File size: 93,428 Bytes
0133738 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 |
---
base_model: sentence-transformers/all-mpnet-base-v2
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:169213
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: This is bullshit. The US government requires taxes to be paid in
USD. There's your intrinsic value. If you want to be compliant with the federal
law, your business and you as an individual are required to convert assets or
labor into USD to pay them.
sentences:
- we love face paint melbourne
- how long to pay off debt
- what is the difference between us tax and mls
- source_sentence: '> There''s always another fresh-faced new grad with dollar
signs in his eyes who doesn''t know enough to ask about outstanding shares, dilution,
or preferences. They''ll learn soon enough. > Very few startups are looking
for penny-ante ''investor'' employees who can only put <$100k. You''ll probably
find that the majority of tech startups are looking for under $100k to get going.
Check out kickstarter.com sometime. > Actual employees are lucky if they can
properly value their options, let alone control how much it ends up being worth
in the end. If you''re asked to put in work without being fully compensated,
you are no longer an employee. You''re an investor. You need to change your way
of thinking.'
sentences:
- how much money is needed to start a company
- capital one interest rate
- can you transfer abc tax directly to a customer
- source_sentence: Let's suppose your friend gave your $100 and you invested all of
it (plus your own money, $500) into one stock. Therefore, the total investment
becomes $100 + $500 = $600. After few months, when you want to sell the stock
or give back the money to your friend, check the percentage of profit/loss. So,
let's assume you get 10% return on total investment of $600. Now, you have two
choices. Either you exit the stock entirely, OR you just sell his portion. If
you want to exit, sell everything and go home with $600 + 10% of 600 = $660. Out
of $660, give you friend his initial capital + 10% of initial capital. Therefore,
your friend will get $100 + 10% of $100 = $110. If you choose the later, to sell
his portion, then you'll need to work everything opposite. Take his initial capital
and add 10% of initial capital to it; which is $100 + 10% of $100 = $110. Sell
the stocks that would be worth equivalent to that money and that's it. Similarly,
you can apply the same logic if you broke his $100 into parts. Do the maths.
sentences:
- what do people think about getting a good job
- how to tell how much to sell a stock after buying one
- how to claim rrsp room allowance
- source_sentence: '"You''re acting like my comments are inconsistent. They''re not. I
think bitcoin''s price is primarily due to Chinese money being moved outside of
China. I don''t think you can point to a price chart and say ""Look, that''s the
Chinese money right there, and look, that part isn''t Chinese money"". That''s
what I said already."'
sentences:
- bitcoin price in china
- can i use tax act to file a spouse's tax
- what to look at if house sells for an appraiser?
- source_sentence: 'It''s simple, really: Practice. Fiscal responsibility is not a
trick you can learn look up on Google, or a service you can buy from your accountant. Being
responsible with your money is a skill that is learned over a lifetime. The only
way to get better at it is to practice, and not get discouraged when you make
mistakes.'
sentences:
- how long does it take for a loan to get paid interest
- whatsapp to use with a foreigner
- why do people have to be fiscally responsible
model-index:
- name: mpnet-base-financial-rag-matryoshka
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.1809635722679201
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.4935370152761457
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5734430082256169
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.663924794359577
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.1809635722679201
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1645123384253819
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.11468860164512337
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.06639247943595769
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.1809635722679201
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.4935370152761457
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5734430082256169
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.663924794359577
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.41746626575107176
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.33849252979687783
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3464380043472146
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.19036427732079905
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.4900117508813161
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5687426556991775
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6533490011750881
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.19036427732079905
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.16333725029377202
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.11374853113983546
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.06533490011750881
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.19036427732079905
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.4900117508813161
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5687426556991775
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.6533490011750881
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4174472433498665
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3417030384421691
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.35038294448729146
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.1797884841363102
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.47473560517038776
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.54524089306698
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6439482961222092
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.1797884841363102
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.15824520172346257
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.10904817861339598
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.06439482961222091
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.1797884841363102
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.47473560517038776
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.54524089306698
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.6439482961222092
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4067526935952037
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3308208829947965
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.33951940009649473
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.18566392479435959
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.4535840188014101
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5240893066980024
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6216216216216216
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.18566392479435959
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.15119467293380337
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.10481786133960047
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.06216216216216215
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.18566392479435959
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.4535840188014101
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5240893066980024
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.6216216216216216
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.39600584846785714
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.324298211254733
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.33327512340163784
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.16333725029377202
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.42420681551116335
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.491186839012926
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5781433607520564
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.16333725029377202
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.14140227183705445
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.09823736780258518
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05781433607520563
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.16333725029377202
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.42420681551116335
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.491186839012926
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5781433607520564
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.36616361619562976
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.2984467386641303
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3078022299669783
name: Cosine Map@100
---
# mpnet-base-financial-rag-matryoshka
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co./sentence-transformers/all-mpnet-base-v2). 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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co./sentence-transformers/all-mpnet-base-v2) <!-- at revision 84f2bcc00d77236f9e89c8a360a00fb1139bf47d -->
- **Maximum Sequence Length:** 384 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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
(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("rbhatia46/mpnet-base-financial-rag-matryoshka")
# Run inference
sentences = [
"It's simple, really: Practice. Fiscal responsibility is not a trick you can learn look up on Google, or a service you can buy from your accountant. Being responsible with your money is a skill that is learned over a lifetime. The only way to get better at it is to practice, and not get discouraged when you make mistakes.",
'why do people have to be fiscally responsible',
'how long does it take for a loan to get paid interest',
]
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 | 0.181 |
| cosine_accuracy@3 | 0.4935 |
| cosine_accuracy@5 | 0.5734 |
| cosine_accuracy@10 | 0.6639 |
| cosine_precision@1 | 0.181 |
| cosine_precision@3 | 0.1645 |
| cosine_precision@5 | 0.1147 |
| cosine_precision@10 | 0.0664 |
| cosine_recall@1 | 0.181 |
| cosine_recall@3 | 0.4935 |
| cosine_recall@5 | 0.5734 |
| cosine_recall@10 | 0.6639 |
| cosine_ndcg@10 | 0.4175 |
| cosine_mrr@10 | 0.3385 |
| **cosine_map@100** | **0.3464** |
#### 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 | 0.1904 |
| cosine_accuracy@3 | 0.49 |
| cosine_accuracy@5 | 0.5687 |
| cosine_accuracy@10 | 0.6533 |
| cosine_precision@1 | 0.1904 |
| cosine_precision@3 | 0.1633 |
| cosine_precision@5 | 0.1137 |
| cosine_precision@10 | 0.0653 |
| cosine_recall@1 | 0.1904 |
| cosine_recall@3 | 0.49 |
| cosine_recall@5 | 0.5687 |
| cosine_recall@10 | 0.6533 |
| cosine_ndcg@10 | 0.4174 |
| cosine_mrr@10 | 0.3417 |
| **cosine_map@100** | **0.3504** |
#### 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 | 0.1798 |
| cosine_accuracy@3 | 0.4747 |
| cosine_accuracy@5 | 0.5452 |
| cosine_accuracy@10 | 0.6439 |
| cosine_precision@1 | 0.1798 |
| cosine_precision@3 | 0.1582 |
| cosine_precision@5 | 0.109 |
| cosine_precision@10 | 0.0644 |
| cosine_recall@1 | 0.1798 |
| cosine_recall@3 | 0.4747 |
| cosine_recall@5 | 0.5452 |
| cosine_recall@10 | 0.6439 |
| cosine_ndcg@10 | 0.4068 |
| cosine_mrr@10 | 0.3308 |
| **cosine_map@100** | **0.3395** |
#### 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 | 0.1857 |
| cosine_accuracy@3 | 0.4536 |
| cosine_accuracy@5 | 0.5241 |
| cosine_accuracy@10 | 0.6216 |
| cosine_precision@1 | 0.1857 |
| cosine_precision@3 | 0.1512 |
| cosine_precision@5 | 0.1048 |
| cosine_precision@10 | 0.0622 |
| cosine_recall@1 | 0.1857 |
| cosine_recall@3 | 0.4536 |
| cosine_recall@5 | 0.5241 |
| cosine_recall@10 | 0.6216 |
| cosine_ndcg@10 | 0.396 |
| cosine_mrr@10 | 0.3243 |
| **cosine_map@100** | **0.3333** |
#### 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 | 0.1633 |
| cosine_accuracy@3 | 0.4242 |
| cosine_accuracy@5 | 0.4912 |
| cosine_accuracy@10 | 0.5781 |
| cosine_precision@1 | 0.1633 |
| cosine_precision@3 | 0.1414 |
| cosine_precision@5 | 0.0982 |
| cosine_precision@10 | 0.0578 |
| cosine_recall@1 | 0.1633 |
| cosine_recall@3 | 0.4242 |
| cosine_recall@5 | 0.4912 |
| cosine_recall@10 | 0.5781 |
| cosine_ndcg@10 | 0.3662 |
| cosine_mrr@10 | 0.2984 |
| **cosine_map@100** | **0.3078** |
<!--
## 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: 169,213 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: 7 tokens</li><li>mean: 158.03 tokens</li><li>max: 384 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.16 tokens</li><li>max: 30 tokens</li></ul> |
* Samples:
| positive | anchor |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------|
| <code>International Trade, the exchange of goods and services between nations. “Goods” can be defined as finished products, as intermediate goods used in producing other goods, or as raw materials such as minerals, agricultural products, and other such commodities. International trade commerce enables a nation to specialize in those goods it can produce most cheaply and efficiently, and sell those that are surplus to its requirements. Trade also enables a country to consume more than it would be able to produce if it depended only on its own resources. Finally, trade encourages economic development by increasing the size of the market to which products can be sold. Trade has always been the major force behind the economic relations among nations; it is a measure of national strength.</code> | <code>what does international trade</code> |
| <code>My wife and I meet in the first few days of each month to create a budget for the coming month. During that meeting we reconcile any spending for the previous month and make sure the amount money in our accounts matches the amount of money in our budget record to the penny. (We use an excel spreadsheet, how you track it matters less than the need to track it and see how much you spent in each category during the previous month.) After we have have reviewed the previous month's spending, we allocate money we made during that previous month to each of the categories. What categories you track and how granular you are is less important than regularly seeing how much you spend so that you can evaluate whether your spending is really matching your priorities. We keep a running total for each category so if we go over on groceries one month, then the following month we have to add more to bring the category back to black as well as enough for our anticipated needs in the coming month. If there is one category that we are consistently underestimating (or overestimating) we talk about why. If there are large purchases that we are planning in the coming month, or even in a few months, we talk about them, why we want them, and we talk about how much we're planning to spend. If we want a new TV or to go on a trip, we may start adding money to the category with no plans to spend in the coming month. The biggest benefit to this process has been that we don't make a lot of impulse purchases, or if we do, they are for small dollar amounts. The simple need to explain what I want and why means I have to put the thought into it myself, and I talk myself out of a lot of purchases during that train of thought. The time spent regularly evaluating what we get for our money has cut waste that wasn't really bringing much happiness. We still buy what we want, but we agree that we want it first.</code> | <code>how to make a budget</code> |
| <code>I just finished my bachelor and I'm doing my masters in Computer Science at a french school in Quebec. I consider myself being in the top 5% and I have an excellent curriculum, having studied abroad, learned 4 languages, participated in student committees, etc. I'm leaning towards IT or business strategy/development...but I'm not sure yet. I guess I'm not that prepared, that's why I wanted a little help.</code> | <code>what school do you want to attend for a masters</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
- `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`: 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`: 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
<details><summary>Click to expand</summary>
| 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 |
|:----------:|:--------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.0303 | 10 | 2.2113 | - | - | - | - | - |
| 0.0605 | 20 | 2.1051 | - | - | - | - | - |
| 0.0908 | 30 | 1.9214 | - | - | - | - | - |
| 0.1210 | 40 | 1.744 | - | - | - | - | - |
| 0.1513 | 50 | 1.5873 | - | - | - | - | - |
| 0.1815 | 60 | 1.3988 | - | - | - | - | - |
| 0.2118 | 70 | 1.263 | - | - | - | - | - |
| 0.2421 | 80 | 1.1082 | - | - | - | - | - |
| 0.2723 | 90 | 1.0061 | - | - | - | - | - |
| 0.3026 | 100 | 1.0127 | - | - | - | - | - |
| 0.3328 | 110 | 0.8644 | - | - | - | - | - |
| 0.3631 | 120 | 0.8006 | - | - | - | - | - |
| 0.3933 | 130 | 0.8067 | - | - | - | - | - |
| 0.4236 | 140 | 0.7624 | - | - | - | - | - |
| 0.4539 | 150 | 0.799 | - | - | - | - | - |
| 0.4841 | 160 | 0.7025 | - | - | - | - | - |
| 0.5144 | 170 | 0.7467 | - | - | - | - | - |
| 0.5446 | 180 | 0.7509 | - | - | - | - | - |
| 0.5749 | 190 | 0.7057 | - | - | - | - | - |
| 0.6051 | 200 | 0.6929 | - | - | - | - | - |
| 0.6354 | 210 | 0.6948 | - | - | - | - | - |
| 0.6657 | 220 | 0.6477 | - | - | - | - | - |
| 0.6959 | 230 | 0.6562 | - | - | - | - | - |
| 0.7262 | 240 | 0.6278 | - | - | - | - | - |
| 0.7564 | 250 | 0.6249 | - | - | - | - | - |
| 0.7867 | 260 | 0.6057 | - | - | - | - | - |
| 0.8169 | 270 | 0.6258 | - | - | - | - | - |
| 0.8472 | 280 | 0.5007 | - | - | - | - | - |
| 0.8775 | 290 | 0.5998 | - | - | - | - | - |
| 0.9077 | 300 | 0.5958 | - | - | - | - | - |
| 0.9380 | 310 | 0.5568 | - | - | - | - | - |
| 0.9682 | 320 | 0.5236 | - | - | - | - | - |
| 0.9985 | 330 | 0.6239 | 0.3189 | 0.3389 | 0.3645 | 0.3046 | 0.3700 |
| 1.0287 | 340 | 0.5106 | - | - | - | - | - |
| 1.0590 | 350 | 0.6022 | - | - | - | - | - |
| 1.0893 | 360 | 0.5822 | - | - | - | - | - |
| 1.1195 | 370 | 0.5094 | - | - | - | - | - |
| 1.1498 | 380 | 0.5037 | - | - | - | - | - |
| 1.1800 | 390 | 0.5415 | - | - | - | - | - |
| 1.2103 | 400 | 0.5011 | - | - | - | - | - |
| 1.2405 | 410 | 0.4571 | - | - | - | - | - |
| 1.2708 | 420 | 0.4587 | - | - | - | - | - |
| 1.3011 | 430 | 0.5065 | - | - | - | - | - |
| 1.3313 | 440 | 0.4589 | - | - | - | - | - |
| 1.3616 | 450 | 0.4165 | - | - | - | - | - |
| 1.3918 | 460 | 0.4215 | - | - | - | - | - |
| 1.4221 | 470 | 0.4302 | - | - | - | - | - |
| 1.4523 | 480 | 0.4556 | - | - | - | - | - |
| 1.4826 | 490 | 0.3793 | - | - | - | - | - |
| 1.5129 | 500 | 0.4586 | - | - | - | - | - |
| 1.5431 | 510 | 0.4327 | - | - | - | - | - |
| 1.5734 | 520 | 0.4207 | - | - | - | - | - |
| 1.6036 | 530 | 0.4042 | - | - | - | - | - |
| 1.6339 | 540 | 0.4019 | - | - | - | - | - |
| 1.6641 | 550 | 0.3804 | - | - | - | - | - |
| 1.6944 | 560 | 0.3796 | - | - | - | - | - |
| 1.7247 | 570 | 0.3476 | - | - | - | - | - |
| 1.7549 | 580 | 0.3871 | - | - | - | - | - |
| 1.7852 | 590 | 0.3602 | - | - | - | - | - |
| 1.8154 | 600 | 0.3711 | - | - | - | - | - |
| 1.8457 | 610 | 0.2879 | - | - | - | - | - |
| 1.8759 | 620 | 0.3497 | - | - | - | - | - |
| 1.9062 | 630 | 0.3346 | - | - | - | - | - |
| 1.9365 | 640 | 0.3426 | - | - | - | - | - |
| 1.9667 | 650 | 0.2977 | - | - | - | - | - |
| 1.9970 | 660 | 0.3783 | - | - | - | - | - |
| 2.0 | 661 | - | 0.3282 | 0.3485 | 0.3749 | 0.2960 | 0.3666 |
| 2.0272 | 670 | 0.3012 | - | - | - | - | - |
| 2.0575 | 680 | 0.3491 | - | - | - | - | - |
| 2.0877 | 690 | 0.3589 | - | - | - | - | - |
| 2.1180 | 700 | 0.2998 | - | - | - | - | - |
| 2.1483 | 710 | 0.2925 | - | - | - | - | - |
| 2.1785 | 720 | 0.3261 | - | - | - | - | - |
| 2.2088 | 730 | 0.2917 | - | - | - | - | - |
| 2.2390 | 740 | 0.2685 | - | - | - | - | - |
| 2.2693 | 750 | 0.2674 | - | - | - | - | - |
| 2.2995 | 760 | 0.3136 | - | - | - | - | - |
| 2.3298 | 770 | 0.2631 | - | - | - | - | - |
| 2.3601 | 780 | 0.2509 | - | - | - | - | - |
| 2.3903 | 790 | 0.2518 | - | - | - | - | - |
| 2.4206 | 800 | 0.2603 | - | - | - | - | - |
| 2.4508 | 810 | 0.2773 | - | - | - | - | - |
| 2.4811 | 820 | 0.245 | - | - | - | - | - |
| 2.5113 | 830 | 0.2746 | - | - | - | - | - |
| 2.5416 | 840 | 0.2747 | - | - | - | - | - |
| 2.5719 | 850 | 0.2426 | - | - | - | - | - |
| 2.6021 | 860 | 0.2593 | - | - | - | - | - |
| 2.6324 | 870 | 0.2482 | - | - | - | - | - |
| 2.6626 | 880 | 0.2344 | - | - | - | - | - |
| 2.6929 | 890 | 0.2452 | - | - | - | - | - |
| 2.7231 | 900 | 0.218 | - | - | - | - | - |
| 2.7534 | 910 | 0.2319 | - | - | - | - | - |
| 2.7837 | 920 | 0.2366 | - | - | - | - | - |
| 2.8139 | 930 | 0.2265 | - | - | - | - | - |
| 2.8442 | 940 | 0.1753 | - | - | - | - | - |
| 2.8744 | 950 | 0.2153 | - | - | - | - | - |
| 2.9047 | 960 | 0.201 | - | - | - | - | - |
| 2.9349 | 970 | 0.2205 | - | - | - | - | - |
| 2.9652 | 980 | 0.1933 | - | - | - | - | - |
| 2.9955 | 990 | 0.2301 | - | - | - | - | - |
| 2.9985 | 991 | - | 0.3285 | 0.3484 | 0.3636 | 0.2966 | 0.3660 |
| 3.0257 | 1000 | 0.1946 | - | - | - | - | - |
| 3.0560 | 1010 | 0.203 | - | - | - | - | - |
| 3.0862 | 1020 | 0.2385 | - | - | - | - | - |
| 3.1165 | 1030 | 0.1821 | - | - | - | - | - |
| 3.1467 | 1040 | 0.1858 | - | - | - | - | - |
| 3.1770 | 1050 | 0.2057 | - | - | - | - | - |
| 3.2073 | 1060 | 0.18 | - | - | - | - | - |
| 3.2375 | 1070 | 0.1751 | - | - | - | - | - |
| 3.2678 | 1080 | 0.1539 | - | - | - | - | - |
| 3.2980 | 1090 | 0.2153 | - | - | - | - | - |
| 3.3283 | 1100 | 0.1739 | - | - | - | - | - |
| 3.3585 | 1110 | 0.1621 | - | - | - | - | - |
| 3.3888 | 1120 | 0.1541 | - | - | - | - | - |
| 3.4191 | 1130 | 0.1642 | - | - | - | - | - |
| 3.4493 | 1140 | 0.1893 | - | - | - | - | - |
| 3.4796 | 1150 | 0.16 | - | - | - | - | - |
| 3.5098 | 1160 | 0.1839 | - | - | - | - | - |
| 3.5401 | 1170 | 0.1748 | - | - | - | - | - |
| 3.5703 | 1180 | 0.1499 | - | - | - | - | - |
| 3.6006 | 1190 | 0.1706 | - | - | - | - | - |
| 3.6309 | 1200 | 0.1541 | - | - | - | - | - |
| 3.6611 | 1210 | 0.1592 | - | - | - | - | - |
| 3.6914 | 1220 | 0.1683 | - | - | - | - | - |
| 3.7216 | 1230 | 0.1408 | - | - | - | - | - |
| 3.7519 | 1240 | 0.1595 | - | - | - | - | - |
| 3.7821 | 1250 | 0.1585 | - | - | - | - | - |
| 3.8124 | 1260 | 0.1521 | - | - | - | - | - |
| 3.8427 | 1270 | 0.1167 | - | - | - | - | - |
| 3.8729 | 1280 | 0.1416 | - | - | - | - | - |
| 3.9032 | 1290 | 0.1386 | - | - | - | - | - |
| 3.9334 | 1300 | 0.1513 | - | - | - | - | - |
| 3.9637 | 1310 | 0.1329 | - | - | - | - | - |
| 3.9939 | 1320 | 0.1565 | - | - | - | - | - |
| 4.0 | 1322 | - | 0.3270 | 0.3575 | 0.3636 | 0.3053 | 0.3660 |
| 4.0242 | 1330 | 0.1253 | - | - | - | - | - |
| 4.0545 | 1340 | 0.1325 | - | - | - | - | - |
| 4.0847 | 1350 | 0.1675 | - | - | - | - | - |
| 4.1150 | 1360 | 0.1291 | - | - | - | - | - |
| 4.1452 | 1370 | 0.1259 | - | - | - | - | - |
| 4.1755 | 1380 | 0.1359 | - | - | - | - | - |
| 4.2057 | 1390 | 0.1344 | - | - | - | - | - |
| 4.2360 | 1400 | 0.1187 | - | - | - | - | - |
| 4.2663 | 1410 | 0.1062 | - | - | - | - | - |
| 4.2965 | 1420 | 0.1653 | - | - | - | - | - |
| 4.3268 | 1430 | 0.1164 | - | - | - | - | - |
| 4.3570 | 1440 | 0.103 | - | - | - | - | - |
| 4.3873 | 1450 | 0.1093 | - | - | - | - | - |
| 4.4175 | 1460 | 0.1156 | - | - | - | - | - |
| 4.4478 | 1470 | 0.1195 | - | - | - | - | - |
| 4.4781 | 1480 | 0.1141 | - | - | - | - | - |
| 4.5083 | 1490 | 0.1233 | - | - | - | - | - |
| 4.5386 | 1500 | 0.1169 | - | - | - | - | - |
| 4.5688 | 1510 | 0.0957 | - | - | - | - | - |
| 4.5991 | 1520 | 0.1147 | - | - | - | - | - |
| 4.6293 | 1530 | 0.1134 | - | - | - | - | - |
| 4.6596 | 1540 | 0.1143 | - | - | - | - | - |
| 4.6899 | 1550 | 0.1125 | - | - | - | - | - |
| 4.7201 | 1560 | 0.0988 | - | - | - | - | - |
| 4.7504 | 1570 | 0.1149 | - | - | - | - | - |
| 4.7806 | 1580 | 0.1154 | - | - | - | - | - |
| 4.8109 | 1590 | 0.1043 | - | - | - | - | - |
| 4.8411 | 1600 | 0.0887 | - | - | - | - | - |
| 4.8714 | 1610 | 0.0921 | - | - | - | - | - |
| 4.9017 | 1620 | 0.1023 | - | - | - | - | - |
| 4.9319 | 1630 | 0.1078 | - | - | - | - | - |
| 4.9622 | 1640 | 0.1053 | - | - | - | - | - |
| 4.9924 | 1650 | 0.1135 | - | - | - | - | - |
| 4.9985 | 1652 | - | 0.3402 | 0.3620 | 0.3781 | 0.3236 | 0.3842 |
| 5.0227 | 1660 | 0.0908 | - | - | - | - | - |
| 5.0530 | 1670 | 0.0908 | - | - | - | - | - |
| 5.0832 | 1680 | 0.1149 | - | - | - | - | - |
| 5.1135 | 1690 | 0.0991 | - | - | - | - | - |
| 5.1437 | 1700 | 0.0864 | - | - | - | - | - |
| 5.1740 | 1710 | 0.0987 | - | - | - | - | - |
| 5.2042 | 1720 | 0.0949 | - | - | - | - | - |
| 5.2345 | 1730 | 0.0893 | - | - | - | - | - |
| 5.2648 | 1740 | 0.0806 | - | - | - | - | - |
| 5.2950 | 1750 | 0.1187 | - | - | - | - | - |
| 5.3253 | 1760 | 0.0851 | - | - | - | - | - |
| 5.3555 | 1770 | 0.0814 | - | - | - | - | - |
| 5.3858 | 1780 | 0.0803 | - | - | - | - | - |
| 5.4160 | 1790 | 0.0816 | - | - | - | - | - |
| 5.4463 | 1800 | 0.0916 | - | - | - | - | - |
| 5.4766 | 1810 | 0.0892 | - | - | - | - | - |
| 5.5068 | 1820 | 0.0935 | - | - | - | - | - |
| 5.5371 | 1830 | 0.0963 | - | - | - | - | - |
| 5.5673 | 1840 | 0.0759 | - | - | - | - | - |
| 5.5976 | 1850 | 0.0908 | - | - | - | - | - |
| 5.6278 | 1860 | 0.0896 | - | - | - | - | - |
| 5.6581 | 1870 | 0.0855 | - | - | - | - | - |
| 5.6884 | 1880 | 0.0849 | - | - | - | - | - |
| 5.7186 | 1890 | 0.0805 | - | - | - | - | - |
| 5.7489 | 1900 | 0.0872 | - | - | - | - | - |
| 5.7791 | 1910 | 0.0853 | - | - | - | - | - |
| 5.8094 | 1920 | 0.0856 | - | - | - | - | - |
| 5.8396 | 1930 | 0.064 | - | - | - | - | - |
| 5.8699 | 1940 | 0.0748 | - | - | - | - | - |
| 5.9002 | 1950 | 0.0769 | - | - | - | - | - |
| 5.9304 | 1960 | 0.0868 | - | - | - | - | - |
| 5.9607 | 1970 | 0.0842 | - | - | - | - | - |
| 5.9909 | 1980 | 0.0825 | - | - | - | - | - |
| 6.0 | 1983 | - | 0.3412 | 0.3542 | 0.3615 | 0.3171 | 0.3676 |
| 6.0212 | 1990 | 0.073 | - | - | - | - | - |
| 6.0514 | 2000 | 0.0708 | - | - | - | - | - |
| 6.0817 | 2010 | 0.0908 | - | - | - | - | - |
| 6.1120 | 2020 | 0.0807 | - | - | - | - | - |
| 6.1422 | 2030 | 0.0665 | - | - | - | - | - |
| 6.1725 | 2040 | 0.0773 | - | - | - | - | - |
| 6.2027 | 2050 | 0.0798 | - | - | - | - | - |
| 6.2330 | 2060 | 0.0743 | - | - | - | - | - |
| 6.2632 | 2070 | 0.0619 | - | - | - | - | - |
| 6.2935 | 2080 | 0.0954 | - | - | - | - | - |
| 6.3238 | 2090 | 0.0682 | - | - | - | - | - |
| 6.3540 | 2100 | 0.0594 | - | - | - | - | - |
| 6.3843 | 2110 | 0.0621 | - | - | - | - | - |
| 6.4145 | 2120 | 0.0674 | - | - | - | - | - |
| 6.4448 | 2130 | 0.069 | - | - | - | - | - |
| 6.4750 | 2140 | 0.0741 | - | - | - | - | - |
| 6.5053 | 2150 | 0.0757 | - | - | - | - | - |
| 6.5356 | 2160 | 0.0781 | - | - | - | - | - |
| 6.5658 | 2170 | 0.0632 | - | - | - | - | - |
| 6.5961 | 2180 | 0.07 | - | - | - | - | - |
| 6.6263 | 2190 | 0.0767 | - | - | - | - | - |
| 6.6566 | 2200 | 0.0674 | - | - | - | - | - |
| 6.6868 | 2210 | 0.0704 | - | - | - | - | - |
| 6.7171 | 2220 | 0.065 | - | - | - | - | - |
| 6.7474 | 2230 | 0.066 | - | - | - | - | - |
| 6.7776 | 2240 | 0.0752 | - | - | - | - | - |
| 6.8079 | 2250 | 0.07 | - | - | - | - | - |
| 6.8381 | 2260 | 0.0602 | - | - | - | - | - |
| 6.8684 | 2270 | 0.0595 | - | - | - | - | - |
| 6.8986 | 2280 | 0.065 | - | - | - | - | - |
| 6.9289 | 2290 | 0.0677 | - | - | - | - | - |
| 6.9592 | 2300 | 0.0708 | - | - | - | - | - |
| 6.9894 | 2310 | 0.0651 | - | - | - | - | - |
| **6.9985** | **2313** | **-** | **0.3484** | **0.3671** | **0.3645** | **0.3214** | **0.3773** |
| 7.0197 | 2320 | 0.0657 | - | - | - | - | - |
| 7.0499 | 2330 | 0.0588 | - | - | - | - | - |
| 7.0802 | 2340 | 0.0701 | - | - | - | - | - |
| 7.1104 | 2350 | 0.0689 | - | - | - | - | - |
| 7.1407 | 2360 | 0.0586 | - | - | - | - | - |
| 7.1710 | 2370 | 0.0626 | - | - | - | - | - |
| 7.2012 | 2380 | 0.0723 | - | - | - | - | - |
| 7.2315 | 2390 | 0.0602 | - | - | - | - | - |
| 7.2617 | 2400 | 0.0541 | - | - | - | - | - |
| 7.2920 | 2410 | 0.0823 | - | - | - | - | - |
| 7.3222 | 2420 | 0.0592 | - | - | - | - | - |
| 7.3525 | 2430 | 0.0535 | - | - | - | - | - |
| 7.3828 | 2440 | 0.0548 | - | - | - | - | - |
| 7.4130 | 2450 | 0.0598 | - | - | - | - | - |
| 7.4433 | 2460 | 0.0554 | - | - | - | - | - |
| 7.4735 | 2470 | 0.0663 | - | - | - | - | - |
| 7.5038 | 2480 | 0.0645 | - | - | - | - | - |
| 7.5340 | 2490 | 0.0638 | - | - | - | - | - |
| 7.5643 | 2500 | 0.0574 | - | - | - | - | - |
| 7.5946 | 2510 | 0.0608 | - | - | - | - | - |
| 7.6248 | 2520 | 0.0633 | - | - | - | - | - |
| 7.6551 | 2530 | 0.0576 | - | - | - | - | - |
| 7.6853 | 2540 | 0.0613 | - | - | - | - | - |
| 7.7156 | 2550 | 0.054 | - | - | - | - | - |
| 7.7458 | 2560 | 0.0591 | - | - | - | - | - |
| 7.7761 | 2570 | 0.0659 | - | - | - | - | - |
| 7.8064 | 2580 | 0.0601 | - | - | - | - | - |
| 7.8366 | 2590 | 0.053 | - | - | - | - | - |
| 7.8669 | 2600 | 0.0536 | - | - | - | - | - |
| 7.8971 | 2610 | 0.0581 | - | - | - | - | - |
| 7.9274 | 2620 | 0.0603 | - | - | - | - | - |
| 7.9576 | 2630 | 0.0661 | - | - | - | - | - |
| 7.9879 | 2640 | 0.0588 | - | - | - | - | - |
| 8.0 | 2644 | - | 0.3340 | 0.3533 | 0.3541 | 0.3163 | 0.3651 |
| 8.0182 | 2650 | 0.0559 | - | - | - | - | - |
| 8.0484 | 2660 | 0.0566 | - | - | - | - | - |
| 8.0787 | 2670 | 0.0666 | - | - | - | - | - |
| 8.1089 | 2680 | 0.0601 | - | - | - | - | - |
| 8.1392 | 2690 | 0.0522 | - | - | - | - | - |
| 8.1694 | 2700 | 0.0527 | - | - | - | - | - |
| 8.1997 | 2710 | 0.0622 | - | - | - | - | - |
| 8.2300 | 2720 | 0.0577 | - | - | - | - | - |
| 8.2602 | 2730 | 0.0467 | - | - | - | - | - |
| 8.2905 | 2740 | 0.0762 | - | - | - | - | - |
| 8.3207 | 2750 | 0.0562 | - | - | - | - | - |
| 8.3510 | 2760 | 0.0475 | - | - | - | - | - |
| 8.3812 | 2770 | 0.0482 | - | - | - | - | - |
| 8.4115 | 2780 | 0.0536 | - | - | - | - | - |
| 8.4418 | 2790 | 0.0534 | - | - | - | - | - |
| 8.4720 | 2800 | 0.0588 | - | - | - | - | - |
| 8.5023 | 2810 | 0.0597 | - | - | - | - | - |
| 8.5325 | 2820 | 0.0587 | - | - | - | - | - |
| 8.5628 | 2830 | 0.0544 | - | - | - | - | - |
| 8.5930 | 2840 | 0.0577 | - | - | - | - | - |
| 8.6233 | 2850 | 0.0592 | - | - | - | - | - |
| 8.6536 | 2860 | 0.0554 | - | - | - | - | - |
| 8.6838 | 2870 | 0.0541 | - | - | - | - | - |
| 8.7141 | 2880 | 0.0495 | - | - | - | - | - |
| 8.7443 | 2890 | 0.0547 | - | - | - | - | - |
| 8.7746 | 2900 | 0.0646 | - | - | - | - | - |
| 8.8048 | 2910 | 0.0574 | - | - | - | - | - |
| 8.8351 | 2920 | 0.0486 | - | - | - | - | - |
| 8.8654 | 2930 | 0.0517 | - | - | - | - | - |
| 8.8956 | 2940 | 0.0572 | - | - | - | - | - |
| 8.9259 | 2950 | 0.0518 | - | - | - | - | - |
| 8.9561 | 2960 | 0.0617 | - | - | - | - | - |
| 8.9864 | 2970 | 0.0572 | - | - | - | - | - |
| 8.9985 | 2974 | - | 0.3434 | 0.3552 | 0.3694 | 0.3253 | 0.3727 |
| 9.0166 | 2980 | 0.0549 | - | - | - | - | - |
| 9.0469 | 2990 | 0.0471 | - | - | - | - | - |
| 9.0772 | 3000 | 0.0629 | - | - | - | - | - |
| 9.1074 | 3010 | 0.058 | - | - | - | - | - |
| 9.1377 | 3020 | 0.0531 | - | - | - | - | - |
| 9.1679 | 3030 | 0.051 | - | - | - | - | - |
| 9.1982 | 3040 | 0.0593 | - | - | - | - | - |
| 9.2284 | 3050 | 0.056 | - | - | - | - | - |
| 9.2587 | 3060 | 0.0452 | - | - | - | - | - |
| 9.2890 | 3070 | 0.0672 | - | - | - | - | - |
| 9.3192 | 3080 | 0.0547 | - | - | - | - | - |
| 9.3495 | 3090 | 0.0477 | - | - | - | - | - |
| 9.3797 | 3100 | 0.0453 | - | - | - | - | - |
| 9.4100 | 3110 | 0.0542 | - | - | - | - | - |
| 9.4402 | 3120 | 0.0538 | - | - | - | - | - |
| 9.4705 | 3130 | 0.0552 | - | - | - | - | - |
| 9.5008 | 3140 | 0.0586 | - | - | - | - | - |
| 9.5310 | 3150 | 0.0567 | - | - | - | - | - |
| 9.5613 | 3160 | 0.0499 | - | - | - | - | - |
| 9.5915 | 3170 | 0.0598 | - | - | - | - | - |
| 9.6218 | 3180 | 0.0546 | - | - | - | - | - |
| 9.6520 | 3190 | 0.0513 | - | - | - | - | - |
| 9.6823 | 3200 | 0.0549 | - | - | - | - | - |
| 9.7126 | 3210 | 0.0513 | - | - | - | - | - |
| 9.7428 | 3220 | 0.0536 | - | - | - | - | - |
| 9.7731 | 3230 | 0.0588 | - | - | - | - | - |
| 9.8033 | 3240 | 0.0531 | - | - | - | - | - |
| 9.8336 | 3250 | 0.0472 | - | - | - | - | - |
| 9.8638 | 3260 | 0.0486 | - | - | - | - | - |
| 9.8941 | 3270 | 0.0576 | - | - | - | - | - |
| 9.9244 | 3280 | 0.0526 | - | - | - | - | - |
| 9.9546 | 3290 | 0.0568 | - | - | - | - | - |
| 9.9849 | 3300 | 0.0617 | 0.3333 | 0.3395 | 0.3504 | 0.3078 | 0.3464 |
* The bold row denotes the saved checkpoint.
</details>
### Framework Versions
- Python: 3.10.8
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.33.0
- 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.*
--> |