File size: 58,693 Bytes
0785fcb b385a28 0785fcb 92ef9d5 0785fcb b385a28 0785fcb b385a28 0785fcb b385a28 0785fcb b385a28 0785fcb b385a28 0785fcb 92ef9d5 0785fcb 2802f8a 0785fcb d5fc350 8484e8c 2628cb6 2802f8a |
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 |
---
language:
- ar
library_name: sentence-transformers
tags:
- mteb
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:557850
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: tomaarsen/mpnet-base-all-nli-triplet
datasets:
- Omartificial-Intelligence-Space/Arabic-NLi-Triplet
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: ذكر متوازن بعناية يقف على قدم واحدة بالقرب من منطقة شاطئ المحيط النظيفة
sentences:
- رجل يقدم عرضاً
- هناك رجل بالخارج قرب الشاطئ
- رجل يجلس على أريكه
- source_sentence: رجل يقفز إلى سريره القذر
sentences:
- السرير قذر.
- رجل يضحك أثناء غسيل الملابس
- الرجل على القمر
- source_sentence: الفتيات بالخارج
sentences:
- امرأة تلف الخيط إلى كرات بجانب كومة من الكرات
- فتيان يركبان في جولة متعة
- >-
ثلاث فتيات يقفون سوية في غرفة واحدة تستمع وواحدة تكتب على الحائط والثالثة
تتحدث إليهن
- source_sentence: الرجل يرتدي قميصاً أزرق.
sentences:
- >-
رجل يرتدي قميصاً أزرق يميل إلى الجدار بجانب الطريق مع شاحنة زرقاء وسيارة
حمراء مع الماء في الخلفية.
- كتاب القصص مفتوح
- رجل يرتدي قميص أسود يعزف على الجيتار.
- source_sentence: يجلس شاب ذو شعر أشقر على الحائط يقرأ جريدة بينما تمر امرأة وفتاة شابة.
sentences:
- ذكر شاب ينظر إلى جريدة بينما تمر إمرأتان بجانبه
- رجل يستلقي على وجهه على مقعد في الحديقة.
- الشاب نائم بينما الأم تقود ابنتها إلى الحديقة
pipeline_tag: sentence-similarity
model-index:
- name: Omartificial-Intelligence-Space/Arabic-mpnet-base-all-nli-triplet
results:
- dataset:
config: default
name: MTEB BIOSSES (default)
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
split: test
type: mteb/biosses-sts
metrics:
- type: cosine_pearson
value: 69.84925402371587
- type: cosine_spearman
value: 67.12261377163864
- type: euclidean_pearson
value: 68.77931734192
- type: euclidean_spearman
value: 67.10454107068325
- type: main_score
value: 67.12261377163864
- type: manhattan_pearson
value: 69.39988076793398
- type: manhattan_spearman
value: 67.68708446481159
task:
type: STS
- dataset:
config: default
name: MTEB SICK-R (default)
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
split: test
type: mteb/sickr-sts
metrics:
- type: cosine_pearson
value: 72.71925116055804
- type: cosine_spearman
value: 68.9386835022992
- type: euclidean_pearson
value: 71.00708266525079
- type: euclidean_spearman
value: 69.07087906196487
- type: main_score
value: 68.9386835022992
- type: manhattan_pearson
value: 70.95266060047263
- type: manhattan_spearman
value: 69.11051988196195
task:
type: STS
- dataset:
config: default
name: MTEB STS12 (default)
revision: a0d554a64d88156834ff5ae9920b964011b16384
split: test
type: mteb/sts12-sts
metrics:
- type: cosine_pearson
value: 71.67274455692545
- type: cosine_spearman
value: 68.71669873972587
- type: euclidean_pearson
value: 69.79037485042406
- type: euclidean_spearman
value: 68.80550150752252
- type: main_score
value: 68.71669873972587
- type: manhattan_pearson
value: 69.7571283034187
- type: manhattan_spearman
value: 68.58306466019968
task:
type: STS
- dataset:
config: default
name: MTEB STS13 (default)
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
split: test
type: mteb/sts13-sts
metrics:
- type: cosine_pearson
value: 54.172888286882504
- type: cosine_spearman
value: 56.04247097489131
- type: euclidean_pearson
value: 57.88587934777827
- type: euclidean_spearman
value: 57.6139294630564
- type: main_score
value: 56.04247097489131
- type: manhattan_pearson
value: 57.616116618991185
- type: manhattan_spearman
value: 57.23150380799801
task:
type: STS
- dataset:
config: default
name: MTEB STS14 (default)
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
split: test
type: mteb/sts14-sts
metrics:
- type: cosine_pearson
value: 59.58820914531488
- type: cosine_spearman
value: 58.80575077741524
- type: euclidean_pearson
value: 61.1884427988923
- type: euclidean_spearman
value: 60.661625936116124
- type: main_score
value: 58.80575077741524
- type: manhattan_pearson
value: 60.800157410891885
- type: manhattan_spearman
value: 60.29447727072491
task:
type: STS
- dataset:
config: default
name: MTEB STS15 (default)
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
split: test
type: mteb/sts15-sts
metrics:
- type: cosine_pearson
value: 73.45220638967554
- type: cosine_spearman
value: 73.74453589715445
- type: euclidean_pearson
value: 73.8887071337604
- type: euclidean_spearman
value: 73.51752094057372
- type: main_score
value: 73.74453589715445
- type: manhattan_pearson
value: 73.45961523235827
- type: manhattan_spearman
value: 73.07675481848841
task:
type: STS
- dataset:
config: default
name: MTEB STS16 (default)
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
split: test
type: mteb/sts16-sts
metrics:
- type: cosine_pearson
value: 66.84132105540075
- type: cosine_spearman
value: 68.24735989887876
- type: euclidean_pearson
value: 68.2712231484699
- type: euclidean_spearman
value: 68.02365271737838
- type: main_score
value: 68.24735989887876
- type: manhattan_pearson
value: 67.87379902773417
- type: manhattan_spearman
value: 67.65342499070456
task:
type: STS
- dataset:
config: ar-ar
name: MTEB STS17 (ar-ar)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cosine_pearson
value: 79.2987412566616
- type: cosine_spearman
value: 79.93275889323859
- type: euclidean_pearson
value: 77.90301430319637
- type: euclidean_spearman
value: 79.12169562085792
- type: main_score
value: 79.93275889323859
- type: manhattan_pearson
value: 77.93298637610417
- type: manhattan_spearman
value: 79.38516109229111
task:
type: STS
- dataset:
config: ar
name: MTEB STS22 (ar)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 46.955019830396445
- type: cosine_spearman
value: 52.44226852669887
- type: euclidean_pearson
value: 42.80891863181744
- type: euclidean_spearman
value: 53.175461247693704
- type: main_score
value: 52.44226852669887
- type: manhattan_pearson
value: 42.97005510727849
- type: manhattan_spearman
value: 53.158087426369825
task:
type: STS
- dataset:
config: default
name: MTEB STSBenchmark (default)
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
split: test
type: mteb/stsbenchmark-sts
metrics:
- type: cosine_pearson
value: 66.99025999216197
- type: cosine_spearman
value: 67.56341643518167
- type: euclidean_pearson
value: 69.73441598964332
- type: euclidean_spearman
value: 68.72541136876826
- type: main_score
value: 67.56341643518167
- type: manhattan_pearson
value: 69.43492004000674
- type: manhattan_spearman
value: 68.39614969063062
task:
type: STS
- dataset:
config: default
name: MTEB SummEval (default)
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
split: test
type: mteb/summeval
metrics:
- type: cosine_pearson
value: 30.13248188083236
- type: cosine_spearman
value: 28.78575545661001
- type: dot_pearson
value: 30.934754821379464
- type: dot_spearman
value: 29.730792596057093
- type: main_score
value: 28.78575545661001
- type: pearson
value: 30.13248188083236
- type: spearman
value: 28.78575545661001
task:
type: Summarization
- name: SentenceTransformer based on tomaarsen/mpnet-base-all-nli-triplet
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 768
type: sts-test-768
metrics:
- type: pearson_cosine
value: 0.66986244175229
name: Pearson Cosine
- type: spearman_cosine
value: 0.675651628513557
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6943200977280434
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6839707658313092
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6973190148612566
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6872926092972673
name: Spearman Euclidean
- type: pearson_dot
value: 0.5534197296097646
name: Pearson Dot
- type: spearman_dot
value: 0.5421965591416092
name: Spearman Dot
- type: pearson_max
value: 0.6973190148612566
name: Pearson Max
- type: spearman_max
value: 0.6872926092972673
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 512
type: sts-test-512
metrics:
- type: pearson_cosine
value: 0.6628171358537143
name: Pearson Cosine
- type: spearman_cosine
value: 0.670314701212355
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6916567677127377
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6815748132707206
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6948756461188812
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.685329042213794
name: Spearman Euclidean
- type: pearson_dot
value: 0.5229142840207227
name: Pearson Dot
- type: spearman_dot
value: 0.5113740757424073
name: Spearman Dot
- type: pearson_max
value: 0.6948756461188812
name: Pearson Max
- type: spearman_max
value: 0.685329042213794
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 256
type: sts-test-256
metrics:
- type: pearson_cosine
value: 0.6368313837029833
name: Pearson Cosine
- type: spearman_cosine
value: 0.6512526280069127
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6832129716443456
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.674638334774044
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6843664039671002
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6760040651639672
name: Spearman Euclidean
- type: pearson_dot
value: 0.4266095536126992
name: Pearson Dot
- type: spearman_dot
value: 0.4179376458107888
name: Spearman Dot
- type: pearson_max
value: 0.6843664039671002
name: Pearson Max
- type: spearman_max
value: 0.6760040651639672
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 128
type: sts-test-128
metrics:
- type: pearson_cosine
value: 0.6147896744901056
name: Pearson Cosine
- type: spearman_cosine
value: 0.6354730852658397
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6730782159165468
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6652649799789521
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.676407799774529
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6691409653459247
name: Spearman Euclidean
- type: pearson_dot
value: 0.35130869784942953
name: Pearson Dot
- type: spearman_dot
value: 0.3445374275232203
name: Spearman Dot
- type: pearson_max
value: 0.676407799774529
name: Pearson Max
- type: spearman_max
value: 0.6691409653459247
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 64
type: sts-test-64
metrics:
- type: pearson_cosine
value: 0.5789158725954748
name: Pearson Cosine
- type: spearman_cosine
value: 0.6081197115891086
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6578631744829946
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6518503436513217
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6629734628760299
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6570510967281272
name: Spearman Euclidean
- type: pearson_dot
value: 0.24034366392620327
name: Pearson Dot
- type: spearman_dot
value: 0.2331392769925126
name: Spearman Dot
- type: pearson_max
value: 0.6629734628760299
name: Pearson Max
- type: spearman_max
value: 0.6570510967281272
name: Spearman Max
license: apache-2.0
---
# SentenceTransformer based on tomaarsen/mpnet-base-all-nli-triplet
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [tomaarsen/mpnet-base-all-nli-triplet](https://huggingface.co/tomaarsen/mpnet-base-all-nli-triplet) on the Omartificial-Intelligence-Space/arabic-n_li-triplet 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:** [tomaarsen/mpnet-base-all-nli-triplet](https://huggingface.co/tomaarsen/mpnet-base-all-nli-triplet) <!-- at revision e88732e5620f3592bf6566604be9a6a5cad814ec -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- Omartificial-Intelligence-Space/arabic-n_li-triplet
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### 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: 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})
)
```
## 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("Omartificial-Intelligence-Space/mpnet-base-all-nli-triplet-Arabic-mpnet_base")
# Run inference
sentences = [
'يجلس شاب ذو شعر أشقر على الحائط يقرأ جريدة بينما تمر امرأة وفتاة شابة.',
'ذكر شاب ينظر إلى جريدة بينما تمر إمرأتان بجانبه',
'الشاب نائم بينما الأم تقود ابنتها إلى الحديقة',
]
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
#### Semantic Similarity
* Dataset: `sts-test-768`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.6699 |
| **spearman_cosine** | **0.6757** |
| pearson_manhattan | 0.6943 |
| spearman_manhattan | 0.684 |
| pearson_euclidean | 0.6973 |
| spearman_euclidean | 0.6873 |
| pearson_dot | 0.5534 |
| spearman_dot | 0.5422 |
| pearson_max | 0.6973 |
| spearman_max | 0.6873 |
#### Semantic Similarity
* Dataset: `sts-test-512`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.6628 |
| **spearman_cosine** | **0.6703** |
| pearson_manhattan | 0.6917 |
| spearman_manhattan | 0.6816 |
| pearson_euclidean | 0.6949 |
| spearman_euclidean | 0.6853 |
| pearson_dot | 0.5229 |
| spearman_dot | 0.5114 |
| pearson_max | 0.6949 |
| spearman_max | 0.6853 |
#### Semantic Similarity
* Dataset: `sts-test-256`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.6368 |
| **spearman_cosine** | **0.6513** |
| pearson_manhattan | 0.6832 |
| spearman_manhattan | 0.6746 |
| pearson_euclidean | 0.6844 |
| spearman_euclidean | 0.676 |
| pearson_dot | 0.4266 |
| spearman_dot | 0.4179 |
| pearson_max | 0.6844 |
| spearman_max | 0.676 |
#### Semantic Similarity
* Dataset: `sts-test-128`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.6148 |
| **spearman_cosine** | **0.6355** |
| pearson_manhattan | 0.6731 |
| spearman_manhattan | 0.6653 |
| pearson_euclidean | 0.6764 |
| spearman_euclidean | 0.6691 |
| pearson_dot | 0.3513 |
| spearman_dot | 0.3445 |
| pearson_max | 0.6764 |
| spearman_max | 0.6691 |
#### Semantic Similarity
* Dataset: `sts-test-64`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.5789 |
| **spearman_cosine** | **0.6081** |
| pearson_manhattan | 0.6579 |
| spearman_manhattan | 0.6519 |
| pearson_euclidean | 0.663 |
| spearman_euclidean | 0.6571 |
| pearson_dot | 0.2403 |
| spearman_dot | 0.2331 |
| pearson_max | 0.663 |
| spearman_max | 0.6571 |
<!--
## 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
#### Omartificial-Intelligence-Space/arabic-n_li-triplet
* Dataset: Omartificial-Intelligence-Space/arabic-n_li-triplet
* Size: 557,850 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 12 tokens</li><li>mean: 23.93 tokens</li><li>max: 155 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 29.62 tokens</li><li>max: 117 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 33.95 tokens</li><li>max: 149 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:------------------------------------------------------------|:--------------------------------------------|:------------------------------------|
| <code>شخص على حصان يقفز فوق طائرة معطلة</code> | <code>شخص في الهواء الطلق، على حصان.</code> | <code>شخص في مطعم، يطلب عجة.</code> |
| <code>أطفال يبتسمون و يلوحون للكاميرا</code> | <code>هناك أطفال حاضرون</code> | <code>الاطفال يتجهمون</code> |
| <code>صبي يقفز على لوح التزلج في منتصف الجسر الأحمر.</code> | <code>الفتى يقوم بخدعة التزلج</code> | <code>الصبي يتزلج على الرصيف</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
}
```
### Evaluation Dataset
#### Omartificial-Intelligence-Space/arabic-n_li-triplet
* Dataset: Omartificial-Intelligence-Space/arabic-n_li-triplet
* Size: 6,584 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 12 tokens</li><li>mean: 49.5 tokens</li><li>max: 246 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 23.66 tokens</li><li>max: 103 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 25.33 tokens</li><li>max: 82 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------|:---------------------------------------------------|
| <code>امرأتان يتعانقان بينما يحملان حزمة</code> | <code>إمرأتان يحملان حزمة</code> | <code>الرجال يتشاجرون خارج مطعم</code> |
| <code>طفلين صغيرين يرتديان قميصاً أزرق، أحدهما يرتدي الرقم 9 والآخر يرتدي الرقم 2 يقفان على خطوات خشبية في الحمام ويغسلان أيديهما في المغسلة.</code> | <code>طفلين يرتديان قميصاً مرقماً يغسلون أيديهم</code> | <code>طفلين يرتديان سترة يذهبان إلى المدرسة</code> |
| <code>رجل يبيع الدونات لعميل خلال معرض عالمي أقيم في مدينة أنجليس</code> | <code>رجل يبيع الدونات لعميل</code> | <code>امرأة تشرب قهوتها في مقهى صغير</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
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `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
- `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`: False
- `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, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `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_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | sts-test-128_spearman_cosine | sts-test-256_spearman_cosine | sts-test-512_spearman_cosine | sts-test-64_spearman_cosine | sts-test-768_spearman_cosine |
|:------:|:-----:|:-------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|
| 0.0229 | 200 | 21.5318 | - | - | - | - | - |
| 0.0459 | 400 | 17.2344 | - | - | - | - | - |
| 0.0688 | 600 | 15.393 | - | - | - | - | - |
| 0.0918 | 800 | 13.7897 | - | - | - | - | - |
| 0.1147 | 1000 | 13.534 | - | - | - | - | - |
| 0.1377 | 1200 | 12.2683 | - | - | - | - | - |
| 0.1606 | 1400 | 10.9271 | - | - | - | - | - |
| 0.1835 | 1600 | 11.071 | - | - | - | - | - |
| 0.2065 | 1800 | 10.0153 | - | - | - | - | - |
| 0.2294 | 2000 | 9.8463 | - | - | - | - | - |
| 0.2524 | 2200 | 10.0194 | - | - | - | - | - |
| 0.2753 | 2400 | 9.8371 | - | - | - | - | - |
| 0.2983 | 2600 | 9.6315 | - | - | - | - | - |
| 0.3212 | 2800 | 8.9858 | - | - | - | - | - |
| 0.3442 | 3000 | 9.1876 | - | - | - | - | - |
| 0.3671 | 3200 | 8.8028 | - | - | - | - | - |
| 0.3900 | 3400 | 8.6075 | - | - | - | - | - |
| 0.4130 | 3600 | 8.4285 | - | - | - | - | - |
| 0.4359 | 3800 | 8.1258 | - | - | - | - | - |
| 0.4589 | 4000 | 8.2508 | - | - | - | - | - |
| 0.4818 | 4200 | 7.8037 | - | - | - | - | - |
| 0.5048 | 4400 | 7.7133 | - | - | - | - | - |
| 0.5277 | 4600 | 7.5006 | - | - | - | - | - |
| 0.5506 | 4800 | 7.7025 | - | - | - | - | - |
| 0.5736 | 5000 | 7.7593 | - | - | - | - | - |
| 0.5965 | 5200 | 7.6305 | - | - | - | - | - |
| 0.6195 | 5400 | 7.7502 | - | - | - | - | - |
| 0.6424 | 5600 | 7.5624 | - | - | - | - | - |
| 0.6654 | 5800 | 7.5287 | - | - | - | - | - |
| 0.6883 | 6000 | 7.4261 | - | - | - | - | - |
| 0.7113 | 6200 | 7.239 | - | - | - | - | - |
| 0.7342 | 6400 | 7.1631 | - | - | - | - | - |
| 0.7571 | 6600 | 7.6865 | - | - | - | - | - |
| 0.7801 | 6800 | 7.6124 | - | - | - | - | - |
| 0.8030 | 7000 | 6.9936 | - | - | - | - | - |
| 0.8260 | 7200 | 6.7331 | - | - | - | - | - |
| 0.8489 | 7400 | 6.4542 | - | - | - | - | - |
| 0.8719 | 7600 | 6.1994 | - | - | - | - | - |
| 0.8948 | 7800 | 5.9798 | - | - | - | - | - |
| 0.9177 | 8000 | 5.7808 | - | - | - | - | - |
| 0.9407 | 8200 | 5.6952 | - | - | - | - | - |
| 0.9636 | 8400 | 5.5082 | - | - | - | - | - |
| 0.9866 | 8600 | 5.4421 | - | - | - | - | - |
| 1.0095 | 8800 | 3.0309 | - | - | - | - | - |
| 1.0026 | 9000 | 1.1835 | - | - | - | - | - |
| 1.0256 | 9200 | 8.1196 | - | - | - | - | - |
| 1.0485 | 9400 | 8.0326 | - | - | - | - | - |
| 1.0715 | 9600 | 8.5028 | - | - | - | - | - |
| 1.0944 | 9800 | 7.6923 | - | - | - | - | - |
| 1.1174 | 10000 | 8.029 | - | - | - | - | - |
| 1.1403 | 10200 | 7.5052 | - | - | - | - | - |
| 1.1632 | 10400 | 7.1177 | - | - | - | - | - |
| 1.1862 | 10600 | 6.9594 | - | - | - | - | - |
| 1.2091 | 10800 | 6.6662 | - | - | - | - | - |
| 1.2321 | 11000 | 6.6903 | - | - | - | - | - |
| 1.2550 | 11200 | 6.9523 | - | - | - | - | - |
| 1.2780 | 11400 | 6.676 | - | - | - | - | - |
| 1.3009 | 11600 | 6.7141 | - | - | - | - | - |
| 1.3238 | 11800 | 6.568 | - | - | - | - | - |
| 1.3468 | 12000 | 6.8938 | - | - | - | - | - |
| 1.3697 | 12200 | 6.3745 | - | - | - | - | - |
| 1.3927 | 12400 | 6.2513 | - | - | - | - | - |
| 1.4156 | 12600 | 6.2589 | - | - | - | - | - |
| 1.4386 | 12800 | 6.1388 | - | - | - | - | - |
| 1.4615 | 13000 | 6.1835 | - | - | - | - | - |
| 1.4845 | 13200 | 5.9004 | - | - | - | - | - |
| 1.5074 | 13400 | 5.7891 | - | - | - | - | - |
| 1.5303 | 13600 | 5.6184 | - | - | - | - | - |
| 1.5533 | 13800 | 5.9762 | - | - | - | - | - |
| 1.5762 | 14000 | 5.9737 | - | - | - | - | - |
| 1.5992 | 14200 | 5.8563 | - | - | - | - | - |
| 1.6221 | 14400 | 5.8904 | - | - | - | - | - |
| 1.6451 | 14600 | 5.8484 | - | - | - | - | - |
| 1.6680 | 14800 | 5.8906 | - | - | - | - | - |
| 1.6909 | 15000 | 5.7613 | - | - | - | - | - |
| 1.7139 | 15200 | 5.5744 | - | - | - | - | - |
| 1.7368 | 15400 | 5.6569 | - | - | - | - | - |
| 1.7598 | 15600 | 5.7439 | - | - | - | - | - |
| 1.7827 | 15800 | 5.5593 | - | - | - | - | - |
| 1.8057 | 16000 | 5.2935 | - | - | - | - | - |
| 1.8286 | 16200 | 5.088 | - | - | - | - | - |
| 1.8516 | 16400 | 5.0167 | - | - | - | - | - |
| 1.8745 | 16600 | 4.84 | - | - | - | - | - |
| 1.8974 | 16800 | 4.6731 | - | - | - | - | - |
| 1.9204 | 17000 | 4.6404 | - | - | - | - | - |
| 1.9433 | 17200 | 4.6413 | - | - | - | - | - |
| 1.9663 | 17400 | 4.4495 | - | - | - | - | - |
| 1.9892 | 17600 | 4.4262 | - | - | - | - | - |
| 2.0122 | 17800 | 2.01 | - | - | - | - | - |
| 2.0053 | 18000 | 1.8418 | - | - | - | - | - |
| 2.0282 | 18200 | 6.2714 | - | - | - | - | - |
| 2.0512 | 18400 | 6.1742 | - | - | - | - | - |
| 2.0741 | 18600 | 6.5996 | - | - | - | - | - |
| 2.0971 | 18800 | 6.0907 | - | - | - | - | - |
| 2.1200 | 19000 | 6.2418 | - | - | - | - | - |
| 2.1429 | 19200 | 5.7817 | - | - | - | - | - |
| 2.1659 | 19400 | 5.7073 | - | - | - | - | - |
| 2.1888 | 19600 | 5.2645 | - | - | - | - | - |
| 2.2118 | 19800 | 5.3451 | - | - | - | - | - |
| 2.2347 | 20000 | 5.2453 | - | - | - | - | - |
| 2.2577 | 20200 | 5.6161 | - | - | - | - | - |
| 2.2806 | 20400 | 5.2289 | - | - | - | - | - |
| 2.3035 | 20600 | 5.3888 | - | - | - | - | - |
| 2.3265 | 20800 | 5.2483 | - | - | - | - | - |
| 2.3494 | 21000 | 5.5791 | - | - | - | - | - |
| 2.3724 | 21200 | 5.1643 | - | - | - | - | - |
| 2.3953 | 21400 | 5.1231 | - | - | - | - | - |
| 2.4183 | 21600 | 5.1055 | - | - | - | - | - |
| 2.4412 | 21800 | 5.1778 | - | - | - | - | - |
| 2.4642 | 22000 | 5.0466 | - | - | - | - | - |
| 2.4871 | 22200 | 4.8321 | - | - | - | - | - |
| 2.5100 | 22400 | 4.7056 | - | - | - | - | - |
| 2.5330 | 22600 | 4.6858 | - | - | - | - | - |
| 2.5559 | 22800 | 4.9189 | - | - | - | - | - |
| 2.5789 | 23000 | 4.912 | - | - | - | - | - |
| 2.6018 | 23200 | 4.8289 | - | - | - | - | - |
| 2.6248 | 23400 | 4.8959 | - | - | - | - | - |
| 2.6477 | 23600 | 4.9441 | - | - | - | - | - |
| 2.6706 | 23800 | 4.9334 | - | - | - | - | - |
| 2.6936 | 24000 | 4.8328 | - | - | - | - | - |
| 2.7165 | 24200 | 4.601 | - | - | - | - | - |
| 2.7395 | 24400 | 4.834 | - | - | - | - | - |
| 2.7624 | 24600 | 5.152 | - | - | - | - | - |
| 2.7854 | 24800 | 4.9232 | - | - | - | - | - |
| 2.8083 | 25000 | 4.6556 | - | - | - | - | - |
| 2.8312 | 25200 | 4.6229 | - | - | - | - | - |
| 2.8542 | 25400 | 4.5768 | - | - | - | - | - |
| 2.8771 | 25600 | 4.3619 | - | - | - | - | - |
| 2.9001 | 25800 | 4.3608 | - | - | - | - | - |
| 2.9230 | 26000 | 4.2834 | - | - | - | - | - |
| 2.9403 | 26151 | - | 0.6355 | 0.6513 | 0.6703 | 0.6081 | 0.6757 |
</details>
### Framework Versions
- Python: 3.9.18
- Sentence Transformers: 3.0.1
- Transformers: 4.40.0
- PyTorch: 2.2.2+cu121
- Accelerate: 0.26.1
- Datasets: 2.19.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## <span style="color:blue">Acknowledgments</span>
The author would like to thank Prince Sultan University for their invaluable support in this project. Their contributions and resources have been instrumental in the development and fine-tuning of these models.
```markdown
## Citation
If you use the Arabic Matryoshka Embeddings Model, please cite it as follows:
@misc{nacar2024enhancingsemanticsimilarityunderstanding,
title={Enhancing Semantic Similarity Understanding in Arabic NLP with Nested Embedding Learning},
author={Omer Nacar and Anis Koubaa},
year={2024},
eprint={2407.21139},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2407.21139},
} |