File size: 29,416 Bytes
c15a5c6 |
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 |
---
base_model: BAAI/bge-large-en-v1.5
datasets: []
language:
- en
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:530
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: If you receive a BharatPe speaker that you didn't order, please
contact BharatPe support immediately. They will assist in resolving the issue
and advise on the next steps.
sentences:
- Can I control multiple BharatPe speakers from one app?
- What to do if the BharatPe speaker's transaction announcements are intermittently
silent?
- What should I do if I receive a BharatPe speaker without ordering it?
- source_sentence: Remote control capabilities depend on the model of the BharatPe
speaker. Check if your model supports remote control through the BharatPe app
or a connected device.
sentences:
- How do I update my personal details in my Bharatpe account?
- What are the benefits of the BharatPe speaker?
- Can I control the BharatPe speaker remotely?
- source_sentence: If the announcements are not clear, check the speaker's volume
settings and ensure it's not placed near noisy equipment. If clarity doesn't improve,
the speaker may need servicing.
sentences:
- What to do if my BharatPe speaker is not syncing with the transaction history
in the app?
- What should I do if the speaker is not announcing payments clearly?
- The speaker doesn't produce any sound, what can be done?
- source_sentence: If the speaker is causing interference, try relocating it or other
devices to reduce the interference. Ensure there's a reasonable distance between
the speaker and other wireless equipment.
sentences:
- Can I use my Bharatpe device for international transactions?
- How do I know if my BharatPe speaker is under warranty?
- What should I do if the BharatPe speaker is causing interference with other wireless
devices?
- source_sentence: I can understand and respond in multiple Indian regional languages.
Feel free to communicate with me in the language you're most comfortable with.
sentences:
- How can I check if the BharatPe speaker is receiving a network signal?
- Bharti, can you provide tips for effective online communication?
- Bharti, what languages can you understand and respond to?
model-index:
- name: BGE large Chatbot Matryoshka
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.8837209302325582
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9534883720930233
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9534883720930233
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9534883720930233
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.8837209302325582
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3178294573643411
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19069767441860463
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09534883720930232
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8837209302325582
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9534883720930233
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9534883720930233
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9534883720930233
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9246944071428586
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9147286821705425
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9186317558410582
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.8837209302325582
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9534883720930233
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9534883720930233
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9534883720930233
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.8837209302325582
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3178294573643411
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19069767441860463
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09534883720930232
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8837209302325582
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9534883720930233
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9534883720930233
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9534883720930233
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9246944071428586
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9147286821705425
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9186317558410582
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.8837209302325582
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9302325581395349
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9534883720930233
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9534883720930233
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.8837209302325582
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.31007751937984496
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19069767441860463
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09534883720930232
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8837209302325582
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9302325581395349
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9534883720930233
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9534883720930233
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9220630770785455
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9116279069767442
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9147848047984846
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.9069767441860465
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9302325581395349
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9302325581395349
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9534883720930233
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.9069767441860465
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.31007751937984496
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.18604651162790697
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09534883720930232
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.9069767441860465
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9302325581395349
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9302325581395349
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9534883720930233
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9299334172251043
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9224806201550388
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.92549351912877
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.8604651162790697
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9534883720930233
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9767441860465116
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9767441860465116
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.8604651162790697
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3178294573643411
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1953488372093023
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09767441860465115
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8604651162790697
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9534883720930233
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9767441860465116
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9767441860465116
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9261271120648318
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9089147286821706
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9089147286821704
name: Cosine Map@100
---
# BGE large Chatbot Matryoshka
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5). 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-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) <!-- at revision d4aa6901d3a41ba39fb536a557fa166f842b0e09 -->
- **Maximum Sequence Length:** 512 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': 512, 'do_lower_case': True}) with Transformer model: BertModel
(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("MANMEET75/bge-large-Chatbot-matryoshka")
# Run inference
sentences = [
"I can understand and respond in multiple Indian regional languages. Feel free to communicate with me in the language you're most comfortable with.",
'Bharti, what languages can you understand and respond to?',
'Bharti, can you provide tips for effective online communication?',
]
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 | 0.8837 |
| cosine_accuracy@3 | 0.9535 |
| cosine_accuracy@5 | 0.9535 |
| cosine_accuracy@10 | 0.9535 |
| cosine_precision@1 | 0.8837 |
| cosine_precision@3 | 0.3178 |
| cosine_precision@5 | 0.1907 |
| cosine_precision@10 | 0.0953 |
| cosine_recall@1 | 0.8837 |
| cosine_recall@3 | 0.9535 |
| cosine_recall@5 | 0.9535 |
| cosine_recall@10 | 0.9535 |
| cosine_ndcg@10 | 0.9247 |
| cosine_mrr@10 | 0.9147 |
| **cosine_map@100** | **0.9186** |
#### 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 | 0.8837 |
| cosine_accuracy@3 | 0.9535 |
| cosine_accuracy@5 | 0.9535 |
| cosine_accuracy@10 | 0.9535 |
| cosine_precision@1 | 0.8837 |
| cosine_precision@3 | 0.3178 |
| cosine_precision@5 | 0.1907 |
| cosine_precision@10 | 0.0953 |
| cosine_recall@1 | 0.8837 |
| cosine_recall@3 | 0.9535 |
| cosine_recall@5 | 0.9535 |
| cosine_recall@10 | 0.9535 |
| cosine_ndcg@10 | 0.9247 |
| cosine_mrr@10 | 0.9147 |
| **cosine_map@100** | **0.9186** |
#### 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 | 0.8837 |
| cosine_accuracy@3 | 0.9302 |
| cosine_accuracy@5 | 0.9535 |
| cosine_accuracy@10 | 0.9535 |
| cosine_precision@1 | 0.8837 |
| cosine_precision@3 | 0.3101 |
| cosine_precision@5 | 0.1907 |
| cosine_precision@10 | 0.0953 |
| cosine_recall@1 | 0.8837 |
| cosine_recall@3 | 0.9302 |
| cosine_recall@5 | 0.9535 |
| cosine_recall@10 | 0.9535 |
| cosine_ndcg@10 | 0.9221 |
| cosine_mrr@10 | 0.9116 |
| **cosine_map@100** | **0.9148** |
#### 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 | 0.907 |
| cosine_accuracy@3 | 0.9302 |
| cosine_accuracy@5 | 0.9302 |
| cosine_accuracy@10 | 0.9535 |
| cosine_precision@1 | 0.907 |
| cosine_precision@3 | 0.3101 |
| cosine_precision@5 | 0.186 |
| cosine_precision@10 | 0.0953 |
| cosine_recall@1 | 0.907 |
| cosine_recall@3 | 0.9302 |
| cosine_recall@5 | 0.9302 |
| cosine_recall@10 | 0.9535 |
| cosine_ndcg@10 | 0.9299 |
| cosine_mrr@10 | 0.9225 |
| **cosine_map@100** | **0.9255** |
#### 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 | 0.8605 |
| cosine_accuracy@3 | 0.9535 |
| cosine_accuracy@5 | 0.9767 |
| cosine_accuracy@10 | 0.9767 |
| cosine_precision@1 | 0.8605 |
| cosine_precision@3 | 0.3178 |
| cosine_precision@5 | 0.1953 |
| cosine_precision@10 | 0.0977 |
| cosine_recall@1 | 0.8605 |
| cosine_recall@3 | 0.9535 |
| cosine_recall@5 | 0.9767 |
| cosine_recall@10 | 0.9767 |
| cosine_ndcg@10 | 0.9261 |
| cosine_mrr@10 | 0.9089 |
| **cosine_map@100** | **0.9089** |
<!--
## 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: 530 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: 35.33 tokens</li><li>max: 99 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 17.3 tokens</li><li>max: 29 tokens</li></ul> |
* Samples:
| positive | anchor |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------|
| <code>BharatPe Speaker comes with the following benefits: - Helps you avoid payment fraud - Lightweight & Easy installation process - Compatible with SIM & GPRS connectivity - Comes with a battery, no hassle of constant charging - Available in 10 Languages - Cashback Offers - Free replacement To Know more and place an order, tap below http://bharatpe.in/speaker.</code> | <code>What are the benefits of the BharatPe speaker?</code> |
| <code>BharatPe Speaker comes with the following benefits: - Helps you avoid payment fraud - Lightweight & Easy installation process - Compatible with SIM & GPRS connectivity - Comes with a battery, no hassle of constant charging - Available in 10 Languages - Cashback Offers - Free replacement To Know more and place an order, tap below http://bharatpe.in/speaker.</code> | <code>What advantages does the BharatPe speaker offer?</code> |
| <code>BharatPe Speaker comes with the following benefits: - Helps you avoid payment fraud - Lightweight & Easy installation process - Compatible with SIM & GPRS connectivity - Comes with a battery, no hassle of constant charging - Available in 10 Languages - Cashback Offers - Free replacement To Know more and place an order, tap below http://bharatpe.in/speaker.</code> | <code>Can you outline the benefits of using the BharatPe speaker?</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`: 10
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `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`: 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`: 10
- `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`: False
- `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
- `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 |
|:----------:|:-----:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.9412 | 1 | - | 0.7980 | 0.8251 | 0.8141 | 0.7124 | 0.8260 |
| 1.8824 | 2 | - | 0.8624 | 0.8619 | 0.8691 | 0.7637 | 0.8557 |
| 2.8235 | 3 | - | 0.8763 | 0.8792 | 0.8770 | 0.8588 | 0.8832 |
| 3.7647 | 4 | - | 0.9007 | 0.9014 | 0.9115 | 0.8820 | 0.9130 |
| 4.7059 | 5 | - | 0.9014 | 0.9146 | 0.9186 | 0.9053 | 0.9185 |
| 5.6471 | 6 | - | 0.9134 | 0.9146 | 0.9186 | 0.9205 | 0.9183 |
| **6.5882** | **7** | **-** | **0.9255** | **0.9146** | **0.9186** | **0.9089** | **0.9185** |
| 7.5294 | 8 | - | 0.9255 | 0.9147 | 0.9186 | 0.9089 | 0.9185 |
| 8.4706 | 9 | - | 0.9255 | 0.9147 | 0.9186 | 0.9089 | 0.9186 |
| 9.4118 | 10 | 2.0337 | 0.9255 | 0.9148 | 0.9186 | 0.9089 | 0.9186 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.32.1
- 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.*
--> |