File size: 28,556 Bytes
45e9f65 |
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 |
---
language:
- en
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:1K<n<10K
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
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
widget:
- source_sentence: What is the title of Item 6 in the text?
sentences:
- What is the title of Item 8 in the document?
- What was the total premiums revenue for the Insurance segment in 2023?
- How much were the net cash flows from investing activities in 2023 and 2022?
- source_sentence: Which markets does Garmin primarily serve?
sentences:
- What types of products are offered in Garmin's Fitness segment?
- In 2023, AbbVie's net revenue in the United States was $41,883 million.
- As of December 31, 2023, the total deferred income tax asset was $1,157,486.
- source_sentence: What was the effective tax rate in 2023?
sentences:
- What was the effective tax rate for 2023 and how did it compare to 2022?
- What are the various diversity, equity, and inclusion councils at AMC?
- What is the title of the section that discusses legal issues in the document?
- source_sentence: What begins on page 105 of this report?
sentences:
- Where can one find the details pertaining to Legal Proceedings in the report?
- What are the technological features of the GeForce RTX 40 Series GPUs?
- Changes in foreign exchange rates reduced cost of sales by $254 million in 2023.
- source_sentence: How is Costco's fiscal year structured?
sentences:
- How many weeks did the fiscal years 2023 and 2022 include?
- What is the process for using reinsurers not on the authorized list?
- What contributed to the increase in Google Services' operating income in 2023?
pipeline_tag: sentence-similarity
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: 0.6814285714285714
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8128571428571428
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.85
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9028571428571428
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6814285714285714
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.27095238095238094
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16999999999999996
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09028571428571427
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6814285714285714
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8128571428571428
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.85
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9028571428571428
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7916721734405803
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7562692743764173
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7609992859917654
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.6842857142857143
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8114285714285714
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8528571428571429
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8985714285714286
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6842857142857143
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2704761904761905
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17057142857142854
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08985714285714284
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6842857142857143
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8114285714285714
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8528571428571429
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8985714285714286
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7909210075399126
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.756487528344671
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.761586340523296
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.6785714285714286
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8085714285714286
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8428571428571429
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8942857142857142
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6785714285714286
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2695238095238095
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16857142857142854
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08942857142857143
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6785714285714286
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8085714285714286
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8428571428571429
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8942857142857142
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7866298497982406
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.752303287981859
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7571741668436585
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.6714285714285714
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7857142857142857
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8257142857142857
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8814285714285715
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6714285714285714
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2619047619047619
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16514285714285715
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08814285714285713
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6714285714285714
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7857142857142857
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8257142857142857
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8814285714285715
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7742856481999635
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.740471655328798
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.745692801681558
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.6371428571428571
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7685714285714286
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8071428571428572
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8614285714285714
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6371428571428571
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2561904761904762
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16142857142857142
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08614285714285713
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6371428571428571
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7685714285714286
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8071428571428572
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8614285714285714
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7500703607138253
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7145918367346937
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7198995734568113
name: Cosine Map@100
---
# BGE base Financial Matryoshka
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("gK29382231121/bge-base-financial-matryoshka")
# Run inference
sentences = [
"How is Costco's fiscal year structured?",
'How many weeks did the fiscal years 2023 and 2022 include?',
'What is the process for using reinsurers not on the authorized list?',
]
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 | 0.6814 |
| cosine_accuracy@3 | 0.8129 |
| cosine_accuracy@5 | 0.85 |
| cosine_accuracy@10 | 0.9029 |
| cosine_precision@1 | 0.6814 |
| cosine_precision@3 | 0.271 |
| cosine_precision@5 | 0.17 |
| cosine_precision@10 | 0.0903 |
| cosine_recall@1 | 0.6814 |
| cosine_recall@3 | 0.8129 |
| cosine_recall@5 | 0.85 |
| cosine_recall@10 | 0.9029 |
| cosine_ndcg@10 | 0.7917 |
| cosine_mrr@10 | 0.7563 |
| **cosine_map@100** | **0.761** |
#### 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 | 0.6843 |
| cosine_accuracy@3 | 0.8114 |
| cosine_accuracy@5 | 0.8529 |
| cosine_accuracy@10 | 0.8986 |
| cosine_precision@1 | 0.6843 |
| cosine_precision@3 | 0.2705 |
| cosine_precision@5 | 0.1706 |
| cosine_precision@10 | 0.0899 |
| cosine_recall@1 | 0.6843 |
| cosine_recall@3 | 0.8114 |
| cosine_recall@5 | 0.8529 |
| cosine_recall@10 | 0.8986 |
| cosine_ndcg@10 | 0.7909 |
| cosine_mrr@10 | 0.7565 |
| **cosine_map@100** | **0.7616** |
#### 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 | 0.6786 |
| cosine_accuracy@3 | 0.8086 |
| cosine_accuracy@5 | 0.8429 |
| cosine_accuracy@10 | 0.8943 |
| cosine_precision@1 | 0.6786 |
| cosine_precision@3 | 0.2695 |
| cosine_precision@5 | 0.1686 |
| cosine_precision@10 | 0.0894 |
| cosine_recall@1 | 0.6786 |
| cosine_recall@3 | 0.8086 |
| cosine_recall@5 | 0.8429 |
| cosine_recall@10 | 0.8943 |
| cosine_ndcg@10 | 0.7866 |
| cosine_mrr@10 | 0.7523 |
| **cosine_map@100** | **0.7572** |
#### 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 | 0.6714 |
| cosine_accuracy@3 | 0.7857 |
| cosine_accuracy@5 | 0.8257 |
| cosine_accuracy@10 | 0.8814 |
| cosine_precision@1 | 0.6714 |
| cosine_precision@3 | 0.2619 |
| cosine_precision@5 | 0.1651 |
| cosine_precision@10 | 0.0881 |
| cosine_recall@1 | 0.6714 |
| cosine_recall@3 | 0.7857 |
| cosine_recall@5 | 0.8257 |
| cosine_recall@10 | 0.8814 |
| cosine_ndcg@10 | 0.7743 |
| cosine_mrr@10 | 0.7405 |
| **cosine_map@100** | **0.7457** |
#### 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 | 0.6371 |
| cosine_accuracy@3 | 0.7686 |
| cosine_accuracy@5 | 0.8071 |
| cosine_accuracy@10 | 0.8614 |
| cosine_precision@1 | 0.6371 |
| cosine_precision@3 | 0.2562 |
| cosine_precision@5 | 0.1614 |
| cosine_precision@10 | 0.0861 |
| cosine_recall@1 | 0.6371 |
| cosine_recall@3 | 0.7686 |
| cosine_recall@5 | 0.8071 |
| cosine_recall@10 | 0.8614 |
| cosine_ndcg@10 | 0.7501 |
| cosine_mrr@10 | 0.7146 |
| **cosine_map@100** | **0.7199** |
<!--
## 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: 6,300 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: 8 tokens</li><li>mean: 45.34 tokens</li><li>max: 439 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 20.47 tokens</li><li>max: 51 tokens</li></ul> |
* Samples:
| positive | anchor |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| <code>The HP GreenValley edge-to-cloud platform is used for software-defined disaggregated storage services that include HPE GreenLake for Block Storage and HPE GreenLake for File Storage, and it provides unified cloud-based management to simplify how customers manage storage.</code> | <code>What are the focus areas for the HP GreenLake platform?</code> |
| <code>Net income | $ | 1,550,190 | | $ | 854,800 | $ | 695,390 | 81.4 | %</code> | <code>By how much did net income increase in 2023 compared to 2022?</code> |
| <code>Deferred tax assets and deferred tax liabilities included in the Consolidated Balance Sheets as follows: As of October 31, 2023: Deferred tax assets were $3,155 million and Deferred tax liabilities were $44 million. As of October 31, 2022: Deferred tax assets were $2,167 million and Deferred tax liabilities were $121 million. The total net deferred tax assets were $3,111 million in 2023 and $2,046 million in 2022.</code> | <code>What was the change in HP's net deferred tax assets from 2022 to 2023?</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
- `num_train_epochs`: 4
- `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`: 4
- `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 |
|:------:|:----:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.8122 | 10 | 1.5361 | - | - | - | - | - |
| 0.9746 | 12 | - | 0.7280 | 0.7414 | 0.7494 | 0.6896 | 0.7470 |
| 1.6244 | 20 | 0.6833 | - | - | - | - | - |
| 1.9492 | 24 | - | 0.7426 | 0.7487 | 0.7573 | 0.7138 | 0.7592 |
| 2.4365 | 30 | 0.4674 | - | - | - | - | - |
| 2.9239 | 36 | - | 0.7452 | 0.7558 | 0.7624 | 0.7190 | 0.7623 |
| 3.2487 | 40 | 0.4038 | - | - | - | - | - |
| 3.8985 | 48 | - | 0.7457 | 0.7572 | 0.7616 | 0.7199 | 0.7610 |
### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.0.0
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.30.1
- Datasets: 2.19.1
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |