File size: 49,261 Bytes
2f989d7 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 |
---
base_model: BAAI/bge-base-en-v1.5
language:
- en
library_name: sentence-transformers
license: apache-2.0
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:1K<n<10K
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: USS Conyngham (DD-58)
sentences:
- '"w jakich patrolach uczestniczył USS ""Conyngham"" (DD-58)?"'
- Jest ona najstarszą skoczkinią w kadrze norweskiej.
- kto uczył malarstwa olimpijczyka Bronisława Czecha?
- source_sentence: Danae (obraz Tycjana)
sentences:
- jakie różnice występują pomiędzy kolejnymi wersjami obrazu Tycjana Danae?
- z czego wykonana jest rzeźba Robotnik i kołchoźnica?
- z jakiego powodu zwołano synod w Whitby?
- source_sentence: dlaczego zapominamy?
sentences:
- Zamek w Haapsalu
- kto był tłumaczem języka angielskiego u Mao Zedonga?
- Najstarszy z trzech synów Hong Xiuquana; jego matką była Lai Lianying.
- source_sentence: kim był Steve Yzerman?
sentences:
- która hala ma najmniejszą widownię w NHL?
- za co krytykowany był papieski wykład ratyzboński?
- ' W 1867 oddano do użytku Kolej Warszawsko-Terespolską (całą linię).'
- source_sentence: Herkules na rozstajach
sentences:
- jak zinterpretować wymowę obrazu Herkules na rozstajach?
- Dowódcą grupy był Wiaczesław Razumowicz ps. „Chmara”.
- z jakiego powodu zwołano synod w Whitby?
model-index:
- name: bge-base-en-v1.5-klej-dyk
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.17307692307692307
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.46153846153846156
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6225961538461539
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7355769230769231
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.17307692307692307
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.15384615384615385
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.12451923076923076
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0735576923076923
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.17307692307692307
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.46153846153846156
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.6225961538461539
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.7355769230769231
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4433646681639308
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.35053323412698395
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3573926265146405
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.16826923076923078
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.4519230769230769
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6009615384615384
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7091346153846154
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.16826923076923078
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.15064102564102563
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1201923076923077
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07091346153846154
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.16826923076923078
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.4519230769230769
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.6009615384615384
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.7091346153846154
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.42955891948336516
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3405992445054941
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3484580834493777
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.19230769230769232
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.4543269230769231
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5913461538461539
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6899038461538461
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.19230769230769232
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.15144230769230768
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.11826923076923078
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0689903846153846
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.19230769230769232
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.4543269230769231
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5913461538461539
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.6899038461538461
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4311008111471328
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3488247863247859
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3560982492053804
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.16346153846153846
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.41586538461538464
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5168269230769231
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5985576923076923
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.16346153846153846
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.13862179487179488
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.10336538461538461
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.059855769230769226
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.16346153846153846
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.41586538461538464
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5168269230769231
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5985576923076923
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.37641559536404157
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3052140567765567
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3151839890893904
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.1658653846153846
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.35096153846153844
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.43990384615384615
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5288461538461539
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.1658653846153846
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.11698717948717949
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.08798076923076924
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.052884615384615384
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.1658653846153846
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.35096153846153844
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.43990384615384615
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5288461538461539
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.33823482580826353
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.27800194597069605
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2876731521968676
name: Cosine Map@100
---
# bge-base-en-v1.5-klej-dyk
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). 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:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0
### 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': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## 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 = [
'Herkules na rozstajach',
'jak zinterpretować wymowę obrazu Herkules na rozstajach?',
'Dowódcą grupy był Wiaczesław Razumowicz ps. „Chmara”.',
]
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
#### Information Retrieval
* Dataset: `dim_768`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.1731 |
| cosine_accuracy@3 | 0.4615 |
| cosine_accuracy@5 | 0.6226 |
| cosine_accuracy@10 | 0.7356 |
| cosine_precision@1 | 0.1731 |
| cosine_precision@3 | 0.1538 |
| cosine_precision@5 | 0.1245 |
| cosine_precision@10 | 0.0736 |
| cosine_recall@1 | 0.1731 |
| cosine_recall@3 | 0.4615 |
| cosine_recall@5 | 0.6226 |
| cosine_recall@10 | 0.7356 |
| cosine_ndcg@10 | 0.4434 |
| cosine_mrr@10 | 0.3505 |
| **cosine_map@100** | **0.3574** |
#### Information Retrieval
* Dataset: `dim_512`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.1683 |
| cosine_accuracy@3 | 0.4519 |
| cosine_accuracy@5 | 0.601 |
| cosine_accuracy@10 | 0.7091 |
| cosine_precision@1 | 0.1683 |
| cosine_precision@3 | 0.1506 |
| cosine_precision@5 | 0.1202 |
| cosine_precision@10 | 0.0709 |
| cosine_recall@1 | 0.1683 |
| cosine_recall@3 | 0.4519 |
| cosine_recall@5 | 0.601 |
| cosine_recall@10 | 0.7091 |
| cosine_ndcg@10 | 0.4296 |
| cosine_mrr@10 | 0.3406 |
| **cosine_map@100** | **0.3485** |
#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.1923 |
| cosine_accuracy@3 | 0.4543 |
| cosine_accuracy@5 | 0.5913 |
| cosine_accuracy@10 | 0.6899 |
| cosine_precision@1 | 0.1923 |
| cosine_precision@3 | 0.1514 |
| cosine_precision@5 | 0.1183 |
| cosine_precision@10 | 0.069 |
| cosine_recall@1 | 0.1923 |
| cosine_recall@3 | 0.4543 |
| cosine_recall@5 | 0.5913 |
| cosine_recall@10 | 0.6899 |
| cosine_ndcg@10 | 0.4311 |
| cosine_mrr@10 | 0.3488 |
| **cosine_map@100** | **0.3561** |
#### Information Retrieval
* Dataset: `dim_128`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.1635 |
| cosine_accuracy@3 | 0.4159 |
| cosine_accuracy@5 | 0.5168 |
| cosine_accuracy@10 | 0.5986 |
| cosine_precision@1 | 0.1635 |
| cosine_precision@3 | 0.1386 |
| cosine_precision@5 | 0.1034 |
| cosine_precision@10 | 0.0599 |
| cosine_recall@1 | 0.1635 |
| cosine_recall@3 | 0.4159 |
| cosine_recall@5 | 0.5168 |
| cosine_recall@10 | 0.5986 |
| cosine_ndcg@10 | 0.3764 |
| cosine_mrr@10 | 0.3052 |
| **cosine_map@100** | **0.3152** |
#### Information Retrieval
* Dataset: `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.1659 |
| cosine_accuracy@3 | 0.351 |
| cosine_accuracy@5 | 0.4399 |
| cosine_accuracy@10 | 0.5288 |
| cosine_precision@1 | 0.1659 |
| cosine_precision@3 | 0.117 |
| cosine_precision@5 | 0.088 |
| cosine_precision@10 | 0.0529 |
| cosine_recall@1 | 0.1659 |
| cosine_recall@3 | 0.351 |
| cosine_recall@5 | 0.4399 |
| cosine_recall@10 | 0.5288 |
| cosine_ndcg@10 | 0.3382 |
| cosine_mrr@10 | 0.278 |
| **cosine_map@100** | **0.2877** |
<!--
## 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: 3,738 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
| | positive | anchor |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 90.01 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 30.82 tokens</li><li>max: 76 tokens</li></ul> |
* Samples:
| positive | anchor |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------|
| <code>Londyńska premiera w Ambassadors Theatre na londyńskim West Endzie miała miejsce 25 listopada 1952 roku, a przedstawione grane jest do dziś (od 1974 r.) w sąsiednim St Martin's Theatre. W Polsce była wystawiana m.in. w Teatrze Nowym w Zabrzu.</code> | <code>w którym londyńskim muzeum wystawiana była instalacja My Bed?</code> |
| <code>Theridion grallator osiąga długość 5 mm. U niektórych postaci na żółtym odwłoku występuje wzór przypominający uśmiechniętą lub śmiejącą się twarz klowna.</code> | <code>które pająki noszą na grzbiecie wzór przypominający uśmiechniętego klauna?</code> |
| <code>W 1998 w wyniku sporów o wytyczenie granicy między dwoma państwami wybuchła wojna erytrejsko-etiopska. Zakończyła się porozumieniem zawartym w Algierze 12 grudnia 2000. Od tego czasu strefa graniczna jest patrolowana przez siły pokojowe ONZ.</code> | <code>jakie były skutki wojny erytrejsko-etiopskiej?</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
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 10
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-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`: 10
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `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`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `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`: True
- `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_fused
- `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
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.0684 | 1 | 7.2706 | - | - | - | - | - |
| 0.1368 | 2 | 8.2776 | - | - | - | - | - |
| 0.2051 | 3 | 7.1399 | - | - | - | - | - |
| 0.2735 | 4 | 6.6905 | - | - | - | - | - |
| 0.3419 | 5 | 6.735 | - | - | - | - | - |
| 0.4103 | 6 | 7.0537 | - | - | - | - | - |
| 0.4786 | 7 | 6.871 | - | - | - | - | - |
| 0.5470 | 8 | 6.7277 | - | - | - | - | - |
| 0.6154 | 9 | 5.9853 | - | - | - | - | - |
| 0.6838 | 10 | 6.0518 | - | - | - | - | - |
| 0.7521 | 11 | 5.8291 | - | - | - | - | - |
| 0.8205 | 12 | 5.0064 | - | - | - | - | - |
| 0.8889 | 13 | 4.8572 | - | - | - | - | - |
| 0.9573 | 14 | 5.1899 | 0.2812 | 0.3335 | 0.3486 | 0.2115 | 0.3639 |
| 1.0256 | 15 | 4.2996 | - | - | - | - | - |
| 1.0940 | 16 | 4.1475 | - | - | - | - | - |
| 1.1624 | 17 | 4.6174 | - | - | - | - | - |
| 1.2308 | 18 | 4.394 | - | - | - | - | - |
| 1.2991 | 19 | 4.0255 | - | - | - | - | - |
| 1.3675 | 20 | 3.9722 | - | - | - | - | - |
| 1.4359 | 21 | 3.9509 | - | - | - | - | - |
| 1.5043 | 22 | 3.7674 | - | - | - | - | - |
| 1.5726 | 23 | 3.7572 | - | - | - | - | - |
| 1.6410 | 24 | 3.9463 | - | - | - | - | - |
| 1.7094 | 25 | 3.7151 | - | - | - | - | - |
| 1.7778 | 26 | 3.7771 | - | - | - | - | - |
| 1.8462 | 27 | 3.5228 | - | - | - | - | - |
| 1.9145 | 28 | 2.7906 | - | - | - | - | - |
| 1.9829 | 29 | 3.4555 | 0.3164 | 0.3529 | 0.3641 | 0.2636 | 0.3681 |
| 2.0513 | 30 | 2.737 | - | - | - | - | - |
| 2.1197 | 31 | 3.1976 | - | - | - | - | - |
| 2.1880 | 32 | 3.1363 | - | - | - | - | - |
| 2.2564 | 33 | 2.9706 | - | - | - | - | - |
| 2.3248 | 34 | 2.9629 | - | - | - | - | - |
| 2.3932 | 35 | 2.7226 | - | - | - | - | - |
| 2.4615 | 36 | 2.4378 | - | - | - | - | - |
| 2.5299 | 37 | 2.7201 | - | - | - | - | - |
| 2.5983 | 38 | 2.6802 | - | - | - | - | - |
| 2.6667 | 39 | 3.1613 | - | - | - | - | - |
| 2.7350 | 40 | 2.9344 | - | - | - | - | - |
| 2.8034 | 41 | 2.5254 | - | - | - | - | - |
| 2.8718 | 42 | 2.5617 | - | - | - | - | - |
| 2.9402 | 43 | 2.459 | 0.3197 | 0.3571 | 0.3640 | 0.2739 | 0.3733 |
| 3.0085 | 44 | 2.3785 | - | - | - | - | - |
| 3.0769 | 45 | 1.9408 | - | - | - | - | - |
| 3.1453 | 46 | 2.7095 | - | - | - | - | - |
| 3.2137 | 47 | 2.4774 | - | - | - | - | - |
| 3.2821 | 48 | 2.2178 | - | - | - | - | - |
| 3.3504 | 49 | 2.0884 | - | - | - | - | - |
| 3.4188 | 50 | 2.1044 | - | - | - | - | - |
| 3.4872 | 51 | 2.1504 | - | - | - | - | - |
| 3.5556 | 52 | 2.1177 | - | - | - | - | - |
| 3.6239 | 53 | 2.2283 | - | - | - | - | - |
| 3.6923 | 54 | 2.3964 | - | - | - | - | - |
| 3.7607 | 55 | 2.0972 | - | - | - | - | - |
| 3.8291 | 56 | 2.0961 | - | - | - | - | - |
| 3.8974 | 57 | 1.783 | - | - | - | - | - |
| **3.9658** | **58** | **2.1031** | **0.3246** | **0.3533** | **0.3603** | **0.2829** | **0.3687** |
| 4.0342 | 59 | 1.6699 | - | - | - | - | - |
| 4.1026 | 60 | 1.6675 | - | - | - | - | - |
| 4.1709 | 61 | 2.1672 | - | - | - | - | - |
| 4.2393 | 62 | 1.8881 | - | - | - | - | - |
| 4.3077 | 63 | 1.701 | - | - | - | - | - |
| 4.3761 | 64 | 1.9154 | - | - | - | - | - |
| 4.4444 | 65 | 1.4549 | - | - | - | - | - |
| 4.5128 | 66 | 1.5444 | - | - | - | - | - |
| 4.5812 | 67 | 1.8352 | - | - | - | - | - |
| 4.6496 | 68 | 1.7908 | - | - | - | - | - |
| 4.7179 | 69 | 1.6876 | - | - | - | - | - |
| 4.7863 | 70 | 1.7366 | - | - | - | - | - |
| 4.8547 | 71 | 1.8689 | - | - | - | - | - |
| 4.9231 | 72 | 1.4676 | - | - | - | - | - |
| 4.9915 | 73 | 1.5045 | 0.3170 | 0.3538 | 0.3606 | 0.2829 | 0.3675 |
| 5.0598 | 74 | 1.2155 | - | - | - | - | - |
| 5.1282 | 75 | 1.4365 | - | - | - | - | - |
| 5.1966 | 76 | 1.7451 | - | - | - | - | - |
| 5.2650 | 77 | 1.4537 | - | - | - | - | - |
| 5.3333 | 78 | 1.3813 | - | - | - | - | - |
| 5.4017 | 79 | 1.4035 | - | - | - | - | - |
| 5.4701 | 80 | 1.3912 | - | - | - | - | - |
| 5.5385 | 81 | 1.3286 | - | - | - | - | - |
| 5.6068 | 82 | 1.5153 | - | - | - | - | - |
| 5.6752 | 83 | 1.6745 | - | - | - | - | - |
| 5.7436 | 84 | 1.4323 | - | - | - | - | - |
| 5.8120 | 85 | 1.5299 | - | - | - | - | - |
| 5.8803 | 86 | 1.488 | - | - | - | - | - |
| 5.9487 | 87 | 1.5195 | 0.3206 | 0.3556 | 0.3530 | 0.2878 | 0.3605 |
| 6.0171 | 88 | 1.2999 | - | - | - | - | - |
| 6.0855 | 89 | 1.1511 | - | - | - | - | - |
| 6.1538 | 90 | 1.552 | - | - | - | - | - |
| 6.2222 | 91 | 1.35 | - | - | - | - | - |
| 6.2906 | 92 | 1.218 | - | - | - | - | - |
| 6.3590 | 93 | 1.1712 | - | - | - | - | - |
| 6.4274 | 94 | 1.3381 | - | - | - | - | - |
| 6.4957 | 95 | 1.1716 | - | - | - | - | - |
| 6.5641 | 96 | 1.2117 | - | - | - | - | - |
| 6.6325 | 97 | 1.5349 | - | - | - | - | - |
| 6.7009 | 98 | 1.4564 | - | - | - | - | - |
| 6.7692 | 99 | 1.3541 | - | - | - | - | - |
| 6.8376 | 100 | 1.2468 | - | - | - | - | - |
| 6.9060 | 101 | 1.1519 | - | - | - | - | - |
| 6.9744 | 102 | 1.2421 | 0.3150 | 0.3555 | 0.3501 | 0.2858 | 0.3575 |
| 7.0427 | 103 | 1.0096 | - | - | - | - | - |
| 7.1111 | 104 | 1.1405 | - | - | - | - | - |
| 7.1795 | 105 | 1.2958 | - | - | - | - | - |
| 7.2479 | 106 | 1.35 | - | - | - | - | - |
| 7.3162 | 107 | 1.1291 | - | - | - | - | - |
| 7.3846 | 108 | 0.9968 | - | - | - | - | - |
| 7.4530 | 109 | 1.0454 | - | - | - | - | - |
| 7.5214 | 110 | 1.102 | - | - | - | - | - |
| 7.5897 | 111 | 1.1328 | - | - | - | - | - |
| 7.6581 | 112 | 1.5988 | - | - | - | - | - |
| 7.7265 | 113 | 1.2992 | - | - | - | - | - |
| 7.7949 | 114 | 1.2572 | - | - | - | - | - |
| 7.8632 | 115 | 1.1414 | - | - | - | - | - |
| 7.9316 | 116 | 1.1432 | - | - | - | - | - |
| 8.0 | 117 | 1.1181 | 0.3154 | 0.3545 | 0.3509 | 0.2884 | 0.3578 |
| 8.0684 | 118 | 0.9365 | - | - | - | - | - |
| 8.1368 | 119 | 1.3286 | - | - | - | - | - |
| 8.2051 | 120 | 1.3711 | - | - | - | - | - |
| 8.2735 | 121 | 1.2001 | - | - | - | - | - |
| 8.3419 | 122 | 1.165 | - | - | - | - | - |
| 8.4103 | 123 | 1.0575 | - | - | - | - | - |
| 8.4786 | 124 | 1.105 | - | - | - | - | - |
| 8.5470 | 125 | 1.077 | - | - | - | - | - |
| 8.6154 | 126 | 1.2217 | - | - | - | - | - |
| 8.6838 | 127 | 1.3254 | - | - | - | - | - |
| 8.7521 | 128 | 1.2165 | - | - | - | - | - |
| 8.8205 | 129 | 1.3021 | - | - | - | - | - |
| 8.8889 | 130 | 1.0927 | - | - | - | - | - |
| 8.9573 | 131 | 1.3961 | 0.3150 | 0.3540 | 0.3490 | 0.2882 | 0.3588 |
| 9.0256 | 132 | 1.0779 | - | - | - | - | - |
| 9.0940 | 133 | 0.901 | - | - | - | - | - |
| 9.1624 | 134 | 1.313 | - | - | - | - | - |
| 9.2308 | 135 | 1.1409 | - | - | - | - | - |
| 9.2991 | 136 | 1.1635 | - | - | - | - | - |
| 9.3675 | 137 | 1.0244 | - | - | - | - | - |
| 9.4359 | 138 | 1.0576 | - | - | - | - | - |
| 9.5043 | 139 | 1.0101 | - | - | - | - | - |
| 9.5726 | 140 | 1.1516 | 0.3152 | 0.3561 | 0.3485 | 0.2877 | 0.3574 |
* The bold row denotes the saved checkpoint.
</details>
### Framework Versions
- Python: 3.12.2
- Sentence Transformers: 3.0.0
- Transformers: 4.41.2
- PyTorch: 2.3.1
- Accelerate: 0.27.2
- Datasets: 2.19.1
- 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.*
--> |