File size: 39,407 Bytes
29efd03 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 |
---
language:
- en
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:100K<n<1M
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: distilbert/distilroberta-base
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: Test Rocks
sentences:
- Number of testimonies
- People are at a pool.
- I've never been to Asia
- source_sentence: No animals.
sentences:
- We don't have a dog.
- These boys are on bikes
- A person is climbing.
- source_sentence: Shrinking.
sentences:
- That doesn't seem fair.
- A man reads the paper.
- I've never been to Asia
- source_sentence: Loire Valley
sentences:
- A Lake in Loire.
- people stand near pole
- A cat is licking itself.
- source_sentence: It is well.
sentences:
- That's convenient.
- away from the children
- She hated the restaurant!
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on distilbert/distilroberta-base
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 768
type: sts-dev-768
metrics:
- type: pearson_cosine
value: 0.8413274730706258
name: Pearson Cosine
- type: spearman_cosine
value: 0.8478057476815382
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8414182910991368
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8394684211369814
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8423380151813549
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8401129676358965
name: Spearman Euclidean
- type: pearson_dot
value: 0.7854982058734802
name: Pearson Dot
- type: spearman_dot
value: 0.7814388303641997
name: Spearman Dot
- type: pearson_max
value: 0.8423380151813549
name: Pearson Max
- type: spearman_max
value: 0.8478057476815382
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 512
type: sts-dev-512
metrics:
- type: pearson_cosine
value: 0.8394744649386727
name: Pearson Cosine
- type: spearman_cosine
value: 0.8469596264857904
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8398552366754626
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8377241640608183
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8406514989809173
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8380050330376462
name: Spearman Euclidean
- type: pearson_dot
value: 0.7811135781647157
name: Pearson Dot
- type: spearman_dot
value: 0.7776714775017128
name: Spearman Dot
- type: pearson_max
value: 0.8406514989809173
name: Pearson Max
- type: spearman_max
value: 0.8469596264857904
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 256
type: sts-dev-256
metrics:
- type: pearson_cosine
value: 0.8326846589795867
name: Pearson Cosine
- type: spearman_cosine
value: 0.8435757360139872
name: Spearman Cosine
- type: pearson_manhattan
value: 0.835121668379584
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.833167770567356
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8359785864160201
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8337674519096212
name: Spearman Euclidean
- type: pearson_dot
value: 0.7499541215721716
name: Pearson Dot
- type: spearman_dot
value: 0.7452815230357489
name: Spearman Dot
- type: pearson_max
value: 0.8359785864160201
name: Pearson Max
- type: spearman_max
value: 0.8435757360139872
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 128
type: sts-dev-128
metrics:
- type: pearson_cosine
value: 0.8243384464323462
name: Pearson Cosine
- type: spearman_cosine
value: 0.8399706247679909
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8281897604718583
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8270317815639731
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8281918243965822
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8267242273030063
name: Spearman Euclidean
- type: pearson_dot
value: 0.7110017325551932
name: Pearson Dot
- type: spearman_dot
value: 0.7049602384186016
name: Spearman Dot
- type: pearson_max
value: 0.8281918243965822
name: Pearson Max
- type: spearman_max
value: 0.8399706247679909
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 64
type: sts-dev-64
metrics:
- type: pearson_cosine
value: 0.811599959622093
name: Pearson Cosine
- type: spearman_cosine
value: 0.8316629408285197
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8113103800424869
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8104916438729426
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8113924334973999
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8110877753624469
name: Spearman Euclidean
- type: pearson_dot
value: 0.641225674602723
name: Pearson Dot
- type: spearman_dot
value: 0.6346995881423587
name: Spearman Dot
- type: pearson_max
value: 0.811599959622093
name: Pearson Max
- type: spearman_max
value: 0.8316629408285197
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 32
type: sts-dev-32
metrics:
- type: pearson_cosine
value: 0.7834130163353433
name: Pearson Cosine
- type: spearman_cosine
value: 0.814057381112976
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7831854350286095
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7859760066096324
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7868628503474937
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7893614397994021
name: Spearman Euclidean
- type: pearson_dot
value: 0.5533705216922039
name: Pearson Dot
- type: spearman_dot
value: 0.5449230360083127
name: Spearman Dot
- type: pearson_max
value: 0.7868628503474937
name: Pearson Max
- type: spearman_max
value: 0.814057381112976
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev 16
type: sts-dev-16
metrics:
- type: pearson_cosine
value: 0.7259201534121641
name: Pearson Cosine
- type: spearman_cosine
value: 0.7751337117844075
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7420762055565752
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7552849049126117
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7483211915991654
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.759888035465032
name: Spearman Euclidean
- type: pearson_dot
value: 0.4387404126202509
name: Pearson Dot
- type: spearman_dot
value: 0.42591442860202633
name: Spearman Dot
- type: pearson_max
value: 0.7483211915991654
name: Pearson Max
- type: spearman_max
value: 0.7751337117844075
name: Spearman Max
---
# SentenceTransformer based on distilbert/distilroberta-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilroberta-base](https://huggingface.co./distilbert/distilroberta-base) on the [sentence-transformers/all-nli](https://huggingface.co./datasets/sentence-transformers/all-nli) dataset. It maps sentences & paragraphs to a 768-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:** [distilbert/distilroberta-base](https://huggingface.co./distilbert/distilroberta-base) <!-- at revision fb53ab8802853c8e4fbdbcd0529f21fc6f459b2b -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [sentence-transformers/all-nli](https://huggingface.co./datasets/sentence-transformers/all-nli)
- **Language:** en
<!-- - **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: RobertaModel
(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, '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("mrm8488/distilroberta-base-ft-allnli-matryoshka-768-16-1e-128bs")
# Run inference
sentences = [
'It is well.',
"That's convenient.",
'away from the children',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# 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.*
-->
## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-dev-768`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8413 |
| **spearman_cosine** | **0.8478** |
| pearson_manhattan | 0.8414 |
| spearman_manhattan | 0.8395 |
| pearson_euclidean | 0.8423 |
| spearman_euclidean | 0.8401 |
| pearson_dot | 0.7855 |
| spearman_dot | 0.7814 |
| pearson_max | 0.8423 |
| spearman_max | 0.8478 |
#### Semantic Similarity
* Dataset: `sts-dev-512`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:----------|
| pearson_cosine | 0.8395 |
| **spearman_cosine** | **0.847** |
| pearson_manhattan | 0.8399 |
| spearman_manhattan | 0.8377 |
| pearson_euclidean | 0.8407 |
| spearman_euclidean | 0.838 |
| pearson_dot | 0.7811 |
| spearman_dot | 0.7777 |
| pearson_max | 0.8407 |
| spearman_max | 0.847 |
#### Semantic Similarity
* Dataset: `sts-dev-256`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8327 |
| **spearman_cosine** | **0.8436** |
| pearson_manhattan | 0.8351 |
| spearman_manhattan | 0.8332 |
| pearson_euclidean | 0.836 |
| spearman_euclidean | 0.8338 |
| pearson_dot | 0.75 |
| spearman_dot | 0.7453 |
| pearson_max | 0.836 |
| spearman_max | 0.8436 |
#### Semantic Similarity
* Dataset: `sts-dev-128`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:---------|
| pearson_cosine | 0.8243 |
| **spearman_cosine** | **0.84** |
| pearson_manhattan | 0.8282 |
| spearman_manhattan | 0.827 |
| pearson_euclidean | 0.8282 |
| spearman_euclidean | 0.8267 |
| pearson_dot | 0.711 |
| spearman_dot | 0.705 |
| pearson_max | 0.8282 |
| spearman_max | 0.84 |
#### Semantic Similarity
* Dataset: `sts-dev-64`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8116 |
| **spearman_cosine** | **0.8317** |
| pearson_manhattan | 0.8113 |
| spearman_manhattan | 0.8105 |
| pearson_euclidean | 0.8114 |
| spearman_euclidean | 0.8111 |
| pearson_dot | 0.6412 |
| spearman_dot | 0.6347 |
| pearson_max | 0.8116 |
| spearman_max | 0.8317 |
#### Semantic Similarity
* Dataset: `sts-dev-32`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.7834 |
| **spearman_cosine** | **0.8141** |
| pearson_manhattan | 0.7832 |
| spearman_manhattan | 0.786 |
| pearson_euclidean | 0.7869 |
| spearman_euclidean | 0.7894 |
| pearson_dot | 0.5534 |
| spearman_dot | 0.5449 |
| pearson_max | 0.7869 |
| spearman_max | 0.8141 |
#### Semantic Similarity
* Dataset: `sts-dev-16`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.7259 |
| **spearman_cosine** | **0.7751** |
| pearson_manhattan | 0.7421 |
| spearman_manhattan | 0.7553 |
| pearson_euclidean | 0.7483 |
| spearman_euclidean | 0.7599 |
| pearson_dot | 0.4387 |
| spearman_dot | 0.4259 |
| pearson_max | 0.7483 |
| spearman_max | 0.7751 |
<!--
## 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
#### sentence-transformers/all-nli
* Dataset: [sentence-transformers/all-nli](https://huggingface.co./datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co./datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* Size: 557,850 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 7 tokens</li><li>mean: 10.38 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.8 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.4 tokens</li><li>max: 50 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------|
| <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | <code>A person is at a diner, ordering an omelette.</code> |
| <code>Children smiling and waving at camera</code> | <code>There are children present</code> | <code>The kids are frowning</code> |
| <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64,
32,
16
],
"matryoshka_weights": [
1,
1,
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Evaluation Dataset
#### sentence-transformers/all-nli
* Dataset: [sentence-transformers/all-nli](https://huggingface.co./datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co./datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* Size: 6,584 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 18.02 tokens</li><li>max: 66 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 9.81 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.37 tokens</li><li>max: 29 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------|
| <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code> | <code>The men are fighting outside a deli.</code> |
| <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>Two kids in numbered jerseys wash their hands.</code> | <code>Two kids in jackets walk to school.</code> |
| <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> | <code>A man selling donuts to a customer.</code> | <code>A woman drinks her coffee in a small cafe.</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64,
32,
16
],
"matryoshka_weights": [
1,
1,
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_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`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `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}
- `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`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | sts-dev-128_spearman_cosine | sts-dev-16_spearman_cosine | sts-dev-256_spearman_cosine | sts-dev-32_spearman_cosine | sts-dev-512_spearman_cosine | sts-dev-64_spearman_cosine | sts-dev-768_spearman_cosine |
|:------:|:----:|:-------------:|:-------:|:---------------------------:|:--------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|
| 0.0229 | 100 | 29.0917 | 14.1514 | 0.7659 | 0.7440 | 0.7915 | 0.7749 | 0.7999 | 0.7909 | 0.7918 |
| 0.0459 | 200 | 15.6915 | 11.7031 | 0.7718 | 0.7487 | 0.7940 | 0.7776 | 0.8005 | 0.7931 | 0.7871 |
| 0.0688 | 300 | 14.3136 | 11.1970 | 0.7744 | 0.7389 | 0.7952 | 0.7728 | 0.8036 | 0.7925 | 0.7938 |
| 0.0918 | 400 | 12.8122 | 10.4416 | 0.7899 | 0.7536 | 0.8040 | 0.7764 | 0.8065 | 0.7953 | 0.8018 |
| 0.1147 | 500 | 12.1747 | 10.5491 | 0.7871 | 0.7513 | 0.8035 | 0.7785 | 0.8094 | 0.7978 | 0.8008 |
| 0.1376 | 600 | 11.6784 | 9.6618 | 0.7785 | 0.7465 | 0.7956 | 0.7762 | 0.8027 | 0.7953 | 0.7935 |
| 0.1606 | 700 | 11.9351 | 9.3279 | 0.7907 | 0.7403 | 0.7995 | 0.7706 | 0.8036 | 0.7894 | 0.7982 |
| 0.1835 | 800 | 10.4998 | 9.1538 | 0.7911 | 0.7516 | 0.8043 | 0.7820 | 0.8078 | 0.8025 | 0.8010 |
| 0.2065 | 900 | 10.6069 | 9.0531 | 0.7874 | 0.7371 | 0.7974 | 0.7704 | 0.8042 | 0.7910 | 0.8010 |
| 0.2294 | 1000 | 10.0316 | 8.9759 | 0.7842 | 0.7356 | 0.7981 | 0.7721 | 0.8024 | 0.7905 | 0.7955 |
| 0.2524 | 1100 | 10.199 | 8.5398 | 0.7863 | 0.7322 | 0.7961 | 0.7691 | 0.8002 | 0.7910 | 0.7936 |
| 0.2753 | 1200 | 9.9393 | 8.1356 | 0.7860 | 0.7304 | 0.7990 | 0.7682 | 0.8025 | 0.7908 | 0.7954 |
| 0.2982 | 1300 | 9.8711 | 7.9177 | 0.7932 | 0.7319 | 0.8028 | 0.7708 | 0.8067 | 0.7924 | 0.8013 |
| 0.3212 | 1400 | 9.3594 | 7.8870 | 0.7892 | 0.7296 | 0.8032 | 0.7710 | 0.8070 | 0.7961 | 0.8030 |
| 0.3441 | 1500 | 9.4534 | 7.5756 | 0.8003 | 0.7518 | 0.8078 | 0.7857 | 0.8112 | 0.8063 | 0.8068 |
| 0.3671 | 1600 | 8.9061 | 7.8164 | 0.7781 | 0.7390 | 0.7942 | 0.7761 | 0.8002 | 0.7968 | 0.7941 |
| 0.3900 | 1700 | 8.5164 | 7.4869 | 0.7934 | 0.7530 | 0.8063 | 0.7864 | 0.8120 | 0.8055 | 0.8080 |
| 0.4129 | 1800 | 8.9262 | 7.7155 | 0.7846 | 0.7301 | 0.7991 | 0.7728 | 0.8065 | 0.7945 | 0.8003 |
| 0.4359 | 1900 | 8.3242 | 7.3068 | 0.7850 | 0.7273 | 0.7976 | 0.7710 | 0.8020 | 0.7904 | 0.7976 |
| 0.4588 | 2000 | 8.5374 | 7.1026 | 0.7845 | 0.7272 | 0.7993 | 0.7717 | 0.8042 | 0.7925 | 0.7963 |
| 0.4818 | 2100 | 8.2304 | 7.1601 | 0.7879 | 0.7354 | 0.8015 | 0.7719 | 0.8059 | 0.7944 | 0.8029 |
| 0.5047 | 2200 | 8.1347 | 7.8267 | 0.7715 | 0.7230 | 0.7889 | 0.7626 | 0.7956 | 0.7849 | 0.7930 |
| 0.5276 | 2300 | 8.3057 | 8.0057 | 0.7622 | 0.7148 | 0.7814 | 0.7572 | 0.7881 | 0.7769 | 0.7836 |
| 0.5506 | 2400 | 8.215 | 7.6922 | 0.7772 | 0.7210 | 0.7929 | 0.7637 | 0.7995 | 0.7858 | 0.7956 |
| 0.5735 | 2500 | 8.4343 | 7.2104 | 0.7869 | 0.7307 | 0.8017 | 0.7707 | 0.8071 | 0.7929 | 0.8048 |
| 0.5965 | 2600 | 8.159 | 6.9977 | 0.7893 | 0.7297 | 0.8031 | 0.7733 | 0.8071 | 0.7928 | 0.8045 |
| 0.6194 | 2700 | 8.2048 | 6.9465 | 0.7859 | 0.7280 | 0.8006 | 0.7725 | 0.8052 | 0.7926 | 0.8004 |
| 0.6423 | 2800 | 8.187 | 7.3185 | 0.7790 | 0.7266 | 0.7960 | 0.7690 | 0.8018 | 0.7911 | 0.7964 |
| 0.6653 | 2900 | 8.4768 | 7.5535 | 0.7756 | 0.7192 | 0.7913 | 0.7618 | 0.7958 | 0.7827 | 0.7907 |
| 0.6882 | 3000 | 8.4153 | 7.3732 | 0.7825 | 0.7276 | 0.7988 | 0.7692 | 0.8029 | 0.7899 | 0.7988 |
| 0.7112 | 3100 | 7.9226 | 6.8469 | 0.7912 | 0.7311 | 0.8055 | 0.7765 | 0.8101 | 0.7977 | 0.8058 |
| 0.7341 | 3200 | 8.1155 | 6.7604 | 0.7880 | 0.7298 | 0.8024 | 0.7747 | 0.8071 | 0.7959 | 0.8025 |
| 0.7571 | 3300 | 6.8463 | 5.4863 | 0.8357 | 0.7638 | 0.8407 | 0.8085 | 0.8431 | 0.8283 | 0.8440 |
| 0.7800 | 3400 | 5.2008 | 5.2472 | 0.8362 | 0.7655 | 0.8401 | 0.8105 | 0.8429 | 0.8279 | 0.8445 |
| 0.8029 | 3500 | 4.5415 | 5.1649 | 0.8385 | 0.7700 | 0.8421 | 0.8138 | 0.8454 | 0.8304 | 0.8465 |
| 0.8259 | 3600 | 4.4474 | 5.0933 | 0.8371 | 0.7693 | 0.8410 | 0.8112 | 0.8443 | 0.8288 | 0.8451 |
| 0.8488 | 3700 | 4.12 | 5.0555 | 0.8396 | 0.7718 | 0.8439 | 0.8140 | 0.8463 | 0.8311 | 0.8471 |
| 0.8718 | 3800 | 3.9104 | 5.0147 | 0.8386 | 0.7749 | 0.8432 | 0.8129 | 0.8459 | 0.8304 | 0.8471 |
| 0.8947 | 3900 | 3.9054 | 4.9966 | 0.8379 | 0.7733 | 0.8424 | 0.8125 | 0.8456 | 0.8296 | 0.8464 |
| 0.9176 | 4000 | 3.757 | 4.9892 | 0.8407 | 0.7763 | 0.8447 | 0.8156 | 0.8478 | 0.8326 | 0.8488 |
| 0.9406 | 4100 | 3.7729 | 4.9859 | 0.8400 | 0.7751 | 0.8436 | 0.8141 | 0.8470 | 0.8317 | 0.8478 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.0
- Transformers: 4.41.1
- PyTorch: 2.3.0+cu121
- Accelerate: 0.30.1
- Datasets: 2.19.2
- Tokenizers: 0.19.1
## 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",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### 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.*
--> |