File size: 32,229 Bytes
62789bc |
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 |
---
base_model: BAAI/bge-m3
library_name: sentence-transformers
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:2844
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: La unió d’aquests dos documents conforma l’Informe d’Avaluació
de l’Edifici (IAE).
sentences:
- Quin és el requisit per a rebre els ajuts econòmics per a les empreses?
- Quin és el resultat de la unió de la Inspecció Tècnica de l’Edifici (ITE) i dels
certificats energètics?
- Quin és el termini per sol·licitar la renovació del carnet de persona cuidadora?
- source_sentence: La Inspecció Tècnica dels Edificis (ITE) permet identificar les
oportunitats de millora de l'eficiència energètica i implementar mesures de rehabilitació.
sentences:
- Quin és el benefici de l'activitat del Viver dels Avis de Sitges per a la qualitat
de vida?
- Com puc saber si puc ser cuidador?
- Quin és el paper de la Inspecció Tècnica dels Edificis (ITE) en la millora de
l'eficiència energètica?
- source_sentence: A les zones blaves els parquímetres i serveis de pagament reconeixen
les matricules dels vehicles acreditats.
sentences:
- Quin és el paper de la mediació en una denúncia?
- Quin és el paper de les persones físiques?
- Quin és el procediment per estacionar a les zones blaves amb l'acreditació de
resident?
- source_sentence: Els establiments oberts al públic destinats a espectacles cinematogràfics.
Els establiments oberts al públic destinats a espectacles públics i activitats
recreatives musicals amb un aforament autoritzat fins a 150 persones.
sentences:
- Quin és el resultat esperat després de la intervenció de l'Ajuntament en les denúncies
sanitàries?
- Quin és el requisit de superfície construïda per als restaurants musicals?
- Quins establiments oberts al públic han de comunicar la seva obertura a l'Ajuntament?
- source_sentence: El Decret 97/2002, de 5 de març, regula la concessió de la targeta
d’aparcament per a persones amb disminució i altres mesures adreçades a facilitar
el desplaçament de les persones amb mobilitat reduïda.
sentences:
- Quin és el benefici de la targeta d'aparcament per a les persones amb disminució?
- Quin és el paper de la Junta de Govern Local?
- Quin és l'organisme que emet el certificat de serveis prestats?
model-index:
- name: SentenceTransformer based on BAAI/bge-m3
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 1024
type: dim_1024
metrics:
- type: cosine_accuracy@1
value: 0.11814345991561181
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.23277074542897327
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.3129395218002813
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.4644163150492264
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.11814345991561181
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.07759024847632442
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.06258790436005626
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.046441631504922636
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.11814345991561181
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.23277074542897327
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3129395218002813
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.4644163150492264
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.26553370933458276
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.20527392672962277
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.22599508422976106
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.11575246132208157
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.2289732770745429
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.3112517580872011
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.46568213783403656
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.11575246132208157
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.07632442569151429
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.062250351617440226
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.04656821378340366
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.11575246132208157
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.2289732770745429
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3112517580872011
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.46568213783403656
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.26414039995115557
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.20311873507021158
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.22355973027797246
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.11912798874824192
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.23277074542897327
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.31758087201125174
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.46582278481012657
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.11912798874824192
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.07759024847632444
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.06351617440225035
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.04658227848101265
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.11912798874824192
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.23277074542897327
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.31758087201125174
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.46582278481012657
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.26671990925029193
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.20635646194717913
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.22673055490318922
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.11533052039381153
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.22658227848101264
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.30857946554149085
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.45668073136427567
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.11533052039381153
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.07552742616033756
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.06171589310829817
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.04566807313642757
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.11533052039381153
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.22658227848101264
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.30857946554149085
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.45668073136427567
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.26044811042246035
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.20098218471636187
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.22169039893772347
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.11181434599156118
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.22334739803094233
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.30253164556962026
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.45288326300984527
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.11181434599156118
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.07444913267698076
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.06050632911392405
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.045288326300984526
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.11181434599156118
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.22334739803094233
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.30253164556962026
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.45288326300984527
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2566428043422134
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.19724806331346384
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.21784479785600805
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.10689170182841069
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.21251758087201125
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.28846694796061884
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.42967651195499296
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.10689170182841069
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.07083919362400375
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.05769338959212378
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0429676511954993
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.10689170182841069
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.21251758087201125
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.28846694796061884
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.42967651195499296
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2438421466584992
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.1875642957604982
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2080904354707231
name: Cosine Map@100
---
# SentenceTransformer based on BAAI/bge-m3
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) on the json dataset. 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:**
- json
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 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("adriansanz/ST-tramits-sitges-006-5ep")
# Run inference
sentences = [
'El Decret 97/2002, de 5 de març, regula la concessió de la targeta d’aparcament per a persones amb disminució i altres mesures adreçades a facilitar el desplaçament de les persones amb mobilitat reduïda.',
"Quin és el benefici de la targeta d'aparcament per a les persones amb disminució?",
'Quin és el paper de la Junta de Govern Local?',
]
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 | 0.1181 |
| cosine_accuracy@3 | 0.2328 |
| cosine_accuracy@5 | 0.3129 |
| cosine_accuracy@10 | 0.4644 |
| cosine_precision@1 | 0.1181 |
| cosine_precision@3 | 0.0776 |
| cosine_precision@5 | 0.0626 |
| cosine_precision@10 | 0.0464 |
| cosine_recall@1 | 0.1181 |
| cosine_recall@3 | 0.2328 |
| cosine_recall@5 | 0.3129 |
| cosine_recall@10 | 0.4644 |
| cosine_ndcg@10 | 0.2655 |
| cosine_mrr@10 | 0.2053 |
| **cosine_map@100** | **0.226** |
#### 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 | 0.1158 |
| cosine_accuracy@3 | 0.229 |
| cosine_accuracy@5 | 0.3113 |
| cosine_accuracy@10 | 0.4657 |
| cosine_precision@1 | 0.1158 |
| cosine_precision@3 | 0.0763 |
| cosine_precision@5 | 0.0623 |
| cosine_precision@10 | 0.0466 |
| cosine_recall@1 | 0.1158 |
| cosine_recall@3 | 0.229 |
| cosine_recall@5 | 0.3113 |
| cosine_recall@10 | 0.4657 |
| cosine_ndcg@10 | 0.2641 |
| cosine_mrr@10 | 0.2031 |
| **cosine_map@100** | **0.2236** |
#### 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 | 0.1191 |
| cosine_accuracy@3 | 0.2328 |
| cosine_accuracy@5 | 0.3176 |
| cosine_accuracy@10 | 0.4658 |
| cosine_precision@1 | 0.1191 |
| cosine_precision@3 | 0.0776 |
| cosine_precision@5 | 0.0635 |
| cosine_precision@10 | 0.0466 |
| cosine_recall@1 | 0.1191 |
| cosine_recall@3 | 0.2328 |
| cosine_recall@5 | 0.3176 |
| cosine_recall@10 | 0.4658 |
| cosine_ndcg@10 | 0.2667 |
| cosine_mrr@10 | 0.2064 |
| **cosine_map@100** | **0.2267** |
#### 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 | 0.1153 |
| cosine_accuracy@3 | 0.2266 |
| cosine_accuracy@5 | 0.3086 |
| cosine_accuracy@10 | 0.4567 |
| cosine_precision@1 | 0.1153 |
| cosine_precision@3 | 0.0755 |
| cosine_precision@5 | 0.0617 |
| cosine_precision@10 | 0.0457 |
| cosine_recall@1 | 0.1153 |
| cosine_recall@3 | 0.2266 |
| cosine_recall@5 | 0.3086 |
| cosine_recall@10 | 0.4567 |
| cosine_ndcg@10 | 0.2604 |
| cosine_mrr@10 | 0.201 |
| **cosine_map@100** | **0.2217** |
#### 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 | 0.1118 |
| cosine_accuracy@3 | 0.2233 |
| cosine_accuracy@5 | 0.3025 |
| cosine_accuracy@10 | 0.4529 |
| cosine_precision@1 | 0.1118 |
| cosine_precision@3 | 0.0744 |
| cosine_precision@5 | 0.0605 |
| cosine_precision@10 | 0.0453 |
| cosine_recall@1 | 0.1118 |
| cosine_recall@3 | 0.2233 |
| cosine_recall@5 | 0.3025 |
| cosine_recall@10 | 0.4529 |
| cosine_ndcg@10 | 0.2566 |
| cosine_mrr@10 | 0.1972 |
| **cosine_map@100** | **0.2178** |
#### 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 | 0.1069 |
| cosine_accuracy@3 | 0.2125 |
| cosine_accuracy@5 | 0.2885 |
| cosine_accuracy@10 | 0.4297 |
| cosine_precision@1 | 0.1069 |
| cosine_precision@3 | 0.0708 |
| cosine_precision@5 | 0.0577 |
| cosine_precision@10 | 0.043 |
| cosine_recall@1 | 0.1069 |
| cosine_recall@3 | 0.2125 |
| cosine_recall@5 | 0.2885 |
| cosine_recall@10 | 0.4297 |
| cosine_ndcg@10 | 0.2438 |
| cosine_mrr@10 | 0.1876 |
| **cosine_map@100** | **0.2081** |
<!--
## 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
#### json
* Dataset: json
* Size: 2,844 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: 3 tokens</li><li>mean: 49.45 tokens</li><li>max: 148 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 20.94 tokens</li><li>max: 45 tokens</li></ul> |
* Samples:
| positive | anchor |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------|
| <code>L'Ajuntament de Sitges atorga subvencions per a projectes i activitats d'interès públic o social que tinguin per finalitat les activitats esportives federades, escolars o populars desenvolupades per les entitats esportives i esportistes del municipi de Sitges.</code> | <code>Quin és el benefici de les subvencions per a les entitats esportives?</code> |
| <code>Per a poder ser beneficiari d'una subvenció per a un projecte o activitat cultural, les entitats o associacions culturals de Sitges han de tenir una seu social a la ciutat de Sitges i estar inscrites en el Registre d'Entitats de la Generalitat de Catalunya.</code> | <code>Quin és el requisit per a poder ser beneficiari d'una subvenció per a un projecte o activitat cultural?</code> |
| <code>La cessió entre tercers, només es contempla en el cas de sepultures de construcció particular que hagin estat donades d'alta amb una anterioritat de 10 anys a la data de sol·licitud de la cessió.</code> | <code>Quin és el paper de la persona que, legalment hi tingui dret, en la cessió entre tercers?</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
1024,
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
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`: 5
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.2
- `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
- `torch_empty_cache_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`: 5
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.2
- `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
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training 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 |
|:----------:|:------:|:-------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.8989 | 10 | 3.2114 | - | - | - | - | - | - |
| 0.9888 | 11 | - | 0.2144 | 0.2008 | 0.2070 | 0.2126 | 0.1842 | 0.2126 |
| 1.7978 | 20 | 1.5622 | - | - | - | - | - | - |
| 1.9775 | 22 | - | 0.2179 | 0.2101 | 0.2169 | 0.2180 | 0.2012 | 0.2193 |
| 2.6966 | 30 | 0.7882 | - | - | - | - | - | - |
| 2.9663 | 33 | - | 0.2239 | 0.2162 | 0.2220 | 0.2238 | 0.2070 | 0.2222 |
| 3.5955 | 40 | 0.4956 | - | - | - | - | - | - |
| 3.9551 | 44 | - | 0.2270 | 0.2177 | 0.2231 | 0.2278 | 0.2084 | 0.2255 |
| 4.4944 | 50 | 0.392 | - | - | - | - | - | - |
| **4.9438** | **55** | **-** | **0.226** | **0.2178** | **0.2217** | **0.2267** | **0.2081** | **0.2236** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.1
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Accelerate: 0.35.0.dev0
- Datasets: 3.0.1
- 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.*
--> |