File size: 28,993 Bytes
c98c0eb |
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 |
---
base_model: projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base
datasets: []
language:
- ca
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:4173
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 'Queixa: Deixar constància de la vostra disconformitat per un mal
servei (un tracte inapropiat, un temps d''espera excessiu, etc.), sense demanar
cap indemnització.'
sentences:
- Quin és el format de sortida del tràmit de baixa de la llicència de gual?
- Quin és el tipus de venda que es realitza en els mercats setmanals?
- Quin és el paper de la queixa en la resolució de conflictes?
- source_sentence: L'empleat que en l'exercici de les seves tasques tingui assignada
la funció de conducció de vehicles municipals, pot sol·licitar un ajut per les
despeses ocasionades per a la renovació del carnet de conduir (certificat mèdic
i administratiu).
sentences:
- Quin és el resultat esperat de les escoles que reben les subvencions?
- Quin és el requisit per obtenir una autorització d'estacionament?
- Quin és el requisit per a sol·licitar l'ajut social?
- source_sentence: Aportació de documentació. Subvencions per finançar despeses d'hipoteca,
subministrament i altres serveis i la manca d'ingressos de lloguer de les entitats
culturals
sentences:
- Quin és el propòsit de la documentació?
- Quin és el paper del públic assistent en el Ple Municipal?
- Quin és el paper de l'ajuntament en la renovació del carnet de persona cuidadora?
- source_sentence: la Fira de la Vila del Llibre de Sitges consistent en un conjunt
de parades instal·lades al Passeig Marítim
sentences:
- Quin és el paper de la llicència de parcel·lació en la construcció d'edificacions?
- Quin és l'objectiu del tràmit de participació en processos de selecció de personal
de l'Ajuntament?
- Quin és el lloc on es desenvolupa la Fira de la Vila del Llibre de Sitges?
- source_sentence: Mitjançant aquest tràmit la persona interessada posa en coneixement
de l'Ajuntament de Sitges l'inici d'un espectacle públic o activitat recreativa
de caràcter extraordinari...
sentences:
- Quin és el paper de la persona interessada en la llicència per a espectacles públics
o activitats recreatives de caràcter extraordinari?
- Quin és el paper del Registre de Sol·licitants d'Habitatge amb Protecció Oficial
en la gestió d'habitatges?
- Quin és el tipus de familiars que es tenen en compte per l'ajut especial?
model-index:
- name: BGE SITGES CAT
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.07327586206896551
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.15732758620689655
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.21767241379310345
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.39439655172413796
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.07327586206896551
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.05244252873563218
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.043534482758620686
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.03943965517241379
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.07327586206896551
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.15732758620689655
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.21767241379310345
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.39439655172413796
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.20125893142070614
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.14385604816639316
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.17098930660026063
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.07327586206896551
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.15086206896551724
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.21767241379310345
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.39439655172413796
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.07327586206896551
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.050287356321839075
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.04353448275862069
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.03943965517241379
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.07327586206896551
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.15086206896551724
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.21767241379310345
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.39439655172413796
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2016207682773376
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.14438799945265474
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.1715919733142084
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.07327586206896551
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.14870689655172414
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.21120689655172414
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.40086206896551724
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.07327586206896551
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.04956896551724138
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.04224137931034483
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.04008620689655173
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.07327586206896551
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.14870689655172414
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.21120689655172414
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.40086206896551724
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2021149795452301
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.1433856732348113
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.16973847535400444
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.06896551724137931
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.14655172413793102
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.21767241379310345
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.38146551724137934
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.06896551724137931
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.048850574712643674
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.04353448275862069
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.03814655172413793
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.06896551724137931
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.14655172413793102
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.21767241379310345
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.38146551724137934
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.19535554125135882
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.1398416119321293
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.16597320243564267
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.05603448275862069
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.13793103448275862
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.1939655172413793
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.36853448275862066
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.05603448275862069
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.04597701149425287
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.03879310344827586
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.03685344827586207
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.05603448275862069
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.13793103448275862
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.1939655172413793
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.36853448275862066
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.18225870966588442
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.12688492063492074
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.15425908300208627
name: Cosine Map@100
---
# BGE SITGES CAT
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base](https://huggingface.co./projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base). 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:** [projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base](https://huggingface.co./projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base) <!-- at revision 3354aea2cb9d91091495e9f1e1241b488f32e47c -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** ca
- **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': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("adriansanz/SITGES-aina4_moreseq")
# Run inference
sentences = [
"Mitjançant aquest tràmit la persona interessada posa en coneixement de l'Ajuntament de Sitges l'inici d'un espectacle públic o activitat recreativa de caràcter extraordinari...",
'Quin és el paper de la persona interessada en la llicència per a espectacles públics o activitats recreatives de caràcter extraordinari?',
"Quin és el paper del Registre de Sol·licitants d'Habitatge amb Protecció Oficial en la gestió d'habitatges?",
]
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 | 0.0733 |
| cosine_accuracy@3 | 0.1573 |
| cosine_accuracy@5 | 0.2177 |
| cosine_accuracy@10 | 0.3944 |
| cosine_precision@1 | 0.0733 |
| cosine_precision@3 | 0.0524 |
| cosine_precision@5 | 0.0435 |
| cosine_precision@10 | 0.0394 |
| cosine_recall@1 | 0.0733 |
| cosine_recall@3 | 0.1573 |
| cosine_recall@5 | 0.2177 |
| cosine_recall@10 | 0.3944 |
| cosine_ndcg@10 | 0.2013 |
| cosine_mrr@10 | 0.1439 |
| **cosine_map@100** | **0.171** |
#### 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 | 0.0733 |
| cosine_accuracy@3 | 0.1509 |
| cosine_accuracy@5 | 0.2177 |
| cosine_accuracy@10 | 0.3944 |
| cosine_precision@1 | 0.0733 |
| cosine_precision@3 | 0.0503 |
| cosine_precision@5 | 0.0435 |
| cosine_precision@10 | 0.0394 |
| cosine_recall@1 | 0.0733 |
| cosine_recall@3 | 0.1509 |
| cosine_recall@5 | 0.2177 |
| cosine_recall@10 | 0.3944 |
| cosine_ndcg@10 | 0.2016 |
| cosine_mrr@10 | 0.1444 |
| **cosine_map@100** | **0.1716** |
#### 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 | 0.0733 |
| cosine_accuracy@3 | 0.1487 |
| cosine_accuracy@5 | 0.2112 |
| cosine_accuracy@10 | 0.4009 |
| cosine_precision@1 | 0.0733 |
| cosine_precision@3 | 0.0496 |
| cosine_precision@5 | 0.0422 |
| cosine_precision@10 | 0.0401 |
| cosine_recall@1 | 0.0733 |
| cosine_recall@3 | 0.1487 |
| cosine_recall@5 | 0.2112 |
| cosine_recall@10 | 0.4009 |
| cosine_ndcg@10 | 0.2021 |
| cosine_mrr@10 | 0.1434 |
| **cosine_map@100** | **0.1697** |
#### 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 | 0.069 |
| cosine_accuracy@3 | 0.1466 |
| cosine_accuracy@5 | 0.2177 |
| cosine_accuracy@10 | 0.3815 |
| cosine_precision@1 | 0.069 |
| cosine_precision@3 | 0.0489 |
| cosine_precision@5 | 0.0435 |
| cosine_precision@10 | 0.0381 |
| cosine_recall@1 | 0.069 |
| cosine_recall@3 | 0.1466 |
| cosine_recall@5 | 0.2177 |
| cosine_recall@10 | 0.3815 |
| cosine_ndcg@10 | 0.1954 |
| cosine_mrr@10 | 0.1398 |
| **cosine_map@100** | **0.166** |
#### 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 | 0.056 |
| cosine_accuracy@3 | 0.1379 |
| cosine_accuracy@5 | 0.194 |
| cosine_accuracy@10 | 0.3685 |
| cosine_precision@1 | 0.056 |
| cosine_precision@3 | 0.046 |
| cosine_precision@5 | 0.0388 |
| cosine_precision@10 | 0.0369 |
| cosine_recall@1 | 0.056 |
| cosine_recall@3 | 0.1379 |
| cosine_recall@5 | 0.194 |
| cosine_recall@10 | 0.3685 |
| cosine_ndcg@10 | 0.1823 |
| cosine_mrr@10 | 0.1269 |
| **cosine_map@100** | **0.1543** |
<!--
## 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`: False
- `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`: False
- `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_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|:----------:|:------:|:-------------:|:----------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.3065 | 5 | 3.3947 | - | - | - | - | - | - |
| 0.6130 | 10 | 2.6401 | - | - | - | - | - | - |
| 0.9195 | 15 | 2.0152 | - | - | - | - | - | - |
| 0.9808 | 16 | - | 1.3404 | 0.1639 | 0.1577 | 0.1694 | 0.1503 | 0.1638 |
| 1.2261 | 20 | 1.4542 | - | - | - | - | - | - |
| 1.5326 | 25 | 1.0135 | - | - | - | - | - | - |
| 1.8391 | 30 | 0.8437 | - | - | - | - | - | - |
| 1.9617 | 32 | - | 0.9436 | 0.1556 | 0.1596 | 0.1600 | 0.1467 | 0.1701 |
| 2.1456 | 35 | 0.7676 | - | - | - | - | - | - |
| 2.4521 | 40 | 0.5126 | - | - | - | - | - | - |
| 2.7586 | 45 | 0.4358 | - | - | - | - | - | - |
| 2.9425 | 48 | - | 0.7852 | 0.1650 | 0.1693 | 0.1720 | 0.1511 | 0.1686 |
| 3.0651 | 50 | 0.4192 | - | - | - | - | - | - |
| 3.3716 | 55 | 0.3429 | - | - | - | - | - | - |
| 3.6782 | 60 | 0.3025 | - | - | - | - | - | - |
| 3.9847 | 65 | 0.2863 | 0.7401 | 0.1646 | 0.1706 | 0.1759 | 0.1480 | 0.1694 |
| 4.2912 | 70 | 0.2474 | - | - | - | - | - | - |
| 4.5977 | 75 | 0.2324 | - | - | - | - | - | - |
| 4.9042 | 80 | 0.2344 | - | - | - | - | - | - |
| 4.9655 | 81 | - | 0.7217 | 0.1663 | 0.1699 | 0.1767 | 0.1512 | 0.1696 |
| 5.2107 | 85 | 0.2181 | - | - | - | - | - | - |
| 5.5172 | 90 | 0.2116 | - | - | - | - | - | - |
| 5.8238 | 95 | 0.1926 | - | - | - | - | - | - |
| **5.8851** | **96** | **-** | **0.7154** | **0.166** | **0.1697** | **0.1716** | **0.1543** | **0.171** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.3
- PyTorch: 2.3.1+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.*
--> |