File size: 30,365 Bytes
58da7ec |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 |
---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:73
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: thenlper/gte-base
widget:
- source_sentence: What is the maximum value of equipment that can be purchased with
a CUE Student Research Project Grant?
sentences:
- Equipment costs (valued up to $1000).
- Variable awards to recognize and reward academic achievement at the senior high
school level and to encourage students to pursue post -secondary studies.
- The Amazon Future Engineer Scholarship provides students with an opportunity to
upgrade their careers with a $7,500 CAD/year scholarship available for up to four
years.
- source_sentence: What is the minimum distance a recipient's hometown must be from
Concordia University of Edmonton to be eligible for the Alberta Blue Cross Away
from Home Scholarship?
sentences:
- Three awards are available
- The recipient’s hometown must be at least 100 kilometres from Concordia University
of Edmonton.
- 'Application Deadline: September 1'
- source_sentence: According to the selection criteria, what level of subjects are
used to determine the academic standing of a potential Alberta Blue Cross Away
from Home Scholarship recipient?
sentences:
- Selection is ba sed on the academic standing of 30 -level subjects used for admission.
- 'These eligible and ineligible lists are not exhaustive. Doubts about the eligibility
of expenses should be directed to the ORI’s Research Administration Service s
(RAS): [email protected] .'
- '*Value: $11000 Master’s; $14,000 Doctoral'
- source_sentence: According to the text, how many days does a grant recipient have
to submit a final report after the grant ends?
sentences:
- All Fall grant recipients are expected to submit an abstract to present an oral
and/or poster presentation of their work, either in its progression or final stage.
- a business program offered by an Alberta college, polytechnic, or university that
offers the prerequisite courses required for entrance into the CPA Professional
Education Program (CPA PEP).
- The applicant is required to complete and submit a final report within 5 days
of the end of the grant.
- source_sentence: In what format should applicants acknowledge the funding provided
by Concordia University of Edmonton for their Student Project Grant?
sentences:
- All oral or poster presentations, publications, including public messages, arising
from research supported by CUE grants must acknowledge the support of the institution.
Acknowledgement can be in the written format, such as " This research is funded
by the generous support of Concordia University of Edmonton through their CUE
Student Research Project Grants program ", or similar phrasing.
- This $1,000 scholarship is awarded to post -secondary students who have completed
at least one year towards their Bachelor of Science with a focus on Computer Science,
achieved an average GPA of 3.5 or higher, and are still enrolled in post -secondary
studie s.
- The recipient will be selected based on the highest grade in MARK320. In the event
of a tie, preference will be given to the student with the highest cumulative
GPA.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
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: 0.5555555555555556
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 1.0
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 1.0
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5555555555555556
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.2
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.1
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5555555555555556
name: Cosine Recall@1
- type: cosine_recall@3
value: 1.0
name: Cosine Recall@3
- type: cosine_recall@5
value: 1.0
name: Cosine Recall@5
- type: cosine_recall@10
value: 1.0
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8214210289682637
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7592592592592592
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7592592592592592
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.4444444444444444
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8888888888888888
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 1.0
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.4444444444444444
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2962962962962963
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.2
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.1
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.4444444444444444
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8888888888888888
name: Cosine Recall@3
- type: cosine_recall@5
value: 1.0
name: Cosine Recall@5
- type: cosine_recall@10
value: 1.0
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7678413135022636
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6888888888888889
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6888888888888889
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.4444444444444444
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 1.0
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 1.0
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.4444444444444444
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.2
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.1
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.4444444444444444
name: Cosine Recall@1
- type: cosine_recall@3
value: 1.0
name: Cosine Recall@3
- type: cosine_recall@5
value: 1.0
name: Cosine Recall@5
- type: cosine_recall@10
value: 1.0
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7658654734127082
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6851851851851851
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6851851851851851
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.4444444444444444
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8888888888888888
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8888888888888888
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8888888888888888
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.4444444444444444
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2962962962962963
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17777777777777778
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08888888888888889
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.4444444444444444
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8888888888888888
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8888888888888888
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8888888888888888
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7103099178571526
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6481481481481483
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6521164021164021
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.6666666666666666
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.6666666666666666
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7777777777777778
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8888888888888888
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6666666666666666
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2222222222222222
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.15555555555555556
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08888888888888889
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6666666666666666
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.6666666666666666
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.7777777777777778
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8888888888888888
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7515566546007473
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7103174603174602
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.71494708994709
name: Cosine Map@100
---
# BGE base Financial Matryoshka
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [thenlper/gte-base](https://huggingface.co/thenlper/gte-base) on the json dataset. 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:** [thenlper/gte-base](https://huggingface.co/thenlper/gte-base) <!-- at revision c078288308d8dee004ab72c6191778064285ec0c -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- json
- **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': False}) with Transformer model: BertModel
(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})
(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("TatvaRA/gte-base-finetuned-schorlaships-matryonshka")
# Run inference
sentences = [
'In what format should applicants acknowledge the funding provided by Concordia University of Edmonton for their Student Project Grant?',
'All oral or poster presentations, publications, including public messages, arising from research supported by CUE grants must acknowledge the support of the institution. Acknowledgement can be in the written format, such as " This research is funded by the generous support of Concordia University of Edmonton through their CUE Student Research Project Grants program ", or similar phrasing.',
'The recipient will be selected based on the highest grade in MARK320. In the event of a tie, preference will be given to the student with the highest cumulative GPA.',
]
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) with these parameters:
```json
{
"truncate_dim": 768
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.5556 |
| cosine_accuracy@3 | 1.0 |
| cosine_accuracy@5 | 1.0 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.5556 |
| cosine_precision@3 | 0.3333 |
| cosine_precision@5 | 0.2 |
| cosine_precision@10 | 0.1 |
| cosine_recall@1 | 0.5556 |
| cosine_recall@3 | 1.0 |
| cosine_recall@5 | 1.0 |
| cosine_recall@10 | 1.0 |
| **cosine_ndcg@10** | **0.8214** |
| cosine_mrr@10 | 0.7593 |
| cosine_map@100 | 0.7593 |
#### Information Retrieval
* Dataset: `dim_512`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 512
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.4444 |
| cosine_accuracy@3 | 0.8889 |
| cosine_accuracy@5 | 1.0 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.4444 |
| cosine_precision@3 | 0.2963 |
| cosine_precision@5 | 0.2 |
| cosine_precision@10 | 0.1 |
| cosine_recall@1 | 0.4444 |
| cosine_recall@3 | 0.8889 |
| cosine_recall@5 | 1.0 |
| cosine_recall@10 | 1.0 |
| **cosine_ndcg@10** | **0.7678** |
| cosine_mrr@10 | 0.6889 |
| cosine_map@100 | 0.6889 |
#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 256
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.4444 |
| cosine_accuracy@3 | 1.0 |
| cosine_accuracy@5 | 1.0 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.4444 |
| cosine_precision@3 | 0.3333 |
| cosine_precision@5 | 0.2 |
| cosine_precision@10 | 0.1 |
| cosine_recall@1 | 0.4444 |
| cosine_recall@3 | 1.0 |
| cosine_recall@5 | 1.0 |
| cosine_recall@10 | 1.0 |
| **cosine_ndcg@10** | **0.7659** |
| cosine_mrr@10 | 0.6852 |
| cosine_map@100 | 0.6852 |
#### Information Retrieval
* Dataset: `dim_128`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 128
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.4444 |
| cosine_accuracy@3 | 0.8889 |
| cosine_accuracy@5 | 0.8889 |
| cosine_accuracy@10 | 0.8889 |
| cosine_precision@1 | 0.4444 |
| cosine_precision@3 | 0.2963 |
| cosine_precision@5 | 0.1778 |
| cosine_precision@10 | 0.0889 |
| cosine_recall@1 | 0.4444 |
| cosine_recall@3 | 0.8889 |
| cosine_recall@5 | 0.8889 |
| cosine_recall@10 | 0.8889 |
| **cosine_ndcg@10** | **0.7103** |
| cosine_mrr@10 | 0.6481 |
| cosine_map@100 | 0.6521 |
#### Information Retrieval
* Dataset: `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 64
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.6667 |
| cosine_accuracy@3 | 0.6667 |
| cosine_accuracy@5 | 0.7778 |
| cosine_accuracy@10 | 0.8889 |
| cosine_precision@1 | 0.6667 |
| cosine_precision@3 | 0.2222 |
| cosine_precision@5 | 0.1556 |
| cosine_precision@10 | 0.0889 |
| cosine_recall@1 | 0.6667 |
| cosine_recall@3 | 0.6667 |
| cosine_recall@5 | 0.7778 |
| cosine_recall@10 | 0.8889 |
| **cosine_ndcg@10** | **0.7516** |
| cosine_mrr@10 | 0.7103 |
| cosine_map@100 | 0.7149 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### json
* Dataset: json
* Size: 73 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 73 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 14 tokens</li><li>mean: 23.0 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 32.74 tokens</li><li>max: 346 tokens</li></ul> |
* Samples:
| anchor | positive |
|:----------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What specific type of students are the Alberta Innovates Graduate Student Scholarships designed to support?</code> | <code>The Alberta Innovates Graduate Student Scholarships support academically superior graduate students <br>who are receiving training and conducting research in areas that are strategically important to Alberta’s <br>economy.</code> |
| <code>What is the specific date by which students must submit their reports for the Spring 2025 grant period?</code> | <code>Report due date April 20th (5 days post grant closure)</code> |
| <code>In what format should applicants acknowledge the funding provided by Concordia University of Edmonton for their Student Project Grant?</code> | <code>All oral or poster presentations, publications, including public messages, arising from research supported by CUE grants must acknowledge the support of the institution. Acknowledgement can be in the written format, such as " This research is funded by the generous support of Concordia University of Edmonton through their CUE Student Research Project Grants program ", or similar phrasing.</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
- `fp16`: 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`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `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
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
|:-------:|:-----:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 1.0 | 1 | 0.7249 | 0.7249 | 0.7473 | 0.7026 | 0.6686 |
| 2.0 | 2 | 0.7619 | 0.7249 | 0.7533 | 0.7026 | 0.7480 |
| **3.0** | **3** | **0.7804** | **0.7619** | **0.7659** | **0.7103** | **0.7496** |
| 4.0 | 4 | 0.8214 | 0.7678 | 0.7659 | 0.7103 | 0.7516 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.11.12
- Sentence Transformers: 4.1.0
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 1.5.2
- 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.*
--> |