File size: 45,923 Bytes
88edc46 07ce3f6 88edc46 07ce3f6 88edc46 |
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 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 |
---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:100K<n<1M
- loss:AnglELoss
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: 有些人在路上溜达。
sentences:
- Folk går
- Otururken gitar çalan adam.
- ארה"ב קבעה שסוריה השתמשה בנשק כימי
- source_sentence: 緬甸以前稱為緬甸。
sentences:
- 缅甸以前叫缅甸。
- This is very contradictory.
- 한 남자가 아기를 안고 의자에 앉아 잠들어 있다.
- source_sentence: אדם כותב.
sentences:
- האדם כותב.
- questa non è una risposta.
- 7 שוטרים נהרגו ו-4 שוטרים נפצעו.
- source_sentence: הם מפחדים.
sentences:
- liên quan đến rủi ro đáng kể;
- A man is playing a guitar.
- A man is playing a piano.
- source_sentence: 一个女人正在洗澡。
sentences:
- A woman is taking a bath.
- En jente børster håret sitt
- אדם מחלק תפוח אדמה.
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev
type: sts-dev
metrics:
- type: pearson_cosine
value: 0.9551466915019567
name: Pearson Cosine
- type: spearman_cosine
value: 0.9592676437617756
name: Spearman Cosine
- type: pearson_manhattan
value: 0.9270103565661432
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.9382925369644322
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.9278315400036575
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.9393641949848517
name: Spearman Euclidean
- type: pearson_dot
value: 0.8760113280718741
name: Pearson Dot
- type: spearman_dot
value: 0.8864509380027734
name: Spearman Dot
- type: pearson_max
value: 0.9551466915019567
name: Pearson Max
- type: spearman_max
value: 0.9592676437617756
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test
type: sts-test
metrics:
- type: pearson_cosine
value: 0.9479585032380113
name: Pearson Cosine
- type: spearman_cosine
value: 0.9514910354916427
name: Spearman Cosine
- type: pearson_manhattan
value: 0.925192141913064
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.9351648026362221
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.9258239806908134
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.9363652577900217
name: Spearman Euclidean
- type: pearson_dot
value: 0.8442947652156254
name: Pearson Dot
- type: spearman_dot
value: 0.8435104766124126
name: Spearman Dot
- type: pearson_max
value: 0.9479585032380113
name: Pearson Max
- type: spearman_max
value: 0.9514910354916427
name: Spearman Max
- type: pearson_cosine
value: 0.9725274765440489
name: Pearson Cosine
- type: spearman_cosine
value: 0.9766335692570665
name: Spearman Cosine
- type: pearson_manhattan
value: 0.9382317294386867
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.948654920505423
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.9392057529290415
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.9500099103637895
name: Spearman Euclidean
- type: pearson_dot
value: 0.8531236460319379
name: Pearson Dot
- type: spearman_dot
value: 0.8611492409185547
name: Spearman Dot
- type: pearson_max
value: 0.9725274765440489
name: Pearson Max
- type: spearman_max
value: 0.9766335692570665
name: Spearman Max
- type: pearson_cosine
value: 0.8026922386812214
name: Pearson Cosine
- type: spearman_cosine
value: 0.8124393788492182
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7839394479918361
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7899571854314883
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7835912695413444
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7920219916708612
name: Spearman Euclidean
- type: pearson_dot
value: 0.7698701769634279
name: Pearson Dot
- type: spearman_dot
value: 0.781996122357711
name: Spearman Dot
- type: pearson_max
value: 0.8026922386812214
name: Pearson Max
- type: spearman_max
value: 0.8124393788492182
name: Spearman Max
- type: pearson_cosine
value: 0.7795928581740468
name: Pearson Cosine
- type: spearman_cosine
value: 0.7703365842088069
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7903764226370217
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7829879213871844
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7911863454505806
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7841695636601043
name: Spearman Euclidean
- type: pearson_dot
value: 0.7077312955932407
name: Pearson Dot
- type: spearman_dot
value: 0.6914225616023565
name: Spearman Dot
- type: pearson_max
value: 0.7911863454505806
name: Pearson Max
- type: spearman_max
value: 0.7841695636601043
name: Spearman Max
- type: pearson_cosine
value: 0.9112700251605085
name: Pearson Cosine
- type: spearman_cosine
value: 0.9109414091487618
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8969826303560867
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8934356058163047
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8986106629139636
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8954517657266873
name: Spearman Euclidean
- type: pearson_dot
value: 0.884386067267308
name: Pearson Dot
- type: spearman_dot
value: 0.8922685778872441
name: Spearman Dot
- type: pearson_max
value: 0.9112700251605085
name: Pearson Max
- type: spearman_max
value: 0.9109414091487618
name: Spearman Max
- type: pearson_cosine
value: 0.9361870787330656
name: Pearson Cosine
- type: spearman_cosine
value: 0.9378741534997558
name: Spearman Cosine
- type: pearson_manhattan
value: 0.9230051982649123
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.9244721677465636
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.9230904520135751
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.9251248730902872
name: Spearman Euclidean
- type: pearson_dot
value: 0.9069963151228692
name: Pearson Dot
- type: spearman_dot
value: 0.9185797530151516
name: Spearman Dot
- type: pearson_max
value: 0.9361870787330656
name: Pearson Max
- type: spearman_max
value: 0.9378741534997558
name: Spearman Max
- type: pearson_cosine
value: 0.8048757108412675
name: Pearson Cosine
- type: spearman_cosine
value: 0.7987027653005363
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8017660413612523
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7828168153285264
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8006665075585622
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7824761741785664
name: Spearman Euclidean
- type: pearson_dot
value: 0.7894710045147775
name: Pearson Dot
- type: spearman_dot
value: 0.7819409907917216
name: Spearman Dot
- type: pearson_max
value: 0.8048757108412675
name: Pearson Max
- type: spearman_max
value: 0.7987027653005363
name: Spearman Max
- type: pearson_cosine
value: 0.8520160385093393
name: Pearson Cosine
- type: spearman_cosine
value: 0.8553203530552356
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8464006282913296
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8409514527398295
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8467543977447098
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8458591066828018
name: Spearman Euclidean
- type: pearson_dot
value: 0.8093136598158064
name: Pearson Dot
- type: spearman_dot
value: 0.8153571493902085
name: Spearman Dot
- type: pearson_max
value: 0.8520160385093393
name: Pearson Max
- type: spearman_max
value: 0.8553203530552356
name: Spearman Max
- type: pearson_cosine
value: 0.8751983236341568
name: Pearson Cosine
- type: spearman_cosine
value: 0.872701191632785
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8744834146908832
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8661385734785878
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.874802989814616
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8668384026485944
name: Spearman Euclidean
- type: pearson_dot
value: 0.8603441420083793
name: Pearson Dot
- type: spearman_dot
value: 0.8519571499551175
name: Spearman Dot
- type: pearson_max
value: 0.8751983236341568
name: Pearson Max
- type: spearman_max
value: 0.872701191632785
name: Spearman Max
- type: pearson_cosine
value: 0.9082404991830442
name: Pearson Cosine
- type: spearman_cosine
value: 0.9067607122592818
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8908378724095692
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.885184918244054
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8907567800603056
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8850799779856109
name: Spearman Euclidean
- type: pearson_dot
value: 0.8888621290344544
name: Pearson Dot
- type: spearman_dot
value: 0.8965880419316619
name: Spearman Dot
- type: pearson_max
value: 0.9082404991830442
name: Pearson Max
- type: spearman_max
value: 0.9067607122592818
name: Spearman Max
- type: pearson_cosine
value: 0.9249796814520836
name: Pearson Cosine
- type: spearman_cosine
value: 0.9246785886944904
name: Spearman Cosine
- type: pearson_manhattan
value: 0.9083667986520362
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.90288714821411
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.9115880396459031
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.9083794061358542
name: Spearman Euclidean
- type: pearson_dot
value: 0.9000889923763985
name: Pearson Dot
- type: spearman_dot
value: 0.9070443969139744
name: Spearman Dot
- type: pearson_max
value: 0.9249796814520836
name: Pearson Max
- type: spearman_max
value: 0.9246785886944904
name: Spearman Max
- type: pearson_cosine
value: 0.9133091498737149
name: Pearson Cosine
- type: spearman_cosine
value: 0.9114826394926738
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8977113793113364
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8933433506440468
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8979058595014344
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8937323599537337
name: Spearman Euclidean
- type: pearson_dot
value: 0.891219202934611
name: Pearson Dot
- type: spearman_dot
value: 0.8987764114969254
name: Spearman Dot
- type: pearson_max
value: 0.9133091498737149
name: Pearson Max
- type: spearman_max
value: 0.9114826394926738
name: Spearman Max
- type: pearson_cosine
value: 0.8984578585216539
name: Pearson Cosine
- type: spearman_cosine
value: 0.8451542547285167
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8714879175346363
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8451542547285167
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8809190484217423
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8451542547285167
name: Spearman Euclidean
- type: pearson_dot
value: 0.8537957222589418
name: Pearson Dot
- type: spearman_dot
value: 0.8451542547285167
name: Spearman Dot
- type: pearson_max
value: 0.8984578585216539
name: Pearson Max
- type: spearman_max
value: 0.8451542547285167
name: Spearman Max
- type: pearson_cosine
value: 0.6494815112978085
name: Pearson Cosine
- type: spearman_cosine
value: 0.6385354535483773
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6429493098908716
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6473666993823523
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6442945700268683
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6444758519763731
name: Spearman Euclidean
- type: pearson_dot
value: 0.6128358976757747
name: Pearson Dot
- type: spearman_dot
value: 0.6108258021881942
name: Spearman Dot
- type: pearson_max
value: 0.6494815112978085
name: Pearson Max
- type: spearman_max
value: 0.6473666993823523
name: Spearman Max
- type: pearson_cosine
value: 0.7441341150359049
name: Pearson Cosine
- type: spearman_cosine
value: 0.7518021273920814
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7339108684091178
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7367402927783612
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7336764576613932
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.734241088471987
name: Spearman Euclidean
- type: pearson_dot
value: 0.6886320720189693
name: Pearson Dot
- type: spearman_dot
value: 0.698561864698337
name: Spearman Dot
- type: pearson_max
value: 0.7441341150359049
name: Pearson Max
- type: spearman_max
value: 0.7518021273920814
name: Spearman Max
- type: pearson_cosine
value: 0.6278594754203957
name: Pearson Cosine
- type: spearman_cosine
value: 0.6319430830291571
name: Spearman Cosine
- type: pearson_manhattan
value: 0.543548091135791
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6002053211770223
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.5399866615749636
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.5955360076924765
name: Spearman Euclidean
- type: pearson_dot
value: 0.5657998544710718
name: Pearson Dot
- type: spearman_dot
value: 0.6068611192160528
name: Spearman Dot
- type: pearson_max
value: 0.6278594754203957
name: Pearson Max
- type: spearman_max
value: 0.6319430830291571
name: Spearman Max
- type: pearson_cosine
value: 0.7778538763931996
name: Pearson Cosine
- type: spearman_cosine
value: 0.7875616631597785
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7425757616272681
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7789392103102715
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7437054735775576
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.780583955651507
name: Spearman Euclidean
- type: pearson_dot
value: 0.7214423493083364
name: Pearson Dot
- type: spearman_dot
value: 0.7489073787091952
name: Spearman Dot
- type: pearson_max
value: 0.7778538763931996
name: Pearson Max
- type: spearman_max
value: 0.7875616631597785
name: Spearman Max
- type: pearson_cosine
value: 0.526790729806662
name: Pearson Cosine
- type: spearman_cosine
value: 0.5774252131250034
name: Spearman Cosine
- type: pearson_manhattan
value: 0.41713442172065224
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.5599676717727231
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.42192411421528214
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.5665444422359257
name: Spearman Euclidean
- type: pearson_dot
value: 0.49809047501575476
name: Pearson Dot
- type: spearman_dot
value: 0.5367148143234142
name: Spearman Dot
- type: pearson_max
value: 0.526790729806662
name: Pearson Max
- type: spearman_max
value: 0.5774252131250034
name: Spearman Max
- type: pearson_cosine
value: 0.6306061651851392
name: Pearson Cosine
- type: spearman_cosine
value: 0.6383757017928495
name: Spearman Cosine
- type: pearson_manhattan
value: 0.603366556372183
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6167955278711116
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6081018686388112
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6219639110001453
name: Spearman Euclidean
- type: pearson_dot
value: 0.5767081284665276
name: Pearson Dot
- type: spearman_dot
value: 0.5831358067917275
name: Spearman Dot
- type: pearson_max
value: 0.6306061651851392
name: Pearson Max
- type: spearman_max
value: 0.6383757017928495
name: Spearman Max
- type: pearson_cosine
value: 0.5568482062575557
name: Pearson Cosine
- type: spearman_cosine
value: 0.5866853707548388
name: Spearman Cosine
- type: pearson_manhattan
value: 0.49244450938868833
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.5737511662255662
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.49058760093828624
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.5762095703672849
name: Spearman Euclidean
- type: pearson_dot
value: 0.4306984514506903
name: Pearson Dot
- type: spearman_dot
value: 0.5470683854030187
name: Spearman Dot
- type: pearson_max
value: 0.5568482062575557
name: Pearson Max
- type: spearman_max
value: 0.5866853707548388
name: Spearman Max
- type: pearson_cosine
value: 0.5776222742798018
name: Pearson Cosine
- type: spearman_cosine
value: 0.5749790581441845
name: Spearman Cosine
- type: pearson_manhattan
value: 0.571787148920759
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.5500811027014174
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.5695499775959532
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.5532223379017994
name: Spearman Euclidean
- type: pearson_dot
value: 0.53146407233978
name: Pearson Dot
- type: spearman_dot
value: 0.5190797374963447
name: Spearman Dot
- type: pearson_max
value: 0.5776222742798018
name: Pearson Max
- type: spearman_max
value: 0.5749790581441845
name: Spearman Max
- type: pearson_cosine
value: 0.3571900232473057
name: Pearson Cosine
- type: spearman_cosine
value: 0.4335552432730643
name: Spearman Cosine
- type: pearson_manhattan
value: 0.20808854264339055
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.4354537154533896
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.208616390027902
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.440246452767669
name: Spearman Euclidean
- type: pearson_dot
value: 0.22336496195751424
name: Pearson Dot
- type: spearman_dot
value: 0.3706905558756734
name: Spearman Dot
- type: pearson_max
value: 0.3571900232473057
name: Pearson Max
- type: spearman_max
value: 0.440246452767669
name: Spearman Max
- type: pearson_cosine
value: 0.6863427356006826
name: Pearson Cosine
- type: spearman_cosine
value: 0.6620948502618977
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6428578762643233
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6483663123081533
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6424050032110411
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6485902628925195
name: Spearman Euclidean
- type: pearson_dot
value: 0.6352371374824808
name: Pearson Dot
- type: spearman_dot
value: 0.6159110999161411
name: Spearman Dot
- type: pearson_max
value: 0.6863427356006826
name: Pearson Max
- type: spearman_max
value: 0.6620948502618977
name: Spearman Max
- type: pearson_cosine
value: 0.7570295008280781
name: Pearson Cosine
- type: spearman_cosine
value: 0.7510805416538202
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7191097960855934
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7140422377894933
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7204228437397647
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7257632200250398
name: Spearman Euclidean
- type: pearson_dot
value: 0.7144336778935939
name: Pearson Dot
- type: spearman_dot
value: 0.7284199759984302
name: Spearman Dot
- type: pearson_max
value: 0.7570295008280781
name: Pearson Max
- type: spearman_max
value: 0.7510805416538202
name: Spearman Max
- type: pearson_cosine
value: 0.6502825737911098
name: Pearson Cosine
- type: spearman_cosine
value: 0.6624635951676386
name: Spearman Cosine
- type: pearson_manhattan
value: 0.647419285100459
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6589805549915764
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6516956762905051
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6667221229271868
name: Spearman Euclidean
- type: pearson_dot
value: 0.5646710115576599
name: Pearson Dot
- type: spearman_dot
value: 0.570198719868156
name: Spearman Dot
- type: pearson_max
value: 0.6516956762905051
name: Pearson Max
- type: spearman_max
value: 0.6667221229271868
name: Spearman Max
- type: pearson_cosine
value: 0.6774230420538705
name: Pearson Cosine
- type: spearman_cosine
value: 0.6537294853166558
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6824702119604247
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6324707043840341
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6905615468119815
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.640725065351179
name: Spearman Euclidean
- type: pearson_dot
value: 0.5834798827905125
name: Pearson Dot
- type: spearman_dot
value: 0.5962447037764929
name: Spearman Dot
- type: pearson_max
value: 0.6905615468119815
name: Pearson Max
- type: spearman_max
value: 0.6537294853166558
name: Spearman Max
- type: pearson_cosine
value: 0.6709478850576526
name: Pearson Cosine
- type: spearman_cosine
value: 0.6847049462613332
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6612883666796053
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6906896123993531
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.66070522554664
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6880796473119815
name: Spearman Euclidean
- type: pearson_dot
value: 0.609762034287328
name: Pearson Dot
- type: spearman_dot
value: 0.6194587632000961
name: Spearman Dot
- type: pearson_max
value: 0.6709478850576526
name: Pearson Max
- type: spearman_max
value: 0.6906896123993531
name: Spearman Max
- type: pearson_cosine
value: 0.5977420246846783
name: Pearson Cosine
- type: spearman_cosine
value: 0.5798716781400349
name: Spearman Cosine
- type: pearson_manhattan
value: 0.5974348978243684
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.5952597125560467
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.5949256850264925
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.5935900431326085
name: Spearman Euclidean
- type: pearson_dot
value: 0.5042542872226021
name: Pearson Dot
- type: spearman_dot
value: 0.4968394689744579
name: Spearman Dot
- type: pearson_max
value: 0.5977420246846783
name: Pearson Max
- type: spearman_max
value: 0.5952597125560467
name: Spearman Max
- type: pearson_cosine
value: 0.45623521030042163
name: Pearson Cosine
- type: spearman_cosine
value: 0.44220332625465214
name: Spearman Cosine
- type: pearson_manhattan
value: 0.4154787596532877
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.3836945296053597
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.4111357738180186
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.3821548244303783
name: Spearman Euclidean
- type: pearson_dot
value: 0.48625234725541483
name: Pearson Dot
- type: spearman_dot
value: 0.5302744622635869
name: Spearman Dot
- type: pearson_max
value: 0.48625234725541483
name: Pearson Max
- type: spearman_max
value: 0.5302744622635869
name: Spearman Max
- type: pearson_cosine
value: 0.5929570742517215
name: Pearson Cosine
- type: spearman_cosine
value: 0.6266361518449931
name: Spearman Cosine
- type: pearson_manhattan
value: 0.5608268850302591
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6228972623939251
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.5579847474929831
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6202030126844109
name: Spearman Euclidean
- type: pearson_dot
value: 0.4578333834889949
name: Pearson Dot
- type: spearman_dot
value: 0.5628471668594075
name: Spearman Dot
- type: pearson_max
value: 0.5929570742517215
name: Pearson Max
- type: spearman_max
value: 0.6266361518449931
name: Spearman Max
---
/!\ This model achieves SOTA results in the MTEB STS multilingual Leaderboard (in "other"). Here is the comparison
State-of-the-art results (Multi) STSb-XLM-RoBERTa-base Paraphrase Multilingual MPNet base v2
Average 73.17 71.68 **73.89**
STS17 (ar-ar) **81.87** 80.43 81.24
STS17 (en-ar) **81.22** 76.3 77.03
STS17 (en-de) 87.3 91.06 **91.09**
STS17 (en-tr) 77.18 **80.74** 79.87
STS17 (es-en) **88.24** 83.09 85.53
STS17 (es-es) **88.25** 84.16 87.27
STS17 (fr-en) 88.06 **91.33** 90.68
STS17 (it-en) 89.68 **92.87** 92.47
STS17 (ko-ko) 83.69 **97.67** 97.66
STS17 (nl-en) 88.25 **92.13** 91.15
STS22 (ar) 58.67 58.67 **62.66**
STS22 (de) **60.12** 52.17 57.74
STS22 (de-en) **60.92** 58.5 57.5
STS22 (de-fr) **67.79** 51.28 57.99
STS22 (de-pl) **58.69** 44.56 44.22
STS22 (es) **68.57** 63.68 66.21
STS22 (es-en) **78.8** 70.65 75.18
STS22 (es-it) **75.04** 60.88 66.25
STS22 (fr) **83.75** 76.46 78.76
STS22 (fr-pl) 84.52 84.52 **84.52**
STS22 (it) **79.28** 66.73 68.47
STS22 (pl) 42.08 41.18 **43.36**
STS22 (pl-en) **77.5** 64.35 75.11
STS22 (ru) **61.71** 58.59 58.67
STS22 (tr) **68.72** 57.52 63.84
STS22 (zh-en) **71.88** 60.69 65.37
STSb 89.86 95.05 **95.15**
# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-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/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) <!-- at revision 79f2382ceacceacdf38563d7c5d16b9ff8d725d6 -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **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': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(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("Gameselo/STS-multilingual-mpnet-base-v2")
# Run inference
sentences = [
'一个女人正在洗澡。',
'A woman is taking a bath.',
'En jente børster håret sitt',
]
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-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.9551 |
| **spearman_cosine** | **0.9593** |
| pearson_manhattan | 0.927 |
| spearman_manhattan | 0.9383 |
| pearson_euclidean | 0.9278 |
| spearman_euclidean | 0.9394 |
| pearson_dot | 0.876 |
| spearman_dot | 0.8865 |
| pearson_max | 0.9551 |
| spearman_max | 0.9593 |
#### Evalutation results vs SOTA results
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.948 |
| **spearman_cosine** | **0.9515** |
| pearson_manhattan | 0.9252 |
| spearman_manhattan | 0.9352 |
| pearson_euclidean | 0.9258 |
| spearman_euclidean | 0.9364 |
| pearson_dot | 0.8443 |
| spearman_dot | 0.8435 |
| pearson_max | 0.948 |
| spearman_max | 0.9515 |
<!--
## 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: 226,547 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | label |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 3 tokens</li><li>mean: 20.05 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 19.94 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 1.92</li><li>max: 398.6</li></ul> |
* Samples:
| sentence_0 | sentence_1 | label |
|:-------------------------------------------------------------------|:----------------------------------------------------------------|:---------------------------------|
| <code>Bir kadın makineye dikiş dikiyor.</code> | <code>Bir kadın biraz et ekiyor.</code> | <code>0.12</code> |
| <code>Snowden 'gegeven vluchtelingendocument door Ecuador'.</code> | <code>Snowden staat op het punt om uit Moskou te vliegen</code> | <code>0.24000000953674316</code> |
| <code>Czarny pies idzie mostem przez wodę</code> | <code>Czarny pies nie idzie mostem przez wodę</code> | <code>0.74000000954</code> |
* Loss: [<code>AnglELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#angleloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_angle_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 256
- `num_train_epochs`: 10
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 256
- `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
- `num_train_epochs`: 10
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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`: False
- `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`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### Training Logs
| Epoch | Step | Training Loss | sts-dev_spearman_cosine | sts-test_spearman_cosine |
|:------:|:----:|:-------------:|:-----------------------:|:------------------------:|
| 0.5650 | 500 | 10.9426 | - | - |
| 1.0 | 885 | - | 0.9202 | - |
| 1.1299 | 1000 | 9.7184 | - | - |
| 1.6949 | 1500 | 9.5348 | - | - |
| 2.0 | 1770 | - | 0.9400 | - |
| 2.2599 | 2000 | 9.4412 | - | - |
| 2.8249 | 2500 | 9.3097 | - | - |
| 3.0 | 2655 | - | 0.9489 | - |
| 3.3898 | 3000 | 9.2357 | - | - |
| 3.9548 | 3500 | 9.1594 | - | - |
| 4.0 | 3540 | - | 0.9528 | - |
| 4.5198 | 4000 | 9.0963 | - | - |
| 5.0 | 4425 | - | 0.9553 | - |
| 5.0847 | 4500 | 9.0382 | - | - |
| 5.6497 | 5000 | 8.9837 | - | - |
| 6.0 | 5310 | - | 0.9567 | - |
| 6.2147 | 5500 | 8.9403 | - | - |
| 6.7797 | 6000 | 8.8841 | - | - |
| 7.0 | 6195 | - | 0.9581 | - |
| 7.3446 | 6500 | 8.8513 | - | - |
| 7.9096 | 7000 | 8.81 | - | - |
| 8.0 | 7080 | - | 0.9582 | - |
| 8.4746 | 7500 | 8.8069 | - | - |
| 9.0 | 7965 | - | 0.9589 | - |
| 9.0395 | 8000 | 8.7616 | - | - |
| 9.6045 | 8500 | 8.7521 | - | - |
| 10.0 | 8850 | - | 0.9593 | 0.6266 |
### Framework Versions
- Python: 3.9.7
- Sentence Transformers: 3.0.0
- Transformers: 4.40.1
- PyTorch: 2.3.0+cu121
- Accelerate: 0.29.3
- 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",
}
```
#### AnglELoss
```bibtex
@misc{li2023angleoptimized,
title={AnglE-optimized Text Embeddings},
author={Xianming Li and Jing Li},
year={2023},
eprint={2309.12871},
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.*
--> |