File size: 27,193 Bytes
ee1fedc c890bd2 ee1fedc |
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 |
---
base_model: BAAI/bge-m3
language:
- ko
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: 하이브리다이저란 무엇인가요?
sentences:
- 하이퍼바이저는 보안에서 어떤 역할을 합니까?
- 지난 몇 년간 CUDA 생태계는 어떻게 발전해 왔나요?
- 로컬 메모리 액세스 성능을 결정하는 요소는 무엇입니까?
- source_sentence: 임시 구독의 용도는 무엇입니까?
sentences:
- 메모리 액세스 최적화에서 프리패치의 역할은 무엇입니까?
- CUDA 인식 MPI는 확장 측면에서 어떻게 작동합니까?
- CUDA 8이 해결하는 계산상의 과제에는 어떤 것이 있습니까?
- source_sentence: '''saxpy''는 무엇을 뜻하나요?'
sentences:
- CUDA C/C++의 맥락에서 SAXPY는 무엇입니까?
- Numba는 다른 GPU 가속 방법과 어떻게 다른가요?
- 장치 LTO는 CUDA 애플리케이션에 어떤 이점을 제공합니까?
- source_sentence: USD/Hydra란 무엇인가요?
sentences:
- 쿠다란 무엇인가요?
- y 미분 계산에 사용되는 접근 방식의 단점은 무엇입니까?
- Pascal 아키텍처는 통합 메모리를 어떻게 개선합니까?
- source_sentence: CUDAcast란 무엇인가요?
sentences:
- CUDACast 시리즈에서는 어떤 주제를 다룰 예정인가요?
- 이 게시물에 기여한 것으로 인정받은 사람은 누구입니까?
- WSL 2에서 NVML의 목적은 무엇입니까?
model-index:
- name: BGE base Financial Matryoshka
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.5443037974683544
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7749648382559775
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8523206751054853
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9409282700421941
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5443037974683544
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2583216127519925
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17046413502109703
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09409282700421939
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5443037974683544
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7749648382559775
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8523206751054853
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9409282700421941
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7411108924386547
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.677065054807671
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6802131506478553
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.5386779184247539
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7749648382559775
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8593530239099859
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9451476793248945
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5386779184247539
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2583216127519925
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17187060478199717
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09451476793248943
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5386779184247539
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7749648382559775
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8593530239099859
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9451476793248945
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7413571133247474
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6759917844306029
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.678939165210132
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.540084388185654
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7791842475386779
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8621659634317862
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9423347398030942
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.540084388185654
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.25972808251289264
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1724331926863572
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09423347398030943
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.540084388185654
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7791842475386779
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8621659634317862
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9423347398030942
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7403981257690416
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6756379344986938
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6787046866761269
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.5218002812939522
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7679324894514767
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8635724331926864
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9367088607594937
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5218002812939522
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2559774964838256
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17271448663853725
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09367088607594935
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5218002812939522
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7679324894514767
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8635724331926864
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9367088607594937
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7305864977688176
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6641673922264634
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6671648971944116
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.509142053445851
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7426160337552743
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8284106891701828
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9310829817158931
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.509142053445851
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.24753867791842477
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16568213783403654
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09310829817158929
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.509142053445851
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7426160337552743
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8284106891701828
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9310829817158931
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7135661304090457
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6444829549259928
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6474431148702396
name: Cosine Map@100
---
# BGE base Financial Matryoshka
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:** 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': 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("sentence_transformers_model_id")
# Run inference
sentences = [
'CUDAcast란 무엇인가요?',
'CUDACast 시리즈에서는 어떤 주제를 다룰 예정인가요?',
'이 게시물에 기여한 것으로 인정받은 사람은 누구입니까?',
]
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_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 | 0.5443 |
| cosine_accuracy@3 | 0.775 |
| cosine_accuracy@5 | 0.8523 |
| cosine_accuracy@10 | 0.9409 |
| cosine_precision@1 | 0.5443 |
| cosine_precision@3 | 0.2583 |
| cosine_precision@5 | 0.1705 |
| cosine_precision@10 | 0.0941 |
| cosine_recall@1 | 0.5443 |
| cosine_recall@3 | 0.775 |
| cosine_recall@5 | 0.8523 |
| cosine_recall@10 | 0.9409 |
| cosine_ndcg@10 | 0.7411 |
| cosine_mrr@10 | 0.6771 |
| **cosine_map@100** | **0.6802** |
#### 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 | 0.5387 |
| cosine_accuracy@3 | 0.775 |
| cosine_accuracy@5 | 0.8594 |
| cosine_accuracy@10 | 0.9451 |
| cosine_precision@1 | 0.5387 |
| cosine_precision@3 | 0.2583 |
| cosine_precision@5 | 0.1719 |
| cosine_precision@10 | 0.0945 |
| cosine_recall@1 | 0.5387 |
| cosine_recall@3 | 0.775 |
| cosine_recall@5 | 0.8594 |
| cosine_recall@10 | 0.9451 |
| cosine_ndcg@10 | 0.7414 |
| cosine_mrr@10 | 0.676 |
| **cosine_map@100** | **0.6789** |
#### 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 | 0.5401 |
| cosine_accuracy@3 | 0.7792 |
| cosine_accuracy@5 | 0.8622 |
| cosine_accuracy@10 | 0.9423 |
| cosine_precision@1 | 0.5401 |
| cosine_precision@3 | 0.2597 |
| cosine_precision@5 | 0.1724 |
| cosine_precision@10 | 0.0942 |
| cosine_recall@1 | 0.5401 |
| cosine_recall@3 | 0.7792 |
| cosine_recall@5 | 0.8622 |
| cosine_recall@10 | 0.9423 |
| cosine_ndcg@10 | 0.7404 |
| cosine_mrr@10 | 0.6756 |
| **cosine_map@100** | **0.6787** |
#### 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 | 0.5218 |
| cosine_accuracy@3 | 0.7679 |
| cosine_accuracy@5 | 0.8636 |
| cosine_accuracy@10 | 0.9367 |
| cosine_precision@1 | 0.5218 |
| cosine_precision@3 | 0.256 |
| cosine_precision@5 | 0.1727 |
| cosine_precision@10 | 0.0937 |
| cosine_recall@1 | 0.5218 |
| cosine_recall@3 | 0.7679 |
| cosine_recall@5 | 0.8636 |
| cosine_recall@10 | 0.9367 |
| cosine_ndcg@10 | 0.7306 |
| cosine_mrr@10 | 0.6642 |
| **cosine_map@100** | **0.6672** |
#### 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 | 0.5091 |
| cosine_accuracy@3 | 0.7426 |
| cosine_accuracy@5 | 0.8284 |
| cosine_accuracy@10 | 0.9311 |
| cosine_precision@1 | 0.5091 |
| cosine_precision@3 | 0.2475 |
| cosine_precision@5 | 0.1657 |
| cosine_precision@10 | 0.0931 |
| cosine_recall@1 | 0.5091 |
| cosine_recall@3 | 0.7426 |
| cosine_recall@5 | 0.8284 |
| cosine_recall@10 | 0.9311 |
| cosine_ndcg@10 | 0.7136 |
| cosine_mrr@10 | 0.6445 |
| **cosine_map@100** | **0.6474** |
<!--
## 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: 6,397 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: 11 tokens</li><li>mean: 48.46 tokens</li><li>max: 107 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 21.0 tokens</li><li>max: 48 tokens</li></ul> |
* Samples:
| positive | anchor |
|:------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------|
| <code>Warp-stride 및 block-stride 루프는 스레드 동작을 재구성하고 공유 메모리 액세스 패턴을 최적화하는 데 사용되었습니다.</code> | <code>코드에서 공유 메모리 액세스 패턴을 최적화하기 위해 어떤 유형의 루프가 사용되었습니까?</code> |
| <code>Nsight Compute의 규칙은 성능 병목 현상을 식별하기 위한 구조화된 프레임워크를 제공하고 최적화 프로세스를 간소화하기 위한 실행 가능한 통찰력을 제공합니다.</code> | <code>Nsight Compute의 맥락에서 규칙이 중요한 이유는 무엇입니까?</code> |
| <code>NVIDIA Nsight와 같은 도구의 가용성으로 인해 개발자가 단일 GPU에서 디버깅할 수 있게 되어 CUDA 개발 속도가 크게 향상되었습니다. CUDA 메모리 검사기는 메모리 액세스 문제를 식별하여 코드 품질을 향상시키는 데 도움이 됩니다.</code> | <code>디버깅 도구의 가용성이 CUDA 개발에 어떤 영향을 미쳤습니까?</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`: 32
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `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`: 32
- `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`: 3
- `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
| 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 |
|:--------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.8 | 10 | 1.3103 | - | - | - | - | - |
| 0.96 | 12 | - | 0.6512 | 0.6539 | 0.6688 | 0.6172 | 0.6679 |
| 1.6 | 20 | 0.4148 | - | - | - | - | - |
| 2.0 | 25 | - | 0.6615 | 0.6688 | 0.6783 | 0.6417 | 0.6763 |
| 2.4 | 30 | 0.2683 | - | - | - | - | - |
| **2.88** | **36** | **-** | **0.6672** | **0.6787** | **0.6789** | **0.6474** | **0.6802** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.0
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.31.0
- Datasets: 2.18.0
- 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.*
--> |