). \| \| Motivation \| To build an efficient and accurate base model for several downstream language tasks. \| \| Preprocessing \| "We use the English Wikipedia dataset (2500M words) for training the models on the pre-training task. We split the data into train (95%) and validation (5%) sets. Both sets are preprocessed as described in the models’ original papers ([Devlin et al., 2019](https://arxiv.org/abs/1810.04805), [Sanh et al., 2019](https://arxiv.org/abs/1910.01108)). We process the data to use the maximum sequence length allowed by the models, however, we allow shorter sequences at a probability of 0:1." \| \| Ethical Considerations \| Description \| \| ----------- \| ----------- \| \| Data \| The training data come from Wikipedia articles \| \| Human life \| The model is not intended to inform decisions central to human life or flourishing. It is an aggregated set of labelled Wikipedia articles. \| \| Mitigations \| No additional risk mitigation strategies were considered during model development. \| \| Risks and harms \| Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al., 2021](https://aclanthology.org/2021.acl-long.330.pdf), and [Bender et al., 2021](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. Beyond this, the extent of the risks involved by using the model remain unknown.\| \| Use cases \| - \| \| Caveats and Recommendations \| \| ----------- \| \| Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. There are no additional caveats or recommendations for this model. \| ### BibTeX entry and citation info ```bibtex @article{zafrir2021prune, title={Prune Once for All: Sparse Pre-Trained Language Models}, author={Zafrir, Ofir and Larey, Ariel and Boudoukh, Guy and Shen, Haihao and Wasserblat, Moshe}, journal={arXiv preprint arXiv:2111.05754}, year={2021} } ```	{"language": "en", "license": "apache-2.0", "tags": ["fill-mask"], "datasets": ["wikipedia", "bookcorpus"]}	fill-mask	Intel/bert-base-uncased-sparse-85-unstructured-pruneofa	[ "transformers", "pytorch", "tf", "bert", "pretraining", "fill-mask", "en", "dataset:wikipedia", "dataset:bookcorpus", "arxiv:2111.05754", "arxiv:1810.04805", "arxiv:1910.01108", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[ "2111.05754", "1810.04805", "1910.01108" ]	[ "en" ]	TAGS #transformers #pytorch #tf #bert #pretraining #fill-mask #en #dataset-wikipedia #dataset-bookcorpus #arxiv-2111.05754 #arxiv-1810.04805 #arxiv-1910.01108 #license-apache-2.0 #endpoints_compatible #region-us	Model Details: 85% Sparse BERT-Base (uncased) Prune Once for All ---------------------------------------------------------------- This model is a sparse pre-trained model that can be fine-tuned for a wide range of language tasks. The process of weight pruning is forcing some of the weights of the neural network to zero. Setting some of the weights to zero results in sparser matrices. Updating neural network weights does involve matrix multiplication, and if we can keep the matrices sparse while retaining enough important information, we can reduce the overall computational overhead. The term "sparse" in the title of the model indicates a ratio of sparsity in the weights; for more details, you can read Zafrir et al. (2021). Visualization of Prunce Once for All method from Zafrir et al. (2021): !Zafrir2021\_Fig1.png ### How to use Here is an example of how to import this model in Python: For more code examples, refer to the GitHub Repo. ### Metrics (Model Performance): All the results are the mean of two seperate experiments with the same hyper-parameters and different seeds. ### BibTeX entry and citation info	[ "### How to use\n\n\nHere is an example of how to import this model in Python:\n\n\nFor more code examples, refer to the GitHub Repo.", "### Metrics (Model Performance):\n\n\n\nAll the results are the mean of two seperate experiments with the same hyper-parameters and different seeds.", "### BibTeX entry and citation info" ]	[ "TAGS\n#transformers #pytorch #tf #bert #pretraining #fill-mask #en #dataset-wikipedia #dataset-bookcorpus #arxiv-2111.05754 #arxiv-1810.04805 #arxiv-1910.01108 #license-apache-2.0 #endpoints_compatible #region-us \n", "### How to use\n\n\nHere is an example of how to import this model in Python:\n\n\nFor more code examples, refer to the GitHub Repo.", "### Metrics (Model Performance):\n\n\n\nAll the results are the mean of two seperate experiments with the same hyper-parameters and different seeds.", "### BibTeX entry and citation info" ]	[ 82, 33, 36, 11 ]	[ "passage: TAGS\n#transformers #pytorch #tf #bert #pretraining #fill-mask #en #dataset-wikipedia #dataset-bookcorpus #arxiv-2111.05754 #arxiv-1810.04805 #arxiv-1910.01108 #license-apache-2.0 #endpoints_compatible #region-us \n### How to use\n\n\nHere is an example of how to import this model in Python:\n\n\nFor more code examples, refer to the GitHub Repo.### Metrics (Model Performance):\n\n\n\nAll the results are the mean of two seperate experiments with the same hyper-parameters and different seeds.### BibTeX entry and citation info" ]	[ -0.04646419733762741, 0.09619906544685364, -0.003505624597892165, 0.13187673687934875, 0.06881929188966751, 0.02042018435895443, 0.10129685699939728, 0.0937829241156578, 0.04263918101787567, -0.0022539393976330757, 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null	null	transformers	## Model Details: 90% Sparse BERT-Base (uncased) Prune Once for All This model is a sparse pre-trained model that can be fine-tuned for a wide range of language tasks. The process of weight pruning is forcing some of the weights of the neural network to zero. Setting some of the weights to zero results in sparser matrices. Updating neural network weights does involve matrix multiplication, and if we can keep the matrices sparse while retaining enough important information, we can reduce the overall computational overhead. The term "sparse" in the title of the model indicates a ratio of sparsity in the weights; for more details, you can read [Zafrir et al. (2021)](https://arxiv.org/abs/2111.05754). Visualization of Prunce Once for All method from [Zafrir et al. (2021)](https://arxiv.org/abs/2111.05754): ![Zafrir2021_Fig1.png](https://s3.amazonaws.com/moonup/production/uploads/6297f0e30bd2f58c647abb1d/nSDP62H9NHC1FA0C429Xo.png) \| Model Detail \| Description \| \| ----------- \| ----------- \| \| Model Authors - Company \| Intel \| \| Date \| September 30, 2021 \| \| Version \| 1 \| \| Type \| NLP - General sparse language model \| \| Architecture \| "The method consists of two steps, teacher preparation and student pruning. The sparse pre-trained model we trained is the model we use for transfer learning while maintaining its sparsity pattern. We call the method Prune Once for All since we show how to fine-tune the sparse pre-trained models for several language tasks while we prune the pre-trained model only once." [(Zafrir et al., 2021)](https://arxiv.org/abs/2111.05754) \| \| Paper or Other Resources \| [Zafrir et al. (2021)](https://arxiv.org/abs/2111.05754); [GitHub Repo](https://github.com/IntelLabs/Model-Compression-Research-Package/tree/main/research/prune-once-for-all) \| \| License \| Apache 2.0 \| \| Questions or Comments \| [Community Tab](https://huggingface.co/Intel/bert-base-uncased-sparse-90-unstructured-pruneofa/discussions) and [Intel Developers Discord](https://discord.gg/rv2Gp55UJQ)\| \| Intended Use \| Description \| \| ----------- \| ----------- \| \| Primary intended uses \| This is a general sparse language model; in its current form, it is not ready for downstream prediction tasks, but it can be fine-tuned for several language tasks including (but not limited to) question-answering, genre natural language inference, and sentiment classification. \| \| Primary intended users \| Anyone who needs an efficient general language model for other downstream tasks. \| \| Out-of-scope uses \| The model should not be used to intentionally create hostile or alienating environments for people.\| ### How to use Here is an example of how to import this model in Python: ```python import transformers model = transformers.AutoModelForQuestionAnswering.from_pretrained('Intel/bert-base-uncased-sparse-90-unstructured-pruneofa') ``` For more code examples, refer to the [GitHub Repo](https://github.com/IntelLabs/Model-Compression-Research-Package/tree/main/research/prune-once-for-all). ### Metrics (Model Performance): \| Model \| Model Size \| SQuADv1.1 (EM/F1) \| MNLI-m (Acc) \| MNLI-mm (Acc) \| QQP (Acc/F1) \| QNLI (Acc) \| SST-2 (Acc) \| \|-------------------------------\|:----------:\|:-----------------:\|:------------:\|:-------------:\|:------------:\|:----------:\|:-----------:\| \| [80% Sparse BERT-Base uncased fine-tuned on SQuAD1.1](https://huggingface.co/Intel/bert-base-uncased-squadv1.1-sparse-80-1x4-block-pruneofa) \| - \| 81.29/88.47 \| - \| - \| - \| - \| - \| \| [85% Sparse BERT-Base uncased](https://huggingface.co/Intel/bert-base-uncased-sparse-85-unstructured-pruneofa) \| Medium \| 81.10/88.42 \| 82.71 \| 83.67 \| 91.15/88.00 \| 90.34 \| 91.46 \| \| [90% Sparse BERT-Base uncased](https://huggingface.co/Intel/bert-base-uncased-sparse-90-unstructured-pruneofa) \| Medium \| 79.83/87.25 \| 81.45 \| 82.43 \| 90.93/87.72 \| 89.07 \| 90.88 \| \| [90% Sparse BERT-Large uncased](https://huggingface.co/Intel/bert-large-uncased-sparse-90-unstructured-pruneofa) \| Large \| 83.35/90.20 \| 83.74 \| 84.20 \| 91.48/88.43 \| 91.39 \| 92.95 \| \| [85% Sparse DistilBERT uncased](https://huggingface.co/Intel/distilbert-base-uncased-sparse-85-unstructured-pruneofa) \| Small \| 78.10/85.82 \| 81.35 \| 82.03 \| 90.29/86.97 \| 88.31 \| 90.60 \| \| [90% Sparse DistilBERT uncased](https://huggingface.co/Intel/distilbert-base-uncased-sparse-90-unstructured-pruneofa) \| Small \| 76.91/84.82 \| 80.68 \| 81.47 \| 90.05/86.67 \| 87.66 \| 90.02 \| All the results are the mean of two seperate experiments with the same hyper-parameters and different seeds. \| Training and Evaluation Data \| Description \| \| ----------- \| ----------- \| \| Datasets \| [English Wikipedia Dataset](https://huggingface.co/datasets/wikipedia) (2500M words). \| \| Motivation \| To build an efficient and accurate base model for several downstream language tasks. \| \| Preprocessing \| "We use the English Wikipedia dataset (2500M words) for training the models on the pre-training task. We split the data into train (95%) and validation (5%) sets. Both sets are preprocessed as described in the models’ original papers ([Devlin et al., 2019](https://arxiv.org/abs/1810.04805), [Sanh et al., 2019](https://arxiv.org/abs/1910.01108)). We process the data to use the maximum sequence length allowed by the models, however, we allow shorter sequences at a probability of 0:1." \| \| Ethical Considerations \| Description \| \| ----------- \| ----------- \| \| Data \| The training data come from Wikipedia articles \| \| Human life \| The model is not intended to inform decisions central to human life or flourishing. It is an aggregated set of labelled Wikipedia articles. \| \| Mitigations \| No additional risk mitigation strategies were considered during model development. \| \| Risks and harms \| Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al., 2021](https://aclanthology.org/2021.acl-long.330.pdf), and [Bender et al., 2021](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. Beyond this, the extent of the risks involved by using the model remain unknown.\| \| Use cases \| - \| \| Caveats and Recommendations \| \| ----------- \| \| Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. There are no additional caveats or recommendations for this model. \| ### BibTeX entry and citation info ```bibtex @article{zafrir2021prune, title={Prune Once for All: Sparse Pre-Trained Language Models}, author={Zafrir, Ofir and Larey, Ariel and Boudoukh, Guy and Shen, Haihao and Wasserblat, Moshe}, journal={arXiv preprint arXiv:2111.05754}, year={2021} } ```	{"language": "en", "license": "apache-2.0", "tags": ["fill-mask", "bert"], "datasets": ["wikipedia", "bookcorpus"]}	fill-mask	Intel/bert-base-uncased-sparse-90-unstructured-pruneofa	[ "transformers", "pytorch", "tf", "bert", "pretraining", "fill-mask", "en", "dataset:wikipedia", "dataset:bookcorpus", "arxiv:2111.05754", "arxiv:1810.04805", "arxiv:1910.01108", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[ "2111.05754", "1810.04805", "1910.01108" ]	[ "en" ]	TAGS #transformers #pytorch #tf #bert #pretraining #fill-mask #en #dataset-wikipedia #dataset-bookcorpus #arxiv-2111.05754 #arxiv-1810.04805 #arxiv-1910.01108 #license-apache-2.0 #endpoints_compatible #region-us	Model Details: 90% Sparse BERT-Base (uncased) Prune Once for All ---------------------------------------------------------------- This model is a sparse pre-trained model that can be fine-tuned for a wide range of language tasks. The process of weight pruning is forcing some of the weights of the neural network to zero. Setting some of the weights to zero results in sparser matrices. Updating neural network weights does involve matrix multiplication, and if we can keep the matrices sparse while retaining enough important information, we can reduce the overall computational overhead. The term "sparse" in the title of the model indicates a ratio of sparsity in the weights; for more details, you can read Zafrir et al. (2021). Visualization of Prunce Once for All method from Zafrir et al. (2021): !Zafrir2021\_Fig1.png ### How to use Here is an example of how to import this model in Python: For more code examples, refer to the GitHub Repo. ### Metrics (Model Performance): All the results are the mean of two seperate experiments with the same hyper-parameters and different seeds. ### BibTeX entry and citation info	[ "### How to use\n\n\nHere is an example of how to import this model in Python:\n\n\nFor more code examples, refer to the GitHub Repo.", "### Metrics (Model Performance):\n\n\n\nAll the results are the mean of two seperate experiments with the same hyper-parameters and different seeds.", "### BibTeX entry and citation info" ]	[ "TAGS\n#transformers #pytorch #tf #bert #pretraining #fill-mask #en #dataset-wikipedia #dataset-bookcorpus #arxiv-2111.05754 #arxiv-1810.04805 #arxiv-1910.01108 #license-apache-2.0 #endpoints_compatible #region-us \n", "### How to use\n\n\nHere is an example of how to import this model in Python:\n\n\nFor more code examples, refer to the GitHub Repo.", "### Metrics (Model Performance):\n\n\n\nAll the results are the mean of two seperate experiments with the same hyper-parameters and different seeds.", "### BibTeX entry and citation info" ]	[ 82, 33, 36, 11 ]	[ "passage: TAGS\n#transformers #pytorch #tf #bert #pretraining #fill-mask #en #dataset-wikipedia #dataset-bookcorpus #arxiv-2111.05754 #arxiv-1810.04805 #arxiv-1910.01108 #license-apache-2.0 #endpoints_compatible #region-us \n### How to use\n\n\nHere is an example of how to import this model in Python:\n\n\nFor more code examples, refer to the GitHub Repo.### Metrics (Model Performance):\n\n\n\nAll the results are the mean of two seperate experiments with the same hyper-parameters and different seeds.### BibTeX entry and citation info" ]	[ -0.04646419733762741, 0.09619906544685364, -0.003505624597892165, 0.13187673687934875, 0.06881929188966751, 0.02042018435895443, 0.10129685699939728, 0.0937829241156578, 0.04263918101787567, -0.0022539393976330757, 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null	null	transformers	## Model Details: 80% 1x4 Block Sparse BERT-Base (uncased) Fine Tuned on SQuADv1.1 This model has been fine-tuned for the NLP task of question answering, trained on the SQuAD 1.1 dataset. It is a result of fine-tuning a Prune Once For All 80% 1x4 block sparse pre-trained BERT-Base model, combined with knowledge distillation. > We present a new method for training sparse pre-trained Transformer language models by integrating weight pruning and model distillation. These sparse pre-trained models can be used to transfer learning for a wide range of tasks while maintaining their sparsity pattern. We show how the compressed sparse pre-trained models we trained transfer their knowledge to five different downstream natural language tasks with minimal accuracy loss. \| Model Detail \| Description \| \| ----------- \| ----------- \| \| Model Authors - Company \| Intel \| \| Model Card Authors \| Intel \| \| Date \| February 27, 2022 \| \| Version \| 1 \| \| Type \| NLP - Question Answering \| \| Architecture \| "The method consists of two steps, teacher preparation and student pruning. The sparse pre-trained model we trained is the model we use for transfer learning while maintaining its sparsity pattern. We call the method Prune Once for All since we show how to fine-tune the sparse pre-trained models for several language tasks while we prune the pre-trained model only once." [(Zafrir et al., 2021)](https://arxiv.org/abs/2111.05754) \| \| Paper or Other Resources \| [Paper: Zafrir et al. (2021)](https://arxiv.org/abs/2111.05754); [GitHub Repo](https://github.com/IntelLabs/Model-Compression-Research-Package/tree/main/research/prune-once-for-all) \| \| License \| Apache 2.0 \| \| Questions or Comments \| [Community Tab](https://huggingface.co/Intel/bert-base-uncased-squadv1.1-sparse-80-1x4-block-pruneofa/discussions) and [Intel Developers Discord](https://discord.gg/rv2Gp55UJQ)\| Visualization of Prunce Once for All method from [Zafrir et al. (2021)](https://arxiv.org/abs/2111.05754). More details can be found in their paper. ![Zafrir2021_Fig1.png](https://s3.amazonaws.com/moonup/production/uploads/6297f0e30bd2f58c647abb1d/nSDP62H9NHC1FA0C429Xo.png) \| Intended Use \| Description \| \| ----------- \| ----------- \| \| Primary intended uses \| You can use the model for the NLP task of question answering: given a corpus of text, you can ask it a question about that text, and it will find the answer in the text. \| \| Primary intended users \| Anyone doing question answering \| \| Out-of-scope uses \| The model should not be used to intentionally create hostile or alienating environments for people.\| ### How to use Here is how to import this model in Python: ```python import transformers import model_compression_research as model_comp model = transformers.AutoModelForQuestionAnswering.from_pretrained('Intel/bert-base-uncased-squadv1.1-sparse-80-1x4-block-pruneofa') scheduler = mcr.pruning_scheduler_factory(model, '../../examples/transformers/question-answering/config/lock_config.json') # Train your model... scheduler.remove_pruning() ``` For more code examples, refer to the [GitHub Repo](https://github.com/IntelLabs/Model-Compression-Research-Package/tree/main/research/prune-once-for-all). ### Metrics (Model Performance): \| Model \| Model Size \| SQuADv1.1 (EM/F1) \| MNLI-m (Acc) \| MNLI-mm (Acc) \| QQP (Acc/F1) \| QNLI (Acc) \| SST-2 (Acc) \| \|-------------------------------\|:----------:\|:-----------------:\|:------------:\|:-------------:\|:------------:\|:----------:\|:-----------:\| \| [80% 1x4 Block Sparse BERT-Base uncased](https://huggingface.co/Intel/bert-base-uncased-squadv1.1-sparse-80-1x4-block-pruneofa) \| - \| 81.29/88.47 \| - \| - \| - \| - \| - \| \| [85% Sparse BERT-Base uncased](https://huggingface.co/Intel/bert-base-uncased-sparse-85-unstructured-pruneofa) \| Medium \| 81.10/88.42 \| 82.71 \| 83.67 \| 91.15/88.00 \| 90.34 \| 91.46 \| \| [90% Sparse BERT-Base uncased](https://huggingface.co/Intel/bert-base-uncased-sparse-90-unstructured-pruneofa) \| Medium \| 79.83/87.25 \| 81.45 \| 82.43 \| 90.93/87.72 \| 89.07 \| 90.88 \| \| [90% Sparse BERT-Large uncased](https://huggingface.co/Intel/bert-large-uncased-sparse-90-unstructured-pruneofa) \| Large \| 83.35/90.20 \| 83.74 \| 84.20 \| 91.48/88.43 \| 91.39 \| 92.95 \| \| [85% Sparse DistilBERT uncased](https://huggingface.co/Intel/distilbert-base-uncased-sparse-85-unstructured-pruneofa) \| Small \| 78.10/85.82 \| 81.35 \| 82.03 \| 90.29/86.97 \| 88.31 \| 90.60 \| \| [90% Sparse DistilBERT uncased](https://huggingface.co/Intel/distilbert-base-uncased-sparse-90-unstructured-pruneofa) \| Small \| 76.91/84.82 \| 80.68 \| 81.47 \| 90.05/86.67 \| 87.66 \| 90.02 \| All the results are the mean of two seperate experiments with the same hyper-parameters and different seeds. \| Training and Evaluation Data \| Description \| \| ----------- \| ----------- \| \| Datasets \| SQuAD1.1: "Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable." (https://huggingface.co/datasets/squad)\| \| Motivation \| To build an efficient and accurate model for the question answering task. \| \| Preprocessing \| "We use the English Wikipedia dataset (2500M words) for training the models on the pre-training task. We split the data into train (95%) and validation (5%) sets. Both sets are preprocessed as described in the models’ original papers ([Devlin et al., 2019](https://arxiv.org/abs/1810.04805), [Sanh et al., 2019](https://arxiv.org/abs/1910.01108)). We process the data to use the maximum sequence length allowed by the models, however, we allow shorter sequences at a probability of 0:1." Following the pre-training on Wikipedia, fine-tuning is completed on the SQuAD1.1 dataset. \| \| Ethical Considerations \| Description \| \| ----------- \| ----------- \| \| Data \| The training data come from Wikipedia articles \| \| Human life \| The model is not intended to inform decisions central to human life or flourishing. It is an aggregated set of labelled Wikipedia articles. \| \| Mitigations \| No additional risk mitigation strategies were considered during model development. \| \| Risks and harms \| Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al., 2021](https://aclanthology.org/2021.acl-long.330.pdf), and [Bender et al., 2021](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. Beyond this, the extent of the risks involved by using the model remain unknown.\| \| Use cases \| - \| \| Caveats and Recommendations \| \| ----------- \| \| Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. There are no additional caveats or recommendations for this model. \| ### BibTeX entry and citation info ```bibtex @article{zafrir2021prune, title={Prune Once for All: Sparse Pre-Trained Language Models}, author={Zafrir, Ofir and Larey, Ariel and Boudoukh, Guy and Shen, Haihao and Wasserblat, Moshe}, journal={arXiv preprint arXiv:2111.05754}, year={2021} } ```	{"language": "en", "license": "apache-2.0", "tags": ["question-answering", "bert"], "datasets": ["squad"]}	question-answering	Intel/bert-base-uncased-squadv1.1-sparse-80-1x4-block-pruneofa	[ "transformers", "pytorch", "bert", "question-answering", "en", "dataset:squad", "arxiv:2111.05754", "arxiv:1810.04805", "arxiv:1910.01108", "license:apache-2.0", "model-index", "endpoints_compatible", "has_space", "region:us" ]	2022-03-02T23:29:04+00:00	[ "2111.05754", "1810.04805", "1910.01108" ]	[ "en" ]	TAGS #transformers #pytorch #bert #question-answering #en #dataset-squad #arxiv-2111.05754 #arxiv-1810.04805 #arxiv-1910.01108 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us	Model Details: 80% 1x4 Block Sparse BERT-Base (uncased) Fine Tuned on SQuADv1.1 ------------------------------------------------------------------------------- This model has been fine-tuned for the NLP task of question answering, trained on the SQuAD 1.1 dataset. It is a result of fine-tuning a Prune Once For All 80% 1x4 block sparse pre-trained BERT-Base model, combined with knowledge distillation. > > We present a new method for training sparse pre-trained Transformer language models by integrating weight pruning and model distillation. These sparse pre-trained models can be used to transfer learning for a wide range of tasks while maintaining their sparsity pattern. We show how the compressed sparse pre-trained models we trained transfer their knowledge to five different downstream natural language tasks with minimal accuracy loss. > > > Visualization of Prunce Once for All method from Zafrir et al. (2021). More details can be found in their paper. !Zafrir2021\_Fig1.png ### How to use Here is how to import this model in Python: For more code examples, refer to the GitHub Repo. ### Metrics (Model Performance): All the results are the mean of two seperate experiments with the same hyper-parameters and different seeds. ### BibTeX entry and citation info	[ "### How to use\n\n\nHere is how to import this model in Python:\n\n\nFor more code examples, refer to the GitHub Repo.", "### Metrics (Model Performance):\n\n\n\nAll the results are the mean of two seperate experiments with the same hyper-parameters and different seeds.", "### BibTeX entry and citation info" ]	[ "TAGS\n#transformers #pytorch #bert #question-answering #en #dataset-squad #arxiv-2111.05754 #arxiv-1810.04805 #arxiv-1910.01108 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n", "### How to use\n\n\nHere is how to import this model in Python:\n\n\nFor more code examples, refer to the GitHub Repo.", 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null	null	transformers	## Model Details: 90% Sparse BERT-Large (uncased) Prune Once for All This model is a sparse pre-trained model that can be fine-tuned for a wide range of language tasks. The process of weight pruning is forcing some of the weights of the neural network to zero. Setting some of the weights to zero results in sparser matrices. Updating neural network weights does involve matrix multiplication, and if we can keep the matrices sparse while retaining enough important information, we can reduce the overall computational overhead. The term "sparse" in the title of the model indicates a ratio of sparsity in the weights; for more details, you can read [Zafrir et al. (2021)](https://arxiv.org/abs/2111.05754). Visualization of Prunce Once for All method from [Zafrir et al. (2021)](https://arxiv.org/abs/2111.05754): ![Zafrir2021_Fig1.png](https://s3.amazonaws.com/moonup/production/uploads/6297f0e30bd2f58c647abb1d/nSDP62H9NHC1FA0C429Xo.png) \| Model Detail \| Description \| \| ----------- \| ----------- \| \| Model Authors - Company \| Intel \| \| Date \| September 30, 2021 \| \| Version \| 1 \| \| Type \| NLP - General sparse language model \| \| Architecture \| "The method consists of two steps, teacher preparation and student pruning. The sparse pre-trained model we trained is the model we use for transfer learning while maintaining its sparsity pattern. We call the method Prune Once for All since we show how to fine-tune the sparse pre-trained models for several language tasks while we prune the pre-trained model only once." [(Zafrir et al., 2021)](https://arxiv.org/abs/2111.05754) \| \| Paper or Other Resources \| [Zafrir et al. (2021)](https://arxiv.org/abs/2111.05754); [GitHub Repo](https://github.com/IntelLabs/Model-Compression-Research-Package/tree/main/research/prune-once-for-all) \| \| License \| Apache 2.0 \| \| Questions or Comments \| [Community Tab](https://huggingface.co/Intel/bert-large-uncased-sparse-90-unstructured-pruneofa/discussions) and [Intel Developers Discord](https://discord.gg/rv2Gp55UJQ)\| \| Intended Use \| Description \| \| ----------- \| ----------- \| \| Primary intended uses \| This is a general sparse language model; in its current form, it is not ready for downstream prediction tasks, but it can be fine-tuned for several language tasks including (but not limited to) question-answering, genre natural language inference, and sentiment classification. \| \| Primary intended users \| Anyone who needs an efficient general language model for other downstream tasks. \| \| Out-of-scope uses \| The model should not be used to intentionally create hostile or alienating environments for people.\| ### How to use Here is an example of how to import this model in Python: ```python import transformers model = transformers.AutoModelForQuestionAnswering.from_pretrained('Intel/bert-large-uncased-sparse-90-unstructured-pruneofa') ``` For more code examples, refer to the [GitHub Repo](https://github.com/IntelLabs/Model-Compression-Research-Package/tree/main/research/prune-once-for-all). ### Metrics (Model Performance): \| Model \| Model Size \| SQuADv1.1 (EM/F1) \| MNLI-m (Acc) \| MNLI-mm (Acc) \| QQP (Acc/F1) \| QNLI (Acc) \| SST-2 (Acc) \| \|-------------------------------\|:----------:\|:-----------------:\|:------------:\|:-------------:\|:------------:\|:----------:\|:-----------:\| \| [80% Sparse BERT-Base uncased fine-tuned on SQuAD1.1](https://huggingface.co/Intel/bert-base-uncased-squadv1.1-sparse-80-1x4-block-pruneofa) \| - \| 81.29/88.47 \| - \| - \| - \| - \| - \| \| [85% Sparse BERT-Base uncased](https://huggingface.co/Intel/bert-base-uncased-sparse-85-unstructured-pruneofa) \| Medium \| 81.10/88.42 \| 82.71 \| 83.67 \| 91.15/88.00 \| 90.34 \| 91.46 \| \| [90% Sparse BERT-Base uncased](https://huggingface.co/Intel/bert-base-uncased-sparse-90-unstructured-pruneofa) \| Medium \| 79.83/87.25 \| 81.45 \| 82.43 \| 90.93/87.72 \| 89.07 \| 90.88 \| \| [90% Sparse BERT-Large uncased](https://huggingface.co/Intel/bert-large-uncased-sparse-90-unstructured-pruneofa) \| Large \| 83.35/90.20 \| 83.74 \| 84.20 \| 91.48/88.43 \| 91.39 \| 92.95 \| \| [85% Sparse DistilBERT uncased](https://huggingface.co/Intel/distilbert-base-uncased-sparse-85-unstructured-pruneofa) \| Small \| 78.10/85.82 \| 81.35 \| 82.03 \| 90.29/86.97 \| 88.31 \| 90.60 \| \| [90% Sparse DistilBERT uncased](https://huggingface.co/Intel/distilbert-base-uncased-sparse-90-unstructured-pruneofa) \| Small \| 76.91/84.82 \| 80.68 \| 81.47 \| 90.05/86.67 \| 87.66 \| 90.02 \| All the results are the mean of two seperate experiments with the same hyper-parameters and different seeds. \| Training and Evaluation Data \| Description \| \| ----------- \| ----------- \| \| Datasets \| [English Wikipedia Dataset](https://huggingface.co/datasets/wikipedia) (2500M words). \| \| Motivation \| To build an efficient and accurate base model for several downstream language tasks. \| \| Preprocessing \| "We use the English Wikipedia dataset (2500M words) for training the models on the pre-training task. We split the data into train (95%) and validation (5%) sets. Both sets are preprocessed as described in the models’ original papers ([Devlin et al., 2019](https://arxiv.org/abs/1810.04805), [Sanh et al., 2019](https://arxiv.org/abs/1910.01108)). We process the data to use the maximum sequence length allowed by the models, however, we allow shorter sequences at a probability of 0:1." \| \| Ethical Considerations \| Description \| \| ----------- \| ----------- \| \| Data \| The training data come from Wikipedia articles \| \| Human life \| The model is not intended to inform decisions central to human life or flourishing. It is an aggregated set of labelled Wikipedia articles. \| \| Mitigations \| No additional risk mitigation strategies were considered during model development. \| \| Risks and harms \| Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al., 2021](https://aclanthology.org/2021.acl-long.330.pdf), and [Bender et al., 2021](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. Beyond this, the extent of the risks involved by using the model remain unknown.\| \| Use cases \| - \| \| Caveats and Recommendations \| \| ----------- \| \| Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. There are no additional caveats or recommendations for this model. \| ### BibTeX entry and citation info ```bibtex @article{zafrir2021prune, title={Prune Once for All: Sparse Pre-Trained Language Models}, author={Zafrir, Ofir and Larey, Ariel and Boudoukh, Guy and Shen, Haihao and Wasserblat, Moshe}, journal={arXiv preprint arXiv:2111.05754}, year={2021} } ```	{"language": "en", "license": "apache-2.0", "tags": ["fill-mask"], "datasets": ["wikipedia", "bookcorpus"]}	fill-mask	Intel/bert-large-uncased-sparse-90-unstructured-pruneofa	[ "transformers", "pytorch", "tf", "bert", "pretraining", "fill-mask", "en", "dataset:wikipedia", "dataset:bookcorpus", "arxiv:2111.05754", "arxiv:1810.04805", "arxiv:1910.01108", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[ "2111.05754", "1810.04805", "1910.01108" ]	[ "en" ]	TAGS #transformers #pytorch #tf #bert #pretraining #fill-mask #en #dataset-wikipedia #dataset-bookcorpus #arxiv-2111.05754 #arxiv-1810.04805 #arxiv-1910.01108 #license-apache-2.0 #endpoints_compatible #region-us	Model Details: 90% Sparse BERT-Large (uncased) Prune Once for All ----------------------------------------------------------------- This model is a sparse pre-trained model that can be fine-tuned for a wide range of language tasks. The process of weight pruning is forcing some of the weights of the neural network to zero. Setting some of the weights to zero results in sparser matrices. Updating neural network weights does involve matrix multiplication, and if we can keep the matrices sparse while retaining enough important information, we can reduce the overall computational overhead. The term "sparse" in the title of the model indicates a ratio of sparsity in the weights; for more details, you can read Zafrir et al. (2021). Visualization of Prunce Once for All method from Zafrir et al. (2021): !Zafrir2021\_Fig1.png ### How to use Here is an example of how to import this model in Python: For more code examples, refer to the GitHub Repo. ### Metrics (Model Performance): All the results are the mean of two seperate experiments with the same hyper-parameters and different seeds. ### BibTeX entry and citation info	[ "### How to use\n\n\nHere is an example of how to import this model in Python:\n\n\nFor more code examples, refer to the GitHub Repo.", "### Metrics (Model Performance):\n\n\n\nAll the results are the mean of two seperate experiments with the same hyper-parameters and different seeds.", "### BibTeX entry and citation info" ]	[ "TAGS\n#transformers #pytorch #tf #bert #pretraining #fill-mask #en #dataset-wikipedia #dataset-bookcorpus #arxiv-2111.05754 #arxiv-1810.04805 #arxiv-1910.01108 #license-apache-2.0 #endpoints_compatible #region-us \n", "### How to use\n\n\nHere is an example of how to import this model in Python:\n\n\nFor more code examples, refer to the GitHub Repo.", "### Metrics (Model Performance):\n\n\n\nAll the results are the mean of two seperate experiments with the same hyper-parameters and different seeds.", "### BibTeX entry and citation info" ]	[ 82, 33, 36, 11 ]	[ "passage: TAGS\n#transformers #pytorch #tf #bert #pretraining #fill-mask #en #dataset-wikipedia #dataset-bookcorpus #arxiv-2111.05754 #arxiv-1810.04805 #arxiv-1910.01108 #license-apache-2.0 #endpoints_compatible #region-us \n### How to use\n\n\nHere is an example of how to import this model in Python:\n\n\nFor more code examples, refer to the GitHub Repo.### Metrics (Model Performance):\n\n\n\nAll the results are the mean of two seperate experiments with the same hyper-parameters and different seeds.### BibTeX entry and citation info" ]	[ -0.04646419733762741, 0.09619906544685364, -0.003505624597892165, 0.13187673687934875, 0.06881929188966751, 0.02042018435895443, 0.10129685699939728, 0.0937829241156578, 0.04263918101787567, -0.0022539393976330757, 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null	null	transformers	# 90% Sparse BERT-Large (uncased) Fine Tuned on SQuADv1.1 This model is a result of fine-tuning a Prune OFA 90% sparse pre-trained BERT-Large combined with knowledge distillation. This model yields the following results on SQuADv1.1 development set:<br> `{"exact_match": 83.56669820245979, "f1": 90.20829352733487}` For further details see our paper, [Prune Once for All: Sparse Pre-Trained Language Models](https://arxiv.org/abs/2111.05754), and our open source implementation available [here](https://github.com/IntelLabs/Model-Compression-Research-Package/tree/main/research/prune-once-for-all).	{"language": "en"}	question-answering	Intel/bert-large-uncased-squadv1.1-sparse-90-unstructured	[ "transformers", "pytorch", "tf", "bert", "question-answering", "en", "arxiv:2111.05754", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[ "2111.05754" ]	[ "en" ]	TAGS #transformers #pytorch #tf #bert #question-answering #en #arxiv-2111.05754 #endpoints_compatible #region-us	# 90% Sparse BERT-Large (uncased) Fine Tuned on SQuADv1.1 This model is a result of fine-tuning a Prune OFA 90% sparse pre-trained BERT-Large combined with knowledge distillation. This model yields the following results on SQuADv1.1 development set:<br> '{"exact_match": 83.56669820245979, "f1": 90.20829352733487}' For further details see our paper, Prune Once for All: Sparse Pre-Trained Language Models, and our open source implementation available here.	[ "# 90% Sparse BERT-Large (uncased) Fine Tuned on SQuADv1.1\nThis model is a result of fine-tuning a Prune OFA 90% sparse pre-trained BERT-Large combined with knowledge distillation.\nThis model yields the following results on SQuADv1.1 development set:<br>\n'{\"exact_match\": 83.56669820245979, \"f1\": 90.20829352733487}'\n\nFor further details see our paper, Prune Once for All: Sparse Pre-Trained Language Models, and our open source implementation available here." ]	[ "TAGS\n#transformers #pytorch #tf #bert #question-answering #en #arxiv-2111.05754 #endpoints_compatible #region-us \n", "# 90% Sparse BERT-Large (uncased) Fine Tuned on SQuADv1.1\nThis model is a result of fine-tuning a Prune OFA 90% sparse pre-trained BERT-Large combined with knowledge distillation.\nThis model yields the following results on SQuADv1.1 development set:<br>\n'{\"exact_match\": 83.56669820245979, \"f1\": 90.20829352733487}'\n\nFor further details see our paper, Prune Once for All: Sparse Pre-Trained Language Models, and our open source implementation available here." ]	[ 43, 141 ]	[ "passage: TAGS\n#transformers #pytorch #tf #bert #question-answering #en #arxiv-2111.05754 #endpoints_compatible #region-us \n# 90% Sparse BERT-Large (uncased) Fine Tuned on SQuADv1.1\nThis model is a result of fine-tuning a Prune OFA 90% sparse pre-trained BERT-Large combined with knowledge distillation.\nThis model yields the following results on SQuADv1.1 development set:<br>\n'{\"exact_match\": 83.56669820245979, \"f1\": 90.20829352733487}'\n\nFor further details see our paper, Prune Once for All: Sparse Pre-Trained Language Models, and our open source implementation available here." ]	[ -0.10429621487855911, 0.019643407315015793, -0.0018323519034311175, 0.10917486995458603, 0.12274379283189774, -0.0521886870265007, -0.015588962472975254, 0.049708396196365356, -0.09426082670688629, 0.033667974174022675, 0.13270647823810577, 0.11039821058511734, -0.023196838796138763, 0.046532101929187775, 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null	null	transformers	## Model Details: 85% Sparse DistilBERT-Base (uncased) Prune Once for All This model is a sparse pre-trained model that can be fine-tuned for a wide range of language tasks. The process of weight pruning is forcing some of the weights of the neural network to zero. Setting some of the weights to zero results in sparser matrices. Updating neural network weights does involve matrix multiplication, and if we can keep the matrices sparse while retaining enough important information, we can reduce the overall computational overhead. The term "sparse" in the title of the model indicates a ratio of sparsity in the weights; for more details, you can read [Zafrir et al. (2021)](https://arxiv.org/abs/2111.05754). Visualization of Prunce Once for All method from [Zafrir et al. (2021)](https://arxiv.org/abs/2111.05754): ![Zafrir2021_Fig1.png](https://s3.amazonaws.com/moonup/production/uploads/6297f0e30bd2f58c647abb1d/nSDP62H9NHC1FA0C429Xo.png) \| Model Detail \| Description \| \| ----------- \| ----------- \| \| Model Authors - Company \| Intel \| \| Date \| September 30, 2021 \| \| Version \| 1 \| \| Type \| NLP - General sparse language model \| \| Architecture \| "The method consists of two steps, teacher preparation and student pruning. The sparse pre-trained model we trained is the model we use for transfer learning while maintaining its sparsity pattern. We call the method Prune Once for All since we show how to fine-tune the sparse pre-trained models for several language tasks while we prune the pre-trained model only once." [(Zafrir et al., 2021)](https://arxiv.org/abs/2111.05754) \| \| Paper or Other Resources \| [Zafrir et al. (2021)](https://arxiv.org/abs/2111.05754); [GitHub Repo](https://github.com/IntelLabs/Model-Compression-Research-Package/tree/main/research/prune-once-for-all) \| \| License \| Apache 2.0 \| \| Questions or Comments \| [Community Tab](https://huggingface.co/Intel/distilbert-base-uncased-sparse-85-unstructured-pruneofa/discussions) and [Intel Developers Discord](https://discord.gg/rv2Gp55UJQ)\| \| Intended Use \| Description \| \| ----------- \| ----------- \| \| Primary intended uses \| This is a general sparse language model; in its current form, it is not ready for downstream prediction tasks, but it can be fine-tuned for several language tasks including (but not limited to) question-answering, genre natural language inference, and sentiment classification. \| \| Primary intended users \| Anyone who needs an efficient general language model for other downstream tasks. \| \| Out-of-scope uses \| The model should not be used to intentionally create hostile or alienating environments for people.\| ### How to use Here is an example of how to import this model in Python: ```python import transformers model = transformers.AutoModelForQuestionAnswering.from_pretrained('Intel/distilbert-base-uncased-sparse-85-unstructured-pruneofa') ``` For more code examples, refer to the [GitHub Repo](https://github.com/IntelLabs/Model-Compression-Research-Package/tree/main/research/prune-once-for-all). ### Metrics (Model Performance): \| Model \| Model Size \| SQuADv1.1 (EM/F1) \| MNLI-m (Acc) \| MNLI-mm (Acc) \| QQP (Acc/F1) \| QNLI (Acc) \| SST-2 (Acc) \| \|-------------------------------\|:----------:\|:-----------------:\|:------------:\|:-------------:\|:------------:\|:----------:\|:-----------:\| \| [80% Sparse BERT-Base uncased fine-tuned on SQuAD1.1](https://huggingface.co/Intel/bert-base-uncased-squadv1.1-sparse-80-1x4-block-pruneofa) \| - \| 81.29/88.47 \| - \| - \| - \| - \| - \| \| [85% Sparse BERT-Base uncased](https://huggingface.co/Intel/bert-base-uncased-sparse-85-unstructured-pruneofa) \| Medium \| 81.10/88.42 \| 82.71 \| 83.67 \| 91.15/88.00 \| 90.34 \| 91.46 \| \| [90% Sparse BERT-Base uncased](https://huggingface.co/Intel/bert-base-uncased-sparse-90-unstructured-pruneofa) \| Medium \| 79.83/87.25 \| 81.45 \| 82.43 \| 90.93/87.72 \| 89.07 \| 90.88 \| \| [90% Sparse BERT-Large uncased](https://huggingface.co/Intel/bert-large-uncased-sparse-90-unstructured-pruneofa) \| Large \| 83.35/90.20 \| 83.74 \| 84.20 \| 91.48/88.43 \| 91.39 \| 92.95 \| \| [85% Sparse DistilBERT uncased](https://huggingface.co/Intel/distilbert-base-uncased-sparse-85-unstructured-pruneofa) \| Small \| 78.10/85.82 \| 81.35 \| 82.03 \| 90.29/86.97 \| 88.31 \| 90.60 \| \| [90% Sparse DistilBERT uncased](https://huggingface.co/Intel/distilbert-base-uncased-sparse-90-unstructured-pruneofa) \| Small \| 76.91/84.82 \| 80.68 \| 81.47 \| 90.05/86.67 \| 87.66 \| 90.02 \| All the results are the mean of two seperate experiments with the same hyper-parameters and different seeds. \| Training and Evaluation Data \| Description \| \| ----------- \| ----------- \| \| Datasets \| [English Wikipedia Dataset](https://huggingface.co/datasets/wikipedia) (2500M words). \| \| Motivation \| To build an efficient and accurate base model for several downstream language tasks. \| \| Preprocessing \| "We use the English Wikipedia dataset (2500M words) for training the models on the pre-training task. We split the data into train (95%) and validation (5%) sets. Both sets are preprocessed as described in the models’ original papers ([Devlin et al., 2019](https://arxiv.org/abs/1810.04805), [Sanh et al., 2019](https://arxiv.org/abs/1910.01108)). We process the data to use the maximum sequence length allowed by the models, however, we allow shorter sequences at a probability of 0:1." \| \| Ethical Considerations \| Description \| \| ----------- \| ----------- \| \| Data \| The training data come from Wikipedia articles \| \| Human life \| The model is not intended to inform decisions central to human life or flourishing. It is an aggregated set of labelled Wikipedia articles. \| \| Mitigations \| No additional risk mitigation strategies were considered during model development. \| \| Risks and harms \| Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al., 2021](https://aclanthology.org/2021.acl-long.330.pdf), and [Bender et al., 2021](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. Beyond this, the extent of the risks involved by using the model remain unknown.\| \| Use cases \| - \| \| Caveats and Recommendations \| \| ----------- \| \| Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. There are no additional caveats or recommendations for this model. \| ### BibTeX entry and citation info ```bibtex @article{zafrir2021prune, title={Prune Once for All: Sparse Pre-Trained Language Models}, author={Zafrir, Ofir and Larey, Ariel and Boudoukh, Guy and Shen, Haihao and Wasserblat, Moshe}, journal={arXiv preprint arXiv:2111.05754}, year={2021} } ```	{"language": "en", "license": "apache-2.0", "datasets": ["wikipedia"]}	fill-mask	Intel/distilbert-base-uncased-sparse-85-unstructured-pruneofa	[ "transformers", "pytorch", "tf", "distilbert", "fill-mask", "en", "dataset:wikipedia", "arxiv:2111.05754", "arxiv:1810.04805", "arxiv:1910.01108", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[ "2111.05754", "1810.04805", "1910.01108" ]	[ "en" ]	TAGS #transformers #pytorch #tf #distilbert #fill-mask #en #dataset-wikipedia #arxiv-2111.05754 #arxiv-1810.04805 #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	Model Details: 85% Sparse DistilBERT-Base (uncased) Prune Once for All ---------------------------------------------------------------------- This model is a sparse pre-trained model that can be fine-tuned for a wide range of language tasks. The process of weight pruning is forcing some of the weights of the neural network to zero. Setting some of the weights to zero results in sparser matrices. Updating neural network weights does involve matrix multiplication, and if we can keep the matrices sparse while retaining enough important information, we can reduce the overall computational overhead. The term "sparse" in the title of the model indicates a ratio of sparsity in the weights; for more details, you can read Zafrir et al. (2021). Visualization of Prunce Once for All method from Zafrir et al. (2021): !Zafrir2021\_Fig1.png ### How to use Here is an example of how to import this model in Python: For more code examples, refer to the GitHub Repo. ### Metrics (Model Performance): All the results are the mean of two seperate experiments with the same hyper-parameters and different seeds. ### BibTeX entry and citation info	[ "### How to use\n\n\nHere is an example of how to import this model in Python:\n\n\nFor more code examples, refer to the GitHub Repo.", "### Metrics (Model Performance):\n\n\n\nAll the results are the mean of two seperate experiments with the same hyper-parameters and different seeds.", "### BibTeX entry and citation info" ]	[ "TAGS\n#transformers #pytorch #tf #distilbert #fill-mask #en #dataset-wikipedia #arxiv-2111.05754 #arxiv-1810.04805 #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### How to use\n\n\nHere is an example of how to import this model in Python:\n\n\nFor more code examples, refer to the GitHub Repo.", "### Metrics (Model Performance):\n\n\n\nAll the results are the mean of two seperate experiments with the same hyper-parameters and different seeds.", "### BibTeX entry and citation info" ]	[ 82, 33, 36, 11 ]	[ "passage: TAGS\n#transformers #pytorch #tf #distilbert #fill-mask #en #dataset-wikipedia #arxiv-2111.05754 #arxiv-1810.04805 #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### How to use\n\n\nHere is an example of how to import this model in Python:\n\n\nFor more code examples, refer to the GitHub Repo.### Metrics (Model Performance):\n\n\n\nAll the results are the mean of two seperate experiments with the same hyper-parameters and different seeds.### BibTeX entry and citation info" ]	[ -0.04864472150802612, 0.0966891348361969, -0.0036465052980929613, 0.13952533900737762, 0.09196031838655472, 0.01174522191286087, 0.10593844950199127, 0.0841037929058075, 0.035174984484910965, -0.013614946976304054, 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null	null	transformers	### Model Details: 90% Sparse DistilBERT-Base (uncased) Prune Once for All This model is a sparse pre-trained model that can be fine-tuned for a wide range of language tasks. The process of weight pruning is forcing some of the weights of the neural network to zero. Setting some of the weights to zero results in sparser matrices. Updating neural network weights does involve matrix multiplication, and if we can keep the matrices sparse while retaining enough important information, we can reduce the overall computational overhead. The term "sparse" in the title of the model indicates a ratio of sparsity in the weights; for more details, you can read [Zafrir et al. (2021)](https://arxiv.org/abs/2111.05754). Visualization of Prunce Once for All method from [Zafrir et al. (2021)](https://arxiv.org/abs/2111.05754): ![Zafrir2021_Fig1.png](https://s3.amazonaws.com/moonup/production/uploads/6297f0e30bd2f58c647abb1d/nSDP62H9NHC1FA0C429Xo.png) \| Model Detail \| Description \| \| ----------- \| ----------- \| \| Model Authors - Company \| Intel \| \| Date \| September 30, 2021 \| \| Version \| 1 \| \| Type \| NLP - General sparse language model \| \| Architecture \| "The method consists of two steps, teacher preparation and student pruning. The sparse pre-trained model we trained is the model we use for transfer learning while maintaining its sparsity pattern. We call the method Prune Once for All since we show how to fine-tune the sparse pre-trained models for several language tasks while we prune the pre-trained model only once." [(Zafrir et al., 2021)](https://arxiv.org/abs/2111.05754) \| \| Paper or Other Resources \| [Zafrir et al. (2021)](https://arxiv.org/abs/2111.05754); [GitHub Repo](https://github.com/IntelLabs/Model-Compression-Research-Package/tree/main/research/prune-once-for-all) \| \| License \| Apache 2.0 \| \| Questions or Comments \| [Community Tab](https://huggingface.co/Intel/distilbert-base-uncased-sparse-90-unstructured-pruneofa/discussions) and [Intel Developers Discord](https://discord.gg/rv2Gp55UJQ)\| \| Intended Use \| Description \| \| ----------- \| ----------- \| \| Primary intended uses \| This is a general sparse language model; in its current form, it is not ready for downstream prediction tasks, but it can be fine-tuned for several language tasks including (but not limited to) question-answering, genre natural language inference, and sentiment classification. \| \| Primary intended users \| Anyone who needs an efficient general language model for other downstream tasks. \| \| Out-of-scope uses \| The model should not be used to intentionally create hostile or alienating environments for people.\| ### How to use Here is an example of how to import this model in Python: ```python import transformers model = transformers.AutoModelForQuestionAnswering.from_pretrained('Intel/distilbert-base-uncased-sparse-90-unstructured-pruneofa') ``` For more code examples, refer to the [GitHub Repo](https://github.com/IntelLabs/Model-Compression-Research-Package/tree/main/research/prune-once-for-all). ### Metrics (Model Performance): \| Model \| Model Size \| SQuADv1.1 (EM/F1) \| MNLI-m (Acc) \| MNLI-mm (Acc) \| QQP (Acc/F1) \| QNLI (Acc) \| SST-2 (Acc) \| \|-------------------------------\|:----------:\|:-----------------:\|:------------:\|:-------------:\|:------------:\|:----------:\|:-----------:\| \| [80% Sparse BERT-Base uncased fine-tuned on SQuAD1.1](https://huggingface.co/Intel/bert-base-uncased-squadv1.1-sparse-80-1x4-block-pruneofa) \| - \| 81.29/88.47 \| - \| - \| - \| - \| - \| \| [85% Sparse BERT-Base uncased](https://huggingface.co/Intel/bert-base-uncased-sparse-85-unstructured-pruneofa) \| Medium \| 81.10/88.42 \| 82.71 \| 83.67 \| 91.15/88.00 \| 90.34 \| 91.46 \| \| [90% Sparse BERT-Base uncased](https://huggingface.co/Intel/bert-base-uncased-sparse-90-unstructured-pruneofa) \| Medium \| 79.83/87.25 \| 81.45 \| 82.43 \| 90.93/87.72 \| 89.07 \| 90.88 \| \| [90% Sparse BERT-Large uncased](https://huggingface.co/Intel/bert-large-uncased-sparse-90-unstructured-pruneofa) \| Large \| 83.35/90.20 \| 83.74 \| 84.20 \| 91.48/88.43 \| 91.39 \| 92.95 \| \| [85% Sparse DistilBERT uncased](https://huggingface.co/Intel/distilbert-base-uncased-sparse-85-unstructured-pruneofa) \| Small \| 78.10/85.82 \| 81.35 \| 82.03 \| 90.29/86.97 \| 88.31 \| 90.60 \| \| [90% Sparse DistilBERT uncased](https://huggingface.co/Intel/distilbert-base-uncased-sparse-90-unstructured-pruneofa) \| Small \| 76.91/84.82 \| 80.68 \| 81.47 \| 90.05/86.67 \| 87.66 \| 90.02 \| All the results are the mean of two seperate experiments with the same hyper-parameters and different seeds. \| Training and Evaluation Data \| Description \| \| ----------- \| ----------- \| \| Datasets \| [English Wikipedia Dataset](https://huggingface.co/datasets/wikipedia) (2500M words). \| \| Motivation \| To build an efficient and accurate base model for several downstream language tasks. \| \| Preprocessing \| "We use the English Wikipedia dataset (2500M words) for training the models on the pre-training task. We split the data into train (95%) and validation (5%) sets. Both sets are preprocessed as described in the models’ original papers ([Devlin et al., 2019](https://arxiv.org/abs/1810.04805), [Sanh et al., 2019](https://arxiv.org/abs/1910.01108)). We process the data to use the maximum sequence length allowed by the models, however, we allow shorter sequences at a probability of 0:1." \| \| Ethical Considerations \| Description \| \| ----------- \| ----------- \| \| Data \| The training data come from Wikipedia articles \| \| Human life \| The model is not intended to inform decisions central to human life or flourishing. It is an aggregated set of labelled Wikipedia articles. \| \| Mitigations \| No additional risk mitigation strategies were considered during model development. \| \| Risks and harms \| Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al., 2021](https://aclanthology.org/2021.acl-long.330.pdf), and [Bender et al., 2021](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. Beyond this, the extent of the risks involved by using the model remain unknown.\| \| Use cases \| - \| \| Caveats and Recommendations \| \| ----------- \| \| Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. There are no additional caveats or recommendations for this model. \| ### BibTeX entry and citation info ```bibtex @article{zafrir2021prune, title={Prune Once for All: Sparse Pre-Trained Language Models}, author={Zafrir, Ofir and Larey, Ariel and Boudoukh, Guy and Shen, Haihao and Wasserblat, Moshe}, journal={arXiv preprint arXiv:2111.05754}, year={2021} } ```	{"language": "en", "license": "apache-2.0", "datasets": ["wikipedia"]}	fill-mask	Intel/distilbert-base-uncased-sparse-90-unstructured-pruneofa	[ "transformers", "pytorch", "tf", "distilbert", "fill-mask", "en", "dataset:wikipedia", "arxiv:2111.05754", "arxiv:1810.04805", "arxiv:1910.01108", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[ "2111.05754", "1810.04805", "1910.01108" ]	[ "en" ]	TAGS #transformers #pytorch #tf #distilbert #fill-mask #en #dataset-wikipedia #arxiv-2111.05754 #arxiv-1810.04805 #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	### Model Details: 90% Sparse DistilBERT-Base (uncased) Prune Once for All This model is a sparse pre-trained model that can be fine-tuned for a wide range of language tasks. The process of weight pruning is forcing some of the weights of the neural network to zero. Setting some of the weights to zero results in sparser matrices. Updating neural network weights does involve matrix multiplication, and if we can keep the matrices sparse while retaining enough important information, we can reduce the overall computational overhead. The term "sparse" in the title of the model indicates a ratio of sparsity in the weights; for more details, you can read Zafrir et al. (2021). Visualization of Prunce Once for All method from Zafrir et al. (2021): !Zafrir2021\_Fig1.png ### How to use Here is an example of how to import this model in Python: For more code examples, refer to the GitHub Repo. ### Metrics (Model Performance): All the results are the mean of two seperate experiments with the same hyper-parameters and different seeds. ### BibTeX entry and citation info	[ "### Model Details: 90% Sparse DistilBERT-Base (uncased) Prune Once for All\n\n\nThis model is a sparse pre-trained model that can be fine-tuned for a wide range of language tasks. The process of weight pruning is forcing some of the weights of the neural network to zero. Setting some of the weights to zero results in sparser matrices. Updating neural network weights does involve matrix multiplication, and if we can keep the matrices sparse while retaining enough important information, we can reduce the overall computational overhead. The term \"sparse\" in the title of the model indicates a ratio of sparsity in the weights; for more details, you can read Zafrir et al. (2021).\n\n\nVisualization of Prunce Once for All method from Zafrir et al. (2021):\n!Zafrir2021\\_Fig1.png", "### How to use\n\n\nHere is an example of how to import this model in Python:\n\n\nFor more code examples, refer to the GitHub Repo.", "### Metrics (Model Performance):\n\n\n\nAll the results are the mean of two seperate experiments with the same hyper-parameters and different seeds.", "### BibTeX entry and citation info" ]	[ "TAGS\n#transformers #pytorch #tf #distilbert #fill-mask #en #dataset-wikipedia #arxiv-2111.05754 #arxiv-1810.04805 #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Model Details: 90% Sparse DistilBERT-Base (uncased) Prune Once for All\n\n\nThis model is a sparse pre-trained model that can be fine-tuned for a wide range of language tasks. The process of weight pruning is forcing some of the weights of the neural network to zero. Setting some of the weights to zero results in sparser matrices. Updating neural network weights does involve matrix multiplication, and if we can keep the matrices sparse while retaining enough important information, we can reduce the overall computational overhead. The term \"sparse\" in the title of the model indicates a ratio of sparsity in the weights; for more details, you can read Zafrir et al. (2021).\n\n\nVisualization of Prunce Once for All method from Zafrir et al. (2021):\n!Zafrir2021\\_Fig1.png", "### How to use\n\n\nHere is an example of how to import this model in Python:\n\n\nFor more code examples, refer to the GitHub Repo.", "### Metrics (Model Performance):\n\n\n\nAll the results are the mean of two seperate experiments with the same hyper-parameters and different seeds.", "### BibTeX entry and citation info" ]	[ 82, 203, 33, 36, 11 ]	[ "passage: TAGS\n#transformers #pytorch #tf #distilbert #fill-mask #en #dataset-wikipedia #arxiv-2111.05754 #arxiv-1810.04805 #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Model Details: 90% Sparse DistilBERT-Base (uncased) Prune Once for All\n\n\nThis model is a sparse pre-trained model that can be fine-tuned for a wide range of language tasks. The process of weight pruning is forcing some of the weights of the neural network to zero. Setting some of the weights to zero results in sparser matrices. Updating neural network weights does involve matrix multiplication, and if we can keep the matrices sparse while retaining enough important information, we can reduce the overall computational overhead. The term \"sparse\" in the title of the model indicates a ratio of sparsity in the weights; for more details, you can read Zafrir et al. (2021).\n\n\nVisualization of Prunce Once for All method from Zafrir et al. (2021):\n!Zafrir2021\\_Fig1.png### How to use\n\n\nHere is an example of how to import this model in Python:\n\n\nFor more code examples, refer to the GitHub Repo.### Metrics (Model Performance):\n\n\n\nAll the results are the mean of two seperate experiments with the same hyper-parameters and different seeds.### BibTeX entry and citation info" ]	[ -0.020796973258256912, 0.10594657808542252, -0.005017620511353016, 0.09171494096517563, 0.14268235862255096, 0.021284013986587524, 0.054298337548971176, 0.0627967044711113, -0.011991120874881744, 0.14330120384693146, 0.07680238038301468, 0.09305538982152939, 0.07426553219556808, 0.1149081364274025, 0.052237022668123245, -0.2781694829463959, 0.048309117555618286, -0.06740029901266098, 0.06710059195756912, 0.08541073650121689, 0.09717031568288803, -0.07277456670999527, 0.09646616876125336, 0.033457960933446884, -0.1432129293680191, 0.00690682465210557, 0.016918644309043884, -0.01326915342360735, 0.07475345581769943, 0.04525911062955856, 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null	null	transformers	## Model Details: Dynamic-TinyBERT: Boost TinyBERT's Inference Efficiency by Dynamic Sequence Length Dynamic-TinyBERT has been fine-tuned for the NLP task of question answering, trained on the SQuAD 1.1 dataset. [Guskin et al. (2021)](https://neurips2021-nlp.github.io/papers/16/CameraReady/Dynamic_TinyBERT_NLSP2021_camera_ready.pdf) note: > Dynamic-TinyBERT is a TinyBERT model that utilizes sequence-length reduction and Hyperparameter Optimization for enhanced inference efficiency per any computational budget. Dynamic-TinyBERT is trained only once, performing on-par with BERT and achieving an accuracy-speedup trade-off superior to any other efficient approaches (up to 3.3x with <1% loss-drop). \| Model Detail \| Description \| \| ----------- \| ----------- \| \| Model Authors - Company \| Intel \| \| Model Card Authors \| Intel in collaboration with Hugging Face \| \| Date \| November 22, 2021 \| \| Version \| 1 \| \| Type \| NLP - Question Answering \| \| Architecture \| "For our Dynamic-TinyBERT model we use the architecture of TinyBERT6L: a small BERT model with 6 layers, a hidden size of 768, a feed forward size of 3072 and 12 heads." [Guskin et al. (2021)](https://gyuwankim.github.io/publication/dynamic-tinybert/poster.pdf) \| \| Paper or Other Resources \| [Paper](https://neurips2021-nlp.github.io/papers/16/CameraReady/Dynamic_TinyBERT_NLSP2021_camera_ready.pdf); [Poster](https://gyuwankim.github.io/publication/dynamic-tinybert/poster.pdf); [GitHub Repo](https://github.com/IntelLabs/Model-Compression-Research-Package) \| \| License \| Apache 2.0 \| \| Questions or Comments \| [Community Tab](https://huggingface.co/Intel/dynamic_tinybert/discussions) and [Intel Developers Discord](https://discord.gg/rv2Gp55UJQ)\| \| Intended Use \| Description \| \| ----------- \| ----------- \| \| Primary intended uses \| You can use the model for the NLP task of question answering: given a corpus of text, you can ask it a question about that text, and it will find the answer in the text. \| \| Primary intended users \| Anyone doing question answering \| \| Out-of-scope uses \| The model should not be used to intentionally create hostile or alienating environments for people.\| ### How to use Here is how to import this model in Python: <details> <summary> Click to expand </summary> ```python from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Intel/dynamic_tinybert") model = AutoModelForQuestionAnswering.from_pretrained("Intel/dynamic_tinybert") ``` </details> \| Factors \| Description \| \| ----------- \| ----------- \| \| Groups \| Many Wikipedia articles with question and answer labels are contained in the training data \| \| Instrumentation \| - \| \| Environment \| Training was completed on a Titan GPU. \| \| Card Prompts \| Model deployment on alternate hardware and software will change model performance \| \| Metrics \| Description \| \| ----------- \| ----------- \| \| Model performance measures \| F1 \| \| Decision thresholds \| - \| \| Approaches to uncertainty and variability \| - \| \| Training and Evaluation Data \| Description \| \| ----------- \| ----------- \| \| Datasets \| SQuAD1.1: "Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable." (https://huggingface.co/datasets/squad)\| \| Motivation \| To build an efficient and accurate model for the question answering task. \| \| Preprocessing \| "We start with a pre-trained general-TinyBERT student, which was trained to learn the general knowledge of BERT using the general-distillation method presented by TinyBERT. We perform transformer distillation from a fine- tuned BERT teacher to the student, following the same training steps used in the original TinyBERT: (1) intermediate-layer distillation (ID) — learning the knowledge residing in the hidden states and attentions matrices, and (2) prediction-layer distillation (PD) — fitting the predictions of the teacher." ([Guskin et al., 2021](https://neurips2021-nlp.github.io/papers/16/CameraReady/Dynamic_TinyBERT_NLSP2021_camera_ready.pdf))\| Model Performance Analysis: \| Model \| Max F1 (full model) \| Best Speedup within BERT-1% \| \|------------------\|---------------------\|-----------------------------\| \| Dynamic-TinyBERT \| 88.71 \| 3.3x \| \| Ethical Considerations \| Description \| \| ----------- \| ----------- \| \| Data \| The training data come from Wikipedia articles \| \| Human life \| The model is not intended to inform decisions central to human life or flourishing. It is an aggregated set of labelled Wikipedia articles. \| \| Mitigations \| No additional risk mitigation strategies were considered during model development. \| \| Risks and harms \| Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al., 2021](https://aclanthology.org/2021.acl-long.330.pdf), and [Bender et al., 2021](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. Beyond this, the extent of the risks involved by using the model remain unknown.\| \| Use cases \| - \| \| Caveats and Recommendations \| \| ----------- \| \| Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. There are no additional caveats or recommendations for this model. \| ### BibTeX entry and citation info ```bibtex @misc{https://doi.org/10.48550/arxiv.2111.09645, doi = {10.48550/ARXIV.2111.09645}, url = {https://arxiv.org/abs/2111.09645}, author = {Guskin, Shira and Wasserblat, Moshe and Ding, Ke and Kim, Gyuwan}, keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Dynamic-TinyBERT: Boost TinyBERT's Inference Efficiency by Dynamic Sequence Length}, publisher = {arXiv}, year = {2021}, ```	{"language": ["en"], "license": "apache-2.0", "tags": ["question-answering", "bert"], "datasets": ["squad"]}	question-answering	Intel/dynamic_tinybert	[ "transformers", "pytorch", "bert", "question-answering", "en", "dataset:squad", "arxiv:2111.09645", "license:apache-2.0", "model-index", "endpoints_compatible", "has_space", "region:us" ]	2022-03-02T23:29:04+00:00	[ "2111.09645" ]	[ "en" ]	TAGS #transformers #pytorch #bert #question-answering #en #dataset-squad #arxiv-2111.09645 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us	Model Details: Dynamic-TinyBERT: Boost TinyBERT's Inference Efficiency by Dynamic Sequence Length ------------------------------------------------------------------------------------------------- Dynamic-TinyBERT has been fine-tuned for the NLP task of question answering, trained on the SQuAD 1.1 dataset. Guskin et al. (2021) note: > > Dynamic-TinyBERT is a TinyBERT model that utilizes sequence-length reduction and Hyperparameter Optimization for enhanced inference efficiency per any computational budget. Dynamic-TinyBERT is trained only once, performing on-par with BERT and achieving an accuracy-speedup trade-off superior to any other efficient approaches (up to 3.3x with <1% loss-drop). > > > ### How to use Here is how to import this model in Python: Click to expand Model Performance Analysis: Model: Dynamic-TinyBERT, Max F1 (full model): 88.71, Best Speedup within BERT-1%: 3.3x ### BibTeX entry and citation info	[ "### How to use\n\n\nHere is how to import this model in Python:\n\n\n\n Click to expand \n\n\n\n\nModel Performance Analysis:\n\n\nModel: Dynamic-TinyBERT, Max F1 (full model): 88.71, Best Speedup within BERT-1%: 3.3x", "### BibTeX entry and citation info" ]	[ "TAGS\n#transformers #pytorch #bert #question-answering #en #dataset-squad #arxiv-2111.09645 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n", "### How to use\n\n\nHere is how to import this model in Python:\n\n\n\n Click to expand \n\n\n\n\nModel Performance Analysis:\n\n\nModel: Dynamic-TinyBERT, Max F1 (full model): 88.71, Best Speedup within BERT-1%: 3.3x", "### BibTeX entry and citation info" ]	[ 62, 55, 11 ]	[ "passage: TAGS\n#transformers #pytorch #bert #question-answering #en #dataset-squad #arxiv-2111.09645 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n### How to use\n\n\nHere is how to import this model in Python:\n\n\n\n Click to expand \n\n\n\n\nModel Performance Analysis:\n\n\nModel: Dynamic-TinyBERT, Max F1 (full model): 88.71, Best Speedup within BERT-1%: 3.3x### BibTeX entry and citation info" ]	[ -0.10313211381435394, 0.11044196039438248, -0.0005834275507368147, 0.09411479532718658, 0.041704993695020676, 0.012820381671190262, -0.029769159853458405, 0.10355652123689651, 0.02789015881717205, -0.018844211474061012, 0.1749873012304306, 0.11994818598031998, 0.026807617396116257, 0.1325293928384781, -0.008661337196826935, -0.2403373271226883, 0.11071619391441345, 0.07797316461801529, -0.065947987139225, 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null	null	transformers	#harry potter	{"tags": ["conversational"]}	text-generation	Invincible/Chat_bot-Harrypotter-medium	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	#harry potter	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 51 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.009697278961539268, 0.03208012506365776, -0.007204889785498381, 0.004809224978089333, 0.16726240515708923, 0.014898733235895634, 0.09765533357858658, 0.13672804832458496, 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null	null	transformers	#harry potter Model	{"tags": ["conversational"]}	text-generation	Invincible/Chat_bot-Harrypotter-small	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "has_space", "text-generation-inference", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us	#harry potter Model	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n" ]	[ 55 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n" ]	[ -0.0020731890108436346, 0.034266941249370575, -0.005774117540568113, 0.004248267505317926, 0.14393343031406403, 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null	null	null	#Harry Potter DialoDPT Model	{"tags": ["conversational"]}	text-generation	Invincible/DialoGPT-medium-harryPotter	[ "conversational", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #conversational #region-us	#Harry Potter DialoDPT Model	[]	[ "TAGS\n#conversational #region-us \n" ]	[ 10 ]	[ "passage: TAGS\n#conversational #region-us \n" ]	[ 0.04639962688088417, 0.010033470578491688, -0.010517087765038013, -0.09196841716766357, 0.07825888693332672, 0.025966141372919083, 0.0816626027226448, 0.03981694206595421, 0.1679982990026474, -0.043665021657943726, 0.11948301643133163, 0.05959230661392212, -0.03424782678484917, -0.034178510308265686, -0.03257700800895691, -0.1518038958311081, 0.018241867423057556, -0.03490595519542694, 0.09715951234102249, 0.046818334609270096, -0.022061990574002266, -0.08366407454013824, 0.008475017733871937, -0.06431017816066742, -0.04472750052809715, 0.10980460792779922, 0.019003495573997498, -0.011012797243893147, 0.15410423278808594, 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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. --> # roberta-base-bne-finetuned-amazon_reviews_multi This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi 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 ### Framework versions - Transformers 4.9.2 - Pytorch 1.9.0+cu102 - Datasets 1.11.0 - Tokenizers 0.10.3	{"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "model_index": [{"name": "roberta-base-bne-finetuned-amazon_reviews_multi", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "amazon_reviews_multi", "type": "amazon_reviews_multi", "args": "es"}}]}]}	text-classification	IsabellaKarabasz/roberta-base-bne-finetuned-amazon_reviews_multi	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "dataset:amazon_reviews_multi", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us	# roberta-base-bne-finetuned-amazon_reviews_multi This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the amazon_reviews_multi 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 ### Framework versions - Transformers 4.9.2 - Pytorch 1.9.0+cu102 - Datasets 1.11.0 - Tokenizers 0.10.3	[ "# roberta-base-bne-finetuned-amazon_reviews_multi\n\nThis model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the amazon_reviews_multi 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", "### Framework versions\n\n- Transformers 4.9.2\n- Pytorch 1.9.0+cu102\n- Datasets 1.11.0\n- Tokenizers 0.10.3" ]	[ "TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# roberta-base-bne-finetuned-amazon_reviews_multi\n\nThis model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the amazon_reviews_multi 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", "### Framework versions\n\n- Transformers 4.9.2\n- Pytorch 1.9.0+cu102\n- Datasets 1.11.0\n- Tokenizers 0.10.3" ]	[ 64, 55, 6, 12, 8, 3, 90, 34 ]	[ "passage: TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n# roberta-base-bne-finetuned-amazon_reviews_multi\n\nThis model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the amazon_reviews_multi 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### Framework versions\n\n- Transformers 4.9.2\n- Pytorch 1.9.0+cu102\n- Datasets 1.11.0\n- Tokenizers 0.10.3" ]	[ -0.06923318654298782, 0.15117035806179047, -0.003487436566501856, 0.100824274122715, 0.13460879027843475, 0.02384221740067005, 0.1518108993768692, 0.13672751188278198, -0.10227956622838974, 0.017931077629327774, 0.06974198669195175, 0.0466911680996418, 0.04018523916602135, 0.15156802535057068, -0.036817584186792374, -0.22626231610774994, 0.020160827785730362, 0.010358340106904507, 0.0019566502887755632, 0.113700732588768, 0.12807650864124298, -0.09231914579868317, 0.08930063247680664, -0.011499404907226562, -0.12876737117767334, 0.013458937406539917, -0.0019482114585116506, -0.09016718715429306, 0.06989605724811554, 0.010718491859734058, 0.08745279908180237, 0.013989703729748726, 0.10987730324268341, -0.23577134311199188, 0.006594392005354166, 0.050010185688734055, 0.0355403795838356, 0.0882439911365509, 0.006983253639191389, -0.04730434715747833, 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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. --> # This model is a fine-tuned version of [hf-test/xls-r-dummy](https://huggingface.co/hf-test/xls-r-dummy) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset. It achieves the following results on the evaluation set: - Loss: 156.8789 - Wer: 1.3456 ## 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: 0.0003 - 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 - training_steps: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.18.1.dev0 - Tokenizers 0.11.0	{"language": ["ab"], "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]}	automatic-speech-recognition	Iskaj/hf-challenge-test	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer", "ab", "dataset:common_voice", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "ab" ]	TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #ab #dataset-common_voice #endpoints_compatible #region-us	# This model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset. It achieves the following results on the evaluation set: - Loss: 156.8789 - Wer: 1.3456 ## 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: 0.0003 - 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 - training_steps: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.18.1.dev0 - Tokenizers 0.11.0	[ "# \n\nThis model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 156.8789\n- Wer: 1.3456", "## 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: 0.0003\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- training_steps: 10\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.16.0.dev0\n- Pytorch 1.10.1+cu102\n- Datasets 1.18.1.dev0\n- Tokenizers 0.11.0" ]	[ "TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #ab #dataset-common_voice #endpoints_compatible #region-us \n", "# \n\nThis model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 156.8789\n- Wer: 1.3456", "## 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: 0.0003\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- training_steps: 10\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.16.0.dev0\n- Pytorch 1.10.1+cu102\n- Datasets 1.18.1.dev0\n- Tokenizers 0.11.0" ]	[ 75, 70, 6, 12, 8, 3, 101, 4, 41 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #ab #dataset-common_voice #endpoints_compatible #region-us \n# \n\nThis model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 156.8789\n- Wer: 1.3456## 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: 0.0003\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- training_steps: 10\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.16.0.dev0\n- Pytorch 1.10.1+cu102\n- Datasets 1.18.1.dev0\n- Tokenizers 0.11.0" ]	[ -0.11955516040325165, 0.1077391505241394, -0.0046463776379823685, 0.031557124108076096, 0.13835538923740387, 0.01547481119632721, 0.06890618056058884, 0.17141468822956085, 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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. --> # newnew This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset. It achieves the following results on the evaluation set: - Loss: 11.4375 - Wer: 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: 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_steps: 4000 - num_epochs: 50.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.3.dev0 - Tokenizers 0.11.0	{"language": ["nl"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "newnew", "results": []}]}	automatic-speech-recognition	Iskaj/newnew	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "nl", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "nl" ]	TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #nl #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us	# newnew This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset. It achieves the following results on the evaluation set: - Loss: 11.4375 - Wer: 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: 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_steps: 4000 - num_epochs: 50.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.3.dev0 - Tokenizers 0.11.0	[ "# newnew\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 11.4375\n- Wer: 1.0", "## 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: 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_steps: 4000\n- num_epochs: 50.0\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.17.0.dev0\n- Pytorch 1.10.2+cu102\n- Datasets 1.18.3.dev0\n- Tokenizers 0.11.0" ]	[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #nl #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n", "# newnew\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 11.4375\n- Wer: 1.0", "## 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: 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_steps: 4000\n- num_epochs: 50.0\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.17.0.dev0\n- Pytorch 1.10.2+cu102\n- Datasets 1.18.3.dev0\n- Tokenizers 0.11.0" ]	[ 79, 73, 6, 12, 8, 3, 142, 4, 39 ]	[ "passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #nl #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n# newnew\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 11.4375\n- Wer: 1.0## 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: 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_steps: 4000\n- num_epochs: 50.0\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.17.0.dev0\n- Pytorch 1.10.2+cu102\n- Datasets 1.18.3.dev0\n- Tokenizers 0.11.0" ]	[ -0.13116024434566498, 0.16673648357391357, -0.00418110890313983, 0.04085119813680649, 0.1036147028207779, 0.0032772975973784924, 0.057705819606781006, 0.17463469505310059, -0.057642143219709396, 0.11373009532690048, 0.01916438899934292, 0.025421367958188057, 0.08967834711074829, 0.10937341302633286, -0.004373957868665457, -0.2069254219532013, 0.024055682122707367, -0.036594539880752563, -0.028843311592936516, 0.08299528807401657, 0.09173385798931122, -0.08349284529685974, 0.03165556490421295, 0.03630552440881729, -0.06161891669034958, -0.0009263341198675334, -0.04047930985689163, -0.06126882880926132, 0.05745520070195198, 0.007738828659057617, 0.00800394732505083, 0.019192514941096306, 0.06702042371034622, 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null	null	transformers	Copy of "facebook/wav2vec2-large-xlsr-53-dutch"	{}	automatic-speech-recognition	Iskaj/w2v-xlsr-dutch-lm-added	[ "transformers", "pytorch", "jax", "wav2vec2", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us	Copy of "facebook/wav2vec2-large-xlsr-53-dutch"	[]	[ "TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n" ]	[ 40 ]	[ "passage: TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n" ]	[ -0.05520883575081825, -0.008646681904792786, -0.008018726482987404, -0.060088928788900375, 0.1007847860455513, -0.0292293019592762, 0.05964343622326851, 0.10686095058917999, 0.10739415138959885, -0.01164542231708765, 0.11132402718067169, 0.20903414487838745, 0.005836382042616606, 0.013538715429604053, 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null	null	transformers	Model cloned from https://huggingface.co/facebook/wav2vec2-large-xlsr-53-dutch Currently bugged: Logits size 48, vocab size 50	{}	automatic-speech-recognition	Iskaj/w2v-xlsr-dutch-lm	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us	Model cloned from URL Currently bugged: Logits size 48, vocab size 50	[]	[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n" ]	[ 37 ]	[ "passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n" ]	[ -0.0721188485622406, -0.028641358017921448, -0.008131618611514568, -0.06536999344825745, 0.10369497537612915, -0.029162423685193062, 0.05449262633919716, 0.09215618669986725, 0.09012462943792343, 0.0024160705506801605, 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null	null	transformers	# xlsr300m_cv_7.0_nl_lm	{"language": ["nl"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "nl", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "XLS-R-300M - Dutch", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8 NL", "type": "mozilla-foundation/common_voice_8_0", "args": "nl"}, "metrics": [{"type": "wer", "value": 32, "name": "Test WER"}, {"type": "cer", "value": 17, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "nl"}, "metrics": [{"type": "wer", "value": 37.44, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "nl"}, "metrics": [{"type": "wer", "value": 38.74, "name": "Test WER"}]}]}]}	automatic-speech-recognition	Iskaj/xlsr300m_cv_7.0_nl_lm	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "nl", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard", "dataset:mozilla-foundation/common_voice_8_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "nl" ]	TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #nl #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us	# xlsr300m_cv_7.0_nl_lm	[ "# xlsr300m_cv_7.0_nl_lm" ]	[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #nl #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "# xlsr300m_cv_7.0_nl_lm" ]	[ 110, 15 ]	[ "passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #nl #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# xlsr300m_cv_7.0_nl_lm" ]	[ -0.14019881188869476, 0.06807677447795868, -0.006067667622119188, -0.0576205737888813, 0.08792361617088318, -0.07807305455207825, 0.12916088104248047, 0.09787455201148987, 0.03690830245614052, 0.019406389445066452, 0.07089699059724808, 0.1296369880437851, 0.04368612915277481, -0.007270239293575287, -0.00296089518815279, -0.1517094224691391, 0.08624736219644547, -0.010226907208561897, 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null	null	transformers	# xlsr300m_cv_8.0_nl #### Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id Iskaj/xlsr300m_cv_8.0_nl --dataset mozilla-foundation/common_voice_8_0 --config nl --split test ``` 2. To evaluate on `speech-recognition-community-v2/dev_data` ```bash python eval.py --model_id Iskaj/xlsr300m_cv_8.0_nl --dataset speech-recognition-community-v2/dev_data --config nl --split validation --chunk_length_s 5.0 --stride_length_s 1.0 ``` ### Inference ```python import torch from datasets import load_dataset from transformers import AutoModelForCTC, AutoProcessor import torchaudio.functional as F model_id = "Iskaj/xlsr300m_cv_8.0_nl" sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "nl", split="test", streaming=True, use_auth_token=True)) sample = next(sample_iter) resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy() model = AutoModelForCTC.from_pretrained(model_id) processor = AutoProcessor.from_pretrained(model_id) inputs = processor(resampled_audio, sampling_rate=16_000, return_tensors="pt") with torch.no_grad(): logits = model(**inputs).logits predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) transcription[0].lower() #'het kontine schip lag aangemeert in de aven' ```	{"language": ["nl"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "mozilla-foundation/common_voice_7_0", "nl", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "XLS-R-300M - Dutch", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8 NL", "type": "mozilla-foundation/common_voice_8_0", "args": "nl"}, "metrics": [{"type": "wer", "value": 46.94, "name": "Test WER"}, {"type": "cer", "value": 21.65, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "nl"}, "metrics": [{"type": "wer", "value": "???", "name": "Test WER"}, {"type": "cer", "value": "???", "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "nl"}, "metrics": [{"type": "wer", "value": 42.56, "name": "Test WER"}]}]}]}	automatic-speech-recognition	Iskaj/xlsr300m_cv_8.0_nl	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "mozilla-foundation/common_voice_7_0", "nl", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard", "dataset:mozilla-foundation/common_voice_8_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "nl" ]	TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #mozilla-foundation/common_voice_7_0 #nl #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us	# xlsr300m_cv_8.0_nl #### Evaluation Commands 1. To evaluate on 'mozilla-foundation/common_voice_8_0' with split 'test' 2. To evaluate on 'speech-recognition-community-v2/dev_data' ### Inference	[ "# xlsr300m_cv_8.0_nl", "#### Evaluation Commands\n1. To evaluate on 'mozilla-foundation/common_voice_8_0' with split 'test'\n\n\n\n2. To evaluate on 'speech-recognition-community-v2/dev_data'", "### Inference" ]	[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #mozilla-foundation/common_voice_7_0 #nl #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "# xlsr300m_cv_8.0_nl", "#### Evaluation Commands\n1. To evaluate on 'mozilla-foundation/common_voice_8_0' with split 'test'\n\n\n\n2. To evaluate on 'speech-recognition-community-v2/dev_data'", "### Inference" ]	[ 126, 15, 56, 5 ]	[ "passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #mozilla-foundation/common_voice_7_0 #nl #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# xlsr300m_cv_8.0_nl#### Evaluation Commands\n1. To evaluate on 'mozilla-foundation/common_voice_8_0' with split 'test'\n\n\n\n2. 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null	null	transformers	# xlsr_300m_CV_8.0_50_EP_new_params_nl	{"language": ["nl"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "hf-asr-leaderboard", "model_for_talk", "mozilla-foundation/common_voice_8_0", "nl", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "XLS-R-300M - Dutch", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8 NL", "type": "mozilla-foundation/common_voice_8_0", "args": "nl"}, "metrics": [{"type": "wer", "value": 35.44, "name": "Test WER"}, {"type": "cer", "value": 19.57, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "nl"}, "metrics": [{"type": "wer", "value": 37.17, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "nl"}, "metrics": [{"type": "wer", "value": 38.73, "name": "Test WER"}]}]}]}	automatic-speech-recognition	Iskaj/xlsr_300m_CV_8.0_50_EP_new_params_nl	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "hf-asr-leaderboard", "model_for_talk", "mozilla-foundation/common_voice_8_0", "nl", "robust-speech-event", "dataset:mozilla-foundation/common_voice_8_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "nl" ]	TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #nl #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us	# xlsr_300m_CV_8.0_50_EP_new_params_nl	[ "# xlsr_300m_CV_8.0_50_EP_new_params_nl" ]	[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #nl #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "# xlsr_300m_CV_8.0_50_EP_new_params_nl" ]	[ 110, 24 ]	[ "passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #nl #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# xlsr_300m_CV_8.0_50_EP_new_params_nl" ]	[ -0.15578420460224152, 0.05097978562116623, -0.005861265119165182, -0.07213830202817917, 0.10517104715108871, -0.06951741129159927, 0.12145137786865234, 0.08898612856864929, -0.008940470404922962, 0.03246443346142769, 0.07357454299926758, 0.1122710183262825, 0.05755456164479256, 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null	null	null	#sherlock	{"tags": ["conversational"]}	text-generation	Istiaque190515/Sherlock	[ "conversational", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #conversational #region-us	#sherlock	[]	[ "TAGS\n#conversational #region-us \n" ]	[ 10 ]	[ "passage: TAGS\n#conversational #region-us \n" ]	[ 0.04639962688088417, 0.010033470578491688, -0.010517087765038013, -0.09196841716766357, 0.07825888693332672, 0.025966141372919083, 0.0816626027226448, 0.03981694206595421, 0.1679982990026474, -0.043665021657943726, 0.11948301643133163, 0.05959230661392212, -0.03424782678484917, -0.034178510308265686, -0.03257700800895691, -0.1518038958311081, 0.018241867423057556, -0.03490595519542694, 0.09715951234102249, 0.046818334609270096, -0.022061990574002266, -0.08366407454013824, 0.008475017733871937, -0.06431017816066742, -0.04472750052809715, 0.10980460792779922, 0.019003495573997498, -0.011012797243893147, 0.15410423278808594, -0.0074986726976931095, 0.14888158440589905, 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null	null	transformers	#harry_bot	{"tags": ["conversational"]}	text-generation	Istiaque190515/harry_bot_discord	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	#harry_bot	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 51 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.009697278961539268, 0.03208012506365776, -0.007204889785498381, 0.004809224978089333, 0.16726240515708923, 0.014898733235895634, 0.09765533357858658, 0.13672804832458496, 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null	null	transformers	#harry_potter	{"tags": ["conversational"]}	text-generation	Istiaque190515/harry_potter	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	#harry_potter	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 51 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.009697278961539268, 0.03208012506365776, -0.007204889785498381, 0.004809224978089333, 0.16726240515708923, 0.014898733235895634, 0.09765533357858658, 0.13672804832458496, 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null	null	transformers	# Tohru DialoGPT model	{"tags": ["conversational"]}	text-generation	ItoYagura/DialoGPT-medium-tohru	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Tohru DialoGPT model	[ "# Tohru DialoGPT model" ]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Tohru DialoGPT model" ]	[ 51, 8 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Tohru DialoGPT model" ]	[ -0.01709621399641037, 0.03250964730978012, -0.00497213052585721, 0.01048628892749548, 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null	null	transformers	# Pickle Rick DialoGPT Model	{"tags": ["conversational"]}	text-generation	ItzJorinoPlays/DialoGPT-small-PickleRick	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Pickle Rick DialoGPT Model	[ "# Pickle Rick DialoGPT Model" ]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Pickle Rick DialoGPT Model" ]	[ 51, 9 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Pickle Rick DialoGPT Model" ]	[ 0.0058993385173380375, 0.1619562804698944, 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null	null	transformers	# Thor DialogGPT Model	{"tags": ["conversational"]}	text-generation	J-Chiang/DialoGPT-small-thor	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Thor DialogGPT Model	[ "# Thor DialogGPT Model" ]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Thor DialogGPT Model" ]	[ 51, 6 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Thor DialogGPT Model" ]	[ -0.005856621079146862, 0.04284031689167023, -0.004534080158919096, 0.06394263356924057, 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null	null	transformers	## Model description This model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset. ## How to use ```python from transformers.pipelines import pipeline model_name = "JAlexis/PruebaBert" nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) inputs = { 'question': 'How can I protect myself against covid-19?', 'context': 'Preventative measures consist of recommendations to wear a mask in public, maintain social distancing of at least six feet, wash hands regularly, and use hand sanitizer. To facilitate this aim, we adapt the conceptual model and measures of Liao et al. [6] to the current context of the COVID-19 pandemic and the culture of the USA. Applying this model in a different time and context provides an opportunity to make comparisons of reactions to information sources across a decade of evolving attitudes toward media and government, between two cultures (Hong Kong vs. the USA), and between two considerably different global pandemics (H1N1 vs. COVID-19). ', 'question': 'How can I protect myself against covid-19?', 'context': ' ', } nlp(inputs) ``` ## Overview ``` Language model: deepset/bert-base-cased-squad2 Language: English Downstream-task: Q&A Datasets: CORD-19 from 31rd January 2022 Code: Haystack and FARM Infrastructure: Tesla T4 ``` ## Hyperparameters ``` batch_size = 8 n_epochs = 7 max_seq_len = max_length learning_rate = AdamW: 2e-5 ```	{"language": "en", "tags": ["pytorch", "question-answering"], "datasets": ["squad2", "cord19"], "metrics": ["f1"], "widget": [{"text": "How can I protect myself against covid-19?", "context": "Preventative measures consist of recommendations to wear a mask in public, maintain social distancing of at least six feet, wash hands regularly, and use hand sanitizer. To facilitate this aim, we adapt the conceptual model and measures of Liao et al. [6] to the current context of the COVID-19 pandemic and the culture of the USA. Applying this model in a different time and context provides an opportunity to make comparisons of reactions to information sources across a decade of evolving attitudes toward media and government, between two cultures (Hong Kong vs. the USA), and between two considerably different global pandemics (H1N1 vs. COVID-19)."}, {"text": "How can I protect myself against covid-19?", "context": " "}]}	question-answering	JAlexis/Bertv1_fine	[ "transformers", "pytorch", "bert", "question-answering", "en", "dataset:squad2", "dataset:cord19", "endpoints_compatible", "has_space", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #bert #question-answering #en #dataset-squad2 #dataset-cord19 #endpoints_compatible #has_space #region-us	## Model description This model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset. ## How to use ## Overview ## Hyperparameters	[ "## Model description \nThis model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset.", "## How to use", "## Overview", "## Hyperparameters" ]	[ "TAGS\n#transformers #pytorch #bert #question-answering #en #dataset-squad2 #dataset-cord19 #endpoints_compatible #has_space #region-us \n", "## Model description \nThis model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset.", "## How to use", "## Overview", "## Hyperparameters" ]	[ 48, 32, 4, 3, 5 ]	[ "passage: TAGS\n#transformers #pytorch #bert #question-answering #en #dataset-squad2 #dataset-cord19 #endpoints_compatible #has_space #region-us \n## Model description \nThis model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset.## How to use## Overview## Hyperparameters" ]	[ -0.09651433676481247, 0.012336142361164093, -0.002330036601051688, 0.018268417567014694, 0.08749478310346603, 0.05608363449573517, 0.03669748082756996, 0.09330844134092331, -0.008716179057955742, 0.05186276137828827, 0.1418342888355255, 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null	null	transformers	## Model description This model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset. ## How to use ```python from transformers.pipelines import pipeline model_name = "JAlexis/PruebaBert" nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) inputs = { 'question': 'How can I protect myself against covid-19?', 'context': 'Preventative measures consist of recommendations to wear a mask in public, maintain social distancing of at least six feet, wash hands regularly, and use hand sanitizer. To facilitate this aim, we adapt the conceptual model and measures of Liao et al. [6] to the current context of the COVID-19 pandemic and the culture of the USA. Applying this model in a different time and context provides an opportunity to make comparisons of reactions to information sources across a decade of evolving attitudes toward media and government, between two cultures (Hong Kong vs. the USA), and between two considerably different global pandemics (H1N1 vs. COVID-19). ', 'question': 'How can I protect myself against covid-19?', 'context': ' ', } nlp(inputs) ``` ## Overview ``` Language model: deepset/bert-base-cased-squad2 Language: English Downstream-task: Q&A Datasets: CORD-19 from 31rd January 2022 Code: Haystack and FARM Infrastructure: Tesla T4 ``` ## Hyperparameters ``` batch_size = 8 n_epochs = 9 max_seq_len = max_length learning_rate = AdamW: 1e-5 ```	{"language": "en", "tags": ["pytorch", "question-answering"], "datasets": ["squad2", "cord19"], "metrics": ["EM (exact match)"], "widget": [{"text": "How can I protect myself against covid-19?", "context": "Preventative measures consist of recommendations to wear a mask in public, maintain social distancing of at least six feet, wash hands regularly, and use hand sanitizer. To facilitate this aim, we adapt the conceptual model and measures of Liao et al. [6] to the current context of the COVID-19 pandemic and the culture of the USA. Applying this model in a different time and context provides an opportunity to make comparisons of reactions to information sources across a decade of evolving attitudes toward media and government, between two cultures (Hong Kong vs. the USA), and between two considerably different global pandemics (H1N1 vs. COVID-19)."}, {"text": "How can I protect myself against covid-19?", "context": " "}]}	question-answering	JAlexis/PruebaBert	[ "transformers", "pytorch", "bert", "question-answering", "en", "dataset:squad2", "dataset:cord19", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #bert #question-answering #en #dataset-squad2 #dataset-cord19 #endpoints_compatible #region-us	## Model description This model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset. ## How to use ## Overview ## Hyperparameters	[ "## Model description \nThis model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset.", "## How to use", "## Overview", "## Hyperparameters" ]	[ "TAGS\n#transformers #pytorch #bert #question-answering #en #dataset-squad2 #dataset-cord19 #endpoints_compatible #region-us \n", "## Model description \nThis model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset.", "## How to use", "## Overview", "## Hyperparameters" ]	[ 44, 32, 4, 3, 5 ]	[ "passage: TAGS\n#transformers #pytorch #bert #question-answering #en #dataset-squad2 #dataset-cord19 #endpoints_compatible #region-us \n## Model description \nThis model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset.## How to use## Overview## Hyperparameters" ]	[ -0.1046748086810112, 0.001475242548622191, -0.003101753769442439, 0.015607194043695927, 0.11349891871213913, 0.05707438290119171, 0.04191378504037857, 0.08935923129320145, -0.021401245146989822, 0.032592032104730606, 0.13996006548404694, 0.14566057920455933, 0.014030168764293194, 0.04123534634709358, 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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-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8366 - Matthews Correlation: 0.5472 ## 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.5224 \| 1.0 \| 535 \| 0.5432 \| 0.4243 \| \| 0.3447 \| 2.0 \| 1070 \| 0.4968 \| 0.5187 \| \| 0.2347 \| 3.0 \| 1605 \| 0.6540 \| 0.5280 \| \| 0.1747 \| 4.0 \| 2140 \| 0.7547 \| 0.5367 \| \| 0.1255 \| 5.0 \| 2675 \| 0.8366 \| 0.5472 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"}, "metrics": [{"type": "matthews_correlation", "value": 0.5471613867597194, "name": "Matthews Correlation"}]}]}]}	text-classification	JBNLRY/distilbert-base-uncased-finetuned-cola	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us	distilbert-base-uncased-finetuned-cola ====================================== This model is a fine-tuned version of distilbert-base-uncased on the glue dataset. It achieves the following results on the evaluation set: * Loss: 0.8366 * Matthews Correlation: 0.5472 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: 5 ### Training results ### Framework versions * Transformers 4.16.2 * Pytorch 1.10.0+cu111 * Datasets 1.18.3 * Tokenizers 0.11.0	[ "### 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: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]	[ "TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #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: 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: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]	[ 67, 98, 4, 35 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #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: 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: 5### Training results### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]	[ -0.10348068922758102, 0.09148605167865753, -0.0022954836022108793, 0.12302655726671219, 0.16473764181137085, 0.029876047745347023, 0.11664267629384995, 0.1302110105752945, -0.0878436341881752, 0.02543756179511547, 0.12662576138973236, 0.16262155771255493, 0.020631218329072, 0.12087254971265793, -0.05344507843255997, -0.26481878757476807, -0.00954040139913559, 0.05130685120820999, -0.04174657538533211, 0.13426357507705688, 0.09329058229923248, 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null	null	transformers	# T5 Question Generation and Question Answering ## Model description This model is a T5 Transformers model (airklizz/t5-base-multi-fr-wiki-news) that was fine-tuned in french on 3 different tasks * question generation * question answering * answer extraction It obtains quite good results on FQuAD validation dataset. ## Intended uses & limitations This model functions for the 3 tasks mentionned earlier and was not tested on other tasks. ```python from transformers import T5ForConditionalGeneration, T5Tokenizer model = T5ForConditionalGeneration.from_pretrained("JDBN/t5-base-fr-qg-fquad") tokenizer = T5Tokenizer.from_pretrained("JDBN/t5-base-fr-qg-fquad") ``` ## Training data The initial model used was https://huggingface.co/airKlizz/t5-base-multi-fr-wiki-news. This model was finetuned on a dataset composed of FQuAD and PIAF on the 3 tasks mentioned previously. The data were preprocessed like this * question generation: "generate question: Barack Hussein Obama, né le 4 aout 1961, est un homme politique américain et avocat. Il a été élu <hl> en 2009 <hl> pour devenir le 44ème président des Etats-Unis d'Amérique." * question answering: "question: Quand Barack Hussein Obamaa-t-il été élu président des Etats-Unis d’Amérique? context: Barack Hussein Obama, né le 4 aout 1961, est un homme politique américain et avocat. Il a été élu en 2009 pour devenir le 44ème président des Etats-Unis d’Amérique." * answer extraction: "extract_answers: Barack Hussein Obama, né le 4 aout 1961, est un homme politique américain et avocat. <hl> Il a été élu en 2009 pour devenir le 44ème président des Etats-Unis d’Amérique <hl>." The preprocessing we used was implemented in https://github.com/patil-suraj/question_generation ## Eval results #### On FQuAD validation set \| BLEU_1 \| BLEU_2 \| BLEU_3 \| BLEU_4 \| METEOR \| ROUGE_L \| CIDEr \| \|--------\|--------\|--------\|--------\|--------\|---------\|-------\| \| 0.290 \| 0.203 \| 0.149 \| 0.111 \| 0.197 \| 0.284 \| 1.038 \| #### Question Answering metrics For these metrics, the performance of this question answering model (https://huggingface.co/illuin/camembert-base-fquad) on FQuAD original question and on T5 generated questions are compared. \| Questions \| Exact Match \| F1 Score \| \|------------------\|--------\|--------\| \|Original FQuAD \| 54.015 \| 77.466 \| \|Generated \| 45.765 \| 67.306 \| ### BibTeX entry and citation info ```bibtex @misc{githubPatil, author = {Patil Suraj}, title = {question generation GitHub repository}, year = {2020}, howpublished={\url{https://github.com/patil-suraj/question_generation}} } @article{T5, title={Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer}, author={Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu}, year={2019}, eprint={1910.10683}, archivePrefix={arXiv}, primaryClass={cs.LG} } @misc{dhoffschmidt2020fquad, title={FQuAD: French Question Answering Dataset}, author={Martin d'Hoffschmidt and Wacim Belblidia and Tom Brendlé and Quentin Heinrich and Maxime Vidal}, year={2020}, eprint={2002.06071}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```	{"language": "fr", "tags": ["pytorch", "t5", "question-generation", "seq2seq"], "datasets": ["fquad", "piaf"], "widget": [{"text": "generate question: Barack Hussein Obama, n\u00e9 le 4 aout 1961, est un homme politique am\u00e9ricain et avocat. Il a \u00e9t\u00e9 \u00e9lu <hl> en 2009 <hl> pour devenir le 44\u00e8me pr\u00e9sident des Etats-Unis d'Am\u00e9rique. </s>"}, {"text": "question: Quand Barack Obama a t'il \u00e9t\u00e9 \u00e9lu pr\u00e9sident? context: Barack Hussein Obama, n\u00e9 le 4 aout 1961, est un homme politique am\u00e9ricain et avocat. Il a \u00e9t\u00e9 \u00e9lu en 2009 pour devenir le 44\u00e8me pr\u00e9sident des Etats-Unis d'Am\u00e9rique. </s>"}]}	text2text-generation	JDBN/t5-base-fr-qg-fquad	[ "transformers", "pytorch", "jax", "t5", "text2text-generation", "question-generation", "seq2seq", "fr", "dataset:fquad", "dataset:piaf", "arxiv:1910.10683", "arxiv:2002.06071", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:04+00:00	[ "1910.10683", "2002.06071" ]	[ "fr" ]	TAGS #transformers #pytorch #jax #t5 #text2text-generation #question-generation #seq2seq #fr #dataset-fquad #dataset-piaf #arxiv-1910.10683 #arxiv-2002.06071 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	T5 Question Generation and Question Answering ============================================= Model description ----------------- This model is a T5 Transformers model (airklizz/t5-base-multi-fr-wiki-news) that was fine-tuned in french on 3 different tasks * question generation * question answering * answer extraction It obtains quite good results on FQuAD validation dataset. Intended uses & limitations --------------------------- This model functions for the 3 tasks mentionned earlier and was not tested on other tasks. Training data ------------- The initial model used was URL This model was finetuned on a dataset composed of FQuAD and PIAF on the 3 tasks mentioned previously. The data were preprocessed like this * question generation: "generate question: Barack Hussein Obama, né le 4 aout 1961, est un homme politique américain et avocat. Il a été élu en 2009 pour devenir le 44ème président des Etats-Unis d'Amérique." * question answering: "question: Quand Barack Hussein Obamaa-t-il été élu président des Etats-Unis d’Amérique? context: Barack Hussein Obama, né le 4 aout 1961, est un homme politique américain et avocat. Il a été élu en 2009 pour devenir le 44ème président des Etats-Unis d’Amérique." * answer extraction: "extract\_answers: Barack Hussein Obama, né le 4 aout 1961, est un homme politique américain et avocat. Il a été élu en 2009 pour devenir le 44ème président des Etats-Unis d’Amérique ." The preprocessing we used was implemented in URL Eval results ------------ #### On FQuAD validation set #### Question Answering metrics For these metrics, the performance of this question answering model (URL on FQuAD original question and on T5 generated questions are compared. Questions: Original FQuAD, Exact Match: 54.015, F1 Score: 77.466 Questions: Generated, Exact Match: 45.765, F1 Score: 67.306 ### BibTeX entry and citation info	[ "#### On FQuAD validation set", "#### Question Answering metrics\n\n\nFor these metrics, the performance of this question answering model (URL on FQuAD original question and on T5 generated questions are compared.\n\n\nQuestions: Original FQuAD, Exact Match: 54.015, F1 Score: 77.466\nQuestions: Generated, Exact Match: 45.765, F1 Score: 67.306", "### BibTeX entry and citation info" ]	[ "TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #question-generation #seq2seq #fr #dataset-fquad #dataset-piaf #arxiv-1910.10683 #arxiv-2002.06071 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "#### On FQuAD validation set", "#### Question Answering metrics\n\n\nFor these metrics, the performance of this question answering model (URL on FQuAD original question and on T5 generated questions are compared.\n\n\nQuestions: Original FQuAD, Exact Match: 54.015, F1 Score: 77.466\nQuestions: Generated, Exact Match: 45.765, F1 Score: 67.306", "### BibTeX entry and citation info" ]	[ 95, 9, 80, 11 ]	[ "passage: TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #question-generation #seq2seq #fr #dataset-fquad #dataset-piaf #arxiv-1910.10683 #arxiv-2002.06071 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n#### On FQuAD validation set#### Question Answering metrics\n\n\nFor these metrics, the performance of this question answering model (URL on FQuAD original question and on T5 generated questions are compared.\n\n\nQuestions: Original FQuAD, Exact Match: 54.015, F1 Score: 77.466\nQuestions: Generated, Exact Match: 45.765, F1 Score: 67.306### BibTeX entry and citation info" ]	[ -0.07673418521881104, 0.1083921492099762, -0.005533802788704634, 0.09081786870956421, 0.059624239802360535, 0.019532131031155586, 0.09229472279548645, 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null	null	transformers	@ Harry Potter DialoGPT Model	{"tags": ["conversational"]}	text-generation	JDS22/DialoGPT-medium-HarryPotterBot	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	@ Harry Potter DialoGPT Model	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 51 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.009697278961539268, 0.03208012506365776, -0.007204889785498381, 0.004809224978089333, 0.16726240515708923, 0.014898733235895634, 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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. --> # bert-base-finetuned-nli This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klue dataset. It achieves the following results on the evaluation set: - Loss: 0.6210 - Accuracy: 0.085 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 196 \| 0.6210 \| 0.085 \| \| No log \| 2.0 \| 392 \| 0.5421 \| 0.0643 \| \| 0.5048 \| 3.0 \| 588 \| 0.5523 \| 0.062 \| \| 0.5048 \| 4.0 \| 784 \| 0.5769 \| 0.0533 \| \| 0.5048 \| 5.0 \| 980 \| 0.5959 \| 0.052 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0	{"tags": ["generated_from_trainer"], "datasets": ["klue"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-finetuned-nli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "klue", "type": "klue", "args": "nli"}, "metrics": [{"type": "accuracy", "value": 0.085, "name": "Accuracy"}]}]}]}	text-classification	JIWON/bert-base-finetuned-nli	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:klue", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-klue #model-index #autotrain_compatible #endpoints_compatible #region-us	bert-base-finetuned-nli ======================= This model is a fine-tuned version of klue/bert-base on the klue dataset. It achieves the following results on the evaluation set: * Loss: 0.6210 * Accuracy: 0.085 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: 128 * eval\_batch\_size: 128 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 5 ### Training results ### Framework versions * Transformers 4.16.2 * Pytorch 1.10.0+cu111 * Datasets 1.18.3 * Tokenizers 0.11.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]	[ "TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-klue #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: 2e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]	[ 53, 98, 4, 35 ]	[ "passage: TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-klue #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: 2e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]	[ -0.09798229485750198, 0.06294521689414978, -0.0006091445684432983, 0.12007969617843628, 0.20726899802684784, 0.03471340984106064, 0.0939624011516571, 0.11239465326070786, -0.09474998712539673, 0.005376458168029785, 0.12819935381412506, 0.17081843316555023, -0.0010147057473659515, 0.1387052685022354, -0.06435191631317139, -0.2879981994628906, -0.02468680776655674, 0.03636010363698006, -0.061347708106040955, 0.1323409527540207, 0.09279569238424301, -0.15465909242630005, 0.08971502631902695, -0.011664710007607937, 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null	null	transformers	# aristoBERTo aristoBERTo is a transformer model for ancient Greek, a low resource language. We initialized the pre-training with weights from [GreekBERT](https://huggingface.co/nlpaueb/bert-base-greek-uncased-v1), a Greek version of BERT which was trained on a large corpus of modern Greek (~ 30 GB of texts). We continued the pre-training with an ancient Greek corpus of about 900 MB, which was scrapped from the web and post-processed. Duplicate texts and editorial punctuation were removed. Applied to the processing of ancient Greek, aristoBERTo outperforms xlm-roberta-base and mdeberta in most downstream tasks like the labeling of POS, MORPH, DEP and LEMMA. aristoBERTo is provided by the [Diogenet project](https://diogenet.ucsd.edu) of the University of California, San Diego. ## Intended uses This model was created for fine-tuning with spaCy and the ancient Greek Universal Dependency datasets as well as a NER corpus produced by the [Diogenet project](https://diogenet.ucsd.edu). As a fill-mask model, AristoBERTo can also be used in the restoration of damaged Greek papyri, inscriptions, and manuscripts. It achieves the following results on the evaluation set: - Loss: 1.6323 ## 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: 20.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-------:\|:---------------:\| \| 1.377 \| 20.0 \| 3414220 \| 1.6314 \| ### Framework versions - Transformers 4.14.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3	{"language": ["grc"], "widget": [{"text": "\u03a0\u03bb\u03ac\u03c4\u03c9\u03bd \u1f41 \u03a0\u03b5\u03c1\u03b9\u03ba\u03c4\u03b9\u03cc\u03bd\u03b7\u03c2 [MASK] \u03b3\u03ad\u03bd\u03bf\u03c2 \u1f00\u03bd\u03ad\u03c6\u03b5\u03c1\u03b5\u03bd \u03b5\u1f30\u03c2 \u03a3\u03cc\u03bb\u03c9\u03bd\u03b1."}, {"text": "\u1f41 \u039a\u03c1\u03b9\u03c4\u03af\u03b1\u03c2 \u1f00\u03c0\u03ad\u03b2\u03bb\u03b5\u03c8\u03b5 [MASK] \u03c4\u1f74\u03bd \u03b8\u03cd\u03c1\u03b1\u03bd."}, {"text": "\u03c0\u03c1\u1ff6\u03c4\u03bf\u03b9 \u03b4\u1f72 \u03ba\u03b1\u1f76 \u03bf\u1f50\u03bd\u03cc\u03bc\u03b1\u03c4\u03b1 \u1f31\u03c1\u1f70 \u1f14\u03b3\u03bd\u03c9\u03c3\u03b1\u03bd \u03ba\u03b1\u1f76 [MASK] \u1f31\u03c1\u03bf\u1f7a\u03c2 \u1f14\u03bb\u03b5\u03be\u03b1\u03bd."}], "model-index": [{"name": "aristoBERTo", "results": []}]}	fill-mask	Jacobo/aristoBERTo	[ "transformers", "pytorch", "bert", "fill-mask", "grc", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "grc" ]	TAGS #transformers #pytorch #bert #fill-mask #grc #autotrain_compatible #endpoints_compatible #region-us	aristoBERTo =========== aristoBERTo is a transformer model for ancient Greek, a low resource language. We initialized the pre-training with weights from GreekBERT, a Greek version of BERT which was trained on a large corpus of modern Greek (~ 30 GB of texts). We continued the pre-training with an ancient Greek corpus of about 900 MB, which was scrapped from the web and post-processed. Duplicate texts and editorial punctuation were removed. Applied to the processing of ancient Greek, aristoBERTo outperforms xlm-roberta-base and mdeberta in most downstream tasks like the labeling of POS, MORPH, DEP and LEMMA. aristoBERTo is provided by the Diogenet project of the University of California, San Diego. Intended uses ------------- This model was created for fine-tuning with spaCy and the ancient Greek Universal Dependency datasets as well as a NER corpus produced by the Diogenet project. As a fill-mask model, AristoBERTo can also be used in the restoration of damaged Greek papyri, inscriptions, and manuscripts. It achieves the following results on the evaluation set: * Loss: 1.6323 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: 20.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.14.0.dev0 * Pytorch 1.10.0+cu102 * Datasets 1.16.1 * Tokenizers 0.10.3	[ "### 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* num\\_epochs: 20.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.14.0.dev0\n* Pytorch 1.10.0+cu102\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]	[ "TAGS\n#transformers #pytorch #bert #fill-mask #grc #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: 20.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.14.0.dev0\n* Pytorch 1.10.0+cu102\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]	[ 39, 114, 4, 36 ]	[ "passage: TAGS\n#transformers #pytorch #bert #fill-mask #grc #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: 20.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.14.0.dev0\n* Pytorch 1.10.0+cu102\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]	[ -0.10261187702417374, -0.006388036534190178, -0.002035397570580244, 0.09576065838336945, 0.18752944469451904, 0.03686103969812393, 0.09718678891658783, 0.09589946269989014, -0.10722286254167557, 0.019301380962133408, 0.1351657509803772, 0.14835666120052338, 0.0028252461925148964, 0.1445840746164322, -0.05520906671881676, -0.26598626375198364, -0.007994115352630615, 0.01998578943312168, -0.09540870040655136, 0.12019436061382294, 0.08839370310306549, -0.15459328889846802, 0.07089490443468094, 0.0013781902380287647, -0.21334846317768097, 0.02394685335457325, 0.031907398253679276, -0.06376001983880997, 0.14150455594062805, 0.023718336597085, 0.1614953875541687, -0.008145829662680626, 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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. --> # axiothea This is an experimental roberta model trained with an ancient Greek corpus of about 900 MB, which was scrapped from the web and post-processed. Duplicate texts and editorial punctuation were removed. The training dataset will be soon available in the Huggingface datasets hub. Training a model of ancient Greek is challenging given that it is a low resource language from which 50% of the register has only survived in fragmentary texts. The model is provided by the Diogenet project at the University of California, San Diego. It achieves the following results on the evaluation set: - Loss: 3.3351 ## 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: 10.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-------:\|:---------------:\| \| 4.7013 \| 1.0 \| 341422 \| 4.8813 \| \| 4.2866 \| 2.0 \| 682844 \| 4.4422 \| \| 4.0496 \| 3.0 \| 1024266 \| 4.2132 \| \| 3.8503 \| 4.0 \| 1365688 \| 4.0246 \| \| 3.6917 \| 5.0 \| 1707110 \| 3.8756 \| \| 3.4917 \| 6.0 \| 2048532 \| 3.7381 \| \| 3.3907 \| 7.0 \| 2389954 \| 3.6107 \| \| 3.2876 \| 8.0 \| 2731376 \| 3.5044 \| \| 3.1994 \| 9.0 \| 3072798 \| 3.3980 \| \| 3.0806 \| 10.0 \| 3414220 \| 3.3095 \| ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.14.0 - Tokenizers 0.10.3	{"language": ["grc"], "tags": ["generated_from_trainer"], "widget": [{"text": "\u03a0\u03bb\u03ac\u03c4\u03c9\u03bd \u1f41 \u03a0\u03b5\u03c1\u03b9\u03ba\u03c4\u03b9\u03cc\u03bd\u03b7\u03c2 <mask> \u03b3\u03ad\u03bd\u03bf\u03c2 \u1f00\u03bd\u03ad\u03c6\u03b5\u03c1\u03b5\u03bd \u03b5\u1f30\u03c2 \u03a3\u03cc\u03bb\u03c9\u03bd\u03b1."}, {"text": "\u1f41 \u039a\u03c1\u03b9\u03c4\u03af\u03b1\u03c2 \u1f00\u03c0\u03ad\u03b2\u03bb\u03b5\u03c8\u03b5 <mask> \u03c4\u1f74\u03bd \u03b8\u03cd\u03c1\u03b1\u03bd."}, {"text": "\u1f6e \u03c6\u03af\u03bb\u03b5 \u039a\u03bb\u03b5\u03b9\u03bd\u03af\u03b1, \u03ba\u03b1\u03bb\u1ff6\u03c2 \u03bc\u1f72\u03bd <mask>."}], "model-index": [{"name": "dioBERTo", "results": []}]}	fill-mask	Jacobo/axiothea	[ "transformers", "pytorch", "roberta", "fill-mask", "generated_from_trainer", "grc", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "grc" ]	TAGS #transformers #pytorch #roberta #fill-mask #generated_from_trainer #grc #autotrain_compatible #endpoints_compatible #region-us	axiothea ======== This is an experimental roberta model trained with an ancient Greek corpus of about 900 MB, which was scrapped from the web and post-processed. Duplicate texts and editorial punctuation were removed. The training dataset will be soon available in the Huggingface datasets hub. Training a model of ancient Greek is challenging given that it is a low resource language from which 50% of the register has only survived in fragmentary texts. The model is provided by the Diogenet project at the University of California, San Diego. It achieves the following results on the evaluation set: * Loss: 3.3351 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: 10.0 ### Training results ### Framework versions * Transformers 4.13.0.dev0 * Pytorch 1.10.0+cu102 * Datasets 1.14.0 * Tokenizers 0.10.3	[ "### 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: 10.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0+cu102\n* Datasets 1.14.0\n* Tokenizers 0.10.3" ]	[ "TAGS\n#transformers #pytorch #roberta #fill-mask #generated_from_trainer #grc #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: 10.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0+cu102\n* Datasets 1.14.0\n* Tokenizers 0.10.3" ]	[ 47, 99, 4, 36 ]	[ "passage: TAGS\n#transformers #pytorch #roberta #fill-mask #generated_from_trainer #grc #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: 10.0### Training results### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0+cu102\n* Datasets 1.14.0\n* Tokenizers 0.10.3" ]	[ -0.10221599787473679, 0.016668546944856644, -0.0018742028623819351, 0.11503934115171432, 0.20093776285648346, 0.04316980391740799, 0.09890982508659363, 0.08746876567602158, -0.11696285009384155, 0.027291348204016685, 0.13907304406166077, 0.14129941165447235, -0.0031425708439201117, 0.1404769867658615, -0.044452305883169174, -0.2481544315814972, -0.011273274198174477, 0.017679724842309952, -0.0981612280011177, 0.12810982763767242, 0.08291041851043701, -0.1473890244960785, 0.08365405350923538, -0.0026951071340590715, -0.2561682462692261, 0.007188761606812477, 0.03958367928862572, 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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. --> # wav2vec2-base-csa-10-rev3 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.5869 - Wer: 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: 0.0001 - 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: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---:\| \| 18.7934 \| 25.0 \| 200 \| 3.5869 \| 1.0 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-csa-10-rev3", "results": []}]}	automatic-speech-recognition	Jainil30/wav2vec2-base-csa-10-rev3	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us	wav2vec2-base-csa-10-rev3 ========================= This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set: * Loss: 3.5869 * Wer: 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: 0.0001 * 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: 1000 * num\_epochs: 30 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.11.3 * Pytorch 1.10.0+cu111 * Datasets 1.13.3 * Tokenizers 0.10.3	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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: 1000\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3" ]	[ "TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #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: 0.0001\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: 1000\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3" ]	[ 56, 130, 4, 33 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #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: 0.0001\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: 1000\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3" ]	[ -0.10880749672651291, 0.09804540872573853, -0.0033000141847878695, 0.06337306648492813, 0.10866135358810425, -0.020470187067985535, 0.12799671292304993, 0.15069246292114258, -0.0926758423447609, 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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. --> # sagemaker-distilbert-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2469 - Accuracy: 0.9165 ## 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: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.9351 \| 1.0 \| 500 \| 0.2469 \| 0.9165 \| ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.1 - Datasets 1.15.1 - Tokenizers 0.10.3	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy"], "model-index": [{"name": "sagemaker-distilbert-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9165, "name": "Accuracy"}]}]}]}	text-classification	JaviBJ/sagemaker-distilbert-emotion	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us	sagemaker-distilbert-emotion ============================ This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset. It achieves the following results on the evaluation set: * Loss: 0.2469 * Accuracy: 0.9165 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: 64 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 500 * num\_epochs: 1 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.12.3 * Pytorch 1.9.1 * Datasets 1.15.1 * Tokenizers 0.10.3	[ "### 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: 64\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* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.1\n* Datasets 1.15.1\n* Tokenizers 0.10.3" ]	[ "TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #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: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 64\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* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.1\n* Datasets 1.15.1\n* Tokenizers 0.10.3" ]	[ 63, 131, 4, 31 ]	[ "passage: TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #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: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 64\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* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.1\n* Datasets 1.15.1\n* Tokenizers 0.10.3" ]	[ -0.11837613582611084, 0.1543351113796234, -0.003603972727432847, 0.11183581501245499, 0.12739664316177368, 0.022878220304846764, 0.10721538960933685, 0.16699880361557007, 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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. --> # bert-base-uncased-finetuned-semeval2020-task4a-append-e2-b32-l5e5 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5466 - Accuracy: 0.8890 ## 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: 4e-05 - 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: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 344 \| 0.3057 \| 0.8630 \| \| 0.4091 \| 2.0 \| 688 \| 0.2964 \| 0.8880 \| \| 0.1322 \| 3.0 \| 1032 \| 0.4465 \| 0.8820 \| \| 0.1322 \| 4.0 \| 1376 \| 0.5466 \| 0.8890 \| ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.1 - Datasets 1.12.1 - Tokenizers 0.10.3	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"]}	multiple-choice	JazibEijaz/bert-base-uncased-finetuned-semeval2020-task4a-append-e2-b32-l5e5	[ "transformers", "pytorch", "tensorboard", "bert", "multiple-choice", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us	bert-base-uncased-finetuned-semeval2020-task4a-append-e2-b32-l5e5 ================================================================= This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.5466 * Accuracy: 0.8890 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: 4e-05 * 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: 4 ### Training results ### Framework versions * Transformers 4.12.3 * Pytorch 1.9.1 * Datasets 1.12.1 * Tokenizers 0.10.3	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\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: 4", "### Training results", "### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.1\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]	[ "TAGS\n#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #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: 4e-05\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: 4", "### Training results", "### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.1\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]	[ 48, 98, 4, 31 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #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: 4e-05\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: 4### Training results### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.1\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]	[ -0.08484116941690445, 0.05078434571623802, -0.001812847563996911, 0.11255830526351929, 0.17951700091362, 0.038746386766433716, 0.12259835749864578, 0.10391149669885635, -0.11441726982593536, 0.02531929686665535, 0.10116814821958542, 0.17127299308776855, 0.010907926596701145, 0.07220199704170227, -0.038858167827129364, -0.2465752810239792, -0.015360175631940365, 0.033297568559646606, -0.09927613288164139, 0.12930132448673248, 0.07311437278985977, -0.15315449237823486, 0.0642530545592308, 0.002215938875451684, -0.22813978791236877, 0.019044270738959312, 0.012373065575957298, -0.04951499029994011, 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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. --> # bert-base-uncased-finetuned-semeval2020-task4a This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the ComVE dataset which was part of SemEval 2020 Task 4. It achieves the following results on the test set: - Loss: 0.2782 - Accuracy: 0.9040 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - 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: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 344 \| 0.2700 \| 0.8940 \| \| 0.349 \| 2.0 \| 688 \| 0.2782 \| 0.9040 \| ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.1 - Datasets 1.12.1 - Tokenizers 0.10.3	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"]}	multiple-choice	JazibEijaz/bert-base-uncased-finetuned-semeval2020-task4a	[ "transformers", "pytorch", "tensorboard", "bert", "multiple-choice", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us	bert-base-uncased-finetuned-semeval2020-task4a ============================================== This model is a fine-tuned version of bert-base-uncased on the ComVE dataset which was part of SemEval 2020 Task 4. It achieves the following results on the test set: * Loss: 0.2782 * Accuracy: 0.9040 ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-05 * 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: 2 ### Training results ### Framework versions * Transformers 4.12.3 * Pytorch 1.9.1 * Datasets 1.12.1 * Tokenizers 0.10.3	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\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: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.1\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]	[ "TAGS\n#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #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: 5e-05\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: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.1\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]	[ 48, 98, 4, 31 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #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: 5e-05\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: 2### Training results### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.1\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]	[ -0.083693727850914, 0.051278844475746155, -0.0018776051001623273, 0.11094808578491211, 0.17900562286376953, 0.03792696073651314, 0.1231539249420166, 0.10390378534793854, -0.11364011466503143, 0.0259530171751976, 0.10162092745304108, 0.17217552661895752, 0.011545269750058651, 0.07436758279800415, -0.04016820341348648, -0.2473057359457016, -0.01449650526046753, 0.032806385308504105, -0.09887902438640594, 0.1302124261856079, 0.0741216242313385, -0.1529828906059265, 0.0631621778011322, 0.0024198824539780617, -0.2273942530155182, 0.019232455641031265, 0.013004211708903313, -0.049648936837911606, 0.15285077691078186, 0.0005242163897491992, 0.14573892951011658, -0.006214610766619444, 0.09597187489271164, -0.19375793635845184, 0.01093299686908722, 0.06327163428068161, 0.010788514278829098, 0.0739767923951149, 0.05426563695073128, 0.007617624942213297, 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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. --> # bert-base-uncased-finetuned-semeval2020-task4b-append-e3-b32-l4e5 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5121 - Accuracy: 0.8700 ## 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: 4e-05 - 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 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 344 \| 0.3603 \| 0.8550 \| \| 0.3894 \| 2.0 \| 688 \| 0.4011 \| 0.8630 \| \| 0.1088 \| 3.0 \| 1032 \| 0.5121 \| 0.8700 \| ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.1 - Datasets 1.12.1 - Tokenizers 0.10.3	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"]}	multiple-choice	JazibEijaz/bert-base-uncased-finetuned-semeval2020-task4b-append-e3-b32-l4e5	[ "transformers", "pytorch", "tensorboard", "bert", "multiple-choice", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us	bert-base-uncased-finetuned-semeval2020-task4b-append-e3-b32-l4e5 ================================================================= This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.5121 * Accuracy: 0.8700 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: 4e-05 * 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 4.12.3 * Pytorch 1.9.1 * Datasets 1.12.1 * Tokenizers 0.10.3	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\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 4.12.3\n* Pytorch 1.9.1\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]	[ "TAGS\n#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #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: 4e-05\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 4.12.3\n* Pytorch 1.9.1\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]	[ 48, 98, 4, 31 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #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: 4e-05\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 4.12.3\n* Pytorch 1.9.1\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]	[ -0.08359528332948685, 0.04865279421210289, -0.0018264508107677102, 0.11178601533174515, 0.1793418675661087, 0.03817156329751015, 0.12305352836847305, 0.10408062487840652, -0.11602725833654404, 0.024864157661795616, 0.10145169496536255, 0.17273560166358948, 0.01089304219931364, 0.07324004173278809, -0.03921469300985336, -0.24676203727722168, -0.014296222478151321, 0.032429613173007965, -0.09796825051307678, 0.13014499843120575, 0.07423095405101776, -0.1523689478635788, 0.06378920376300812, 0.0019320641877129674, -0.22642886638641357, 0.018861236050724983, 0.011471010744571686, -0.04959283769130707, 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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. --> # bert-base-uncased-finetuned-semeval2020-task4b-base-e2-b32-l3e5 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4114 - Accuracy: 0.8700 ## 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: 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: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 344 \| 0.3773 \| 0.8490 \| \| 0.3812 \| 2.0 \| 688 \| 0.4114 \| 0.8700 \| ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.1 - Datasets 1.12.1 - Tokenizers 0.10.3	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"]}	multiple-choice	JazibEijaz/bert-base-uncased-finetuned-semeval2020-task4b-base-e2-b32-l3e5	[ "transformers", "pytorch", "tensorboard", "bert", "multiple-choice", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us	bert-base-uncased-finetuned-semeval2020-task4b-base-e2-b32-l3e5 =============================================================== This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.4114 * Accuracy: 0.8700 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: 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: 2 ### Training results ### Framework versions * Transformers 4.12.3 * Pytorch 1.9.1 * Datasets 1.12.1 * Tokenizers 0.10.3	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\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: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.1\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]	[ "TAGS\n#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #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: 5e-05\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: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.1\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]	[ 48, 98, 4, 31 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #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: 5e-05\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: 2### Training results### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.1\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]	[ -0.083693727850914, 0.051278844475746155, -0.0018776051001623273, 0.11094808578491211, 0.17900562286376953, 0.03792696073651314, 0.1231539249420166, 0.10390378534793854, -0.11364011466503143, 0.0259530171751976, 0.10162092745304108, 0.17217552661895752, 0.011545269750058651, 0.07436758279800415, -0.04016820341348648, -0.2473057359457016, -0.01449650526046753, 0.032806385308504105, -0.09887902438640594, 0.1302124261856079, 0.0741216242313385, -0.1529828906059265, 0.0631621778011322, 0.0024198824539780617, -0.2273942530155182, 0.019232455641031265, 0.013004211708903313, -0.049648936837911606, 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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. --> # bert-base-uncased-finetuned-semeval2020-task4b This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the ComVE dataset which was part of SemEval 2020 Task 4. It achieves the following results on the test set: - Loss: 0.6760 - Accuracy: 0.8760 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-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: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.5016 \| 1.0 \| 688 \| 0.3502 \| 0.8600 \| \| 0.2528 \| 2.0 \| 1376 \| 0.5769 \| 0.8620 \| \| 0.0598 \| 3.0 \| 2064 \| 0.6720 \| 0.8700 \| \| 0.0197 \| 4.0 \| 2752 \| 0.6760 \| 0.8760 \| ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.1 - Datasets 1.12.1 - Tokenizers 0.10.3	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"]}	multiple-choice	JazibEijaz/bert-base-uncased-finetuned-semeval2020-task4b	[ "transformers", "pytorch", "tensorboard", "bert", "multiple-choice", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us	bert-base-uncased-finetuned-semeval2020-task4b ============================================== This model is a fine-tuned version of bert-base-uncased on the ComVE dataset which was part of SemEval 2020 Task 4. It achieves the following results on the test set: * Loss: 0.6760 * Accuracy: 0.8760 ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 3e-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: 4 ### Training results ### Framework versions * Transformers 4.12.3 * Pytorch 1.9.1 * Datasets 1.12.1 * Tokenizers 0.10.3	[ "### 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: 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: 4", "### Training results", "### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.1\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]	[ "TAGS\n#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #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: 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: 4", "### Training results", "### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.1\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]	[ 48, 98, 4, 31 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #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: 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: 4### Training results### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.1\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]	[ -0.08441212773323059, 0.04522351175546646, -0.0018171851988881826, 0.11237913370132446, 0.1806591898202896, 0.037251099944114685, 0.120200015604496, 0.10426123440265656, -0.11477407813072205, 0.025364024564623833, 0.10285168886184692, 0.17121504247188568, 0.010140916332602501, 0.07404649257659912, -0.03822014853358269, -0.24591310322284698, -0.014294499531388283, 0.03381264954805374, -0.09970541298389435, 0.1292993128299713, 0.07408155500888824, -0.15372683107852936, 0.06483427435159683, 0.004420803394168615, -0.22761952877044678, 0.01776343770325184, 0.01206776313483715, -0.04950154572725296, 0.15235239267349243, 0.0013283450389280915, 0.14526666700839996, -0.006151737179607153, 0.09350866079330444, -0.18909955024719238, 0.010636541061103344, 0.06240525096654892, 0.011164969764649868, 0.07495658844709396, 0.05549704283475876, 0.007420581765472889, 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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. --> # bert-base-uncased-finetuned-swag-e1-b16-l5e5 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the swag dataset. It achieves the following results on the evaluation set: - Loss: 0.5202 - Accuracy: 0.7997 ## 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: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.701 \| 1.0 \| 4597 \| 0.5202 \| 0.7997 \| ### Framework versions - Transformers 4.12.2 - Pytorch 1.9.1 - Datasets 1.12.1 - Tokenizers 0.10.3	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["swag"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-finetuned-swag-e1-b16-l5e5", "results": []}]}	multiple-choice	JazibEijaz/bert-base-uncased-finetuned-swag-e1-b16-l5e5	[ "transformers", "pytorch", "tensorboard", "bert", "multiple-choice", "generated_from_trainer", "dataset:swag", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[]	TAGS #transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #dataset-swag #license-apache-2.0 #endpoints_compatible #region-us	bert-base-uncased-finetuned-swag-e1-b16-l5e5 ============================================ This model is a fine-tuned version of bert-base-uncased on the swag dataset. It achieves the following results on the evaluation set: * Loss: 0.5202 * Accuracy: 0.7997 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: 1 ### Training results ### Framework versions * Transformers 4.12.2 * Pytorch 1.9.1 * Datasets 1.12.1 * Tokenizers 0.10.3	[ "### 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* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* Transformers 4.12.2\n* Pytorch 1.9.1\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]	[ "TAGS\n#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #dataset-swag #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: 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: 1", "### Training results", "### Framework versions\n\n\n* Transformers 4.12.2\n* Pytorch 1.9.1\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]	[ 54, 98, 4, 31 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #dataset-swag #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: 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: 1### Training results### Framework versions\n\n\n* Transformers 4.12.2\n* Pytorch 1.9.1\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]	[ -0.10391159355640411, 0.05585082992911339, -0.0020585800521075726, 0.1131148487329483, 0.17113511264324188, 0.04076974838972092, 0.12118707597255707, 0.11254722625017166, -0.09977438300848007, 0.012758931145071983, 0.11550266295671463, 0.18649493157863617, 0.006868424825370312, 0.06540418416261673, -0.03349712863564491, -0.24631336331367493, -0.004882350564002991, 0.036061182618141174, -0.09805320203304291, 0.13843372464179993, 0.08136551827192307, -0.14664120972156525, 0.0741930902004242, 0.01791398413479328, -0.20653317868709564, 0.014369390904903412, 0.0055585759691894054, 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null	null	transformers	# camembert-ner: model fine-tuned from camemBERT for NER task (including DATE tag). ## Introduction [camembert-ner-with-dates] is an extension of french camembert-ner model with an additionnal tag for dates. Model was trained on enriched version of wikiner-fr dataset (~170 634 sentences). On my test data (mix of chat and email), this model got an f1 score of ~83% (in comparison dateparser was ~70%). Dateparser library can still be be used on the output of this model in order to convert text to python datetime object (https://dateparser.readthedocs.io/en/latest/). ## How to use camembert-ner-with-dates with HuggingFace ##### Load camembert-ner-with-dates and its sub-word tokenizer : ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Jean-Baptiste/camembert-ner-with-dates") model = AutoModelForTokenClassification.from_pretrained("Jean-Baptiste/camembert-ner-with-dates") ##### Process text sample (from wikipedia) from transformers import pipeline nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple") nlp("Apple est créée le 1er avril 1976 dans le garage de la maison d'enfance de Steve Jobs à Los Altos en Californie par Steve Jobs, Steve Wozniak et Ronald Wayne14, puis constituée sous forme de société le 3 janvier 1977 à l'origine sous le nom d'Apple Computer, mais pour ses 30 ans et pour refléter la diversification de ses produits, le mot « computer » est retiré le 9 janvier 2015.") [{'entity_group': 'ORG', 'score': 0.9776379466056824, 'word': 'Apple', 'start': 0, 'end': 5}, {'entity_group': 'DATE', 'score': 0.9793774570737567, 'word': 'le 1er avril 1976 dans le', 'start': 15, 'end': 41}, {'entity_group': 'PER', 'score': 0.9958226680755615, 'word': 'Steve Jobs', 'start': 74, 'end': 85}, {'entity_group': 'LOC', 'score': 0.995087186495463, 'word': 'Los Altos', 'start': 87, 'end': 97}, {'entity_group': 'LOC', 'score': 0.9953305125236511, 'word': 'Californie', 'start': 100, 'end': 111}, {'entity_group': 'PER', 'score': 0.9961076378822327, 'word': 'Steve Jobs', 'start': 115, 'end': 126}, {'entity_group': 'PER', 'score': 0.9960325956344604, 'word': 'Steve Wozniak', 'start': 127, 'end': 141}, {'entity_group': 'PER', 'score': 0.9957776467005411, 'word': 'Ronald Wayne', 'start': 144, 'end': 157}, {'entity_group': 'DATE', 'score': 0.994030773639679, 'word': 'le 3 janvier 1977 à', 'start': 198, 'end': 218}, {'entity_group': 'ORG', 'score': 0.9720810294151306, 'word': "d'Apple Computer", 'start': 240, 'end': 257}, {'entity_group': 'DATE', 'score': 0.9924157659212748, 'word': '30 ans et', 'start': 272, 'end': 282}, {'entity_group': 'DATE', 'score': 0.9934852868318558, 'word': 'le 9 janvier 2015.', 'start': 363, 'end': 382}] ``` ## Model performances (metric: seqeval) Global ``` 'precision': 0.928 'recall': 0.928 'f1': 0.928 ``` By entity ``` Label LOC: (precision:0.929, recall:0.932, f1:0.931, support:9510) Label PER: (precision:0.952, recall:0.965, f1:0.959, support:9399) Label MISC: (precision:0.878, recall:0.844, f1:0.860, support:5364) Label ORG: (precision:0.848, recall:0.883, f1:0.865, support:2299) Label DATE: Not relevant because of method used to add date tag on wikiner dataset (estimated f1 ~90%) ```	{"language": "fr", "license": "mit", "datasets": ["Jean-Baptiste/wikiner_fr"], "widget": [{"text": "Je m'appelle jean-baptiste et j'habite \u00e0 montr\u00e9al depuis fevr 2012"}]}	token-classification	Jean-Baptiste/camembert-ner-with-dates	[ "transformers", "pytorch", "onnx", "safetensors", "camembert", "token-classification", "fr", "dataset:Jean-Baptiste/wikiner_fr", "license:mit", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]	2022-03-02T23:29:04+00:00	[]	[ "fr" ]	TAGS #transformers #pytorch #onnx #safetensors #camembert #token-classification #fr #dataset-Jean-Baptiste/wikiner_fr #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us	# camembert-ner: model fine-tuned from camemBERT for NER task (including DATE tag). ## Introduction [camembert-ner-with-dates] is an extension of french camembert-ner model with an additionnal tag for dates. Model was trained on enriched version of wikiner-fr dataset (~170 634 sentences). On my test data (mix of chat and email), this model got an f1 score of ~83% (in comparison dateparser was ~70%). Dateparser library can still be be used on the output of this model in order to convert text to python datetime object (URL ## How to use camembert-ner-with-dates with HuggingFace ##### Load camembert-ner-with-dates and its sub-word tokenizer : ## Model performances (metric: seqeval) Global By entity	[ "# camembert-ner: model fine-tuned from camemBERT for NER task (including DATE tag).", "## Introduction\n\n[camembert-ner-with-dates] is an extension of french camembert-ner model with an additionnal tag for dates.\nModel was trained on enriched version of wikiner-fr dataset (~170 634 sentences).\n\nOn my test data (mix of chat and email), this model got an f1 score of ~83% (in comparison dateparser was ~70%).\nDateparser library can still be be used on the output of this model in order to convert text to python datetime object \n(URL", "## How to use camembert-ner-with-dates with HuggingFace", "##### Load camembert-ner-with-dates and its sub-word tokenizer :", "## Model performances (metric: seqeval)\n\nGlobal\n\n\nBy entity" ]	[ "TAGS\n#transformers #pytorch #onnx #safetensors #camembert #token-classification #fr #dataset-Jean-Baptiste/wikiner_fr #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "# camembert-ner: model fine-tuned from camemBERT for NER task (including DATE tag).", "## Introduction\n\n[camembert-ner-with-dates] is an extension of french camembert-ner model with an additionnal tag for dates.\nModel was trained on enriched version of wikiner-fr dataset (~170 634 sentences).\n\nOn my test data (mix of chat and email), this model got an f1 score of ~83% (in comparison dateparser was ~70%).\nDateparser library can still be be used on the output of this model in order to convert text to python datetime object \n(URL", "## How to use camembert-ner-with-dates with HuggingFace", "##### Load camembert-ner-with-dates and its sub-word tokenizer :", "## Model performances (metric: seqeval)\n\nGlobal\n\n\nBy entity" ]	[ 74, 27, 120, 19, 23, 15 ]	[ "passage: TAGS\n#transformers #pytorch #onnx #safetensors #camembert #token-classification #fr #dataset-Jean-Baptiste/wikiner_fr #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n# camembert-ner: model fine-tuned from camemBERT for NER task (including DATE tag).## Introduction\n\n[camembert-ner-with-dates] is an extension of french camembert-ner model with an additionnal tag for dates.\nModel was trained on enriched version of wikiner-fr dataset (~170 634 sentences).\n\nOn my test data (mix of chat and email), this model got an f1 score of ~83% (in comparison dateparser was ~70%).\nDateparser library can still be be used on the output of this model in order to convert text to python datetime object \n(URL## How to use camembert-ner-with-dates with HuggingFace##### Load camembert-ner-with-dates and its sub-word tokenizer :## Model performances (metric: seqeval)\n\nGlobal\n\n\nBy entity" ]	[ -0.08048323541879654, 0.18632711470127106, 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