File size: 60,446 Bytes
48d168a |
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 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:124788
- loss:GISTEmbedLoss
base_model: Alibaba-NLP/gte-multilingual-base
widget:
- source_sentence: 其他机械、设备和有形货物租赁服务代表
sentences:
- 其他机械和设备租赁服务工作人员
- 电子和电信设备及零部件物流经理
- 工业主厨
- source_sentence: 公交车司机
sentences:
- 表演灯光设计师
- 乙烯基地板安装工
- 国际巴士司机
- source_sentence: online communication manager
sentences:
- trades union official
- social media manager
- budget manager
- source_sentence: Projektmanagerin
sentences:
- Projektmanager/Projektmanagerin
- Category-Manager
- Infanterist
- source_sentence: Volksvertreter
sentences:
- Parlamentarier
- Oberbürgermeister
- Konsul
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@20
- cosine_accuracy@50
- cosine_accuracy@100
- cosine_accuracy@150
- cosine_accuracy@200
- cosine_precision@1
- cosine_precision@20
- cosine_precision@50
- cosine_precision@100
- cosine_precision@150
- cosine_precision@200
- cosine_recall@1
- cosine_recall@20
- cosine_recall@50
- cosine_recall@100
- cosine_recall@150
- cosine_recall@200
- cosine_ndcg@1
- cosine_ndcg@20
- cosine_ndcg@50
- cosine_ndcg@100
- cosine_ndcg@150
- cosine_ndcg@200
- cosine_mrr@1
- cosine_mrr@20
- cosine_mrr@50
- cosine_mrr@100
- cosine_mrr@150
- cosine_mrr@200
- cosine_map@1
- cosine_map@20
- cosine_map@50
- cosine_map@100
- cosine_map@150
- cosine_map@200
- cosine_map@500
model-index:
- name: SentenceTransformer based on Alibaba-NLP/gte-multilingual-base
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: full en
type: full_en
metrics:
- type: cosine_accuracy@1
value: 0.6571428571428571
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.9904761904761905
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.9904761904761905
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.9904761904761905
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.9904761904761905
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.9904761904761905
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.6571428571428571
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.5171428571428571
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.316
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.18895238095238095
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.13384126984126984
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.10433333333333335
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.0678253733846715
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.5470006025464504
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.7399645316315758
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.8452891149669638
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.8838497168796887
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.9109269128757174
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.6571428571428571
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.6953571805621692
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.7150421121165462
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.7679394555495317
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.7856911059911225
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.7969632777290026
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.6571428571428571
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.8138095238095239
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.8138095238095239
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.8138095238095239
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.8138095238095239
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.8138095238095239
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.6571428571428571
name: Cosine Map@1
- type: cosine_map@20
value: 0.5578605627627369
name: Cosine Map@20
- type: cosine_map@50
value: 0.5471407389299809
name: Cosine Map@50
- type: cosine_map@100
value: 0.5795933384755297
name: Cosine Map@100
- type: cosine_map@150
value: 0.5874505508842796
name: Cosine Map@150
- type: cosine_map@200
value: 0.5912226659397186
name: Cosine Map@200
- type: cosine_map@500
value: 0.5952587557760031
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: full es
type: full_es
metrics:
- type: cosine_accuracy@1
value: 0.12432432432432433
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 1.0
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 1.0
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 1.0
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 1.0
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 1.0
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.12432432432432433
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.5718918918918919
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.3885405405405405
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.25172972972972973
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.1904864864864865
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.1521891891891892
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.0036619075252531876
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.3842245968041533
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.5640822196868902
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.6741986120580108
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.7463851968088967
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.7825399601398452
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.12432432432432433
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.6139182209948354
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.5873893466818746
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.6144038475288277
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.6498632077214272
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.6680602466150343
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.12432432432432433
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.5581081081081081
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.5581081081081081
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.5581081081081081
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.5581081081081081
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.5581081081081081
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.12432432432432433
name: Cosine Map@1
- type: cosine_map@20
value: 0.47988875190050484
name: Cosine Map@20
- type: cosine_map@50
value: 0.4249833337950364
name: Cosine Map@50
- type: cosine_map@100
value: 0.430155652024808
name: Cosine Map@100
- type: cosine_map@150
value: 0.4458862132745998
name: Cosine Map@150
- type: cosine_map@200
value: 0.45334655744992447
name: Cosine Map@200
- type: cosine_map@500
value: 0.4656066165331343
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: full de
type: full_de
metrics:
- type: cosine_accuracy@1
value: 0.2955665024630542
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.9704433497536946
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.9852216748768473
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.9852216748768473
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.9901477832512315
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.9901477832512315
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.2955665024630542
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.5083743842364532
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.3654187192118227
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.24133004926108376
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.18036124794745487
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.14467980295566504
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.01108543831680986
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.3221185941380065
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.5024502430161547
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.6247617904371989
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.6829583450315939
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.7216293640715983
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.2955665024630542
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.5393376062142305
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.5267125529267169
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.55793511917882
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.5879547828450983
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.6071252185389439
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.2955665024630542
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.5104381157401634
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.5109752961295605
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.5109752961295605
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.5110222114474118
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.5110222114474118
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.2955665024630542
name: Cosine Map@1
- type: cosine_map@20
value: 0.40097257642946377
name: Cosine Map@20
- type: cosine_map@50
value: 0.35882787401455
name: Cosine Map@50
- type: cosine_map@100
value: 0.3633182590941781
name: Cosine Map@100
- type: cosine_map@150
value: 0.3776727961080201
name: Cosine Map@150
- type: cosine_map@200
value: 0.3848401555555339
name: Cosine Map@200
- type: cosine_map@500
value: 0.3978065874082948
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: full zh
type: full_zh
metrics:
- type: cosine_accuracy@1
value: 0.6601941747572816
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.9805825242718447
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.9902912621359223
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.9902912621359223
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.9902912621359223
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.9902912621359223
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.6601941747572816
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.4781553398058253
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.28951456310679613
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.17572815533980585
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.12595469255663433
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.09815533980582528
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.06151358631979527
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.5107966412908705
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.6922746152164951
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.8004152884148357
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.8465065661615649
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.8770990926698364
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.6601941747572816
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.6539867858378715
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.6707332209240133
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.72342020484322
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.7437750875502527
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.7553648453187212
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.6601941747572816
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.8037216828478965
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.8040950958426687
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.8040950958426687
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.8040950958426687
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.8040950958426687
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.6601941747572816
name: Cosine Map@1
- type: cosine_map@20
value: 0.5087334164702914
name: Cosine Map@20
- type: cosine_map@50
value: 0.49260246320797585
name: Cosine Map@50
- type: cosine_map@100
value: 0.5217412166882693
name: Cosine Map@100
- type: cosine_map@150
value: 0.529859818130126
name: Cosine Map@150
- type: cosine_map@200
value: 0.533378795921413
name: Cosine Map@200
- type: cosine_map@500
value: 0.5386011712914499
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: mix es
type: mix_es
metrics:
- type: cosine_accuracy@1
value: 0.7280291211648466
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.9599583983359334
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.9791991679667187
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.9942797711908476
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.9958398335933437
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.9973998959958398
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.7280291211648466
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.12433697347893914
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.05145085803432139
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.02625065002600105
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.017621771537528162
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.013283931357254294
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.28133620582918556
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.9183394002426764
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.9499306638932224
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.9700901369388107
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.9767724042295025
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.9818166059975733
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.7280291211648466
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.8043549768911603
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.81295852465432
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.817339429558165
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.8186380742931886
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.8195485984235017
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.7280291211648466
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.7968549154271433
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.7974653825839162
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.7976914864910069
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.7977044635908871
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.7977139196654446
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.7280291211648466
name: Cosine Map@1
- type: cosine_map@20
value: 0.7350836192117531
name: Cosine Map@20
- type: cosine_map@50
value: 0.7374205090112232
name: Cosine Map@50
- type: cosine_map@100
value: 0.737988888492803
name: Cosine Map@100
- type: cosine_map@150
value: 0.7381133157945164
name: Cosine Map@150
- type: cosine_map@200
value: 0.7381788581828236
name: Cosine Map@200
- type: cosine_map@500
value: 0.7382854440643231
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: mix de
type: mix_de
metrics:
- type: cosine_accuracy@1
value: 0.6703068122724909
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.9505980239209568
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.9776391055642226
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.9864794591783671
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.9932397295891836
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.9947997919916797
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.6703068122724909
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.1251690067602704
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.052282891315652634
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.026729069162766517
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.01799965331946611
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.013541341653666149
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.25235742763043856
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.9095857167620037
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.9482405962905183
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.96845207141619
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.9781591263650546
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.9810192407696308
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.6703068122724909
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.7735712514376322
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.7843644592705362
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.7889444470773866
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.7908660087982327
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.791403470160319
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.6703068122724909
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.7520307321055828
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.7529374175534339
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.7530616872072472
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.7531202644382351
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.7531293951311296
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.6703068122724909
name: Cosine Map@1
- type: cosine_map@20
value: 0.6967639778693541
name: Cosine Map@20
- type: cosine_map@50
value: 0.699575457224443
name: Cosine Map@50
- type: cosine_map@100
value: 0.70027844357658
name: Cosine Map@100
- type: cosine_map@150
value: 0.7004487000056766
name: Cosine Map@150
- type: cosine_map@200
value: 0.7004863395843564
name: Cosine Map@200
- type: cosine_map@500
value: 0.7005835771389989
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: mix zh
type: mix_zh
metrics:
- type: cosine_accuracy@1
value: 0.19084763390535622
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 1.0
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 1.0
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 1.0
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 1.0
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 1.0
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.19084763390535622
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.15439417576703063
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.0617576703068123
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.03087883515340615
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.020585890102270757
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.015439417576703075
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.06137978852487433
name: Cosine Recall@1
- type: cosine_recall@20
value: 1.0
name: Cosine Recall@20
- type: cosine_recall@50
value: 1.0
name: Cosine Recall@50
- type: cosine_recall@100
value: 1.0
name: Cosine Recall@100
- type: cosine_recall@150
value: 1.0
name: Cosine Recall@150
- type: cosine_recall@200
value: 1.0
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.19084763390535622
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.5474303590499686
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.5474303590499686
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.5474303590499686
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.5474303590499686
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.5474303590499686
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.19084763390535622
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.4093433087972877
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.4093433087972877
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.4093433087972877
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.4093433087972877
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.4093433087972877
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.19084763390535622
name: Cosine Map@1
- type: cosine_map@20
value: 0.32981711891302556
name: Cosine Map@20
- type: cosine_map@50
value: 0.32981711891302556
name: Cosine Map@50
- type: cosine_map@100
value: 0.32981711891302556
name: Cosine Map@100
- type: cosine_map@150
value: 0.32981711891302556
name: Cosine Map@150
- type: cosine_map@200
value: 0.32981711891302556
name: Cosine Map@200
- type: cosine_map@500
value: 0.32981711891302556
name: Cosine Map@500
---
# Job - Job matching Alibaba-NLP/gte-multilingual-base (v1)
Top performing model on [TalentCLEF 2025](https://talentclef.github.io/talentclef/) Task A. Use it for multilingual job title matching
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) <!-- at revision 9fdd4ee8bba0e2808a34e0e739576f6740d2b225 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
- full_en
- full_de
- full_es
- full_zh
- mix
<!-- - **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: NewModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("pj-mathematician/JobGTE-multilingual-base-v1")
# Run inference
sentences = [
'Volksvertreter',
'Parlamentarier',
'Oberbürgermeister',
]
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
* Datasets: `full_en`, `full_es`, `full_de`, `full_zh`, `mix_es`, `mix_de` and `mix_zh`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | full_en | full_es | full_de | full_zh | mix_es | mix_de | mix_zh |
|:---------------------|:----------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|
| cosine_accuracy@1 | 0.6571 | 0.1243 | 0.2956 | 0.6602 | 0.728 | 0.6703 | 0.1908 |
| cosine_accuracy@20 | 0.9905 | 1.0 | 0.9704 | 0.9806 | 0.96 | 0.9506 | 1.0 |
| cosine_accuracy@50 | 0.9905 | 1.0 | 0.9852 | 0.9903 | 0.9792 | 0.9776 | 1.0 |
| cosine_accuracy@100 | 0.9905 | 1.0 | 0.9852 | 0.9903 | 0.9943 | 0.9865 | 1.0 |
| cosine_accuracy@150 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9958 | 0.9932 | 1.0 |
| cosine_accuracy@200 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9974 | 0.9948 | 1.0 |
| cosine_precision@1 | 0.6571 | 0.1243 | 0.2956 | 0.6602 | 0.728 | 0.6703 | 0.1908 |
| cosine_precision@20 | 0.5171 | 0.5719 | 0.5084 | 0.4782 | 0.1243 | 0.1252 | 0.1544 |
| cosine_precision@50 | 0.316 | 0.3885 | 0.3654 | 0.2895 | 0.0515 | 0.0523 | 0.0618 |
| cosine_precision@100 | 0.189 | 0.2517 | 0.2413 | 0.1757 | 0.0263 | 0.0267 | 0.0309 |
| cosine_precision@150 | 0.1338 | 0.1905 | 0.1804 | 0.126 | 0.0176 | 0.018 | 0.0206 |
| cosine_precision@200 | 0.1043 | 0.1522 | 0.1447 | 0.0982 | 0.0133 | 0.0135 | 0.0154 |
| cosine_recall@1 | 0.0678 | 0.0037 | 0.0111 | 0.0615 | 0.2813 | 0.2524 | 0.0614 |
| cosine_recall@20 | 0.547 | 0.3842 | 0.3221 | 0.5108 | 0.9183 | 0.9096 | 1.0 |
| cosine_recall@50 | 0.74 | 0.5641 | 0.5025 | 0.6923 | 0.9499 | 0.9482 | 1.0 |
| cosine_recall@100 | 0.8453 | 0.6742 | 0.6248 | 0.8004 | 0.9701 | 0.9685 | 1.0 |
| cosine_recall@150 | 0.8838 | 0.7464 | 0.683 | 0.8465 | 0.9768 | 0.9782 | 1.0 |
| cosine_recall@200 | 0.9109 | 0.7825 | 0.7216 | 0.8771 | 0.9818 | 0.981 | 1.0 |
| cosine_ndcg@1 | 0.6571 | 0.1243 | 0.2956 | 0.6602 | 0.728 | 0.6703 | 0.1908 |
| cosine_ndcg@20 | 0.6954 | 0.6139 | 0.5393 | 0.654 | 0.8044 | 0.7736 | 0.5474 |
| cosine_ndcg@50 | 0.715 | 0.5874 | 0.5267 | 0.6707 | 0.813 | 0.7844 | 0.5474 |
| cosine_ndcg@100 | 0.7679 | 0.6144 | 0.5579 | 0.7234 | 0.8173 | 0.7889 | 0.5474 |
| cosine_ndcg@150 | 0.7857 | 0.6499 | 0.588 | 0.7438 | 0.8186 | 0.7909 | 0.5474 |
| **cosine_ndcg@200** | **0.797** | **0.6681** | **0.6071** | **0.7554** | **0.8195** | **0.7914** | **0.5474** |
| cosine_mrr@1 | 0.6571 | 0.1243 | 0.2956 | 0.6602 | 0.728 | 0.6703 | 0.1908 |
| cosine_mrr@20 | 0.8138 | 0.5581 | 0.5104 | 0.8037 | 0.7969 | 0.752 | 0.4093 |
| cosine_mrr@50 | 0.8138 | 0.5581 | 0.511 | 0.8041 | 0.7975 | 0.7529 | 0.4093 |
| cosine_mrr@100 | 0.8138 | 0.5581 | 0.511 | 0.8041 | 0.7977 | 0.7531 | 0.4093 |
| cosine_mrr@150 | 0.8138 | 0.5581 | 0.511 | 0.8041 | 0.7977 | 0.7531 | 0.4093 |
| cosine_mrr@200 | 0.8138 | 0.5581 | 0.511 | 0.8041 | 0.7977 | 0.7531 | 0.4093 |
| cosine_map@1 | 0.6571 | 0.1243 | 0.2956 | 0.6602 | 0.728 | 0.6703 | 0.1908 |
| cosine_map@20 | 0.5579 | 0.4799 | 0.401 | 0.5087 | 0.7351 | 0.6968 | 0.3298 |
| cosine_map@50 | 0.5471 | 0.425 | 0.3588 | 0.4926 | 0.7374 | 0.6996 | 0.3298 |
| cosine_map@100 | 0.5796 | 0.4302 | 0.3633 | 0.5217 | 0.738 | 0.7003 | 0.3298 |
| cosine_map@150 | 0.5875 | 0.4459 | 0.3777 | 0.5299 | 0.7381 | 0.7004 | 0.3298 |
| cosine_map@200 | 0.5912 | 0.4533 | 0.3848 | 0.5334 | 0.7382 | 0.7005 | 0.3298 |
| cosine_map@500 | 0.5953 | 0.4656 | 0.3978 | 0.5386 | 0.7383 | 0.7006 | 0.3298 |
<!--
## 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 Datasets
<details><summary>full_en</summary>
#### full_en
* Dataset: full_en
* Size: 28,880 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 5.68 tokens</li><li>max: 11 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.76 tokens</li><li>max: 12 tokens</li></ul> |
* Samples:
| anchor | positive |
|:-----------------------------------------|:-----------------------------------------|
| <code>air commodore</code> | <code>flight lieutenant</code> |
| <code>command and control officer</code> | <code>flight officer</code> |
| <code>air commodore</code> | <code>command and control officer</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, '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()
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
```
</details>
<details><summary>full_de</summary>
#### full_de
* Dataset: full_de
* Size: 23,023 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 7.99 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 8.19 tokens</li><li>max: 30 tokens</li></ul> |
* Samples:
| anchor | positive |
|:----------------------------------|:-----------------------------------------------------|
| <code>Staffelkommandantin</code> | <code>Kommodore</code> |
| <code>Luftwaffenoffizierin</code> | <code>Luftwaffenoffizier/Luftwaffenoffizierin</code> |
| <code>Staffelkommandantin</code> | <code>Luftwaffenoffizierin</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, '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()
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
```
</details>
<details><summary>full_es</summary>
#### full_es
* Dataset: full_es
* Size: 20,724 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 9.13 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 8.84 tokens</li><li>max: 32 tokens</li></ul> |
* Samples:
| anchor | positive |
|:------------------------------------|:-------------------------------------------|
| <code>jefe de escuadrón</code> | <code>instructor</code> |
| <code>comandante de aeronave</code> | <code>instructor de simulador</code> |
| <code>instructor</code> | <code>oficial del Ejército del Aire</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, '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()
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
```
</details>
<details><summary>full_zh</summary>
#### full_zh
* Dataset: full_zh
* Size: 30,401 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 7.15 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 7.46 tokens</li><li>max: 21 tokens</li></ul> |
* Samples:
| anchor | positive |
|:------------------|:---------------------|
| <code>技术总监</code> | <code>技术和运营总监</code> |
| <code>技术总监</code> | <code>技术主管</code> |
| <code>技术总监</code> | <code>技术艺术总监</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, '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()
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
```
</details>
<details><summary>mix</summary>
#### mix
* Dataset: mix
* Size: 21,760 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 2 tokens</li><li>mean: 6.71 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 7.69 tokens</li><li>max: 19 tokens</li></ul> |
* Samples:
| anchor | positive |
|:------------------------------------------|:----------------------------------------------------------------|
| <code>technical manager</code> | <code>Technischer Direktor für Bühne, Film und Fernsehen</code> |
| <code>head of technical</code> | <code>directora técnica</code> |
| <code>head of technical department</code> | <code>技术艺术总监</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, '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()
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
```
</details>
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 128
- `gradient_accumulation_steps`: 2
- `num_train_epochs`: 5
- `warmup_ratio`: 0.05
- `log_on_each_node`: False
- `fp16`: True
- `dataloader_num_workers`: 4
- `ddp_find_unused_parameters`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 128
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 2
- `eval_accumulation_steps`: None
- `torch_empty_cache_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`: 5
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.05
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: False
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 4
- `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}
- `tp_size`: 0
- `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
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: True
- `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`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | full_en_cosine_ndcg@200 | full_es_cosine_ndcg@200 | full_de_cosine_ndcg@200 | full_zh_cosine_ndcg@200 | mix_es_cosine_ndcg@200 | mix_de_cosine_ndcg@200 | mix_zh_cosine_ndcg@200 |
|:------:|:----:|:-------------:|:-----------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|
| -1 | -1 | - | 0.7447 | 0.6125 | 0.5378 | 0.7240 | 0.7029 | 0.6345 | 0.5531 |
| 0.0010 | 1 | 3.4866 | - | - | - | - | - | - | - |
| 0.1027 | 100 | 2.5431 | - | - | - | - | - | - | - |
| 0.2053 | 200 | 1.4536 | 0.7993 | 0.6633 | 0.5974 | 0.7642 | 0.7567 | 0.7011 | 0.5498 |
| 0.3080 | 300 | 1.1018 | - | - | - | - | - | - | - |
| 0.4107 | 400 | 0.9184 | 0.7925 | 0.6586 | 0.6058 | 0.7587 | 0.7749 | 0.7278 | 0.5486 |
| 0.5133 | 500 | 0.8973 | - | - | - | - | - | - | - |
| 0.6160 | 600 | 0.7309 | 0.7951 | 0.6671 | 0.6096 | 0.7708 | 0.7793 | 0.7339 | 0.5525 |
| 0.7187 | 700 | 0.7297 | - | - | - | - | - | - | - |
| 0.8214 | 800 | 0.7281 | 0.7929 | 0.6711 | 0.6088 | 0.7645 | 0.7899 | 0.7444 | 0.5479 |
| 0.9240 | 900 | 0.6607 | - | - | - | - | - | - | - |
| 1.0267 | 1000 | 0.6075 | 0.7915 | 0.6659 | 0.6088 | 0.7665 | 0.7968 | 0.7588 | 0.5482 |
| 1.1294 | 1100 | 0.4553 | - | - | - | - | - | - | - |
| 1.2320 | 1200 | 0.4775 | 0.7979 | 0.6696 | 0.6033 | 0.7669 | 0.7959 | 0.7624 | 0.5484 |
| 1.3347 | 1300 | 0.4838 | - | - | - | - | - | - | - |
| 1.4374 | 1400 | 0.4912 | 0.7973 | 0.6757 | 0.6112 | 0.7656 | 0.7978 | 0.7650 | 0.5487 |
| 1.5400 | 1500 | 0.4732 | - | - | - | - | - | - | - |
| 1.6427 | 1600 | 0.5269 | 0.8031 | 0.6723 | 0.6108 | 0.7654 | 0.8008 | 0.7660 | 0.5492 |
| 1.7454 | 1700 | 0.4822 | - | - | - | - | - | - | - |
| 1.8480 | 1800 | 0.5072 | 0.7962 | 0.6668 | 0.6051 | 0.7592 | 0.8001 | 0.7714 | 0.5486 |
| 1.9507 | 1900 | 0.4709 | - | - | - | - | - | - | - |
| 2.0544 | 2000 | 0.3772 | 0.7940 | 0.6647 | 0.6037 | 0.7579 | 0.8064 | 0.7732 | 0.5479 |
| 2.1571 | 2100 | 0.3982 | - | - | - | - | - | - | - |
| 2.2598 | 2200 | 0.3073 | 0.7969 | 0.6652 | 0.6005 | 0.7625 | 0.8054 | 0.7734 | 0.5493 |
| 2.3624 | 2300 | 0.383 | - | - | - | - | - | - | - |
| 2.4651 | 2400 | 0.3687 | 0.7925 | 0.6690 | 0.5987 | 0.7583 | 0.8081 | 0.7735 | 0.5477 |
| 2.5678 | 2500 | 0.3472 | - | - | - | - | - | - | - |
| 2.6704 | 2600 | 0.3557 | 0.7956 | 0.6758 | 0.6019 | 0.7659 | 0.8082 | 0.7767 | 0.5491 |
| 2.7731 | 2700 | 0.3527 | - | - | - | - | - | - | - |
| 2.8758 | 2800 | 0.3446 | 0.7945 | 0.6719 | 0.6020 | 0.7616 | 0.8124 | 0.7818 | 0.5496 |
| 2.9784 | 2900 | 0.3566 | - | - | - | - | - | - | - |
| 3.0821 | 3000 | 0.3252 | 0.7948 | 0.6682 | 0.6025 | 0.7617 | 0.8152 | 0.7848 | 0.5516 |
| 3.1848 | 3100 | 0.2968 | - | - | - | - | - | - | - |
| 3.2875 | 3200 | 0.2962 | 0.7953 | 0.6717 | 0.6086 | 0.7613 | 0.8110 | 0.7824 | 0.5482 |
| 3.3901 | 3300 | 0.3084 | - | - | - | - | - | - | - |
| 3.4928 | 3400 | 0.2909 | 0.7940 | 0.6634 | 0.6023 | 0.7615 | 0.8138 | 0.7822 | 0.5457 |
| 3.5955 | 3500 | 0.2964 | - | - | - | - | - | - | - |
| 3.6982 | 3600 | 0.3193 | 0.7960 | 0.6635 | 0.6070 | 0.7534 | 0.8164 | 0.7844 | 0.5467 |
| 3.8008 | 3700 | 0.3514 | - | - | - | - | - | - | - |
| 3.9035 | 3800 | 0.3147 | 0.7973 | 0.6696 | 0.6125 | 0.7616 | 0.8176 | 0.7885 | 0.5469 |
| 4.0062 | 3900 | 0.2738 | - | - | - | - | - | - | - |
| 4.1088 | 4000 | 0.2842 | 0.7960 | 0.6672 | 0.6082 | 0.7536 | 0.8174 | 0.7891 | 0.5479 |
| 4.2115 | 4100 | 0.2739 | - | - | - | - | - | - | - |
| 4.3142 | 4200 | 0.2704 | 0.7979 | 0.6681 | 0.6111 | 0.7540 | 0.8180 | 0.7891 | 0.5476 |
| 4.4168 | 4300 | 0.2529 | - | - | - | - | - | - | - |
| 4.5195 | 4400 | 0.272 | 0.7968 | 0.6685 | 0.6087 | 0.7564 | 0.8185 | 0.7901 | 0.5476 |
| 4.6222 | 4500 | 0.3 | - | - | - | - | - | - | - |
| 4.7248 | 4600 | 0.2598 | 0.7972 | 0.6675 | 0.6072 | 0.7556 | 0.8190 | 0.7909 | 0.5478 |
| 4.8275 | 4700 | 0.3101 | - | - | - | - | - | - | - |
| 4.9302 | 4800 | 0.2524 | 0.7970 | 0.6681 | 0.6071 | 0.7554 | 0.8195 | 0.7914 | 0.5474 |
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 4.1.0
- Transformers: 4.51.2
- PyTorch: 2.6.0+cu124
- Accelerate: 1.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## 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",
}
```
#### GISTEmbedLoss
```bibtex
@misc{solatorio2024gistembed,
title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
author={Aivin V. Solatorio},
year={2024},
eprint={2402.16829},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
<!--
## 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.*
--> |