File size: 31,122 Bytes
42d0a03 |
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 |
---
base_model: BAAI/bge-m3
datasets: []
language:
- es
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
- generated_from_trainer
- dataset_size:2947
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Es uso privativo el que determina la ocupación de una porción del
dominio público, de modo que se limita o excluye la utilización del mismo por
otros interesados.
sentences:
- ¿Qué es el uso privativo de los bienes de dominio público?
- ¿Qué es la sanidad ambiental?
- ¿Qué información básica debe contener la información que se facilita al afectado
cuando se obtienen datos personales de él?
- source_sentence: 'Las retribuciones básicas, que se fijan en la Ley de Presupuestos
Generales del Estado, estarán integradas única y exclusivamente por: a) El sueldo
asignado a cada Subgrupo o Grupo de clasificación profesional, en el supuesto
de que éste no tenga Subgrupo. b) Los trienios, que consisten en una cantidad,
que será igual para cada Subgrupo o Grupo de clasificación profesional, en el
supuesto de que éste no tenga Subgrupo, por cada tres años de servicio.'
sentences:
- ¿Qué se entiende por retribuciones básicas?
- ¿Cuál es el título competencial de esta ley orgánica?
- ¿Qué se aprueba a propuesta del Ministro de Hacienda?
- source_sentence: Se reconoce el valor social de las niñas, niños y adolescentes
como personas que realizan un aporte afectivo, cultural y ético al caudal social,
y cuyo protagonismo, creatividad y posicionamiento activo enriquecen la vida colectiva.
sentences:
- ¿Qué sucede si se produce un incumplimiento de las actuaciones establecidas en
el Plan de inclusión sociolaboral?
- ¿Qué se reconoce en cuanto al valor social de la infancia?
- ¿Cuál es el plazo de prescripción de las infracciones?
- source_sentence: Las empresas y las universidades podrán promover y participar en
programas de voluntariado que cumplan los requisitos establecidos en esta Ley.
sentences:
- ¿Cuál es la consideración de las infracciones muy graves?
- ¿Qué tipo de empresas pueden promover y participar en programas de voluntariado?
- ¿Qué tipo de entidades están obligadas a cumplir con las obligaciones de publicidad
activa?
- source_sentence: Artículo 6. Definiciones. 1. Discriminación directa e indirecta.
b) La discriminación indirecta se produce cuando una disposición, criterio o práctica
aparentemente neutros ocasiona o puede ocasionar a una o varias personas una desventaja
particular con respecto a otras por razón de las causas previstas en el apartado
1 del artículo 2.
sentences:
- ¿Cuál es el papel del Consejo de Salud de Área?
- ¿Qué se considera discriminación indirecta?
- ¿Qué tipo de información se considera veraz?
model-index:
- name: BGE large Legal Spanish
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 1024
type: dim_1024
metrics:
- type: cosine_accuracy@1
value: 0.551829268292683
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8048780487804879
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8445121951219512
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9024390243902439
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.551829268292683
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2682926829268293
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16890243902439023
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09024390243902437
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.551829268292683
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8048780487804879
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8445121951219512
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9024390243902439
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7379864083246442
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6841608594657377
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6880865147668174
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.5487804878048781
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8048780487804879
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.850609756097561
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9024390243902439
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5487804878048781
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2682926829268293
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17012195121951218
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09024390243902437
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5487804878048781
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8048780487804879
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.850609756097561
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9024390243902439
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.736128283939538
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6815560878823075
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6854885550473444
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.5579268292682927
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8109756097560976
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.850609756097561
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8932926829268293
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5579268292682927
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.27032520325203246
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17012195121951218
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08932926829268292
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5579268292682927
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8109756097560976
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.850609756097561
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8932926829268293
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7362627915663099
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6845153406891215
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6889302518809046
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.5548780487804879
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7957317073170732
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8323170731707317
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8841463414634146
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5548780487804879
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2652439024390244
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16646341463414632
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08841463414634146
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5548780487804879
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7957317073170732
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8323170731707317
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8841463414634146
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7307377627264078
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6803994870305846
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6851337079025414
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.5213414634146342
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7621951219512195
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8140243902439024
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8658536585365854
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5213414634146342
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.25406504065040647
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16280487804878047
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08658536585365853
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5213414634146342
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7621951219512195
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8140243902439024
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8658536585365854
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7028480041122221
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6495075977545491
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6549966797371862
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.4847560975609756
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.725609756097561
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7804878048780488
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8536585365853658
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.4847560975609756
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.24186991869918703
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.15609756097560976
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08536585365853658
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.4847560975609756
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.725609756097561
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.7804878048780488
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8536585365853658
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.6729421249114532
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6146668118466899
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6198317239083065
name: Cosine Map@100
---
# BGE large Legal Spanish
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3). 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:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** es
- **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': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, '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("dariolopez/bge-m3-es-legal-tmp-6")
# Run inference
sentences = [
'Artículo 6. Definiciones. 1. Discriminación directa e indirecta. b) La discriminación indirecta se produce cuando una disposición, criterio o práctica aparentemente neutros ocasiona o puede ocasionar a una o varias personas una desventaja particular con respecto a otras por razón de las causas previstas en el apartado 1 del artículo 2.',
'¿Qué se considera discriminación indirecta?',
'¿Qué tipo de información se considera veraz?',
]
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.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Dataset: `dim_1024`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.5518 |
| cosine_accuracy@3 | 0.8049 |
| cosine_accuracy@5 | 0.8445 |
| cosine_accuracy@10 | 0.9024 |
| cosine_precision@1 | 0.5518 |
| cosine_precision@3 | 0.2683 |
| cosine_precision@5 | 0.1689 |
| cosine_precision@10 | 0.0902 |
| cosine_recall@1 | 0.5518 |
| cosine_recall@3 | 0.8049 |
| cosine_recall@5 | 0.8445 |
| cosine_recall@10 | 0.9024 |
| cosine_ndcg@10 | 0.738 |
| cosine_mrr@10 | 0.6842 |
| **cosine_map@100** | **0.6881** |
#### 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 | 0.5488 |
| cosine_accuracy@3 | 0.8049 |
| cosine_accuracy@5 | 0.8506 |
| cosine_accuracy@10 | 0.9024 |
| cosine_precision@1 | 0.5488 |
| cosine_precision@3 | 0.2683 |
| cosine_precision@5 | 0.1701 |
| cosine_precision@10 | 0.0902 |
| cosine_recall@1 | 0.5488 |
| cosine_recall@3 | 0.8049 |
| cosine_recall@5 | 0.8506 |
| cosine_recall@10 | 0.9024 |
| cosine_ndcg@10 | 0.7361 |
| cosine_mrr@10 | 0.6816 |
| **cosine_map@100** | **0.6855** |
#### 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 | 0.5579 |
| cosine_accuracy@3 | 0.811 |
| cosine_accuracy@5 | 0.8506 |
| cosine_accuracy@10 | 0.8933 |
| cosine_precision@1 | 0.5579 |
| cosine_precision@3 | 0.2703 |
| cosine_precision@5 | 0.1701 |
| cosine_precision@10 | 0.0893 |
| cosine_recall@1 | 0.5579 |
| cosine_recall@3 | 0.811 |
| cosine_recall@5 | 0.8506 |
| cosine_recall@10 | 0.8933 |
| cosine_ndcg@10 | 0.7363 |
| cosine_mrr@10 | 0.6845 |
| **cosine_map@100** | **0.6889** |
#### 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 | 0.5549 |
| cosine_accuracy@3 | 0.7957 |
| cosine_accuracy@5 | 0.8323 |
| cosine_accuracy@10 | 0.8841 |
| cosine_precision@1 | 0.5549 |
| cosine_precision@3 | 0.2652 |
| cosine_precision@5 | 0.1665 |
| cosine_precision@10 | 0.0884 |
| cosine_recall@1 | 0.5549 |
| cosine_recall@3 | 0.7957 |
| cosine_recall@5 | 0.8323 |
| cosine_recall@10 | 0.8841 |
| cosine_ndcg@10 | 0.7307 |
| cosine_mrr@10 | 0.6804 |
| **cosine_map@100** | **0.6851** |
#### 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 | 0.5213 |
| cosine_accuracy@3 | 0.7622 |
| cosine_accuracy@5 | 0.814 |
| cosine_accuracy@10 | 0.8659 |
| cosine_precision@1 | 0.5213 |
| cosine_precision@3 | 0.2541 |
| cosine_precision@5 | 0.1628 |
| cosine_precision@10 | 0.0866 |
| cosine_recall@1 | 0.5213 |
| cosine_recall@3 | 0.7622 |
| cosine_recall@5 | 0.814 |
| cosine_recall@10 | 0.8659 |
| cosine_ndcg@10 | 0.7028 |
| cosine_mrr@10 | 0.6495 |
| **cosine_map@100** | **0.655** |
#### 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 | 0.4848 |
| cosine_accuracy@3 | 0.7256 |
| cosine_accuracy@5 | 0.7805 |
| cosine_accuracy@10 | 0.8537 |
| cosine_precision@1 | 0.4848 |
| cosine_precision@3 | 0.2419 |
| cosine_precision@5 | 0.1561 |
| cosine_precision@10 | 0.0854 |
| cosine_recall@1 | 0.4848 |
| cosine_recall@3 | 0.7256 |
| cosine_recall@5 | 0.7805 |
| cosine_recall@10 | 0.8537 |
| cosine_ndcg@10 | 0.6729 |
| cosine_mrr@10 | 0.6147 |
| **cosine_map@100** | **0.6198** |
<!--
## 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 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`: 6
- `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`: 6
- `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
- `eval_on_start`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | dim_1024_cosine_map@100 | 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 |
|:----------:|:------:|:-------------:|:----------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.4324 | 5 | 1.6507 | - | - | - | - | - | - | - |
| 0.8649 | 10 | 0.9598 | - | - | - | - | - | - | - |
| 0.9514 | 11 | - | 0.5477 | 0.6833 | 0.6616 | 0.6836 | 0.6758 | 0.5994 | 0.6744 |
| 1.2973 | 15 | 0.8248 | - | - | - | - | - | - | - |
| 1.7297 | 20 | 0.3858 | - | - | - | - | - | - | - |
| 1.9892 | 23 | - | 0.4242 | 0.6748 | 0.6544 | 0.6833 | 0.6740 | 0.6233 | 0.6697 |
| 2.1622 | 25 | 0.32 | - | - | - | - | - | - | - |
| 2.5946 | 30 | 0.1703 | - | - | - | - | - | - | - |
| 2.9405 | 34 | - | 0.3940 | 0.6755 | 0.6523 | 0.6823 | 0.6797 | 0.6196 | 0.6776 |
| 3.0270 | 35 | 0.1337 | - | - | - | - | - | - | - |
| 3.4595 | 40 | 0.0949 | - | - | - | - | - | - | - |
| 3.8919 | 45 | 0.0594 | - | - | - | - | - | - | - |
| **3.9784** | **46** | **-** | **0.3735** | **0.6867** | **0.6588** | **0.6865** | **0.6854** | **0.6189** | **0.6826** |
| 4.3243 | 50 | 0.07 | - | - | - | - | - | - | - |
| 4.7568 | 55 | 0.0524 | - | - | - | - | - | - | - |
| 4.9297 | 57 | - | 0.3642 | 0.6870 | 0.6577 | 0.6858 | 0.6871 | 0.6228 | 0.6853 |
| 5.1892 | 60 | 0.0598 | - | - | - | - | - | - | - |
| 5.6216 | 65 | 0.0491 | - | - | - | - | - | - | - |
| 5.7081 | 66 | - | 0.3626 | 0.6881 | 0.6550 | 0.6851 | 0.6889 | 0.6198 | 0.6855 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.3
- PyTorch: 2.2.0+cu121
- Accelerate: 0.32.1
- Datasets: 2.20.0
- 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.*
--> |