Spaces:
Running
on
CPU Upgrade
Running
on
CPU Upgrade
File size: 109,006 Bytes
4af3178 64dd40c 1380fc9 4af3178 64dd40c 2c63c2f b4966ee 4af3178 78db81b bd1cf3d 46022eb 78db81b 4af3178 003d24d 2458a90 4af3178 2458a90 003d24d 2458a90 cd84165 2458a90 ac3fdf5 2458a90 817663f 4f1ef5f 817663f 2458a90 556c58e 85a6939 2458a90 003d24d 2458a90 4d67578 cd84165 817663f 556c58e 003d24d cd84165 817663f 556c58e 003d24d cd84165 556c58e 96fcd80 556c58e 003d24d cd84165 7287938 b986a91 556c58e b986a91 556c58e 2458a90 2c63c2f 003d24d cd84165 817663f 556c58e 003d24d 556c58e 003d24d cd84165 003d24d cd84165 817663f 556c58e 003d24d 2c63c2f a51beac 3ffdc42 a51beac 3ffdc42 2c63c2f cd84165 7287938 cd84165 bdf66dc cd84165 2c63c2f cd84165 2458a90 2c63c2f e556bec 7287938 e556bec 556c58e e556bec 2c63c2f c9eda65 2458a90 fa91720 2458a90 cd84165 2458a90 cd84165 c9eda65 2c63c2f 7287938 cd84165 2c63c2f 4f1ef5f 2c63c2f 556c58e cd84165 2c63c2f cd84165 2458a90 c1f5045 2458a90 2c63c2f 2458a90 cd84165 2c63c2f cd84165 2c63c2f 817663f cd84165 556c58e cd84165 556c58e 909b95d d2198dc a90dc55 bf18e02 c9eda65 216d974 7c14747 cd84165 2c63c2f c9eda65 cd84165 fda8b01 e982d94 cd84165 2458a90 cd84165 2c63c2f c9eda65 2c63c2f bdf66dc 7287938 cd84165 bdf66dc 5a50b1e bdf66dc 2458a90 cd84165 2458a90 e556bec 7287938 e556bec 556c58e e556bec cd84165 2458a90 c9eda65 cd84165 fa91720 2458a90 cd84165 2458a90 cd84165 c9eda65 2458a90 7287938 c9eda65 2458a90 4f1ef5f 2458a90 556c58e cd84165 2458a90 cd84165 2458a90 c1f5045 2458a90 cd84165 2458a90 cd84165 2458a90 817663f 556c58e 909b95d d2198dc 909b95d 7c14747 cd84165 66b95b9 2458a90 cd84165 fda8b01 b8f175e e982d94 2458a90 2c63c2f 66b95b9 64dd40c bdf66dc 7287938 cd84165 bdf66dc 5a50b1e bdf66dc 64dd40c 17e48df cd84165 2458a90 64dd40c e556bec 7287938 e556bec 556c58e e556bec cd84165 64dd40c 2458a90 cd84165 2458a90 cd84165 2458a90 cd84165 556c58e 2458a90 64dd40c 7287938 2458a90 64dd40c 4f1ef5f 64dd40c 556c58e cd84165 64dd40c cd84165 2458a90 c1f5045 2458a90 cd84165 64dd40c cd84165 2458a90 64dd40c cd84165 64dd40c 817663f 556c58e 909b95d d2198dc a90dc55 64dd40c dda305a 216d974 e559e50 216d974 64dd40c 216d974 2458a90 7c14747 cd84165 556c58e b8f175e cd84165 fda8b01 b8f175e e982d94 2458a90 64dd40c bcadbe0 bdf66dc 7287938 cd84165 bdf66dc b4e6a14 bdf66dc bcadbe0 cd84165 17e48df bcadbe0 e556bec 7287938 e556bec 556c58e cd84165 bcadbe0 2458a90 cd84165 bdf66dc 2458a90 a1e84d6 cd84165 a1e84d6 2458a90 cd84165 2458a90 7287938 2458a90 bcadbe0 4f1ef5f bcadbe0 556c58e cd84165 bcadbe0 cd84165 2458a90 c1f5045 2458a90 cd84165 bcadbe0 cd84165 2458a90 bcadbe0 cd84165 bcadbe0 817663f 556c58e 909b95d d2198dc 909b95d bcadbe0 7c14747 bcadbe0 7c14747 bcadbe0 7c14747 bcadbe0 7c14747 cd84165 2458a90 bcadbe0 cd84165 fda8b01 930f6fc 2458a90 bcadbe0 099d855 bdf66dc 5a50b1e bdf66dc 4af3178 bdf66dc 4af3178 1054b00 4af3178 bdf66dc 4af3178 bdf66dc 4af3178 bdf66dc 4af3178 099d855 bcadbe0 234d367 69d79aa e53fbc1 803802d 4d67578 7f45a23 4613dfa 17518e7 7e96396 f61dd83 a1e84d6 556c58e 817663f be5f904 efed031 091482a 1ff031d 5e01b46 e556bec 83a3876 1c36e9b 4458fa6 e0f10c1 3d8c92a bfda697 f196db6 3468af6 5df8240 80f7cc0 bad02de 415f6ce e87d698 9a0c381 eafd5c8 2c0c3cf 2204347 7b70ae4 76bacb9 b50c34e f5dfa4b b50c34e 1fffb8c 4025917 c57a486 92b270c 92494a9 928dd63 c1f5045 51e109c eae51a5 5c90ee9 97c35aa 35f55aa 8a9c4d0 fe87cc1 234d367 4af3178 2c63c2f cd84165 2c63c2f cd84165 2c63c2f cd84165 2c63c2f cd84165 2c63c2f cd84165 2c63c2f 7287938 2c63c2f cd84165 2c63c2f cd84165 2c63c2f 4af3178 2c63c2f 556c58e 2c63c2f cd84165 2c63c2f 099d855 64dd40c 4af3178 bcadbe0 64dd40c 7d1d0b3 bcadbe0 4af3178 64dd40c ac3fdf5 f61dd83 4af3178 f61dd83 4af3178 f61dd83 4af3178 78db81b 2c63c2f 78db81b 2c63c2f 4d67578 2c63c2f 216d974 4af3178 216d974 d2198dc 2c63c2f 78db81b 234d367 2fc20f3 78db81b 0ef2874 003d24d 4d67578 1bd4020 003d24d 78db81b 0d4db15 4d67578 909b95d 4af3178 909b95d 4d67578 4af3178 78db81b 2458a90 0d4db15 f61dd83 6af949b 003d24d 2c63c2f 556c58e 3ffdc42 556c58e 64dd40c f61dd83 3ffdc42 bd1cf3d 3ffdc42 dbfa15a 3ffdc42 003d24d 17e0108 003d24d 4af3178 556c58e 4af3178 556c58e 4af3178 556c58e 4af3178 556c58e 4af3178 556c58e 4af3178 556c58e 4af3178 556c58e 4af3178 556c58e f61dd83 6af949b 4af3178 556c58e 0d4db15 3ffdc42 78db81b 556c58e 4af3178 556c58e 4af3178 556c58e 4af3178 556c58e 4af3178 556c58e 4af3178 556c58e 4af3178 556c58e 4af3178 556c58e 4af3178 556c58e cd84165 4af3178 cd84165 4af3178 cd84165 4af3178 cd84165 4af3178 cd84165 4af3178 cd84165 4af3178 cd84165 4af3178 cd84165 4af3178 cd84165 817663f 4af3178 817663f 4af3178 817663f 4af3178 817663f 4af3178 817663f 4af3178 817663f 4af3178 817663f 4af3178 817663f 3ffdc42 cd84165 817663f 556c58e 4af3178 f61dd83 556c58e f61dd83 556c58e 4af3178 556c58e cd84165 556c58e 817663f 556c58e cd84165 817663f 556c58e cd84165 817663f 556c58e cd84165 556c58e cd84165 817663f 556c58e 7287938 556c58e cd84165 817663f 556c58e cd84165 556c58e f61dd83 4af3178 f61dd83 556c58e f61dd83 556c58e 3ffdc42 1e84aac 4af3178 1e84aac c05e080 1e84aac 2c58564 1e84aac 4af3178 1e84aac 4af3178 dbfa15a 6135b88 dbfa15a 4af3178 c05fcec 4af3178 d2198dc 4d67578 88c25a0 2a75cd8 4af3178 b4966ee eafd5c8 b4966ee 3ffdc42 17e0108 3ffdc42 1bd4020 |
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 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240 2241 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 2252 2253 2254 2255 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 2268 2269 2270 2271 2272 2273 |
from functools import partial, reduce
import json
import os
import re
from datasets import load_dataset
import gradio as gr
from huggingface_hub import HfApi, hf_hub_download
from huggingface_hub.repocard import metadata_load
import pandas as pd
from tqdm.autonotebook import tqdm
from utils.model_size import get_model_parameters_memory
TASKS = [
"BitextMining",
"Classification",
"Clustering",
"PairClassification",
"Reranking",
"Retrieval",
"STS",
"Summarization",
]
TASK_LIST_BITEXT_MINING = ['BUCC (de-en)', 'BUCC (fr-en)', 'BUCC (ru-en)', 'BUCC (zh-en)', 'Tatoeba (afr-eng)', 'Tatoeba (amh-eng)', 'Tatoeba (ang-eng)', 'Tatoeba (ara-eng)', 'Tatoeba (arq-eng)', 'Tatoeba (arz-eng)', 'Tatoeba (ast-eng)', 'Tatoeba (awa-eng)', 'Tatoeba (aze-eng)', 'Tatoeba (bel-eng)', 'Tatoeba (ben-eng)', 'Tatoeba (ber-eng)', 'Tatoeba (bos-eng)', 'Tatoeba (bre-eng)', 'Tatoeba (bul-eng)', 'Tatoeba (cat-eng)', 'Tatoeba (cbk-eng)', 'Tatoeba (ceb-eng)', 'Tatoeba (ces-eng)', 'Tatoeba (cha-eng)', 'Tatoeba (cmn-eng)', 'Tatoeba (cor-eng)', 'Tatoeba (csb-eng)', 'Tatoeba (cym-eng)', 'Tatoeba (dan-eng)', 'Tatoeba (deu-eng)', 'Tatoeba (dsb-eng)', 'Tatoeba (dtp-eng)', 'Tatoeba (ell-eng)', 'Tatoeba (epo-eng)', 'Tatoeba (est-eng)', 'Tatoeba (eus-eng)', 'Tatoeba (fao-eng)', 'Tatoeba (fin-eng)', 'Tatoeba (fra-eng)', 'Tatoeba (fry-eng)', 'Tatoeba (gla-eng)', 'Tatoeba (gle-eng)', 'Tatoeba (glg-eng)', 'Tatoeba (gsw-eng)', 'Tatoeba (heb-eng)', 'Tatoeba (hin-eng)', 'Tatoeba (hrv-eng)', 'Tatoeba (hsb-eng)', 'Tatoeba (hun-eng)', 'Tatoeba (hye-eng)', 'Tatoeba (ido-eng)', 'Tatoeba (ile-eng)', 'Tatoeba (ina-eng)', 'Tatoeba (ind-eng)', 'Tatoeba (isl-eng)', 'Tatoeba (ita-eng)', 'Tatoeba (jav-eng)', 'Tatoeba (jpn-eng)', 'Tatoeba (kab-eng)', 'Tatoeba (kat-eng)', 'Tatoeba (kaz-eng)', 'Tatoeba (khm-eng)', 'Tatoeba (kor-eng)', 'Tatoeba (kur-eng)', 'Tatoeba (kzj-eng)', 'Tatoeba (lat-eng)', 'Tatoeba (lfn-eng)', 'Tatoeba (lit-eng)', 'Tatoeba (lvs-eng)', 'Tatoeba (mal-eng)', 'Tatoeba (mar-eng)', 'Tatoeba (max-eng)', 'Tatoeba (mhr-eng)', 'Tatoeba (mkd-eng)', 'Tatoeba (mon-eng)', 'Tatoeba (nds-eng)', 'Tatoeba (nld-eng)', 'Tatoeba (nno-eng)', 'Tatoeba (nob-eng)', 'Tatoeba (nov-eng)', 'Tatoeba (oci-eng)', 'Tatoeba (orv-eng)', 'Tatoeba (pam-eng)', 'Tatoeba (pes-eng)', 'Tatoeba (pms-eng)', 'Tatoeba (pol-eng)', 'Tatoeba (por-eng)', 'Tatoeba (ron-eng)', 'Tatoeba (rus-eng)', 'Tatoeba (slk-eng)', 'Tatoeba (slv-eng)', 'Tatoeba (spa-eng)', 'Tatoeba (sqi-eng)', 'Tatoeba (srp-eng)', 'Tatoeba (swe-eng)', 'Tatoeba (swg-eng)', 'Tatoeba (swh-eng)', 'Tatoeba (tam-eng)', 'Tatoeba (tat-eng)', 'Tatoeba (tel-eng)', 'Tatoeba (tgl-eng)', 'Tatoeba (tha-eng)', 'Tatoeba (tuk-eng)', 'Tatoeba (tur-eng)', 'Tatoeba (tzl-eng)', 'Tatoeba (uig-eng)', 'Tatoeba (ukr-eng)', 'Tatoeba (urd-eng)', 'Tatoeba (uzb-eng)', 'Tatoeba (vie-eng)', 'Tatoeba (war-eng)', 'Tatoeba (wuu-eng)', 'Tatoeba (xho-eng)', 'Tatoeba (yid-eng)', 'Tatoeba (yue-eng)', 'Tatoeba (zsm-eng)']
TASK_LIST_BITEXT_MINING_DA = ["BornholmBitextMining"]
TASK_LIST_CLASSIFICATION = [
"AmazonCounterfactualClassification (en)",
"AmazonPolarityClassification",
"AmazonReviewsClassification (en)",
"Banking77Classification",
"EmotionClassification",
"ImdbClassification",
"MassiveIntentClassification (en)",
"MassiveScenarioClassification (en)",
"MTOPDomainClassification (en)",
"MTOPIntentClassification (en)",
"ToxicConversationsClassification",
"TweetSentimentExtractionClassification",
]
TASK_LIST_CLASSIFICATION_DA = [
"AngryTweetsClassification",
"DanishPoliticalCommentsClassification",
"DKHateClassification",
"LccSentimentClassification",
"MassiveIntentClassification (da)",
"MassiveScenarioClassification (da)",
"NordicLangClassification",
"ScalaDaClassification",
]
TASK_LIST_CLASSIFICATION_FR = [
"AmazonReviewsClassification (fr)",
"MasakhaNEWSClassification (fra)",
"MassiveIntentClassification (fr)",
"MassiveScenarioClassification (fr)",
"MTOPDomainClassification (fr)",
"MTOPIntentClassification (fr)",
]
TASK_LIST_CLASSIFICATION_NB = [
"NoRecClassification",
"NordicLangClassification",
"NorwegianParliament",
"MassiveIntentClassification (nb)",
"MassiveScenarioClassification (nb)",
"ScalaNbClassification",
]
TASK_LIST_CLASSIFICATION_PL = [
"AllegroReviews",
"CBD",
"MassiveIntentClassification (pl)",
"MassiveScenarioClassification (pl)",
"PAC",
"PolEmo2.0-IN",
"PolEmo2.0-OUT",
]
TASK_LIST_CLASSIFICATION_SV = [
"DalajClassification",
"MassiveIntentClassification (sv)",
"MassiveScenarioClassification (sv)",
"NordicLangClassification",
"ScalaSvClassification",
"SweRecClassification",
]
TASK_LIST_CLASSIFICATION_ZH = [
"AmazonReviewsClassification (zh)",
"IFlyTek",
"JDReview",
"MassiveIntentClassification (zh-CN)",
"MassiveScenarioClassification (zh-CN)",
"MultilingualSentiment",
"OnlineShopping",
"TNews",
"Waimai",
]
TASK_LIST_CLASSIFICATION_OTHER = ['AmazonCounterfactualClassification (de)', 'AmazonCounterfactualClassification (ja)', 'AmazonReviewsClassification (de)', 'AmazonReviewsClassification (es)', 'AmazonReviewsClassification (fr)', 'AmazonReviewsClassification (ja)', 'AmazonReviewsClassification (zh)', 'MTOPDomainClassification (de)', 'MTOPDomainClassification (es)', 'MTOPDomainClassification (fr)', 'MTOPDomainClassification (hi)', 'MTOPDomainClassification (th)', 'MTOPIntentClassification (de)', 'MTOPIntentClassification (es)', 'MTOPIntentClassification (fr)', 'MTOPIntentClassification (hi)', 'MTOPIntentClassification (th)', 'MassiveIntentClassification (af)', 'MassiveIntentClassification (am)', 'MassiveIntentClassification (ar)', 'MassiveIntentClassification (az)', 'MassiveIntentClassification (bn)', 'MassiveIntentClassification (cy)', 'MassiveIntentClassification (de)', 'MassiveIntentClassification (el)', 'MassiveIntentClassification (es)', 'MassiveIntentClassification (fa)', 'MassiveIntentClassification (fi)', 'MassiveIntentClassification (fr)', 'MassiveIntentClassification (he)', 'MassiveIntentClassification (hi)', 'MassiveIntentClassification (hu)', 'MassiveIntentClassification (hy)', 'MassiveIntentClassification (id)', 'MassiveIntentClassification (is)', 'MassiveIntentClassification (it)', 'MassiveIntentClassification (ja)', 'MassiveIntentClassification (jv)', 'MassiveIntentClassification (ka)', 'MassiveIntentClassification (km)', 'MassiveIntentClassification (kn)', 'MassiveIntentClassification (ko)', 'MassiveIntentClassification (lv)', 'MassiveIntentClassification (ml)', 'MassiveIntentClassification (mn)', 'MassiveIntentClassification (ms)', 'MassiveIntentClassification (my)', 'MassiveIntentClassification (nl)', 'MassiveIntentClassification (pt)', 'MassiveIntentClassification (ro)', 'MassiveIntentClassification (ru)', 'MassiveIntentClassification (sl)', 'MassiveIntentClassification (sq)', 'MassiveIntentClassification (sw)', 'MassiveIntentClassification (ta)', 'MassiveIntentClassification (te)', 'MassiveIntentClassification (th)', 'MassiveIntentClassification (tl)', 'MassiveIntentClassification (tr)', 'MassiveIntentClassification (ur)', 'MassiveIntentClassification (vi)', 'MassiveIntentClassification (zh-TW)', 'MassiveScenarioClassification (af)', 'MassiveScenarioClassification (am)', 'MassiveScenarioClassification (ar)', 'MassiveScenarioClassification (az)', 'MassiveScenarioClassification (bn)', 'MassiveScenarioClassification (cy)', 'MassiveScenarioClassification (de)', 'MassiveScenarioClassification (el)', 'MassiveScenarioClassification (es)', 'MassiveScenarioClassification (fa)', 'MassiveScenarioClassification (fi)', 'MassiveScenarioClassification (fr)', 'MassiveScenarioClassification (he)', 'MassiveScenarioClassification (hi)', 'MassiveScenarioClassification (hu)', 'MassiveScenarioClassification (hy)', 'MassiveScenarioClassification (id)', 'MassiveScenarioClassification (is)', 'MassiveScenarioClassification (it)', 'MassiveScenarioClassification (ja)', 'MassiveScenarioClassification (jv)', 'MassiveScenarioClassification (ka)', 'MassiveScenarioClassification (km)', 'MassiveScenarioClassification (kn)', 'MassiveScenarioClassification (ko)', 'MassiveScenarioClassification (lv)', 'MassiveScenarioClassification (ml)', 'MassiveScenarioClassification (mn)', 'MassiveScenarioClassification (ms)', 'MassiveScenarioClassification (my)', 'MassiveScenarioClassification (nl)', 'MassiveScenarioClassification (pt)', 'MassiveScenarioClassification (ro)', 'MassiveScenarioClassification (ru)', 'MassiveScenarioClassification (sl)', 'MassiveScenarioClassification (sq)', 'MassiveScenarioClassification (sw)', 'MassiveScenarioClassification (ta)', 'MassiveScenarioClassification (te)', 'MassiveScenarioClassification (th)', 'MassiveScenarioClassification (tl)', 'MassiveScenarioClassification (tr)', 'MassiveScenarioClassification (ur)', 'MassiveScenarioClassification (vi)', 'MassiveScenarioClassification (zh-TW)']
TASK_LIST_CLUSTERING = [
"ArxivClusteringP2P",
"ArxivClusteringS2S",
"BiorxivClusteringP2P",
"BiorxivClusteringS2S",
"MedrxivClusteringP2P",
"MedrxivClusteringS2S",
"RedditClustering",
"RedditClusteringP2P",
"StackExchangeClustering",
"StackExchangeClusteringP2P",
"TwentyNewsgroupsClustering",
]
TASK_LIST_CLUSTERING_DE = [
"BlurbsClusteringP2P",
"BlurbsClusteringS2S",
"TenKGnadClusteringP2P",
"TenKGnadClusteringS2S",
]
TASK_LIST_CLUSTERING_FR = [
"AlloProfClusteringP2P",
"AlloProfClusteringS2S",
"HALClusteringS2S",
"MLSUMClusteringP2P",
"MLSUMClusteringS2S",
"MasakhaNEWSClusteringP2P (fra)",
"MasakhaNEWSClusteringS2S (fra)",
]
TASK_LIST_CLUSTERING_PL = [
"8TagsClustering",
]
TASK_LIST_CLUSTERING_ZH = [
"CLSClusteringP2P",
"CLSClusteringS2S",
"ThuNewsClusteringP2P",
"ThuNewsClusteringS2S",
]
TASK_LIST_PAIR_CLASSIFICATION = [
"SprintDuplicateQuestions",
"TwitterSemEval2015",
"TwitterURLCorpus",
]
TASK_LIST_PAIR_CLASSIFICATION_FR = [
"OpusparcusPC (fr)",
"PawsX (fr)",
]
TASK_LIST_PAIR_CLASSIFICATION_PL = [
"CDSC-E",
"PPC",
"PSC",
"SICK-E-PL",
]
TASK_LIST_PAIR_CLASSIFICATION_ZH = [
"Cmnli",
"Ocnli",
]
TASK_LIST_RERANKING = [
"AskUbuntuDupQuestions",
"MindSmallReranking",
"SciDocsRR",
"StackOverflowDupQuestions",
]
TASK_LIST_RERANKING_FR = [
"AlloprofReranking",
"SyntecReranking",
]
TASK_LIST_RERANKING_ZH = [
"CMedQAv1",
"CMedQAv2",
"MMarcoReranking",
"T2Reranking",
]
TASK_LIST_RETRIEVAL = [
"ArguAna",
"ClimateFEVER",
"CQADupstackRetrieval",
"DBPedia",
"FEVER",
"FiQA2018",
"HotpotQA",
"MSMARCO",
"NFCorpus",
"NQ",
"QuoraRetrieval",
"SCIDOCS",
"SciFact",
"Touche2020",
"TRECCOVID",
]
TASK_LIST_RETRIEVAL_FR = [
"AlloprofRetrieval",
"BSARDRetrieval",
"MintakaRetrieval (fr)",
# "MultiLongDocRetrieval",
"SyntecRetrieval",
"XPQARetrieval (fr)",
]
TASK_LIST_RETRIEVAL_LAW = [
"AILACasedocs",
"AILAStatutes",
"GerDaLIRSmall",
"LeCaRDv2",
"LegalBenchConsumerContractsQA",
"LegalBenchCorporateLobbying",
"LegalQuAD",
"LegalSummarization",
]
TASK_LIST_RETRIEVAL_PL = [
"ArguAna-PL",
"DBPedia-PL",
"FiQA-PL",
"HotpotQA-PL",
"MSMARCO-PL",
"NFCorpus-PL",
"NQ-PL",
"Quora-PL",
"SCIDOCS-PL",
"SciFact-PL",
"TRECCOVID-PL",
]
TASK_LIST_RETRIEVAL_ZH = [
"CmedqaRetrieval",
"CovidRetrieval",
"DuRetrieval",
"EcomRetrieval",
"MedicalRetrieval",
"MMarcoRetrieval",
"T2Retrieval",
"VideoRetrieval",
]
TASK_LIST_RETRIEVAL_NORM = TASK_LIST_RETRIEVAL + [
"CQADupstackAndroidRetrieval",
"CQADupstackEnglishRetrieval",
"CQADupstackGamingRetrieval",
"CQADupstackGisRetrieval",
"CQADupstackMathematicaRetrieval",
"CQADupstackPhysicsRetrieval",
"CQADupstackProgrammersRetrieval",
"CQADupstackStatsRetrieval",
"CQADupstackTexRetrieval",
"CQADupstackUnixRetrieval",
"CQADupstackWebmastersRetrieval",
"CQADupstackWordpressRetrieval"
]
TASK_LIST_STS = [
"BIOSSES",
"SICK-R",
"STS12",
"STS13",
"STS14",
"STS15",
"STS16",
"STS17 (en-en)",
"STS22 (en)",
"STSBenchmark",
]
TASK_LIST_STS_FR = [
"STS22 (fr)",
"STSBenchmarkMultilingualSTS (fr)",
"SICKFr",
]
TASK_LIST_STS_PL = [
"CDSC-R",
"SICK-R-PL",
"STS22 (pl)",
]
TASK_LIST_STS_ZH = [
"AFQMC",
"ATEC",
"BQ",
"LCQMC",
"PAWSX",
"QBQTC",
"STS22 (zh)",
"STSB",
]
TASK_LIST_STS_OTHER = ["STS17 (ar-ar)", "STS17 (en-ar)", "STS17 (en-de)", "STS17 (en-tr)", "STS17 (es-en)", "STS17 (es-es)", "STS17 (fr-en)", "STS17 (it-en)", "STS17 (ko-ko)", "STS17 (nl-en)", "STS22 (ar)", "STS22 (de)", "STS22 (de-en)", "STS22 (de-fr)", "STS22 (de-pl)", "STS22 (es)", "STS22 (es-en)", "STS22 (es-it)", "STS22 (fr)", "STS22 (fr-pl)", "STS22 (it)", "STS22 (pl)", "STS22 (pl-en)", "STS22 (ru)", "STS22 (tr)", "STS22 (zh-en)", "STSBenchmark",]
TASK_LIST_SUMMARIZATION = ["SummEval",]
TASK_LIST_SUMMARIZATION_FR = ["SummEvalFr"]
TASK_LIST_EN = TASK_LIST_CLASSIFICATION + TASK_LIST_CLUSTERING + TASK_LIST_PAIR_CLASSIFICATION + TASK_LIST_RERANKING + TASK_LIST_RETRIEVAL + TASK_LIST_STS + TASK_LIST_SUMMARIZATION
TASK_LIST_FR = TASK_LIST_CLASSIFICATION_FR + TASK_LIST_CLUSTERING_FR + TASK_LIST_PAIR_CLASSIFICATION_FR + TASK_LIST_RERANKING_FR + TASK_LIST_RETRIEVAL_FR + TASK_LIST_STS_FR + TASK_LIST_SUMMARIZATION_FR
TASK_LIST_PL = TASK_LIST_CLASSIFICATION_PL + TASK_LIST_CLUSTERING_PL + TASK_LIST_PAIR_CLASSIFICATION_PL + TASK_LIST_RETRIEVAL_PL + TASK_LIST_STS_PL
TASK_LIST_ZH = TASK_LIST_CLASSIFICATION_ZH + TASK_LIST_CLUSTERING_ZH + TASK_LIST_PAIR_CLASSIFICATION_ZH + TASK_LIST_RERANKING_ZH + TASK_LIST_RETRIEVAL_ZH + TASK_LIST_STS_ZH
TASK_TO_METRIC = {
"BitextMining": "f1",
"Clustering": "v_measure",
"Classification": "accuracy",
"PairClassification": "cos_sim_ap",
"Reranking": "map",
"Retrieval": "ndcg_at_10",
"STS": "cos_sim_spearman",
"Summarization": "cos_sim_spearman",
}
def make_clickable_model(model_name, link=None):
if link is None:
link = "https://huggingface.co/" + model_name
# Remove user from model name
return (
f'<a target="_blank" style="text-decoration: underline" href="{link}">{model_name.split("/")[-1]}</a>'
)
# Models without metadata, thus we cannot fetch their results naturally
EXTERNAL_MODELS = [
"Baichuan-text-embedding",
"Cohere-embed-english-v3.0",
"Cohere-embed-multilingual-v3.0",
"Cohere-embed-multilingual-light-v3.0",
"DanskBERT",
"LASER2",
"LLM2Vec-Llama-supervised",
"LLM2Vec-Llama-unsupervised",
"LLM2Vec-Mistral-supervised",
"LLM2Vec-Mistral-unsupervised",
"LLM2Vec-Sheared-Llama-supervised",
"LLM2Vec-Sheared-Llama-unsupervised",
"LaBSE",
"OpenSearch-text-hybrid",
"all-MiniLM-L12-v2",
"all-MiniLM-L6-v2",
"all-mpnet-base-v2",
"allenai-specter",
"bert-base-10lang-cased",
"bert-base-15lang-cased",
"bert-base-25lang-cased",
"bert-base-multilingual-cased",
"bert-base-multilingual-uncased",
"bert-base-swedish-cased",
"bert-base-uncased",
"bge-base-zh-v1.5",
"bge-large-en-v1.5",
"bge-large-zh-v1.5",
"bge-large-zh-noinstruct",
"bge-small-zh-v1.5",
"contriever-base-msmarco",
"cross-en-de-roberta-sentence-transformer",
"dfm-encoder-large-v1",
"dfm-sentence-encoder-large-1",
"distiluse-base-multilingual-cased-v2",
"e5-base",
"e5-large",
"e5-mistral-7b-instruct",
"e5-small",
"electra-small-nordic",
"electra-small-swedish-cased-discriminator",
"flaubert_base_cased",
"flaubert_base_uncased",
"flaubert_large_cased",
"gbert-base",
"gbert-large",
"gelectra-base",
"gelectra-large",
"glove.6B.300d",
"google-gecko.text-embedding-preview-0409",
"google-gecko-256.text-embedding-preview-0409",
"gottbert-base",
"gtr-t5-base",
"gtr-t5-large",
"gtr-t5-xl",
"gtr-t5-xxl",
"herbert-base-retrieval-v2",
"komninos",
"luotuo-bert-medium",
"m3e-base",
"m3e-large",
"mistral-embed",
"msmarco-bert-co-condensor",
"multi-qa-MiniLM-L6-cos-v1",
"multilingual-e5-base",
"multilingual-e5-large",
"multilingual-e5-small",
"nb-bert-base",
"nb-bert-large",
"nomic-embed-text-v1.5-64",
"nomic-embed-text-v1.5-128",
"nomic-embed-text-v1.5-256",
"nomic-embed-text-v1.5-512",
"norbert3-base",
"norbert3-large",
"paraphrase-multilingual-MiniLM-L12-v2",
"paraphrase-multilingual-mpnet-base-v2",
"sentence-bert-swedish-cased",
"sentence-camembert-base",
"sentence-camembert-large",
"sentence-croissant-llm-base",
"sentence-t5-base",
"sentence-t5-large",
"sentence-t5-xl",
"sentence-t5-xxl",
"silver-retriever-base-v1",
"sup-simcse-bert-base-uncased",
"st-polish-paraphrase-from-distilroberta",
"st-polish-paraphrase-from-mpnet",
"text2vec-base-chinese",
"text2vec-base-multilingual",
"text2vec-large-chinese",
"text-embedding-3-small",
"text-embedding-3-large",
"text-embedding-3-large-256",
"text-embedding-ada-002",
"text-similarity-ada-001",
"text-similarity-babbage-001",
"text-similarity-curie-001",
"text-similarity-davinci-001",
"text-search-ada-doc-001",
"text-search-ada-001",
"text-search-babbage-001",
"text-search-curie-001",
"text-search-davinci-001",
"titan-embed-text-v1",
"udever-bloom-1b1",
"udever-bloom-560m",
"universal-sentence-encoder-multilingual-3",
"universal-sentence-encoder-multilingual-large-3",
"unsup-simcse-bert-base-uncased",
"use-cmlm-multilingual",
"voyage-2",
"voyage-code-2",
"voyage-law-2",
"voyage-lite-01-instruct",
"voyage-lite-02-instruct",
"xlm-roberta-base",
"xlm-roberta-large",
]
EXTERNAL_MODEL_TO_LINK = {
"Baichuan-text-embedding": "https://platform.baichuan-ai.com/docs/text-Embedding",
"Cohere-embed-english-v3.0": "https://huggingface.co/Cohere/Cohere-embed-english-v3.0",
"Cohere-embed-multilingual-v3.0": "https://huggingface.co/Cohere/Cohere-embed-multilingual-v3.0",
"Cohere-embed-multilingual-light-v3.0": "https://huggingface.co/Cohere/Cohere-embed-multilingual-light-v3.0",
"DanskBERT": "https://huggingface.co/vesteinn/DanskBERT",
"LASER2": "https://github.com/facebookresearch/LASER",
"LLM2Vec-Llama-supervised": "https://huggingface.co/McGill-NLP/LLM2Vec-Llama-2-7b-chat-hf-mntp-supervised",
"LLM2Vec-Llama-unsupervised": "https://huggingface.co/McGill-NLP/LLM2Vec-Llama-2-7b-chat-hf-mntp",
"LLM2Vec-Mistral-supervised": "https://huggingface.co/McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp-supervised",
"LLM2Vec-Mistral-unsupervised": "https://huggingface.co/McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp",
"LLM2Vec-Sheared-Llama-supervised": "https://huggingface.co/McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp-supervised",
"LLM2Vec-Sheared-Llama-unsupervised": "https://huggingface.co/McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp",
"LaBSE": "https://huggingface.co/sentence-transformers/LaBSE",
"OpenSearch-text-hybrid": "https://help.aliyun.com/zh/open-search/vector-search-edition/hybrid-retrieval",
"allenai-specter": "https://huggingface.co/sentence-transformers/allenai-specter",
"allenai-specter": "https://huggingface.co/sentence-transformers/allenai-specter",
"all-MiniLM-L12-v2": "https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2",
"all-MiniLM-L6-v2": "https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2",
"all-mpnet-base-v2": "https://huggingface.co/sentence-transformers/all-mpnet-base-v2",
"bert-base-10lang-cased": "https://huggingface.co/Geotrend/bert-base-10lang-cased",
"bert-base-15lang-cased": "https://huggingface.co/Geotrend/bert-base-15lang-cased",
"bert-base-25lang-cased": "https://huggingface.co/Geotrend/bert-base-25lang-cased",
"bert-base-multilingual-cased": "https://huggingface.co/google-bert/bert-base-multilingual-cased",
"bert-base-multilingual-uncased": "https://huggingface.co/google-bert/bert-base-multilingual-uncased",
"bert-base-swedish-cased": "https://huggingface.co/KB/bert-base-swedish-cased",
"bert-base-uncased": "https://huggingface.co/bert-base-uncased",
"bge-base-zh-v1.5": "https://huggingface.co/BAAI/bge-base-zh-v1.5",
"bge-large-en-v1.5": "https://huggingface.co/BAAI/bge-large-en-v1.5",
"bge-large-zh-v1.5": "https://huggingface.co/BAAI/bge-large-zh-v1.5",
"bge-large-zh-noinstruct": "https://huggingface.co/BAAI/bge-large-zh-noinstruct",
"bge-small-zh-v1.5": "https://huggingface.co/BAAI/bge-small-zh-v1.5",
"camembert-base": "https://huggingface.co/almanach/camembert-base",
"camembert-large": "https://huggingface.co/almanach/camembert-large",
"contriever-base-msmarco": "https://huggingface.co/nthakur/contriever-base-msmarco",
"cross-en-de-roberta-sentence-transformer": "https://huggingface.co/T-Systems-onsite/cross-en-de-roberta-sentence-transformer",
"distilbert-base-25lang-cased": "https://huggingface.co/Geotrend/distilbert-base-25lang-cased",
"distilbert-base-en-fr-cased": "https://huggingface.co/Geotrend/distilbert-base-en-fr-cased",
"distilbert-base-en-fr-es-pt-it-cased": "https://huggingface.co/Geotrend/distilbert-base-en-fr-es-pt-it-cased",
"distilbert-base-fr-cased": "https://huggingface.co/Geotrend/distilbert-base-fr-cased",
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased",
"distiluse-base-multilingual-cased-v2": "https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2",
"dfm-encoder-large-v1": "https://huggingface.co/chcaa/dfm-encoder-large-v1",
"dfm-sentence-encoder-large-1": "https://huggingface.co/chcaa/dfm-encoder-large-v1",
"e5-base": "https://huggingface.co/intfloat/e5-base",
"e5-large": "https://huggingface.co/intfloat/e5-large",
"e5-mistral-7b-instruct": "https://huggingface.co/intfloat/e5-mistral-7b-instruct",
"e5-small": "https://huggingface.co/intfloat/e5-small",
"electra-small-nordic": "https://huggingface.co/jonfd/electra-small-nordic",
"electra-small-swedish-cased-discriminator": "https://huggingface.co/KBLab/electra-small-swedish-cased-discriminator",
"flaubert_base_cased": "https://huggingface.co/flaubert/flaubert_base_cased",
"flaubert_base_uncased": "https://huggingface.co/flaubert/flaubert_base_uncased",
"flaubert_large_cased": "https://huggingface.co/flaubert/flaubert_large_cased",
"gbert-base": "https://huggingface.co/deepset/gbert-base",
"gbert-large": "https://huggingface.co/deepset/gbert-large",
"gelectra-base": "https://huggingface.co/deepset/gelectra-base",
"gelectra-large": "https://huggingface.co/deepset/gelectra-large",
"glove.6B.300d": "https://huggingface.co/sentence-transformers/average_word_embeddings_glove.6B.300d",
"google-gecko.text-embedding-preview-0409": "https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-text-embeddings#latest_models",
"google-gecko-256.text-embedding-preview-0409": "https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-text-embeddings#latest_models",
"gottbert-base": "https://huggingface.co/uklfr/gottbert-base",
"gtr-t5-base": "https://huggingface.co/sentence-transformers/gtr-t5-base",
"gtr-t5-large": "https://huggingface.co/sentence-transformers/gtr-t5-large",
"gtr-t5-xl": "https://huggingface.co/sentence-transformers/gtr-t5-xl",
"gtr-t5-xxl": "https://huggingface.co/sentence-transformers/gtr-t5-xxl",
"herbert-base-retrieval-v2": "https://huggingface.co/ipipan/herbert-base-retrieval-v2",
"komninos": "https://huggingface.co/sentence-transformers/average_word_embeddings_komninos",
"luotuo-bert-medium": "https://huggingface.co/silk-road/luotuo-bert-medium",
"m3e-base": "https://huggingface.co/moka-ai/m3e-base",
"m3e-large": "https://huggingface.co/moka-ai/m3e-large",
"mistral-embed": "https://docs.mistral.ai/guides/embeddings",
"msmarco-bert-co-condensor": "https://huggingface.co/sentence-transformers/msmarco-bert-co-condensor",
"multi-qa-MiniLM-L6-cos-v1": "https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1",
"multilingual-e5-base": "https://huggingface.co/intfloat/multilingual-e5-base",
"multilingual-e5-large": "https://huggingface.co/intfloat/multilingual-e5-large",
"multilingual-e5-small": "https://huggingface.co/intfloat/multilingual-e5-small",
"nb-bert-base": "https://huggingface.co/NbAiLab/nb-bert-base",
"nb-bert-large": "https://huggingface.co/NbAiLab/nb-bert-large",
"nomic-embed-text-v1.5-64": "https://huggingface.co/nomic-ai/nomic-embed-text-v1.5",
"nomic-embed-text-v1.5-128": "https://huggingface.co/nomic-ai/nomic-embed-text-v1.5",
"nomic-embed-text-v1.5-256": "https://huggingface.co/nomic-ai/nomic-embed-text-v1.5",
"nomic-embed-text-v1.5-512": "https://huggingface.co/nomic-ai/nomic-embed-text-v1.5",
"norbert3-base": "https://huggingface.co/ltg/norbert3-base",
"norbert3-large": "https://huggingface.co/ltg/norbert3-large",
"paraphrase-multilingual-mpnet-base-v2": "https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
"paraphrase-multilingual-MiniLM-L12-v2": "https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
"sentence-camembert-base": "https://huggingface.co/dangvantuan/sentence-camembert-base",
"sentence-camembert-large": "https://huggingface.co/dangvantuan/sentence-camembert-large",
"sentence-croissant-llm-base": "https://huggingface.co/Wissam42/sentence-croissant-llm-base",
"sentence-bert-swedish-cased": "https://huggingface.co/KBLab/sentence-bert-swedish-cased",
"sentence-t5-base": "https://huggingface.co/sentence-transformers/sentence-t5-base",
"sentence-t5-large": "https://huggingface.co/sentence-transformers/sentence-t5-large",
"sentence-t5-xl": "https://huggingface.co/sentence-transformers/sentence-t5-xl",
"sentence-t5-xxl": "https://huggingface.co/sentence-transformers/sentence-t5-xxl",
"silver-retriever-base-v1": "https://huggingface.co/ipipan/silver-retriever-base-v1",
"sup-simcse-bert-base-uncased": "https://huggingface.co/princeton-nlp/sup-simcse-bert-base-uncased",
"st-polish-paraphrase-from-distilroberta": "https://huggingface.co/sdadas/st-polish-paraphrase-from-distilroberta",
"st-polish-paraphrase-from-mpnet": "https://huggingface.co/sdadas/st-polish-paraphrase-from-mpnet",
"text2vec-base-chinese": "https://huggingface.co/shibing624/text2vec-base-chinese",
"text2vec-large-chinese": "https://huggingface.co/GanymedeNil/text2vec-large-chinese",
"text-embedding-3-small": "https://openai.com/blog/new-embedding-models-and-api-updates",
"text-embedding-3-large": "https://openai.com/blog/new-embedding-models-and-api-updates",
"text-embedding-3-large-256": "https://openai.com/blog/new-embedding-models-and-api-updates",
"text-embedding-ada-002": "https://openai.com/blog/new-and-improved-embedding-model",
"text-similarity-ada-001": "https://openai.com/blog/introducing-text-and-code-embeddings",
"text-similarity-babbage-001": "https://openai.com/blog/introducing-text-and-code-embeddings",
"text-similarity-curie-001": "https://openai.com/blog/introducing-text-and-code-embeddings",
"text-similarity-davinci-001": "https://openai.com/blog/introducing-text-and-code-embeddings",
"text-search-ada-doc-001": "https://openai.com/blog/introducing-text-and-code-embeddings",
"text-search-ada-query-001": "https://openai.com/blog/introducing-text-and-code-embeddings",
"text-search-ada-001": "https://openai.com/blog/introducing-text-and-code-embeddings",
"text-search-curie-001": "https://openai.com/blog/introducing-text-and-code-embeddings",
"text-search-babbage-001": "https://openai.com/blog/introducing-text-and-code-embeddings",
"text-search-davinci-001": "https://openai.com/blog/introducing-text-and-code-embeddings",
"titan-embed-text-v1": "https://docs.aws.amazon.com/bedrock/latest/userguide/embeddings.html",
"udever-bloom-1b1": "https://huggingface.co/izhx/udever-bloom-1b1",
"udever-bloom-560m": "https://huggingface.co/izhx/udever-bloom-560m",
"universal-sentence-encoder-multilingual-3": "https://huggingface.co/vprelovac/universal-sentence-encoder-multilingual-3",
"universal-sentence-encoder-multilingual-large-3": "https://huggingface.co/vprelovac/universal-sentence-encoder-multilingual-large-3",
"unsup-simcse-bert-base-uncased": "https://huggingface.co/princeton-nlp/unsup-simcse-bert-base-uncased",
"use-cmlm-multilingual": "https://huggingface.co/sentence-transformers/use-cmlm-multilingual",
"voyage-2": "https://docs.voyageai.com/embeddings/",
"voyage-code-2": "https://docs.voyageai.com/embeddings/",
"voyage-law-2": "https://docs.voyageai.com/embeddings/",
"voyage-lite-01-instruct": "https://docs.voyageai.com/embeddings/",
"voyage-lite-02-instruct": "https://docs.voyageai.com/embeddings/",
"xlm-roberta-base": "https://huggingface.co/xlm-roberta-base",
"xlm-roberta-large": "https://huggingface.co/xlm-roberta-large",
}
EXTERNAL_MODEL_TO_DIM = {
"Baichuan-text-embedding": 1024,
"Cohere-embed-english-v3.0": 1024,
"Cohere-embed-multilingual-v3.0": 1024,
"Cohere-embed-multilingual-light-v3.0": 384,
"DanskBERT": 768,
"LASER2": 1024,
"LLM2Vec-Llama-supervised": 4096,
"LLM2Vec-Llama-unsupervised": 4096,
"LLM2Vec-Mistral-supervised": 4096,
"LLM2Vec-Mistral-unsupervised": 4096,
"LLM2Vec-Sheared-Llama-supervised": 2048,
"LLM2Vec-Sheared-Llama-unsupervised": 2048,
"LaBSE": 768,
"all-MiniLM-L12-v2": 384,
"all-MiniLM-L6-v2": 384,
"all-mpnet-base-v2": 768,
"allenai-specter": 768,
"bert-base-10lang-cased": 768,
"bert-base-15lang-cased": 768,
"bert-base-25lang-cased": 768,
"bert-base-multilingual-cased": 768,
"bert-base-multilingual-uncased": 768,
"bert-base-swedish-cased": 768,
"bert-base-uncased": 768,
"bge-base-zh-v1.5": 768,
"bge-large-en-v1.5": 1024,
"bge-large-zh-v1.5": 1024,
"bge-large-zh-noinstruct": 1024,
"bge-small-zh-v1.5": 512,
"camembert-base": 512,
"camembert-large": 768,
"contriever-base-msmarco": 768,
"cross-en-de-roberta-sentence-transformer": 768,
"distilbert-base-25lang-cased": 768,
"distilbert-base-en-fr-cased": 768,
"distilbert-base-en-fr-es-pt-it-cased": 768,
"distilbert-base-fr-cased": 768,
"distilbert-base-uncased": 768,
"distiluse-base-multilingual-cased-v2": 512,
"dfm-encoder-large-v1": 1024,
"dfm-sentence-encoder-large-1": 1024,
"e5-base": 768,
"e5-large": 1024,
"e5-mistral-7b-instruct": 4096,
"e5-small": 384,
"electra-small-nordic": 256,
"electra-small-swedish-cased-discriminator": 256,
"flaubert_base_cased": 768,
"flaubert_base_uncased": 768,
"flaubert_large_cased": 1024,
"luotuo-bert-medium": 768,
"gbert-base": 768,
"gbert-large": 1024,
"gelectra-base": 768,
"gelectra-large": 1024,
"glove.6B.300d": 300,
"google-gecko.text-embedding-preview-0409": 768,
"google-gecko-256.text-embedding-preview-0409": 256,
"gottbert-base": 768,
"gtr-t5-base": 768,
"gtr-t5-large": 768,
"gtr-t5-xl": 768,
"gtr-t5-xxl": 768,
"herbert-base-retrieval-v2": 768,
"komninos": 300,
"m3e-base": 768,
"m3e-large": 768,
"mistral-embed": 1024,
"msmarco-bert-co-condensor": 768,
"multi-qa-MiniLM-L6-cos-v1": 384,
"multilingual-e5-base": 768,
"multilingual-e5-small": 384,
"multilingual-e5-large": 1024,
"nb-bert-base": 768,
"nb-bert-large": 1024,
"nomic-embed-text-v1.5-64": 64,
"nomic-embed-text-v1.5-128": 128,
"nomic-embed-text-v1.5-256": 256,
"nomic-embed-text-v1.5-512": 512,
"norbert3-base": 768,
"norbert3-large": 1024,
"OpenSearch-text-hybrid": 1792,
"paraphrase-multilingual-MiniLM-L12-v2": 384,
"paraphrase-multilingual-mpnet-base-v2": 768,
"sentence-camembert-base": 768,
"sentence-camembert-large": 1024,
"sentence-croissant-llm-base": 2048,
"sentence-bert-swedish-cased": 768,
"sentence-t5-base": 768,
"sentence-t5-large": 768,
"sentence-t5-xl": 768,
"sentence-t5-xxl": 768,
"silver-retriever-base-v1": 768,
"sup-simcse-bert-base-uncased": 768,
"st-polish-paraphrase-from-distilroberta": 768,
"st-polish-paraphrase-from-mpnet": 768,
"text2vec-base-chinese": 768,
"text2vec-large-chinese": 1024,
"text-embedding-3-large": 3072,
"text-embedding-3-large-256": 256,
"text-embedding-3-small": 1536,
"text-embedding-ada-002": 1536,
"text-similarity-ada-001": 1024,
"text-similarity-babbage-001": 2048,
"text-similarity-curie-001": 4096,
"text-similarity-davinci-001": 12288,
"text-search-ada-doc-001": 1024,
"text-search-ada-query-001": 1024,
"text-search-ada-001": 1024,
"text-search-babbage-001": 2048,
"text-search-curie-001": 4096,
"text-search-davinci-001": 12288,
"titan-embed-text-v1": 1536,
"udever-bloom-1b1": 1536,
"udever-bloom-560m": 1024,
"universal-sentence-encoder-multilingual-3": 512,
"universal-sentence-encoder-multilingual-large-3": 512,
"unsup-simcse-bert-base-uncased": 768,
"use-cmlm-multilingual": 768,
"voyage-2": 1024,
"voyage-code-2": 1536,
"voyage-law-2": 1024,
"voyage-lite-01-instruct": 1024,
"voyage-lite-02-instruct": 1024,
"xlm-roberta-base": 768,
"xlm-roberta-large": 1024,
}
EXTERNAL_MODEL_TO_SEQLEN = {
"Baichuan-text-embedding": 512,
"Cohere-embed-english-v3.0": 512,
"Cohere-embed-multilingual-v3.0": 512,
"Cohere-embed-multilingual-light-v3.0": 512,
"DanskBERT": 514,
"LASER2": "N/A",
"LLM2Vec-Llama-supervised": 4096,
"LLM2Vec-Llama-unsupervised": 4096,
"LLM2Vec-Mistral-supervised": 32768,
"LLM2Vec-Mistral-unsupervised": 32768,
"LLM2Vec-Sheared-Llama-supervised": 4096,
"LLM2Vec-Sheared-Llama-unsupervised": 4096,
"LaBSE": 512,
"all-MiniLM-L12-v2": 512,
"all-MiniLM-L6-v2": 512,
"all-mpnet-base-v2": 514,
"allenai-specter": 512,
"bert-base-10lang-cased": 512,
"bert-base-15lang-cased": 512,
"bert-base-25lang-cased": 512,
"bert-base-multilingual-cased": 512,
"bert-base-multilingual-uncased": 512,
"bert-base-swedish-cased": 512,
"bert-base-uncased": 512,
"bge-base-zh-v1.5": 512,
"bge-large-en-v1.5": 512,
"bge-large-zh-v1.5": 512,
"bge-large-zh-noinstruct": 512,
"bge-small-zh-v1.5": 512,
"camembert-base": 512,
"camembert-large": 512,
"contriever-base-msmarco": 512,
"cross-en-de-roberta-sentence-transformer": 514,
"distilbert-base-25lang-cased": 512,
"distilbert-base-en-fr-cased": 512,
"distilbert-base-en-fr-es-pt-it-cased": 512,
"distilbert-base-fr-cased": 512,
"distilbert-base-uncased": 512,
"dfm-encoder-large-v1": 512,
"dfm-sentence-encoder-large-1": 512,
"distiluse-base-multilingual-cased-v2": 512,
"e5-base": 512,
"e5-large": 512,
"e5-mistral-7b-instruct": 32768,
"e5-small": 512,
"electra-small-nordic": 512,
"electra-small-swedish-cased-discriminator": 512,
"flaubert_base_cased": 512,
"flaubert_base_uncased": 512,
"flaubert_large_cased": 512,
"gbert-base": 512,
"gbert-large": 512,
"gelectra-base": 512,
"gelectra-large": 512,
"google-gecko.text-embedding-preview-0409": 2048,
"google-gecko-256.text-embedding-preview-0409": 2048,
"gottbert-base": 512,
"glove.6B.300d": "N/A",
"gtr-t5-base": 512,
"gtr-t5-large": 512,
"gtr-t5-xl": 512,
"gtr-t5-xxl": 512,
"herbert-base-retrieval-v2": 514,
"komninos": "N/A",
"luotuo-bert-medium": 512,
"m3e-base": 512,
"m3e-large": 512,
# "mistral-embed": "?",
"msmarco-bert-co-condensor": 512,
"multi-qa-MiniLM-L6-cos-v1": 512,
"multilingual-e5-base": 514,
"multilingual-e5-large": 514,
"multilingual-e5-small": 512,
"nb-bert-base": 512,
"nb-bert-large": 512,
"nomic-embed-text-v1.5-64": 8192,
"nomic-embed-text-v1.5-128": 8192,
"nomic-embed-text-v1.5-256": 8192,
"nomic-embed-text-v1.5-512": 8192,
"norbert3-base": 512,
"norbert3-large": 512,
"OpenSearch-text-hybrid": 512,
"paraphrase-multilingual-MiniLM-L12-v2": 512,
"paraphrase-multilingual-mpnet-base-v2": 514,
"sentence-camembert-base": 512,
"sentence-camembert-large": 512,
"sentence-croissant-llm-base": 2048,
"sentence-bert-swedish-cased": 512,
"sentence-t5-base": 512,
"sentence-t5-large": 512,
"sentence-t5-xl": 512,
"sentence-t5-xxl": 512,
"silver-retriever-base-v1": 514,
"sup-simcse-bert-base-uncased": 512,
"st-polish-paraphrase-from-distilroberta": 514,
"st-polish-paraphrase-from-mpnet": 514,
"text2vec-base-chinese": 512,
"text2vec-large-chinese": 512,
"text-embedding-3-large": 8191,
"text-embedding-3-large-256": 8191,
"text-embedding-3-small": 8191,
"text-embedding-ada-002": 8191,
"text-similarity-ada-001": 2046,
"text-similarity-babbage-001": 2046,
"text-similarity-curie-001": 2046,
"text-similarity-davinci-001": 2046,
"text-search-ada-doc-001": 2046,
"text-search-ada-query-001": 2046,
"text-search-ada-001": 2046,
"text-search-babbage-001": 2046,
"text-search-curie-001": 2046,
"text-search-davinci-001": 2046,
"titan-embed-text-v1": 8000,
"udever-bloom-1b1": 2048,
"udever-bloom-560m": 2048,
"universal-sentence-encoder-multilingual-3": 512,
"universal-sentence-encoder-multilingual-large-3": 512,
"use-cmlm-multilingual": 512,
"unsup-simcse-bert-base-uncased": 512,
"voyage-2": 1024,
"voyage-code-2": 16000,
"voyage-law-2": 4000,
"voyage-lite-01-instruct": 4000,
"voyage-lite-02-instruct": 4000,
"xlm-roberta-base": 514,
"xlm-roberta-large": 514,
}
EXTERNAL_MODEL_TO_SIZE = {
"DanskBERT": 125,
"LASER2": 43,
"LLM2Vec-Llama-supervised": 6607,
"LLM2Vec-Llama-unsupervised": 6607,
"LLM2Vec-Mistral-supervised": 7111,
"LLM2Vec-Mistral-unsupervised": 7111,
"LLM2Vec-Sheared-Llama-supervised": 1280,
"LLM2Vec-Sheared-Llama-unsupervised": 1280,
"LaBSE": 471,
"allenai-specter": 110,
"all-MiniLM-L12-v2": 33,
"all-MiniLM-L6-v2": 23,
"all-mpnet-base-v2": 110,
"bert-base-10lang-cased": 138,
"bert-base-15lang-cased": 138,
"bert-base-25lang-cased": 138,
"bert-base-multilingual-cased": 179,
"bert-base-multilingual-uncased": 168,
"bert-base-uncased": 110,
"bert-base-swedish-cased": 125,
"bge-base-zh-v1.5": 102,
"bge-large-zh-v1.5": 326,
"bge-large-zh-noinstruct": 326,
"bge-small-zh-v1.5": 24,
"camembert-base": 111,
"camembert-large": 338,
"cross-en-de-roberta-sentence-transformer": 278,
"contriever-base-msmarco": 110,
"distilbert-base-25lang-cased": 110,
"distilbert-base-en-fr-cased": 110,
"distilbert-base-en-fr-es-pt-it-cased": 110,
"distilbert-base-fr-cased": 110,
"distilbert-base-uncased": 110,
"distiluse-base-multilingual-cased-v2": 135,
"dfm-encoder-large-v1": 355,
"dfm-sentence-encoder-large-1": 355,
"e5-base": 110,
"e5-large": 335,
"e5-mistral-7b-instruct": 7111,
"e5-small": 33,
"electra-small-nordic": 23,
"electra-small-swedish-cased-discriminator": 16,
"flaubert_base_cased": 138,
"flaubert_base_uncased": 138,
"flaubert_large_cased": 372,
"gbert-base": 110,
"gbert-large": 337,
"gelectra-base": 110,
"gelectra-large": 335,
"glove.6B.300d": 120,
"google-gecko.text-embedding-preview-0409": 1200,
"google-gecko-256.text-embedding-preview-0409": 1200,
"gottbert-base": 127,
"gtr-t5-base": 110,
"gtr-t5-large": 168,
"gtr-t5-xl": 1240,
"gtr-t5-xxl": 4865,
"herbert-base-retrieval-v2": 125,
"komninos": 134,
"luotuo-bert-medium": 328,
"m3e-base": 102,
"m3e-large": 102,
"msmarco-bert-co-condensor": 110,
"multi-qa-MiniLM-L6-cos-v1": 23,
"multilingual-e5-base": 278,
"multilingual-e5-small": 118,
"multilingual-e5-large": 560,
"nb-bert-base": 179,
"nb-bert-large": 355,
"nomic-embed-text-v1.5-64": 138,
"nomic-embed-text-v1.5-128": 138,
"nomic-embed-text-v1.5-256": 138,
"nomic-embed-text-v1.5-512": 138,
"norbert3-base": 131,
"norbert3-large": 368,
"paraphrase-multilingual-mpnet-base-v2": 278,
"paraphrase-multilingual-MiniLM-L12-v2": 118,
"sentence-camembert-base": 110,
"sentence-camembert-large": 337,
"sentence-croissant-llm-base": 1280,
"sentence-bert-swedish-cased": 125,
"sentence-t5-base": 110,
"sentence-t5-large": 168,
"sentence-t5-xl": 1240,
"sentence-t5-xxl": 4865,
"silver-retriever-base-v1": 125,
"sup-simcse-bert-base-uncased": 110,
"st-polish-paraphrase-from-distilroberta": 125,
"st-polish-paraphrase-from-mpnet": 125,
"text2vec-base-chinese": 102,
"text2vec-large-chinese": 326,
"unsup-simcse-bert-base-uncased": 110,
"use-cmlm-multilingual": 472,
#"voyage-law-2": 1220,
"voyage-lite-02-instruct": 1220,
"xlm-roberta-base": 279,
"xlm-roberta-large": 560,
}
PROPRIETARY_MODELS = {
"Baichuan-text-embedding",
"Cohere-embed-english-v3.0",
"Cohere-embed-multilingual-v3.0",
"Cohere-embed-multilingual-light-v3.0",
"OpenSearch-text-hybrid",
"mistral-embed",
"text-embedding-3-small",
"text-embedding-3-large",
"text-embedding-3-large-256",
"text-embedding-ada-002",
"text-similarity-ada-001",
"text-similarity-babbage-001",
"text-similarity-curie-001",
"text-similarity-davinci-001",
"text-search-ada-doc-001",
"text-search-ada-query-001",
"text-search-ada-001",
"text-search-curie-001",
"text-search-babbage-001",
"text-search-davinci-001",
"titan-embed-text-v1",
"voyage-2",
"voyage-code-2",
"voyage-law-2",
"voyage-lite-01-instruct",
"voyage-lite-02-instruct",
"google-gecko.text-embedding-preview-0409",
"google-gecko-256.text-embedding-preview-0409",
}
PROPRIETARY_MODELS = {
make_clickable_model(model, link=EXTERNAL_MODEL_TO_LINK.get(model, "https://huggingface.co/spaces/mteb/leaderboard"))
for model in PROPRIETARY_MODELS
}
SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS = {
"allenai-specter",
"allenai-specter",
"all-MiniLM-L12-v2",
"all-MiniLM-L6-v2",
"all-mpnet-base-v2",
"bert-base-10lang-cased",
"bert-base-15lang-cased",
"bert-base-25lang-cased",
"bert-base-multilingual-cased",
"bert-base-multilingual-uncased",
"bert-base-swedish-cased",
"bert-base-uncased",
"bge-base-zh-v1.5",
"bge-large-zh-v1.5",
"bge-large-zh-noinstruct",
"bge-small-zh-v1.5",
"camembert-base",
"camembert-large",
"contriever-base-msmarco",
"cross-en-de-roberta-sentence-transformer",
"DanskBERT",
"distilbert-base-25lang-cased",
"distilbert-base-en-fr-cased",
"distilbert-base-en-fr-es-pt-it-cased",
"distilbert-base-fr-cased",
"distilbert-base-uncased",
"distiluse-base-multilingual-cased-v2",
"dfm-encoder-large-v1",
"dfm-sentence-encoder-large-1",
"e5-base",
"e5-large",
"e5-mistral-7b-instruct",
"e5-small",
"electra-small-nordic",
"electra-small-swedish-cased-discriminator",
"flaubert_base_cased",
"flaubert_base_uncased",
"flaubert_large_cased",
"gbert-base",
"gbert-large",
"gelectra-base",
"gelectra-large",
"glove.6B.300d",
"gottbert-base",
"gtr-t5-base",
"gtr-t5-large",
"gtr-t5-xl",
"gtr-t5-xxl",
"herbert-base-retrieval-v2",
"komninos",
"luotuo-bert-medium",
"LaBSE",
"m3e-base",
"m3e-large",
"msmarco-bert-co-condensor",
"multi-qa-MiniLM-L6-cos-v1",
"multilingual-e5-base",
"multilingual-e5-large",
"multilingual-e5-small",
"nb-bert-base",
"nb-bert-large",
"nomic-embed-text-v1.5-64",
"nomic-embed-text-v1.5-128",
"nomic-embed-text-v1.5-256",
"nomic-embed-text-v1.5-512",
"norbert3-base",
"norbert3-large",
"paraphrase-multilingual-mpnet-base-v2",
"paraphrase-multilingual-MiniLM-L12-v2",
"sentence-camembert-base",
"sentence-camembert-large",
"sentence-croissant-llm-base",
"sentence-bert-swedish-cased",
"sentence-t5-base",
"sentence-t5-large",
"sentence-t5-xl",
"sentence-t5-xxl",
"silver-retriever-base-v1",
"sup-simcse-bert-base-uncased",
"st-polish-paraphrase-from-distilroberta",
"st-polish-paraphrase-from-mpnet",
"text2vec-base-chinese",
"text2vec-large-chinese",
"udever-bloom-1b1",
"udever-bloom-560m",
"universal-sentence-encoder-multilingual-3",
"universal-sentence-encoder-multilingual-large-3",
"unsup-simcse-bert-base-uncased",
"use-cmlm-multilingual",
"xlm-roberta-base",
"xlm-roberta-large",
}
SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS = {
make_clickable_model(model, link=EXTERNAL_MODEL_TO_LINK.get(model, "https://huggingface.co/spaces/mteb/leaderboard"))
for model in SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS
}
MODELS_TO_SKIP = {
"baseplate/instructor-large-1", # Duplicate
"radames/e5-large", # Duplicate
"gentlebowl/instructor-large-safetensors", # Duplicate
"Consensus/instructor-base", # Duplicate
"GovCompete/instructor-xl", # Duplicate
"GovCompete/e5-large-v2", # Duplicate
"t12e/instructor-base", # Duplicate
"michaelfeil/ct2fast-e5-large-v2",
"michaelfeil/ct2fast-e5-large",
"michaelfeil/ct2fast-e5-small-v2",
"newsrx/instructor-xl-newsrx",
"newsrx/instructor-large-newsrx",
"fresha/e5-large-v2-endpoint",
"ggrn/e5-small-v2",
"michaelfeil/ct2fast-e5-small",
"jncraton/e5-small-v2-ct2-int8",
"anttip/ct2fast-e5-small-v2-hfie",
"newsrx/instructor-large",
"newsrx/instructor-xl",
"dmlls/all-mpnet-base-v2",
"cgldo/semanticClone",
"Malmuk1/e5-large-v2_Sharded",
"jncraton/gte-small-ct2-int8",
"Einas/einas_ashkar",
"gruber/e5-small-v2-ggml",
"jncraton/bge-small-en-ct2-int8",
"vectoriseai/bge-small-en",
"recipe/embeddings",
"dhairya0907/thenlper-get-large",
"Narsil/bge-base-en",
"kozistr/fused-large-en",
"sionic-ai/sionic-ai-v2", # Wait for https://huggingface.co/sionic-ai/sionic-ai-v2/discussions/1
"sionic-ai/sionic-ai-v1", # Wait for https://huggingface.co/sionic-ai/sionic-ai-v2/discussions/1
"BAAI/bge-large-en", # Deprecated in favor of v1.5
"BAAI/bge-base-en", # Deprecated in favor of v1.5
"BAAI/bge-small-en", # Deprecated in favor of v1.5
"d0rj/e5-large-en-ru",
"d0rj/e5-base-en-ru",
"d0rj/e5-small-en-ru",
"aident-ai/bge-base-en-onnx",
"barisaydin/bge-base-en",
"barisaydin/gte-large",
"barisaydin/gte-base",
"barisaydin/gte-small",
"barisaydin/bge-small-en",
"odunola/e5-base-v2",
"goldenrooster/multilingual-e5-large",
"davidpeer/gte-small",
"barisaydin/bge-large-en",
"jamesgpt1/english-large-v1",
"vectoriseai/bge-large-en-v1.5",
"vectoriseai/bge-base-en-v1.5",
"vectoriseai/instructor-large",
"vectoriseai/instructor-base",
"vectoriseai/gte-large",
"vectoriseai/gte-base",
"vectoriseai/e5-large-v2",
"vectoriseai/bge-small-en-v1.5",
"vectoriseai/e5-base-v2",
"vectoriseai/e5-large",
"vectoriseai/multilingual-e5-large",
"vectoriseai/gte-small",
"vectoriseai/ember-v1",
"vectoriseai/e5-base",
"vectoriseai/e5-small-v2",
"michaelfeil/ct2fast-bge-large-en-v1.5",
"michaelfeil/ct2fast-bge-large-en-v1.5",
"michaelfeil/ct2fast-bge-base-en-v1.5",
"michaelfeil/ct2fast-gte-large",
"michaelfeil/ct2fast-gte-base",
"michaelfeil/ct2fast-bge-small-en-v1.5",
"rizki/bgr-tf",
"ef-zulla/e5-multi-sml-torch",
"cherubhao/yogamodel",
"morgendigital/multilingual-e5-large-quantized",
"jncraton/gte-tiny-ct2-int8",
"Research2NLP/electrical_stella",
"Intel/bge-base-en-v1.5-sts-int8-static",
"Intel/bge-base-en-v1.5-sts-int8-dynamic",
"Intel/bge-base-en-v1.5-sst2",
"Intel/bge-base-en-v1.5-sst2-int8-static",
"Intel/bge-base-en-v1.5-sst2-int8-dynamic",
"Intel/bge-small-en-v1.5-sst2",
"Intel/bge-small-en-v1.5-sst2-int8-dynamic",
"Intel/bge-small-en-v1.5-sst2-int8-static",
"binqiangliu/EmbeddingModlebgelargeENv1.5",
"DecisionOptimizationSystem/DeepFeatEmbeddingLargeContext",
"woody72/multilingual-e5-base",
"Severian/embed",
"Frazic/udever-bloom-3b-sentence",
"jamesgpt1/zzz",
"karrar-alwaili/UAE-Large-V1",
"odunola/UAE-Large-VI",
"shubham-bgi/UAE-Large",
"retrainai/instructor-xl",
"weakit-v/bge-base-en-v1.5-onnx",
"ieasybooks/multilingual-e5-large-onnx",
"gizmo-ai/Cohere-embed-multilingual-v3.0",
"jingyeom/korean_embedding_model",
"barisaydin/text2vec-base-multilingual",
"mlx-community/multilingual-e5-large-mlx",
"mlx-community/multilingual-e5-base-mlx",
"mlx-community/multilingual-e5-small-mlx",
"maiyad/multilingual-e5-small",
"khoa-klaytn/bge-base-en-v1.5-angle",
"khoa-klaytn/bge-small-en-v1.5-angle",
"mixamrepijey/instructor-small",
"mixamrepijey/instructor-models",
"lsf1000/bge-evaluation", # Empty
"giulio98/placeholder", # Empty
"Severian/nomic", # Copy
"atian-chapters/Chapters-SFR-Embedding-Mistral", # Copy
"rlsChapters/Chapters-SFR-Embedding-Mistral", # Copy
"TitanML/jina-v2-base-en-embed", # Copy
"MaziyarPanahi/GritLM-8x7B-GGUF", # GGUF variant
"Geolumina/instructor-xl", # Duplicate
"krilecy/e5-mistral-7b-instruct",
"beademiguelperez/sentence-transformers-multilingual-e5-small",
"arcdev/SFR-Embedding-Mistral",
"arcdev/e5-mistral-7b-instruct",
"Koat/gte-tiny",
"SmartComponents/bge-micro-v2",
"ildodeltaRule/multilingual-e5-large",
"hsikchi/dump",
"McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp-supervised",
"McGill-NLP/LLM2Vec-Llama-2-7b-chat-hf-mntp-supervised",
"McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp-supervised",
"McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp-unsup-simcse",
"McGill-NLP/LLM2Vec-Llama-2-7b-chat-hf-mntp-unsup-simcse",
"McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp-unsup-simcse",
}
def add_lang(examples):
if not(examples["eval_language"]):
examples["mteb_dataset_name_with_lang"] = examples["mteb_dataset_name"]
else:
examples["mteb_dataset_name_with_lang"] = examples["mteb_dataset_name"] + f' ({examples["eval_language"]})'
return examples
def norm(names): return set([name.split(" ")[0] for name in names])
def add_task(examples):
# Could be added to the dataset loading script instead
if examples["mteb_dataset_name"] in norm(TASK_LIST_CLASSIFICATION + TASK_LIST_CLASSIFICATION_DA + TASK_LIST_CLASSIFICATION_FR + TASK_LIST_CLASSIFICATION_NB + TASK_LIST_CLASSIFICATION_PL + TASK_LIST_CLASSIFICATION_SV + TASK_LIST_CLASSIFICATION_ZH):
examples["mteb_task"] = "Classification"
elif examples["mteb_dataset_name"] in norm(TASK_LIST_CLUSTERING + TASK_LIST_CLUSTERING_DE + TASK_LIST_CLUSTERING_FR + TASK_LIST_CLUSTERING_PL + TASK_LIST_CLUSTERING_ZH):
examples["mteb_task"] = "Clustering"
elif examples["mteb_dataset_name"] in norm(TASK_LIST_PAIR_CLASSIFICATION + TASK_LIST_PAIR_CLASSIFICATION_FR + TASK_LIST_PAIR_CLASSIFICATION_PL + TASK_LIST_PAIR_CLASSIFICATION_ZH):
examples["mteb_task"] = "PairClassification"
elif examples["mteb_dataset_name"] in norm(TASK_LIST_RERANKING + TASK_LIST_RERANKING_FR + TASK_LIST_RERANKING_ZH):
examples["mteb_task"] = "Reranking"
elif examples["mteb_dataset_name"] in norm(TASK_LIST_RETRIEVAL_NORM + TASK_LIST_RETRIEVAL_FR + TASK_LIST_RETRIEVAL_PL + TASK_LIST_RETRIEVAL_ZH + TASK_LIST_RETRIEVAL_LAW):
examples["mteb_task"] = "Retrieval"
elif examples["mteb_dataset_name"] in norm(TASK_LIST_STS + TASK_LIST_STS_FR + TASK_LIST_STS_PL + TASK_LIST_STS_ZH):
examples["mteb_task"] = "STS"
elif examples["mteb_dataset_name"] in norm(TASK_LIST_SUMMARIZATION + TASK_LIST_SUMMARIZATION_FR):
examples["mteb_task"] = "Summarization"
elif examples["mteb_dataset_name"] in norm(TASK_LIST_BITEXT_MINING + TASK_LIST_BITEXT_MINING_DA):
examples["mteb_task"] = "BitextMining"
else:
print("WARNING: Task not found for dataset", examples["mteb_dataset_name"])
examples["mteb_task"] = "Unknown"
return examples
if os.path.exists("EXTERNAL_MODEL_RESULTS.json"):
with open("EXTERNAL_MODEL_RESULTS.json") as f:
EXTERNAL_MODEL_RESULTS = json.load(f)
# Update with models not contained
models_to_run = []
for model in EXTERNAL_MODELS:
if model not in EXTERNAL_MODEL_RESULTS:
models_to_run.append(model)
EXTERNAL_MODEL_RESULTS[model] = {k: {v: []} for k, v in TASK_TO_METRIC.items()}
else:
EXTERNAL_MODEL_RESULTS = {model: {k: {v: []} for k, v in TASK_TO_METRIC.items()} for model in EXTERNAL_MODELS}
models_to_run = EXTERNAL_MODELS
pbar = tqdm(models_to_run, desc="Fetching external model results")
for model in pbar:
pbar.set_description(f"Fetching external model results for {model!r}")
ds = load_dataset("mteb/results", model, trust_remote_code=True)
# For local debugging:
#, download_mode='force_redownload', verification_mode="no_checks")
ds = ds.map(add_lang)
ds = ds.map(add_task)
base_dict = {"Model": make_clickable_model(model, link=EXTERNAL_MODEL_TO_LINK.get(model, "https://huggingface.co/spaces/mteb/leaderboard"))}
# For now only one metric per task - Could add more metrics lateron
for task, metric in TASK_TO_METRIC.items():
ds_dict = ds.filter(lambda x: (x["mteb_task"] == task) and (x["metric"] == metric))["test"].to_dict()
ds_dict = {k: round(v, 2) for k, v in zip(ds_dict["mteb_dataset_name_with_lang"], ds_dict["score"])}
EXTERNAL_MODEL_RESULTS[model][task][metric].append({**base_dict, **ds_dict})
# Save & cache EXTERNAL_MODEL_RESULTS
with open("EXTERNAL_MODEL_RESULTS.json", "w") as f:
json.dump(EXTERNAL_MODEL_RESULTS, f)
def get_dim_seq_size(model):
filenames = [sib.rfilename for sib in model.siblings]
dim, seq = "", ""
for filename in filenames:
if re.match("\d+_Pooling/config.json", filename):
st_config_path = hf_hub_download(model.modelId, filename=filename)
dim = json.load(open(st_config_path)).get("word_embedding_dimension", "")
break
for filename in filenames:
if re.match("\d+_Dense/config.json", filename):
st_config_path = hf_hub_download(model.modelId, filename=filename)
dim = json.load(open(st_config_path)).get("out_features", dim)
if "config.json" in filenames:
config_path = hf_hub_download(model.modelId, filename="config.json")
config = json.load(open(config_path))
if not dim:
dim = config.get("hidden_dim", config.get("hidden_size", config.get("d_model", "")))
seq = config.get("n_positions", config.get("max_position_embeddings", config.get("n_ctx", config.get("seq_length", ""))))
# Get model file size without downloading. Parameters in million parameters and memory in GB
parameters, memory = get_model_parameters_memory(model)
return dim, seq, parameters, memory
def make_datasets_clickable(df):
"""Does not work"""
if "BornholmBitextMining" in df.columns:
link = "https://huggingface.co/datasets/strombergnlp/bornholmsk_parallel"
df = df.rename(
columns={f'BornholmBitextMining': '<a target="_blank" style="text-decoration: underline" href="{link}">BornholmBitextMining</a>',})
return df
def add_rank(df):
cols_to_rank = [col for col in df.columns if col not in ["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Embedding Dimensions", "Max Tokens"]]
if len(cols_to_rank) == 1:
df.sort_values(cols_to_rank[0], ascending=False, inplace=True)
else:
df.insert(len(df.columns) - len(cols_to_rank), "Average", df[cols_to_rank].mean(axis=1, skipna=False))
df.sort_values("Average", ascending=False, inplace=True)
df.insert(0, "Rank", list(range(1, len(df) + 1)))
df = df.round(2)
# Fill NaN after averaging
df.fillna("", inplace=True)
return df
def get_mteb_data(tasks=["Clustering"], langs=[], datasets=[], fillna=True, add_emb_dim=True, task_to_metric=TASK_TO_METRIC, rank=True):
api = HfApi()
models = api.list_models(filter="mteb")
# Initialize list to models that we cannot fetch metadata from
df_list = []
for model in EXTERNAL_MODEL_RESULTS:
results_list = [res for task in tasks for res in EXTERNAL_MODEL_RESULTS[model][task][task_to_metric[task]]]
if len(datasets) > 0:
res = {k: v for d in results_list for k, v in d.items() if (k == "Model") or any([x in k for x in datasets])}
elif langs:
# Would be cleaner to rely on an extra language column instead
langs_format = [f"({lang})" for lang in langs]
res = {k: v for d in results_list for k, v in d.items() if any([k.split(" ")[-1] in (k, x) for x in langs_format])}
else:
res = {k: v for d in results_list for k, v in d.items()}
# Model & at least one result
if len(res) > 1:
if add_emb_dim:
res["Model Size (Million Parameters)"] = EXTERNAL_MODEL_TO_SIZE.get(model, "")
res["Memory Usage (GB, fp32)"] = round(res["Model Size (Million Parameters)"] * 1e6 * 4 / 1024**3, 2) if res["Model Size (Million Parameters)"] != "" else ""
res["Embedding Dimensions"] = EXTERNAL_MODEL_TO_DIM.get(model, "")
res["Max Tokens"] = EXTERNAL_MODEL_TO_SEQLEN.get(model, "")
df_list.append(res)
for model in models:
if model.modelId in MODELS_TO_SKIP: continue
print("MODEL", model)
readme_path = hf_hub_download(model.modelId, filename="README.md")
meta = metadata_load(readme_path)
if "model-index" not in meta:
continue
# meta['model-index'][0]["results"] is list of elements like:
# {
# "task": {"type": "Classification"},
# "dataset": {
# "type": "mteb/amazon_massive_intent",
# "name": "MTEB MassiveIntentClassification (nb)",
# "config": "nb",
# "split": "test",
# },
# "metrics": [
# {"type": "accuracy", "value": 39.81506388702084},
# {"type": "f1", "value": 38.809586587791664},
# ],
# },
# Use "get" instead of dict indexing to skip incompat metadata instead of erroring out
if len(datasets) > 0:
task_results = [sub_res for sub_res in meta["model-index"][0]["results"] if (sub_res.get("task", {}).get("type", "") in tasks) and any([x in sub_res.get("dataset", {}).get("name", "") for x in datasets])]
elif langs:
task_results = [sub_res for sub_res in meta["model-index"][0]["results"] if (sub_res.get("task", {}).get("type", "") in tasks) and (sub_res.get("dataset", {}).get("config", "default") in ("default", *langs))]
else:
task_results = [sub_res for sub_res in meta["model-index"][0]["results"] if (sub_res.get("task", {}).get("type", "") in tasks)]
out = [{res["dataset"]["name"].replace("MTEB ", ""): [round(score["value"], 2) for score in res["metrics"] if score["type"] == task_to_metric.get(res["task"]["type"])][0]} for res in task_results]
out = {k: v for d in out for k, v in d.items()}
out["Model"] = make_clickable_model(model.modelId)
# Model & at least one result
if len(out) > 1:
if add_emb_dim:
try:
# Fails on gated repos, so we only include scores for them
out["Embedding Dimensions"], out["Max Tokens"], out["Model Size (Million Parameters)"], out["Memory Usage (GB, fp32)"] = get_dim_seq_size(model)
except:
pass
df_list.append(out)
if model.library_name == "sentence-transformers" or "sentence-transformers" in model.tags or "modules.json" in {file.rfilename for file in model.siblings}:
SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS.add(out["Model"])
df = pd.DataFrame(df_list)
# If there are any models that are the same, merge them
# E.g. if out["Model"] has the same value in two places, merge & take whichever one is not NaN else just take the first one
df = df.groupby("Model", as_index=False).first()
# Put 'Model' column first
cols = sorted(list(df.columns))
cols.insert(0, cols.pop(cols.index("Model")))
df = df[cols]
if rank:
df = add_rank(df)
if fillna:
df.fillna("", inplace=True)
return df
def get_mteb_average():
global DATA_OVERALL, DATA_CLASSIFICATION_EN, DATA_CLUSTERING, DATA_PAIR_CLASSIFICATION, DATA_RERANKING, DATA_RETRIEVAL, DATA_STS_EN, DATA_SUMMARIZATION
DATA_OVERALL = get_mteb_data(
tasks=[
"Classification",
"Clustering",
"PairClassification",
"Reranking",
"Retrieval",
"STS",
"Summarization",
],
datasets=TASK_LIST_CLASSIFICATION + TASK_LIST_CLUSTERING + TASK_LIST_PAIR_CLASSIFICATION + TASK_LIST_RERANKING + TASK_LIST_RETRIEVAL + TASK_LIST_STS + TASK_LIST_SUMMARIZATION,
fillna=False,
add_emb_dim=True,
rank=False,
)
# Debugging:
# DATA_OVERALL.to_csv("overall.csv")
DATA_OVERALL.insert(1, f"Average ({len(TASK_LIST_EN)} datasets)", DATA_OVERALL[TASK_LIST_EN].mean(axis=1, skipna=False))
DATA_OVERALL.insert(2, f"Classification Average ({len(TASK_LIST_CLASSIFICATION)} datasets)", DATA_OVERALL[TASK_LIST_CLASSIFICATION].mean(axis=1, skipna=False))
DATA_OVERALL.insert(3, f"Clustering Average ({len(TASK_LIST_CLUSTERING)} datasets)", DATA_OVERALL[TASK_LIST_CLUSTERING].mean(axis=1, skipna=False))
DATA_OVERALL.insert(4, f"Pair Classification Average ({len(TASK_LIST_PAIR_CLASSIFICATION)} datasets)", DATA_OVERALL[TASK_LIST_PAIR_CLASSIFICATION].mean(axis=1, skipna=False))
DATA_OVERALL.insert(5, f"Reranking Average ({len(TASK_LIST_RERANKING)} datasets)", DATA_OVERALL[TASK_LIST_RERANKING].mean(axis=1, skipna=False))
DATA_OVERALL.insert(6, f"Retrieval Average ({len(TASK_LIST_RETRIEVAL)} datasets)", DATA_OVERALL[TASK_LIST_RETRIEVAL].mean(axis=1, skipna=False))
DATA_OVERALL.insert(7, f"STS Average ({len(TASK_LIST_STS)} datasets)", DATA_OVERALL[TASK_LIST_STS].mean(axis=1, skipna=False))
DATA_OVERALL.insert(8, f"Summarization Average ({len(TASK_LIST_SUMMARIZATION)} dataset)", DATA_OVERALL[TASK_LIST_SUMMARIZATION].mean(axis=1, skipna=False))
DATA_OVERALL.sort_values(f"Average ({len(TASK_LIST_EN)} datasets)", ascending=False, inplace=True)
# Start ranking from 1
DATA_OVERALL.insert(0, "Rank", list(range(1, len(DATA_OVERALL) + 1)))
DATA_OVERALL = DATA_OVERALL.round(2)
DATA_CLASSIFICATION_EN = add_rank(DATA_OVERALL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_CLASSIFICATION])
# Only keep rows with at least one score in addition to the "Model" & rank column
DATA_CLASSIFICATION_EN = DATA_CLASSIFICATION_EN[DATA_CLASSIFICATION_EN.iloc[:, 4:].ne("").any(axis=1)]
DATA_CLUSTERING = add_rank(DATA_OVERALL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_CLUSTERING])
DATA_CLUSTERING = DATA_CLUSTERING[DATA_CLUSTERING.iloc[:, 4:].ne("").any(axis=1)]
DATA_PAIR_CLASSIFICATION = add_rank(DATA_OVERALL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_PAIR_CLASSIFICATION])
DATA_PAIR_CLASSIFICATION = DATA_PAIR_CLASSIFICATION[DATA_PAIR_CLASSIFICATION.iloc[:, 4:].ne("").any(axis=1)]
DATA_RERANKING = add_rank(DATA_OVERALL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_RERANKING])
DATA_RERANKING = DATA_RERANKING[DATA_RERANKING.iloc[:, 4:].ne("").any(axis=1)]
DATA_RETRIEVAL = add_rank(DATA_OVERALL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_RETRIEVAL])
DATA_RETRIEVAL = DATA_RETRIEVAL[DATA_RETRIEVAL.iloc[:, 4:].ne("").any(axis=1)]
DATA_STS_EN = add_rank(DATA_OVERALL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_STS])
DATA_STS_EN = DATA_STS_EN[DATA_STS_EN.iloc[:, 4:].ne("").any(axis=1)]
DATA_SUMMARIZATION = add_rank(DATA_OVERALL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_SUMMARIZATION])
DATA_SUMMARIZATION = DATA_SUMMARIZATION[DATA_SUMMARIZATION.iloc[:, 1:].ne("").any(axis=1)]
# Fill NaN after averaging
DATA_OVERALL.fillna("", inplace=True)
DATA_OVERALL = DATA_OVERALL[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Embedding Dimensions", "Max Tokens", f"Average ({len(TASK_LIST_EN)} datasets)", f"Classification Average ({len(TASK_LIST_CLASSIFICATION)} datasets)", f"Clustering Average ({len(TASK_LIST_CLUSTERING)} datasets)", f"Pair Classification Average ({len(TASK_LIST_PAIR_CLASSIFICATION)} datasets)", f"Reranking Average ({len(TASK_LIST_RERANKING)} datasets)", f"Retrieval Average ({len(TASK_LIST_RETRIEVAL)} datasets)", f"STS Average ({len(TASK_LIST_STS)} datasets)", f"Summarization Average ({len(TASK_LIST_SUMMARIZATION)} dataset)"]]
DATA_OVERALL = DATA_OVERALL[DATA_OVERALL.iloc[:, 5:].ne("").any(axis=1)]
return DATA_OVERALL
def get_mteb_average_zh():
global DATA_OVERALL_ZH, DATA_CLASSIFICATION_ZH, DATA_CLUSTERING_ZH, DATA_PAIR_CLASSIFICATION_ZH, DATA_RERANKING_ZH, DATA_RETRIEVAL_ZH, DATA_STS_ZH
DATA_OVERALL_ZH = get_mteb_data(
tasks=[
"Classification",
"Clustering",
"PairClassification",
"Reranking",
"Retrieval",
"STS",
],
datasets=TASK_LIST_CLASSIFICATION_ZH + TASK_LIST_CLUSTERING_ZH + TASK_LIST_PAIR_CLASSIFICATION_ZH + TASK_LIST_RERANKING_ZH + TASK_LIST_RETRIEVAL_ZH + TASK_LIST_STS_ZH,
fillna=False,
add_emb_dim=True,
rank=False,
)
# Debugging:
# DATA_OVERALL_ZH.to_csv("overall.csv")
DATA_OVERALL_ZH.insert(1, f"Average ({len(TASK_LIST_ZH)} datasets)", DATA_OVERALL_ZH[TASK_LIST_ZH].mean(axis=1, skipna=False))
DATA_OVERALL_ZH.insert(2, f"Classification Average ({len(TASK_LIST_CLASSIFICATION_ZH)} datasets)", DATA_OVERALL_ZH[TASK_LIST_CLASSIFICATION_ZH].mean(axis=1, skipna=False))
DATA_OVERALL_ZH.insert(3, f"Clustering Average ({len(TASK_LIST_CLUSTERING_ZH)} datasets)", DATA_OVERALL_ZH[TASK_LIST_CLUSTERING_ZH].mean(axis=1, skipna=False))
DATA_OVERALL_ZH.insert(4, f"Pair Classification Average ({len(TASK_LIST_PAIR_CLASSIFICATION_ZH)} datasets)", DATA_OVERALL_ZH[TASK_LIST_PAIR_CLASSIFICATION_ZH].mean(axis=1, skipna=False))
DATA_OVERALL_ZH.insert(5, f"Reranking Average ({len(TASK_LIST_RERANKING_ZH)} datasets)", DATA_OVERALL_ZH[TASK_LIST_RERANKING_ZH].mean(axis=1, skipna=False))
DATA_OVERALL_ZH.insert(6, f"Retrieval Average ({len(TASK_LIST_RETRIEVAL_ZH)} datasets)", DATA_OVERALL_ZH[TASK_LIST_RETRIEVAL_ZH].mean(axis=1, skipna=False))
DATA_OVERALL_ZH.insert(7, f"STS Average ({len(TASK_LIST_STS_ZH)} datasets)", DATA_OVERALL_ZH[TASK_LIST_STS_ZH].mean(axis=1, skipna=False))
DATA_OVERALL_ZH.sort_values(f"Average ({len(TASK_LIST_ZH)} datasets)", ascending=False, inplace=True)
# Start ranking from 1
DATA_OVERALL_ZH.insert(0, "Rank", list(range(1, len(DATA_OVERALL_ZH) + 1)))
DATA_OVERALL_ZH = DATA_OVERALL_ZH.round(2)
DATA_CLASSIFICATION_ZH = add_rank(DATA_OVERALL_ZH[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_CLASSIFICATION_ZH])
# Only keep rows with at least one score in addition to the "Model" & rank column
DATA_CLASSIFICATION_ZH = DATA_CLASSIFICATION_ZH[DATA_CLASSIFICATION_ZH.iloc[:, 4:].ne("").any(axis=1)]
DATA_CLUSTERING_ZH = add_rank(DATA_OVERALL_ZH[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_CLUSTERING_ZH])
DATA_CLUSTERING_ZH = DATA_CLUSTERING_ZH[DATA_CLUSTERING_ZH.iloc[:, 4:].ne("").any(axis=1)]
DATA_PAIR_CLASSIFICATION_ZH = add_rank(DATA_OVERALL_ZH[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_PAIR_CLASSIFICATION_ZH])
DATA_PAIR_CLASSIFICATION_ZH = DATA_PAIR_CLASSIFICATION_ZH[DATA_PAIR_CLASSIFICATION_ZH.iloc[:, 4:].ne("").any(axis=1)]
DATA_RERANKING_ZH = add_rank(DATA_OVERALL_ZH[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_RERANKING_ZH])
DATA_RERANKING_ZH = DATA_RERANKING_ZH[DATA_RERANKING_ZH.iloc[:, 4:].ne("").any(axis=1)]
DATA_RETRIEVAL_ZH = add_rank(DATA_OVERALL_ZH[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_RETRIEVAL_ZH])
DATA_RETRIEVAL_ZH = DATA_RETRIEVAL_ZH[DATA_RETRIEVAL_ZH.iloc[:, 4:].ne("").any(axis=1)]
DATA_STS_ZH = add_rank(DATA_OVERALL_ZH[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_STS_ZH])
DATA_STS_ZH = DATA_STS_ZH[DATA_STS_ZH.iloc[:, 4:].ne("").any(axis=1)]
# Fill NaN after averaging
DATA_OVERALL_ZH.fillna("", inplace=True)
DATA_OVERALL_ZH = DATA_OVERALL_ZH[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Embedding Dimensions", "Max Tokens", f"Average ({len(TASK_LIST_ZH)} datasets)", f"Classification Average ({len(TASK_LIST_CLASSIFICATION_ZH)} datasets)", f"Clustering Average ({len(TASK_LIST_CLUSTERING_ZH)} datasets)", f"Pair Classification Average ({len(TASK_LIST_PAIR_CLASSIFICATION_ZH)} datasets)", f"Reranking Average ({len(TASK_LIST_RERANKING_ZH)} datasets)", f"Retrieval Average ({len(TASK_LIST_RETRIEVAL_ZH)} datasets)", f"STS Average ({len(TASK_LIST_STS_ZH)} datasets)"]]
DATA_OVERALL_ZH = DATA_OVERALL_ZH[DATA_OVERALL_ZH.iloc[:, 5:].ne("").any(axis=1)]
return DATA_OVERALL_ZH
def get_mteb_average_fr():
global DATA_OVERALL_FR, DATA_CLASSIFICATION_FR, DATA_CLUSTERING_FR, DATA_PAIR_CLASSIFICATION_FR, DATA_RERANKING_FR, DATA_RETRIEVAL_FR, DATA_STS_FR, DATA_SUMMARIZATION_FR
DATA_OVERALL_FR = get_mteb_data(
tasks=[
"Classification",
"Clustering",
"PairClassification",
"Reranking",
"Retrieval",
"STS",
"Summarization"
],
datasets=TASK_LIST_CLASSIFICATION_FR + TASK_LIST_CLUSTERING_FR + TASK_LIST_PAIR_CLASSIFICATION_FR + TASK_LIST_RERANKING_FR + TASK_LIST_RETRIEVAL_FR + TASK_LIST_STS_FR + TASK_LIST_SUMMARIZATION_FR,
fillna=False,
add_emb_dim=True,
rank=False,
)
# Debugging:
# DATA_OVERALL_FR.to_csv("overall.csv")
DATA_OVERALL_FR.insert(1, f"Average ({len(TASK_LIST_FR)} datasets)", DATA_OVERALL_FR[TASK_LIST_FR].mean(axis=1, skipna=False))
DATA_OVERALL_FR.insert(2, f"Classification Average ({len(TASK_LIST_CLASSIFICATION_FR)} datasets)", DATA_OVERALL_FR[TASK_LIST_CLASSIFICATION_FR].mean(axis=1, skipna=False))
DATA_OVERALL_FR.insert(3, f"Clustering Average ({len(TASK_LIST_CLUSTERING_FR)} datasets)", DATA_OVERALL_FR[TASK_LIST_CLUSTERING_FR].mean(axis=1, skipna=False))
DATA_OVERALL_FR.insert(4, f"Pair Classification Average ({len(TASK_LIST_PAIR_CLASSIFICATION_FR)} datasets)", DATA_OVERALL_FR[TASK_LIST_PAIR_CLASSIFICATION_FR].mean(axis=1, skipna=False))
DATA_OVERALL_FR.insert(5, f"Reranking Average ({len(TASK_LIST_RERANKING_FR)} datasets)", DATA_OVERALL_FR[TASK_LIST_RERANKING_FR].mean(axis=1, skipna=False))
DATA_OVERALL_FR.insert(6, f"Retrieval Average ({len(TASK_LIST_RETRIEVAL_FR)} datasets)", DATA_OVERALL_FR[TASK_LIST_RETRIEVAL_FR].mean(axis=1, skipna=False))
DATA_OVERALL_FR.insert(7, f"STS Average ({len(TASK_LIST_STS_FR)} datasets)", DATA_OVERALL_FR[TASK_LIST_STS_FR].mean(axis=1, skipna=False))
DATA_OVERALL_FR.insert(8, f"Summarization Average ({len(TASK_LIST_SUMMARIZATION_FR)} dataset)", DATA_OVERALL_FR[TASK_LIST_SUMMARIZATION_FR].mean(axis=1, skipna=False))
DATA_OVERALL_FR.sort_values(f"Average ({len(TASK_LIST_FR)} datasets)", ascending=False, inplace=True)
# Start ranking from 1
DATA_OVERALL_FR.insert(0, "Rank", list(range(1, len(DATA_OVERALL_FR) + 1)))
DATA_OVERALL_FR = DATA_OVERALL_FR.round(2)
DATA_CLASSIFICATION_FR = add_rank(DATA_OVERALL_FR[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_CLASSIFICATION_FR])
DATA_CLASSIFICATION_FR = DATA_CLASSIFICATION_FR[DATA_CLASSIFICATION_FR.iloc[:, 4:].ne("").any(axis=1)]
DATA_CLUSTERING_FR = add_rank(DATA_OVERALL_FR[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_CLUSTERING_FR])
DATA_CLUSTERING_FR = DATA_CLUSTERING_FR[DATA_CLUSTERING_FR.iloc[:, 4:].ne("").any(axis=1)]
DATA_PAIR_CLASSIFICATION_FR = add_rank(DATA_OVERALL_FR[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_PAIR_CLASSIFICATION_FR])
DATA_PAIR_CLASSIFICATION_FR = DATA_PAIR_CLASSIFICATION_FR[DATA_PAIR_CLASSIFICATION_FR.iloc[:, 4:].ne("").any(axis=1)]
DATA_RERANKING_FR = add_rank(DATA_OVERALL_FR[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_RERANKING_FR])
DATA_RERANKING_FR = DATA_RERANKING_FR[DATA_RERANKING_FR.iloc[:, 4:].ne("").any(axis=1)]
DATA_RETRIEVAL_FR = add_rank(DATA_OVERALL_FR[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_RETRIEVAL_FR])
DATA_RETRIEVAL_FR = DATA_RETRIEVAL_FR[DATA_RETRIEVAL_FR.iloc[:, 4:].ne("").any(axis=1)]
DATA_STS_FR = add_rank(DATA_OVERALL_FR[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_STS_FR])
DATA_STS_FR = DATA_STS_FR[DATA_STS_FR.iloc[:, 4:].ne("").any(axis=1)]
DATA_SUMMARIZATION_FR = add_rank(DATA_OVERALL_FR[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_SUMMARIZATION_FR])
DATA_SUMMARIZATION_FR = DATA_SUMMARIZATION_FR[DATA_SUMMARIZATION_FR.iloc[:, 1:].ne("").any(axis=1)]
# Fill NaN after averaging
DATA_OVERALL_FR.fillna("", inplace=True)
DATA_OVERALL_FR = DATA_OVERALL_FR[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Embedding Dimensions", "Max Tokens", f"Average ({len(TASK_LIST_FR)} datasets)", f"Classification Average ({len(TASK_LIST_CLASSIFICATION_FR)} datasets)", f"Clustering Average ({len(TASK_LIST_CLUSTERING_FR)} datasets)", f"Pair Classification Average ({len(TASK_LIST_PAIR_CLASSIFICATION_FR)} datasets)", f"Reranking Average ({len(TASK_LIST_RERANKING_FR)} datasets)", f"Retrieval Average ({len(TASK_LIST_RETRIEVAL_FR)} datasets)", f"STS Average ({len(TASK_LIST_STS_FR)} datasets)", f"Summarization Average ({len(TASK_LIST_SUMMARIZATION_FR)} dataset)"]]
DATA_OVERALL_FR = DATA_OVERALL_FR[DATA_OVERALL_FR.iloc[:, 5:].ne("").any(axis=1)]
return DATA_OVERALL_FR
def get_mteb_average_pl():
global DATA_OVERALL_PL, DATA_CLASSIFICATION_PL, DATA_CLUSTERING_PL, DATA_PAIR_CLASSIFICATION_PL, DATA_RETRIEVAL_PL, DATA_STS_PL
DATA_OVERALL_PL = get_mteb_data(
tasks=[
"Classification",
"Clustering",
"PairClassification",
"Retrieval",
"STS",
],
datasets=TASK_LIST_CLASSIFICATION_PL + TASK_LIST_CLUSTERING_PL + TASK_LIST_PAIR_CLASSIFICATION_PL + TASK_LIST_RETRIEVAL_PL + TASK_LIST_STS_PL,
fillna=False,
add_emb_dim=True,
rank=False,
)
# Debugging:
# DATA_OVERALL_PL.to_csv("overall.csv")
DATA_OVERALL_PL.insert(1, f"Average ({len(TASK_LIST_PL)} datasets)", DATA_OVERALL_PL[TASK_LIST_PL].mean(axis=1, skipna=False))
DATA_OVERALL_PL.insert(2, f"Classification Average ({len(TASK_LIST_CLASSIFICATION_PL)} datasets)", DATA_OVERALL_PL[TASK_LIST_CLASSIFICATION_PL].mean(axis=1, skipna=False))
DATA_OVERALL_PL.insert(3, f"Clustering Average ({len(TASK_LIST_CLUSTERING_PL)} datasets)", DATA_OVERALL_PL[TASK_LIST_CLUSTERING_PL].mean(axis=1, skipna=False))
DATA_OVERALL_PL.insert(4, f"Pair Classification Average ({len(TASK_LIST_PAIR_CLASSIFICATION_PL)} datasets)", DATA_OVERALL_PL[TASK_LIST_PAIR_CLASSIFICATION_PL].mean(axis=1, skipna=False))
DATA_OVERALL_PL.insert(5, f"Retrieval Average ({len(TASK_LIST_RETRIEVAL_PL)} datasets)", DATA_OVERALL_PL[TASK_LIST_RETRIEVAL_PL].mean(axis=1, skipna=False))
DATA_OVERALL_PL.insert(6, f"STS Average ({len(TASK_LIST_STS_PL)} datasets)", DATA_OVERALL_PL[TASK_LIST_STS_PL].mean(axis=1, skipna=False))
DATA_OVERALL_PL.sort_values(f"Average ({len(TASK_LIST_PL)} datasets)", ascending=False, inplace=True)
# Start ranking from 1
DATA_OVERALL_PL.insert(0, "Rank", list(range(1, len(DATA_OVERALL_PL) + 1)))
DATA_OVERALL_PL = DATA_OVERALL_PL.round(2)
DATA_CLASSIFICATION_PL = add_rank(DATA_OVERALL_PL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_CLASSIFICATION_PL])
# Only keep rows with at least one score in addition to the "Model" & rank column
DATA_CLASSIFICATION_PL = DATA_CLASSIFICATION_PL[DATA_CLASSIFICATION_PL.iloc[:, 4:].ne("").any(axis=1)]
DATA_CLUSTERING_PL = add_rank(DATA_OVERALL_PL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_CLUSTERING_PL])
DATA_CLUSTERING_PL = DATA_CLUSTERING_PL[DATA_CLUSTERING_PL.iloc[:, 4:].ne("").any(axis=1)]
DATA_PAIR_CLASSIFICATION_PL = add_rank(DATA_OVERALL_PL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_PAIR_CLASSIFICATION_PL])
DATA_PAIR_CLASSIFICATION_PL = DATA_PAIR_CLASSIFICATION_PL[DATA_PAIR_CLASSIFICATION_PL.iloc[:, 4:].ne("").any(axis=1)]
DATA_RETRIEVAL_PL = add_rank(DATA_OVERALL_PL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_RETRIEVAL_PL])
DATA_RETRIEVAL_PL = DATA_RETRIEVAL_PL[DATA_RETRIEVAL_PL.iloc[:, 4:].ne("").any(axis=1)]
DATA_STS_PL = add_rank(DATA_OVERALL_PL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_STS_PL])
DATA_STS_PL = DATA_STS_PL[DATA_STS_PL.iloc[:, 4:].ne("").any(axis=1)]
# Fill NaN after averaging
DATA_OVERALL_PL.fillna("", inplace=True)
DATA_OVERALL_PL = DATA_OVERALL_PL[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Embedding Dimensions", "Max Tokens", f"Average ({len(TASK_LIST_PL)} datasets)", f"Classification Average ({len(TASK_LIST_CLASSIFICATION_PL)} datasets)", f"Clustering Average ({len(TASK_LIST_CLUSTERING_PL)} datasets)", f"Pair Classification Average ({len(TASK_LIST_PAIR_CLASSIFICATION_PL)} datasets)", f"Retrieval Average ({len(TASK_LIST_RETRIEVAL_PL)} datasets)", f"STS Average ({len(TASK_LIST_STS_PL)} datasets)"]]
DATA_OVERALL_PL = DATA_OVERALL_PL[DATA_OVERALL_PL.iloc[:, 5:].ne("").any(axis=1)]
return DATA_OVERALL_PL
get_mteb_average()
get_mteb_average_fr()
get_mteb_average_pl()
get_mteb_average_zh()
DATA_BITEXT_MINING = get_mteb_data(["BitextMining"], [], TASK_LIST_BITEXT_MINING)[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Average"] + TASK_LIST_BITEXT_MINING]
DATA_BITEXT_MINING_DA = get_mteb_data(["BitextMining"], [], TASK_LIST_BITEXT_MINING_DA)[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + TASK_LIST_BITEXT_MINING_DA]
DATA_CLASSIFICATION_DA = get_mteb_data(["Classification"], [], TASK_LIST_CLASSIFICATION_DA)[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Average"] + TASK_LIST_CLASSIFICATION_DA]
DATA_CLASSIFICATION_NB = get_mteb_data(["Classification"], [], TASK_LIST_CLASSIFICATION_NB)[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Average"] + TASK_LIST_CLASSIFICATION_NB]
DATA_CLASSIFICATION_SV = get_mteb_data(["Classification"], [], TASK_LIST_CLASSIFICATION_SV)[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Average"] + TASK_LIST_CLASSIFICATION_SV]
DATA_CLASSIFICATION_OTHER = get_mteb_data(["Classification"], [], TASK_LIST_CLASSIFICATION_OTHER)[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Average"] + TASK_LIST_CLASSIFICATION_OTHER]
DATA_CLUSTERING_DE = get_mteb_data(["Clustering"], [], TASK_LIST_CLUSTERING_DE)[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Average"] + TASK_LIST_CLUSTERING_DE]
DATA_STS_OTHER = get_mteb_data(["STS"], [], TASK_LIST_STS_OTHER)[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Average"] + TASK_LIST_STS_OTHER]
DATA_RETRIEVAL_LAW = get_mteb_data(["Retrieval"], [], TASK_LIST_RETRIEVAL_LAW)[["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Average"] + TASK_LIST_RETRIEVAL_LAW]
# Exact, add all non-nan integer values for every dataset
NUM_SCORES = 0
DATASETS = []
MODELS = []
# LANGUAGES = []
for d in [
DATA_BITEXT_MINING,
DATA_BITEXT_MINING_DA,
DATA_CLASSIFICATION_EN,
DATA_CLASSIFICATION_DA,
DATA_CLASSIFICATION_FR,
DATA_CLASSIFICATION_NB,
DATA_CLASSIFICATION_PL,
DATA_CLASSIFICATION_SV,
DATA_CLASSIFICATION_ZH,
DATA_CLASSIFICATION_OTHER,
DATA_CLUSTERING,
DATA_CLUSTERING_DE,
DATA_CLUSTERING_FR,
DATA_CLUSTERING_PL,
DATA_CLUSTERING_ZH,
DATA_PAIR_CLASSIFICATION,
DATA_PAIR_CLASSIFICATION_FR,
DATA_PAIR_CLASSIFICATION_PL,
DATA_PAIR_CLASSIFICATION_ZH,
DATA_RERANKING,
DATA_RERANKING_FR,
DATA_RERANKING_ZH,
DATA_RETRIEVAL,
DATA_RETRIEVAL_FR,
DATA_RETRIEVAL_PL,
DATA_RETRIEVAL_ZH,
DATA_RETRIEVAL_LAW,
DATA_STS_EN,
DATA_STS_FR,
DATA_STS_PL,
DATA_STS_ZH,
DATA_STS_OTHER,
DATA_SUMMARIZATION,
DATA_SUMMARIZATION_FR,
]:
# NUM_SCORES += d.iloc[:, 1:].apply(lambda x: sum([1 for y in x if isinstance(y, float) and not np.isnan(y)]), axis=1).sum()
cols_to_ignore = 4 if "Average" in d.columns else 3
# Count number of scores including only non-nan floats & excluding the rank column
NUM_SCORES += d.iloc[:, cols_to_ignore:].notna().sum().sum()
# Exclude rank & model name column (first two); Do not count different language versions as different datasets
DATASETS += [i.split(" ")[0] for i in d.columns[cols_to_ignore:]]
# LANGUAGES += [i.split(" ")[-1] for i in d.columns[cols_to_ignore:]]
MODELS += d["Model"].tolist()
NUM_DATASETS = len(set(DATASETS))
# NUM_LANGUAGES = len(set(LANGUAGES))
NUM_MODELS = len(set(MODELS))
# 1. Force headers to wrap
# 2. Force model column (maximum) width
# 3. Prevent model column from overflowing, scroll instead
# 4. Prevent checkbox groups from taking up too much space
css = """
table > thead {
white-space: normal
}
table {
--cell-width-1: 250px
}
table > tbody > tr > td:nth-child(2) > div {
overflow-x: auto
}
.filter-checkbox-group {
max-width: max-content;
}
"""
"""
Each inner tab can have the following keys:
- language: The language of the leaderboard
- language_long: [optional] The long form of the language
- description: The description of the leaderboard
- credits: [optional] The credits for the leaderboard
- data: The data for the leaderboard
- refresh: The function to refresh the leaderboard
"""
chinese_credits = "[FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding)"
french_credits = "[Lyon-NLP](https://github.com/Lyon-NLP): [Gabriel Sequeira](https://github.com/GabrielSequeira), [Imene Kerboua](https://github.com/imenelydiaker), [Wissam Siblini](https://github.com/wissam-sib), [Mathieu Ciancone](https://github.com/MathieuCiancone), [Marion Schaeffer](https://github.com/schmarion)"
danish_credits = "[Kenneth Enevoldsen](https://github.com/KennethEnevoldsen), [scandinavian-embedding-benchmark](https://kennethenevoldsen.github.io/scandinavian-embedding-benchmark/)"
norwegian_credits = "[Kenneth Enevoldsen](https://github.com/KennethEnevoldsen), [scandinavian-embedding-benchmark](https://kennethenevoldsen.github.io/scandinavian-embedding-benchmark/)"
polish_credits = "[Rafaล Poลwiata](https://github.com/rafalposwiata)"
data = {
"Overall": {
"metric": "Various, refer to task tabs",
"data": [
{
"language": "English",
"description": "**Overall MTEB English leaderboard** ๐ฎ",
"data": DATA_OVERALL,
"refresh": get_mteb_average,
},
{
"language": "Chinese",
"data": DATA_OVERALL_ZH,
"description": "**Overall MTEB Chinese leaderboard (C-MTEB)** ๐ฎ๐จ๐ณ",
"credits": chinese_credits,
"refresh": get_mteb_average_zh,
},
{
"language": "French",
"data": DATA_OVERALL_FR,
"description": "**Overall MTEB French leaderboard (F-MTEB)** ๐ฎ๐ซ๐ท",
"credits": french_credits,
"refresh": get_mteb_average_fr,
},
{
"language": "Polish",
"data": DATA_OVERALL_PL,
"description": "**Overall MTEB Polish leaderboard** ๐ฎ๐ต๐ฑ",
"refresh": get_mteb_average_pl,
},
]
},
"Bitext Mining": {
"metric": "[F1](https://huggingface.co/spaces/evaluate-metric/f1)",
"data": [
{
"language": "English-X",
"language_long": "117 (Pairs of: English & other language)",
"description": "**Bitext Mining English-X Leaderboard** ๐",
"data": DATA_BITEXT_MINING,
"refresh": partial(get_mteb_data, tasks=["BitextMining"], datasets=TASK_LIST_BITEXT_MINING),
},
{
"language": "Danish",
"language_long": "Danish & Bornholmsk (Danish Dialect)",
"description": "**Bitext Mining Danish Leaderboard** ๐๐ฉ๐ฐ",
"credits": danish_credits,
"data": DATA_BITEXT_MINING_DA,
"refresh": partial(get_mteb_data, tasks=["BitextMining"], datasets=TASK_LIST_BITEXT_MINING_DA),
}
]
},
"Classification": {
"metric": "[Accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy)",
"data": [
{
"language": "English",
"description": "**Classification English Leaderboard** โค๏ธ",
"data": DATA_CLASSIFICATION_EN,
"refresh": partial(get_mteb_data, tasks=["Classification"], langs=["en"])
},
{
"language": "Chinese",
"description": "**Classification Chinese Leaderboard** ๐งก๐จ๐ณ",
"credits": chinese_credits,
"data": DATA_CLASSIFICATION_ZH,
"refresh": partial(get_mteb_data, tasks=["Classification"], datasets=TASK_LIST_CLASSIFICATION_ZH)
},
{
"language": "Danish",
"description": "**Classification Danish Leaderboard** ๐ค๐ฉ๐ฐ",
"credits": danish_credits,
"data": DATA_CLASSIFICATION_DA,
"refresh": partial(get_mteb_data, tasks=["Classification"], datasets=TASK_LIST_CLASSIFICATION_DA)
},
{
"language": "French",
"description": "**Classification French Leaderboard** ๐๐ซ๐ท",
"credits": french_credits,
"data": DATA_CLASSIFICATION_FR,
"refresh": partial(get_mteb_data, tasks=["Classification"], datasets=TASK_LIST_CLASSIFICATION_FR)
},
{
"language": "Norwegian",
"language_long": "Norwegian Bokmรฅl",
"description": "**Classification Norwegian Leaderboard** ๐๐ณ๐ด",
"credits": norwegian_credits,
"data": DATA_CLASSIFICATION_NB,
"refresh": partial(get_mteb_data, tasks=["Classification"], datasets=TASK_LIST_CLASSIFICATION_NB)
},
{
"language": "Polish",
"description": "**Classification Polish Leaderboard** ๐ค๐ต๐ฑ",
"credits": polish_credits,
"data": DATA_CLASSIFICATION_PL,
"refresh": partial(get_mteb_data, tasks=["Classification"], datasets=TASK_LIST_CLASSIFICATION_PL)
},
{
"language": "Swedish",
"description": "**Classification Swedish Leaderboard** ๐๐ธ๐ช",
"credits": norwegian_credits,
"data": DATA_CLASSIFICATION_SV,
"refresh": partial(get_mteb_data, tasks=["Classification"], datasets=TASK_LIST_CLASSIFICATION_SV)
},
{
"language": "Other",
"language_long": "47 (Only languages not included in the other tabs)",
"description": "**Classification Other Languages Leaderboard** ๐๐๐",
"data": DATA_CLASSIFICATION_OTHER,
"refresh": partial(get_mteb_data, tasks=["Classification"], datasets=TASK_LIST_CLASSIFICATION_OTHER)
}
]
},
"Clustering": {
"metric": "Validity Measure (v_measure)",
"data": [
{
"language": "English",
"description": "**Clustering Leaderboard** โจ",
"data": DATA_CLUSTERING,
"refresh": partial(get_mteb_data, tasks=["Clustering"], datasets=TASK_LIST_CLUSTERING)
},
{
"language": "Chinese",
"description": "**Clustering Chinese Leaderboard** โจ๐จ๐ณ",
"credits": chinese_credits,
"data": DATA_CLUSTERING_ZH,
"refresh": partial(get_mteb_data, tasks=["Clustering"], datasets=TASK_LIST_CLUSTERING_ZH)
},
{
"language": "French",
"description": "**Clustering French Leaderboard** โจ๐ซ๐ท",
"credits": french_credits,
"data": DATA_CLUSTERING_FR,
"refresh": partial(get_mteb_data, tasks=["Clustering"], datasets=TASK_LIST_CLUSTERING_FR)
},
{
"language": "German",
"description": "**Clustering German Leaderboard** โจ๐ฉ๐ช",
"credits": "[Silvan](https://github.com/slvnwhrl)",
"data": DATA_CLUSTERING_DE,
"refresh": partial(get_mteb_data, tasks=["Clustering"], datasets=TASK_LIST_CLUSTERING_DE)
},
{
"language": "Polish",
"description": "**Clustering Polish Leaderboard** โจ๐ต๐ฑ",
"credits": polish_credits,
"data": DATA_CLUSTERING_PL,
"refresh": partial(get_mteb_data, tasks=["Clustering"], datasets=TASK_LIST_CLUSTERING_PL)
},
]
},
"Pair Classification": {
"metric": "Average Precision based on Cosine Similarities (cos_sim_ap)",
"data": [
{
"language": "English",
"description": "**Pair Classification English Leaderboard** ๐ญ",
"data": DATA_PAIR_CLASSIFICATION,
"refresh": partial(get_mteb_data, tasks=["PairClassification"], datasets=TASK_LIST_PAIR_CLASSIFICATION)
},
{
"language": "Chinese",
"description": "**Pair Classification Chinese Leaderboard** ๐ญ๐จ๐ณ",
"credits": chinese_credits,
"data": DATA_PAIR_CLASSIFICATION_ZH,
"refresh": partial(get_mteb_data, tasks=["PairClassification"], datasets=TASK_LIST_PAIR_CLASSIFICATION_ZH)
},
{
"language": "French",
"description": "**Pair Classification French Leaderboard** ๐ญ๐ซ๐ท",
"credits": french_credits,
"data": DATA_PAIR_CLASSIFICATION_FR,
"refresh": partial(get_mteb_data, tasks=["PairClassification"], datasets=TASK_LIST_PAIR_CLASSIFICATION_FR)
},
{
"language": "Polish",
"description": "**Pair Classification Polish Leaderboard** ๐ญ๐ต๐ฑ",
"credits": polish_credits,
"data": DATA_PAIR_CLASSIFICATION_PL,
"refresh": partial(get_mteb_data, tasks=["PairClassification"], datasets=TASK_LIST_PAIR_CLASSIFICATION_PL)
},
]
},
"Reranking": {
"metric": "Mean Average Precision (MAP)",
"data": [
{
"language": "English",
"description": "**Reranking English Leaderboard** ๐ฅ",
"data": DATA_RERANKING,
"refresh": partial(get_mteb_data, tasks=["Reranking"], datasets=TASK_LIST_RERANKING)
},
{
"language": "Chinese",
"description": "**Reranking Chinese Leaderboard** ๐ฅ๐จ๐ณ",
"credits": chinese_credits,
"data": DATA_RERANKING_ZH,
"refresh": partial(get_mteb_data, tasks=["Reranking"], datasets=TASK_LIST_RERANKING_ZH)
},
{
"language": "French",
"description": "**Reranking French Leaderboard** ๐ฅ๐ซ๐ท",
"credits": french_credits,
"data": DATA_RERANKING_FR,
"refresh": partial(get_mteb_data, tasks=["Reranking"], datasets=TASK_LIST_RERANKING_FR)
}
]
},
"Retrieval": {
"metric": "Normalized Discounted Cumulative Gain @ k (ndcg_at_10)",
"data": [
{
"language": "English",
"description": "**Retrieval English Leaderboard** ๐",
"data": DATA_RETRIEVAL,
"refresh": partial(get_mteb_data, tasks=["Retrieval"], datasets=TASK_LIST_RETRIEVAL)
},
{
"language": "Chinese",
"description": "**Retrieval Chinese Leaderboard** ๐๐จ๐ณ",
"credits": chinese_credits,
"data": DATA_RETRIEVAL_ZH,
"refresh": partial(get_mteb_data, tasks=["Retrieval"], datasets=TASK_LIST_RETRIEVAL_ZH)
},
{
"language": "French",
"description": "**Retrieval French Leaderboard** ๐๐ซ๐ท",
"credits": french_credits,
"data": DATA_RETRIEVAL_FR,
"refresh": partial(get_mteb_data, tasks=["Retrieval"], datasets=TASK_LIST_RETRIEVAL_FR)
},
{
"language": "Law",
"language_long": "English, German, Chinese",
"description": "**Retrieval Law Leaderboard** ๐โ๏ธ",
"credits": "[Voyage AI](https://www.voyageai.com/)",
"data": DATA_RETRIEVAL_LAW,
"refresh": partial(get_mteb_data, tasks=["Retrieval"], datasets=TASK_LIST_RETRIEVAL_LAW)
},
{
"language": "Polish",
"description": "**Retrieval Polish Leaderboard** ๐๐ต๐ฑ",
"credits": polish_credits,
"data": DATA_RETRIEVAL_PL,
"refresh": partial(get_mteb_data, tasks=["Retrieval"], datasets=TASK_LIST_RETRIEVAL_PL)
}
]
},
"STS": {
"metric": "Spearman correlation based on cosine similarity",
"data": [
{
"language": "English",
"description": "**STS English Leaderboard** ๐ค",
"data": DATA_STS_EN,
"refresh": partial(get_mteb_data, tasks=["STS"], datasets=TASK_LIST_STS)
},
{
"language": "Chinese",
"description": "**STS Chinese Leaderboard** ๐ค๐จ๐ณ",
"credits": chinese_credits,
"data": DATA_STS_ZH,
"refresh": partial(get_mteb_data, tasks=["STS"], datasets=TASK_LIST_STS_ZH)
},
{
"language": "French",
"description": "**STS French Leaderboard** ๐ค๐ซ๐ท",
"credits": french_credits,
"data": DATA_STS_FR,
"refresh": partial(get_mteb_data, tasks=["STS"], datasets=TASK_LIST_STS_FR)
},
{
"language": "Polish",
"description": "**STS Polish Leaderboard** ๐ค๐ต๐ฑ",
"credits": polish_credits,
"data": DATA_STS_PL,
"refresh": partial(get_mteb_data, tasks=["STS"], datasets=TASK_LIST_STS_PL)
},
{
"language": "Other",
"language_long": "Arabic, Chinese, Dutch, English, French, German, Italian, Korean, Polish, Russian, Spanish (Only language combos not included in the other tabs)",
"description": "**STS Other Leaderboard** ๐ฝ",
"data": DATA_STS_OTHER,
"refresh": partial(get_mteb_data, tasks=["STS"], datasets=TASK_LIST_STS_OTHER)
},
]
},
"Summarization": {
"metric": "Spearman correlation based on cosine similarity",
"data": [
{
"language": "English",
"description": "**Summarization Leaderboard** ๐",
"data": DATA_SUMMARIZATION,
"refresh": partial(get_mteb_data, tasks=TASK_LIST_SUMMARIZATION)
},
{
"language": "French",
"description": "**Summarization Leaderboard** ๐",
"credits": french_credits,
"data": DATA_SUMMARIZATION_FR,
"refresh": partial(get_mteb_data, tasks=TASK_LIST_SUMMARIZATION_FR)
}
]
}
}
dataframes = []
full_dataframes = []
tabs = []
# The following JavaScript function updates the URL parameters based on the selected task and language
# Additionally, `update_url_task` and `update_url_language` are used to update the current task and language
# The current task and language are stored in the `current_task_language` and `language_per_task` JSON objects
# This is all a bit hacky, but it might be the only way to pass options to a JavaScript function via Gradio
set_window_url_params = """
function(goalUrlObject) {
const params = new URLSearchParams(window.location.search);
for (const [key, value] of Object.entries(goalUrlObject)) {
params.set(key, value);
};
const queryString = '?' + params.toString();
console.log(queryString);
window.history.replaceState({}, '', queryString);
return [];
}
"""
def update_url_task(event: gr.SelectData, current_task_language: dict, language_per_task: dict):
current_task_language["task"] = event.target.id
# Either use the cached language for this task or the 1st language
current_task_language["language"] = language_per_task.get(event.target.id, event.target.children[0].children[0].id)
return current_task_language, language_per_task
def update_url_language(event: gr.SelectData, current_task_language: dict, language_per_task: dict):
current_task_language["language"] = event.target.id
if "task" not in current_task_language:
current_task_language["task"] = "overall"
language_per_task[current_task_language["task"]] = event.target.id
return current_task_language, language_per_task
NUMERIC_INTERVALS = {
"<100M": pd.Interval(0, 100, closed="right"),
"100M to 250M": pd.Interval(100, 250, closed="right"),
"250M to 500M": pd.Interval(250, 500, closed="right"),
"500M to 1B": pd.Interval(500, 1000, closed="right"),
">1B": pd.Interval(1000, 1_000_000, closed="right"),
}
MODEL_TYPES = [
"Open",
"Proprietary",
"Sentence Transformers",
]
def filter_data(search_query, model_types, model_sizes, *full_dataframes):
output_dataframes = []
for df in full_dataframes:
# Apply the search query
if search_query:
names = df["Model"].map(lambda x: re.match("<a .+?>(.+)</a>", x).group(1))
masks = []
for query in search_query.split(";"):
masks.append(names.str.contains(query))
df = df[reduce(lambda a, b: a | b, masks)]
# Apply the model type filtering
if set(model_types) != set(MODEL_TYPES):
masks = []
for model_type in model_types:
if model_type == "Open":
masks.append(~df["Model"].isin(PROPRIETARY_MODELS))
elif model_type == "Proprietary":
masks.append(df["Model"].isin(PROPRIETARY_MODELS))
elif model_type == "Sentence Transformers":
masks.append(df["Model"].isin(SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS))
if masks:
df = df[reduce(lambda a, b: a | b, masks)]
else:
df = pd.DataFrame(columns=df.columns)
# Apply the model size filtering
if set(model_sizes) != set(NUMERIC_INTERVALS.keys()):
numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[model_size] for model_size in model_sizes]))
sizes = df["Model Size (Million Parameters)"].replace('', 0)
mask = sizes.apply(lambda size: any(numeric_interval.contains(size)))
df = df[mask]
output_dataframes.append(df)
return output_dataframes
with gr.Blocks(css=css) as block:
# Store the current task and language for updating the URL. This is a bit hacky, but it works
# for passing the current task and language to the JavaScript function via Gradio
current_task_language = gr.JSON(value=dict(), visible=False)
language_per_task = gr.JSON(value=dict(), visible=False)
gr.Markdown(f"""
Massive Text Embedding Benchmark (MTEB) Leaderboard. To submit, refer to the <a href="https://github.com/embeddings-benchmark/mteb/blob/main/docs/adding_a_model.md" target="_blank" style="text-decoration: underline">MTEB GitHub repository</a> ๐ค Refer to the [MTEB paper](https://arxiv.org/abs/2210.07316) for details on metrics, tasks and models.
""")
with gr.Row():
search_bar = gr.Textbox(
label="Search Bar (separate multiple queries with `;`)",
placeholder=" ๐ Search for a model and press enter...",
)
filter_model_type = gr.CheckboxGroup(
label="Model types",
choices=MODEL_TYPES,
value=MODEL_TYPES,
interactive=True,
elem_classes=["filter-checkbox-group"]
)
filter_model_sizes = gr.CheckboxGroup(
label="Model sizes (in number of parameters)",
choices=list(NUMERIC_INTERVALS.keys()),
value=list(NUMERIC_INTERVALS.keys()),
interactive=True,
elem_classes=["filter-checkbox-group"],
scale=2,
)
with gr.Tabs() as outer_tabs:
# Store the tabs for updating them on load based on URL parameters
tabs.append(outer_tabs)
for task, task_values in data.items():
metric = task_values["metric"]
task_tab_id = task.lower().replace(" ", "-")
# Overall, Bitext Mining, Classification, etc.
with gr.Tab(task, id=task_tab_id) as task_tab:
# For updating the 'task' in the URL
task_tab.select(update_url_task, [current_task_language, language_per_task], [current_task_language, language_per_task]).then(None, [current_task_language], [], js=set_window_url_params)
with gr.Tabs() as task_tabs:
# Store the task tabs for updating them on load based on URL parameters
tabs.append(task_tabs)
for item in task_values["data"]:
item_tab_id = item["language"].lower().replace(" ", "-")
# English, Chinese, French, etc.
with gr.Tab(item["language"], id=item_tab_id) as item_tab:
# For updating the 'language' in the URL
item_tab.select(update_url_language, [current_task_language, language_per_task], [current_task_language, language_per_task], trigger_mode="always_last").then(None, [current_task_language], [], js=set_window_url_params)
with gr.Row():
gr.Markdown(f"""
{item['description']}
- **Metric:** {metric}
- **Languages:** {item['language_long'] if 'language_long' in item else item['language']}
{"- **Credits:** " + item['credits'] if "credits" in item else ''}
""")
with gr.Row():
datatype = ["number", "markdown"] + ["number"] * len(item["data"])
dataframe = gr.Dataframe(item["data"], datatype=datatype, type="pandas", height=500)
dataframes.append(dataframe)
full_dataframe = gr.Dataframe(item["data"], datatype=datatype, type="pandas", visible=False)
full_dataframes.append(full_dataframe)
with gr.Row():
refresh_button = gr.Button("Refresh")
refresh_button.click(item["refresh"], inputs=None, outputs=dataframe, concurrency_limit=20)
gr.Markdown(f"""
- **Total Datasets**: {NUM_DATASETS}
- **Total Languages**: 113
- **Total Scores**: {NUM_SCORES}
- **Total Models**: {NUM_MODELS}
""" + r"""
Made with โค๏ธ for NLP. If this work is useful to you, please consider citing:
```bibtex
@article{muennighoff2022mteb,
doi = {10.48550/ARXIV.2210.07316},
url = {https://arxiv.org/abs/2210.07316},
author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
title = {MTEB: Massive Text Embedding Benchmark},
publisher = {arXiv},
journal={arXiv preprint arXiv:2210.07316},
year = {2022}
}
```
""")
def set_tabs_on_load(request: gr.Request):
"""Set the selected tab based on the URL parameters on load."""
global tabs
valid_task_keys = [child.id for child in tabs[0].children]
return_tabs = [gr.Tabs()] * len(tabs)
query_params = request.request.query_params
task_key = query_params.get("task", "overall")
if task_key not in valid_task_keys:
task_key = "overall"
return_tabs[0] = gr.Tabs(selected=task_key)
tabs_idx = valid_task_keys.index(task_key) + 1
language_key = query_params.get("language", "english")
return_tabs[tabs_idx] = gr.Tabs(selected=language_key)
current_task_language = {"task": task_key, "language": language_key}
language_per_task = {task_key: language_key}
return return_tabs + [current_task_language, language_per_task]
block.load(set_tabs_on_load, inputs=[], outputs=tabs + [current_task_language, language_per_task])
search_bar.submit(filter_data, inputs=[search_bar, filter_model_type, filter_model_sizes] + full_dataframes, outputs=dataframes)
filter_model_type.change(filter_data, inputs=[search_bar, filter_model_type, filter_model_sizes] + full_dataframes, outputs=dataframes)
filter_model_sizes.change(filter_data, inputs=[search_bar, filter_model_type, filter_model_sizes] + full_dataframes, outputs=dataframes)
block.queue(max_size=10)
block.launch()
# Possible changes:
# Could add graphs / other visual content
# Could add verification marks
# Sources:
# https://huggingface.co/spaces/gradio/leaderboard
# https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard
# https://getemoji.com/
|