File size: 34,368 Bytes
e5d1750 b297632 e5d1750 b297632 e5d1750 b297632 e5d1750 b297632 e5d1750 b297632 e5d1750 b297632 e5d1750 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:499184
- loss:MultipleNegativesRankingLoss
base_model: answerdotai/ModernBERT-large
widget:
- source_sentence: how long will rotisserie chicken keep in refridgerator
sentences:
- >-
1 Meats with gravy or sauces: 1 to 2 days refrigerator or 6 months
(freezer). 2 Rotisserie chicken: 3 to 4 days (refrigerator) or 2 to 3
months (freezer). 3 Opened package of hot dogs: 1 week (refrigerator) or 1
to 2 months (freezer).4 Opened package of deli meat: 3 to 4 days
(refrigerator) or 1 to 2 months (freezer). Rotisserie chicken: 3 to 4 days
(refrigerator) or 2 to 3 months (freezer). 2 Opened package of hot dogs: 1
week (refrigerator) or 1 to 2 months (freezer). 3 Opened package of deli
meat: 3 to 4 days (refrigerator) or 1 to 2 months (freezer).
- >-
Can Spinach Cause Constipation? Those who have problems with constipation
will want to stay away from certain foods including spinach. Because spinach
has so much fiber in it, it can cause constipation in some people,
especially those who are already prone to it. Other foods which you will
want to avoid if you problems with constipation include apples, peaches, raw
carrots, zucchini, kidney beans, lima beans, and whole-grain cereal.
- >-
Brush the chickens with oil and season the outside and cavities with salt
and pepper. Skewer the chickens onto the rotisserie rod and grill, on the
rotisserie, for 30 to 35 minutes, or until the chicken is golden brown and
just cooked through. Remove from grill and let rest for 10 minutes before
serving.
- source_sentence: empyema causes
sentences:
- "Causes of an Empyema. Most cases of an empyema are related to bacterial pneumonia (infection of the lung). Pneumonia tends to cause a pleural effusion â\x80\x93 para-pneumonic effusion. This can be uncomplicated (containing exudate), complicated (exudate with high concentrations of neurophils) or empyema thoracis (pus in the pleural space)."
- >-
empyema - a collection of pus in a body cavity (especially in the lung
cavity) inflammatory disease - a disease characterized by inflammation.
purulent pleurisy - a collection of pus in the lung cavity. Translations.
- >-
Laminar Flow. The resistance to flow in a liquid can be characterized in
terms of the viscosity of the fluid if the flow is smooth. In the case of a
moving plate in a liquid, it is found that there is a layer or lamina which
moves with the plate, and a layer which is essentially stationary if it is
next to a stationary plate.
- source_sentence: why is coal found in layers
sentences:
- >-
Email the author | Follow on Twitter. on March 06, 2015 at 6:03 PM, updated
March 06, 2015 at 6:35 PM. Comments. CLEVELAND, Ohio -- The first day of
spring 2015 will be on March 20, with winter officially ending at 6:45 p.m.
that day. Summer 2015 will begin on June 21, fall on Sept. 23 and winter on
Dec. 21.
- >-
EXPERT ANSWER. Coal if formed when dead animals and plants got buried inside
the layer of Earth. The layers increase form time to time and more dead
plants and animals get buried in the layers.Therefore, coal is found in
layers.For example, let us consider the layers of sandwich, on the first
bread we apply the toppings and cover it another slice. Then some more
topping is added to second slice and is covered by third slide.XPERT ANSWER.
Coal if formed when dead animals and plants got buried inside the layer of
Earth. The layers increase form time to time and more dead plants and
animals get buried in the layers.
- >-
Why is Coal not classified as a Mineral? July 8, 2011, shiela, Leave a
comment. Why is Coal not classified as a Mineral? Coal is not a mineral
because it does not qualify to be one. A mineral is made of rocks. It is
non-living and made up of atoms of elements. Coals on the other hand are
carbon-based and came from fossilized plants. By just looking into the
origin of coals, these are not qualified to be minerals because they come
from organic material and it has no definite chemical composition. Minerals
are not formed from living things such as plants or animals. They are
building blocks of rocks and are formed thousands of years ago. Coals on the
other hand came from dead plants and animals. The coals are formed when
these living creatures will decay. Again, it takes thousands of years to
form a coal.
- source_sentence: where is the ford edge built
sentences:
- >-
Amongst fruit-bearing cherry trees, there are two main types: Prunus avium
(sweet cherries), which are the kind sold in produce sections for eating,
and Prunus cerasus (sour cherries), which are the kind used in cooking and
baking.mongst fruit-bearing cherry trees, there are two main types: Prunus
avium (sweet cherries), which are the kind sold in produce sections for
eating, and Prunus cerasus (sour cherries), which are the kind used in
cooking and baking.
- >-
Ford is recalling 204,448 Edge and Lincoln MKX crossovers in North America
for fuel-tank brackets that can rust and cause gas to leak, the automaker
said.
- >-
Ford Edge to be built at new $760 million plant in China. DETROIT, MI - Ford
Motor Co. announced Tuesday it has opened its sixth assembly plant in China,
with a $760 million investment for the Changan Ford Hangzhou Plant.
- source_sentence: what is a tensilon universal testing instrument
sentences:
- >-
Universal Material Testing Instrument. The TENSILON RTF is our newest
universal testing machine offering innovative measuring possibilities, based
on A&D's newly-developed and extensive technological knowledge.The RTF
Series is a world-class Class 0.5 testing machine.Having improved the
overall design and structure of the machine, we achieved a very strong load
frame stiffness enabling super-high accuracy in measurement.he RTF Series is
a world-class Class 0.5 testing machine. Having improved the overall design
and structure of the machine, we achieved a very strong load frame stiffness
enabling super-high accuracy in measurement.
- >-
The term ectopic pregnancy frequently refers to a pregnancy that has
occurred in one of the fallopian tubes, instead of the uterus. This is the
case about 95 percent of the time, but ectopic pregnancies can also be
abdominal, ovarian, cornual, or cervical.
- >-
The McDonald Patent Universal String Tension Calculator (MPUSTC) is a handy
calculator to figure string tensions in steel-string instruments. If you
plug in your scale length, string gauges and tuning, it will give you a
readout of the tension on each of the strings. This is useful when you're
trying to fine-tune a set of custom gauges, or when you're working out how
far you can push a drop tuning before it becomes unmanageable.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
license: mit
---
# SentenceTransformer based on answerdotai/ModernBERT-large
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) <!-- at revision 45bb4654a4d5aaff24dd11d4781fa46d39bf8c13 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: ModernBertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'what is a tensilon universal testing instrument',
"Universal Material Testing Instrument. The TENSILON RTF is our newest universal testing machine offering innovative measuring possibilities, based on A&D's newly-developed and extensive technological knowledge.The RTF Series is a world-class Class 0.5 testing machine.Having improved the overall design and structure of the machine, we achieved a very strong load frame stiffness enabling super-high accuracy in measurement.he RTF Series is a world-class Class 0.5 testing machine. Having improved the overall design and structure of the machine, we achieved a very strong load frame stiffness enabling super-high accuracy in measurement.",
"The McDonald Patent Universal String Tension Calculator (MPUSTC) is a handy calculator to figure string tensions in steel-string instruments. If you plug in your scale length, string gauges and tuning, it will give you a readout of the tension on each of the strings. This is useful when you're trying to fine-tune a set of custom gauges, or when you're working out how far you can push a drop tuning before it becomes unmanageable.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 499,184 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | sentence_2 |
|:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 9.07 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 80.89 tokens</li><li>max: 254 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 79.05 tokens</li><li>max: 226 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 | sentence_2 |
|:---------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>what is a dependent person</code> | <code>1. depending on a person or thing for aid, support, life, etc. 2. (postpositive; foll by on or upon) influenced or conditioned (by); contingent (on) 3. subordinate; subject: a dependent prince. 4. obsolete hanging down.</code> | <code>Dependent personality disorder (DPD) is one of the most frequently diagnosed personality disorders. It occurs equally in men and women, usually becoming apparent in young adulthood or later as important adult relationships form. People with DPD become emotionally dependent on other people and spend great effort trying to please others. People with DPD tend to display needy, passive, and clinging behavior, and have a fear of separation. Other common characteristics of this personality disorder include:</code> |
| <code>what is the hat trick in hockey</code> | <code>Definition of hat trick. 1 1 : the retiring of three batsmen with three consecutive balls by a bowler in cricket. 2 2 : the scoring of three goals in one game (as of hockey or soccer) by a single player. 3 3 : a series of three victories, successes, or related accomplishments scored a hat trick when her three best steers corralled top honors â People.</code> | <code>Hat trick was first recorded in print in the 1870s, but has since been widened to apply to any sport in which the person competing carries off some feat three times in quick succession, such as scoring three goals in one game of soccer.</code> |
| <code>what is an egalitarian</code> | <code>An egalitarian is defined as a person who believes all people were created equal and should be treated equal. An example of an egalitarian is a person who fights for civil rights, like Martin Luther King Jr.</code> | <code>About Egalitarian Companies. In the tradition hierarchical corporate structure, each employee operates under a specific job description. Each employee also reports to a superior who monitors his progress and issues instructions. Egalitarian-style companies eliminate most of this structure. Employees in an egalitarian company have general job descriptions, rather than specific ones. Instead of reporting to a superior, all employees in an egalitarian company work collaboratively on tasks and behave as equals.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 10
- `fp16`: True
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 10
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `tp_size`: 0
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss |
|:------:|:------:|:-------------:|
| 0.0321 | 500 | 1.1178 |
| 0.0641 | 1000 | 0.293 |
| 0.0962 | 1500 | 0.2542 |
| 0.1282 | 2000 | 0.2357 |
| 0.1603 | 2500 | 0.2187 |
| 0.1923 | 3000 | 0.2107 |
| 0.2244 | 3500 | 0.1959 |
| 0.2564 | 4000 | 0.2049 |
| 0.2885 | 4500 | 0.1945 |
| 0.3205 | 5000 | 0.1848 |
| 0.3526 | 5500 | 0.1846 |
| 0.3846 | 6000 | 0.1736 |
| 0.4167 | 6500 | 0.1795 |
| 0.4487 | 7000 | 0.1767 |
| 0.4808 | 7500 | 0.1727 |
| 0.5128 | 8000 | 0.1688 |
| 0.5449 | 8500 | 0.1708 |
| 0.5769 | 9000 | 0.1663 |
| 0.6090 | 9500 | 0.1654 |
| 0.6410 | 10000 | 0.1637 |
| 0.6731 | 10500 | 0.1651 |
| 0.7051 | 11000 | 0.1625 |
| 0.7372 | 11500 | 0.1584 |
| 0.7692 | 12000 | 0.1607 |
| 0.8013 | 12500 | 0.156 |
| 0.8333 | 13000 | 0.1548 |
| 0.8654 | 13500 | 0.1484 |
| 0.8974 | 14000 | 0.1527 |
| 0.9295 | 14500 | 0.1555 |
| 0.9615 | 15000 | 0.1528 |
| 0.9936 | 15500 | 0.1533 |
| 1.0256 | 16000 | 0.0827 |
| 1.0577 | 16500 | 0.0597 |
| 1.0897 | 17000 | 0.0599 |
| 1.1218 | 17500 | 0.0592 |
| 1.1538 | 18000 | 0.0592 |
| 1.1859 | 18500 | 0.0584 |
| 1.2179 | 19000 | 0.0615 |
| 1.25 | 19500 | 0.0589 |
| 1.2821 | 20000 | 0.0612 |
| 1.3141 | 20500 | 0.0618 |
| 1.3462 | 21000 | 0.0606 |
| 1.3782 | 21500 | 0.0587 |
| 1.4103 | 22000 | 0.0611 |
| 1.4423 | 22500 | 0.0616 |
| 1.4744 | 23000 | 0.0623 |
| 1.5064 | 23500 | 0.0615 |
| 1.5385 | 24000 | 0.0602 |
| 1.5705 | 24500 | 0.0658 |
| 1.6026 | 25000 | 0.068 |
| 1.6346 | 25500 | 0.0649 |
| 1.6667 | 26000 | 0.0645 |
| 1.6987 | 26500 | 0.0652 |
| 1.7308 | 27000 | 0.0632 |
| 1.7628 | 27500 | 0.0631 |
| 1.7949 | 28000 | 0.0655 |
| 1.8269 | 28500 | 0.0633 |
| 1.8590 | 29000 | 0.0607 |
| 1.8910 | 29500 | 0.0633 |
| 1.9231 | 30000 | 0.0612 |
| 1.9551 | 30500 | 0.0631 |
| 1.9872 | 31000 | 0.0616 |
| 2.0192 | 31500 | 0.0382 |
| 2.0513 | 32000 | 0.0178 |
| 2.0833 | 32500 | 0.0177 |
| 2.1154 | 33000 | 0.0178 |
| 2.1474 | 33500 | 0.0171 |
| 2.1795 | 34000 | 0.0188 |
| 2.2115 | 34500 | 0.0186 |
| 2.2436 | 35000 | 0.0177 |
| 2.2756 | 35500 | 0.0183 |
| 2.3077 | 36000 | 0.0195 |
| 2.3397 | 36500 | 0.0202 |
| 2.3718 | 37000 | 0.0199 |
| 2.4038 | 37500 | 0.0197 |
| 2.4359 | 38000 | 0.019 |
| 2.4679 | 38500 | 0.021 |
| 2.5 | 39000 | 0.0195 |
| 2.5321 | 39500 | 0.0211 |
| 2.5641 | 40000 | 0.0205 |
| 2.5962 | 40500 | 0.0207 |
| 2.6282 | 41000 | 0.0222 |
| 2.6603 | 41500 | 0.0204 |
| 2.6923 | 42000 | 0.0205 |
| 2.7244 | 42500 | 0.0211 |
| 2.7564 | 43000 | 0.0232 |
| 2.7885 | 43500 | 0.0202 |
| 2.8205 | 44000 | 0.0207 |
| 2.8526 | 44500 | 0.0225 |
| 2.8846 | 45000 | 0.0224 |
| 2.9167 | 45500 | 0.0203 |
| 2.9487 | 46000 | 0.0215 |
| 2.9808 | 46500 | 0.0218 |
| 3.0128 | 47000 | 0.0159 |
| 3.0449 | 47500 | 0.0064 |
| 3.0769 | 48000 | 0.0069 |
| 3.1090 | 48500 | 0.0074 |
| 3.1410 | 49000 | 0.0075 |
| 3.1731 | 49500 | 0.0066 |
| 3.2051 | 50000 | 0.0076 |
| 3.2372 | 50500 | 0.0073 |
| 3.2692 | 51000 | 0.0077 |
| 3.3013 | 51500 | 0.0075 |
| 3.3333 | 52000 | 0.0079 |
| 3.3654 | 52500 | 0.008 |
| 3.3974 | 53000 | 0.0087 |
| 3.4295 | 53500 | 0.0077 |
| 3.4615 | 54000 | 0.0084 |
| 3.4936 | 54500 | 0.0086 |
| 3.5256 | 55000 | 0.009 |
| 3.5577 | 55500 | 0.0082 |
| 3.5897 | 56000 | 0.0084 |
| 3.6218 | 56500 | 0.0084 |
| 3.6538 | 57000 | 0.008 |
| 3.6859 | 57500 | 0.0079 |
| 3.7179 | 58000 | 0.0085 |
| 3.75 | 58500 | 0.0096 |
| 3.7821 | 59000 | 0.0087 |
| 3.8141 | 59500 | 0.0086 |
| 3.8462 | 60000 | 0.0089 |
| 3.8782 | 60500 | 0.0081 |
| 3.9103 | 61000 | 0.0087 |
| 3.9423 | 61500 | 0.0085 |
| 3.9744 | 62000 | 0.0082 |
| 4.0064 | 62500 | 0.0076 |
| 4.0385 | 63000 | 0.0037 |
| 4.0705 | 63500 | 0.0035 |
| 4.1026 | 64000 | 0.0037 |
| 4.1346 | 64500 | 0.004 |
| 4.1667 | 65000 | 0.0037 |
| 4.1987 | 65500 | 0.0036 |
| 4.2308 | 66000 | 0.0042 |
| 4.2628 | 66500 | 0.0044 |
| 4.2949 | 67000 | 0.0041 |
| 4.3269 | 67500 | 0.004 |
| 4.3590 | 68000 | 0.0037 |
| 4.3910 | 68500 | 0.0043 |
| 4.4231 | 69000 | 0.0035 |
| 4.4551 | 69500 | 0.0045 |
| 4.4872 | 70000 | 0.0042 |
| 4.5192 | 70500 | 0.0043 |
| 4.5513 | 71000 | 0.0042 |
| 4.5833 | 71500 | 0.0049 |
| 4.6154 | 72000 | 0.0041 |
| 4.6474 | 72500 | 0.0041 |
| 4.6795 | 73000 | 0.0044 |
| 4.7115 | 73500 | 0.0038 |
| 4.7436 | 74000 | 0.0039 |
| 4.7756 | 74500 | 0.0049 |
| 4.8077 | 75000 | 0.0041 |
| 4.8397 | 75500 | 0.0044 |
| 4.8718 | 76000 | 0.0043 |
| 4.9038 | 76500 | 0.0053 |
| 4.9359 | 77000 | 0.0043 |
| 4.9679 | 77500 | 0.0049 |
| 5.0 | 78000 | 0.0042 |
| 5.0321 | 78500 | 0.0022 |
| 5.0641 | 79000 | 0.0023 |
| 5.0962 | 79500 | 0.0021 |
| 5.1282 | 80000 | 0.003 |
| 5.1603 | 80500 | 0.0024 |
| 5.1923 | 81000 | 0.0022 |
| 5.2244 | 81500 | 0.0023 |
| 5.2564 | 82000 | 0.0022 |
| 5.2885 | 82500 | 0.0027 |
| 5.3205 | 83000 | 0.0023 |
| 5.3526 | 83500 | 0.0029 |
| 5.3846 | 84000 | 0.0027 |
| 5.4167 | 84500 | 0.0025 |
| 5.4487 | 85000 | 0.0029 |
| 5.4808 | 85500 | 0.0029 |
| 5.5128 | 86000 | 0.0024 |
| 5.5449 | 86500 | 0.0026 |
| 5.5769 | 87000 | 0.0026 |
| 5.6090 | 87500 | 0.0028 |
| 5.6410 | 88000 | 0.0025 |
| 5.6731 | 88500 | 0.0026 |
| 5.7051 | 89000 | 0.0023 |
| 5.7372 | 89500 | 0.0029 |
| 5.7692 | 90000 | 0.0027 |
| 5.8013 | 90500 | 0.0019 |
| 5.8333 | 91000 | 0.0023 |
| 5.8654 | 91500 | 0.0022 |
| 5.8974 | 92000 | 0.003 |
| 5.9295 | 92500 | 0.0023 |
| 5.9615 | 93000 | 0.0026 |
| 5.9936 | 93500 | 0.0027 |
| 6.0256 | 94000 | 0.0015 |
| 6.0577 | 94500 | 0.0012 |
| 6.0897 | 95000 | 0.0016 |
| 6.1218 | 95500 | 0.0018 |
| 6.1538 | 96000 | 0.0017 |
| 6.1859 | 96500 | 0.0014 |
| 6.2179 | 97000 | 0.0013 |
| 6.25 | 97500 | 0.0022 |
| 6.2821 | 98000 | 0.0015 |
| 6.3141 | 98500 | 0.002 |
| 6.3462 | 99000 | 0.0021 |
| 6.3782 | 99500 | 0.0016 |
| 6.4103 | 100000 | 0.0024 |
| 6.4423 | 100500 | 0.002 |
| 6.4744 | 101000 | 0.0014 |
| 6.5064 | 101500 | 0.0019 |
| 6.5385 | 102000 | 0.0017 |
| 6.5705 | 102500 | 0.0019 |
| 6.6026 | 103000 | 0.0016 |
| 6.6346 | 103500 | 0.0013 |
| 6.6667 | 104000 | 0.0012 |
| 6.6987 | 104500 | 0.0015 |
| 6.7308 | 105000 | 0.0015 |
| 6.7628 | 105500 | 0.0018 |
| 6.7949 | 106000 | 0.0018 |
| 6.8269 | 106500 | 0.0016 |
| 6.8590 | 107000 | 0.0018 |
| 6.8910 | 107500 | 0.0026 |
| 6.9231 | 108000 | 0.0013 |
| 6.9551 | 108500 | 0.0019 |
| 6.9872 | 109000 | 0.0015 |
| 7.0192 | 109500 | 0.0014 |
| 7.0513 | 110000 | 0.0009 |
| 7.0833 | 110500 | 0.0012 |
| 7.1154 | 111000 | 0.0016 |
| 7.1474 | 111500 | 0.0014 |
| 7.1795 | 112000 | 0.0013 |
| 7.2115 | 112500 | 0.0009 |
| 7.2436 | 113000 | 0.0015 |
| 7.2756 | 113500 | 0.0011 |
| 7.3077 | 114000 | 0.0011 |
| 7.3397 | 114500 | 0.0011 |
| 7.3718 | 115000 | 0.0013 |
| 7.4038 | 115500 | 0.001 |
| 7.4359 | 116000 | 0.0012 |
| 7.4679 | 116500 | 0.0012 |
| 7.5 | 117000 | 0.0013 |
| 7.5321 | 117500 | 0.0014 |
| 7.5641 | 118000 | 0.0013 |
| 7.5962 | 118500 | 0.0013 |
| 7.6282 | 119000 | 0.0014 |
| 7.6603 | 119500 | 0.001 |
| 7.6923 | 120000 | 0.0012 |
| 7.7244 | 120500 | 0.0018 |
| 7.7564 | 121000 | 0.001 |
| 7.7885 | 121500 | 0.0014 |
| 7.8205 | 122000 | 0.0011 |
| 7.8526 | 122500 | 0.0012 |
| 7.8846 | 123000 | 0.0012 |
| 7.9167 | 123500 | 0.0008 |
| 7.9487 | 124000 | 0.0013 |
| 7.9808 | 124500 | 0.0014 |
| 8.0128 | 125000 | 0.001 |
| 8.0449 | 125500 | 0.0007 |
| 8.0769 | 126000 | 0.001 |
| 8.1090 | 126500 | 0.0009 |
| 8.1410 | 127000 | 0.0007 |
| 8.1731 | 127500 | 0.0007 |
| 8.2051 | 128000 | 0.001 |
| 8.2372 | 128500 | 0.0011 |
| 8.2692 | 129000 | 0.0008 |
| 8.3013 | 129500 | 0.0007 |
| 8.3333 | 130000 | 0.0013 |
| 8.3654 | 130500 | 0.0012 |
| 8.3974 | 131000 | 0.001 |
| 8.4295 | 131500 | 0.001 |
| 8.4615 | 132000 | 0.0007 |
| 8.4936 | 132500 | 0.001 |
| 8.5256 | 133000 | 0.001 |
| 8.5577 | 133500 | 0.001 |
| 8.5897 | 134000 | 0.0011 |
| 8.6218 | 134500 | 0.0013 |
| 8.6538 | 135000 | 0.0007 |
| 8.6859 | 135500 | 0.001 |
| 8.7179 | 136000 | 0.0008 |
| 8.75 | 136500 | 0.001 |
| 8.7821 | 137000 | 0.0008 |
| 8.8141 | 137500 | 0.0006 |
| 8.8462 | 138000 | 0.0006 |
| 8.8782 | 138500 | 0.0009 |
| 8.9103 | 139000 | 0.0007 |
| 8.9423 | 139500 | 0.0009 |
| 8.9744 | 140000 | 0.0006 |
| 9.0064 | 140500 | 0.0018 |
| 9.0385 | 141000 | 0.0008 |
| 9.0705 | 141500 | 0.0008 |
| 9.1026 | 142000 | 0.0009 |
| 9.1346 | 142500 | 0.0006 |
| 9.1667 | 143000 | 0.0009 |
| 9.1987 | 143500 | 0.0007 |
| 9.2308 | 144000 | 0.0007 |
| 9.2628 | 144500 | 0.0006 |
| 9.2949 | 145000 | 0.0008 |
| 9.3269 | 145500 | 0.0009 |
| 9.3590 | 146000 | 0.0005 |
| 9.3910 | 146500 | 0.001 |
| 9.4231 | 147000 | 0.001 |
| 9.4551 | 147500 | 0.0011 |
| 9.4872 | 148000 | 0.0011 |
| 9.5192 | 148500 | 0.0012 |
| 9.5513 | 149000 | 0.0011 |
| 9.5833 | 149500 | 0.0007 |
| 9.6154 | 150000 | 0.0008 |
| 9.6474 | 150500 | 0.0005 |
| 9.6795 | 151000 | 0.0007 |
| 9.7115 | 151500 | 0.0008 |
| 9.7436 | 152000 | 0.0007 |
| 9.7756 | 152500 | 0.0009 |
| 9.8077 | 153000 | 0.0007 |
| 9.8397 | 153500 | 0.0012 |
| 9.8718 | 154000 | 0.0005 |
| 9.9038 | 154500 | 0.0008 |
| 9.9359 | 155000 | 0.0007 |
| 9.9679 | 155500 | 0.0007 |
| 10.0 | 156000 | 0.0011 |
</details>
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.50.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.2
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |