Spaces:
Runtime error
Runtime error
File size: 120,405 Bytes
e50fe35 |
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 |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "view-in-github"
},
"source": [
"<a href=\"https://colab.research.google.com/github/gowtham1997/indicTrans-1/blob/main/indicTrans_Finetuning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "rE4MO-8bDtwD",
"outputId": "e54447b4-2b04-44c4-96a2-a79e7ed014ae"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/content/finetuning\n"
]
}
],
"source": [
"# create a seperate folder to store everything\n",
"!mkdir finetuning\n",
"%cd finetuning"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-2Rs6_WkD_gF",
"outputId": "95d19041-0e73-406c-a3c2-c7bddbfda916"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cloning into 'indicTrans'...\n",
"remote: Enumerating objects: 398, done.\u001b[K\n",
"remote: Counting objects: 100% (398/398), done.\u001b[K\n",
"remote: Compressing objects: 100% (267/267), done.\u001b[K\n",
"remote: Total 398 (delta 231), reused 251 (delta 126), pack-reused 0\u001b[K\n",
"Receiving objects: 100% (398/398), 1.41 MiB | 17.84 MiB/s, done.\n",
"Resolving deltas: 100% (231/231), done.\n",
"/content/finetuning/indicTrans\n",
"Cloning into 'indic_nlp_library'...\n",
"remote: Enumerating objects: 1325, done.\u001b[K\n",
"remote: Counting objects: 100% (147/147), done.\u001b[K\n",
"remote: Compressing objects: 100% (103/103), done.\u001b[K\n",
"remote: Total 1325 (delta 84), reused 89 (delta 41), pack-reused 1178\u001b[K\n",
"Receiving objects: 100% (1325/1325), 9.57 MiB | 14.30 MiB/s, done.\n",
"Resolving deltas: 100% (688/688), done.\n",
"Cloning into 'indic_nlp_resources'...\n",
"remote: Enumerating objects: 133, done.\u001b[K\n",
"remote: Counting objects: 100% (7/7), done.\u001b[K\n",
"remote: Compressing objects: 100% (7/7), done.\u001b[K\n",
"remote: Total 133 (delta 0), reused 2 (delta 0), pack-reused 126\u001b[K\n",
"Receiving objects: 100% (133/133), 149.77 MiB | 35.48 MiB/s, done.\n",
"Resolving deltas: 100% (51/51), done.\n",
"Cloning into 'subword-nmt'...\n",
"remote: Enumerating objects: 580, done.\u001b[K\n",
"remote: Counting objects: 100% (4/4), done.\u001b[K\n",
"remote: Compressing objects: 100% (4/4), done.\u001b[K\n",
"remote: Total 580 (delta 0), reused 0 (delta 0), pack-reused 576\u001b[K\n",
"Receiving objects: 100% (580/580), 237.41 KiB | 18.26 MiB/s, done.\n",
"Resolving deltas: 100% (349/349), done.\n",
"/content/finetuning\n"
]
}
],
"source": [
"# clone the repo for running finetuning\n",
"!git clone https://github.com/AI4Bharat/indicTrans.git\n",
"%cd indicTrans\n",
"# clone requirements repositories\n",
"!git clone https://github.com/anoopkunchukuttan/indic_nlp_library.git\n",
"!git clone https://github.com/anoopkunchukuttan/indic_nlp_resources.git\n",
"!git clone https://github.com/rsennrich/subword-nmt.git\n",
"%cd .."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "duwTvJ9xEBJ1",
"outputId": "98445af3-041d-415d-97f3-a322939260e4"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reading package lists... Done\n",
"Building dependency tree \n",
"Reading state information... Done\n",
"The following NEW packages will be installed:\n",
" tree\n",
"0 upgraded, 1 newly installed, 0 to remove and 39 not upgraded.\n",
"Need to get 40.7 kB of archives.\n",
"After this operation, 105 kB of additional disk space will be used.\n",
"Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 tree amd64 1.7.0-5 [40.7 kB]\n",
"Fetched 40.7 kB in 0s (121 kB/s)\n",
"debconf: unable to initialize frontend: Dialog\n",
"debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 1.)\n",
"debconf: falling back to frontend: Readline\n",
"debconf: unable to initialize frontend: Readline\n",
"debconf: (This frontend requires a controlling tty.)\n",
"debconf: falling back to frontend: Teletype\n",
"dpkg-preconfigure: unable to re-open stdin: \n",
"Selecting previously unselected package tree.\n",
"(Reading database ... 160772 files and directories currently installed.)\n",
"Preparing to unpack .../tree_1.7.0-5_amd64.deb ...\n",
"Unpacking tree (1.7.0-5) ...\n",
"Setting up tree (1.7.0-5) ...\n",
"Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
"Collecting sacremoses\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/75/ee/67241dc87f266093c533a2d4d3d69438e57d7a90abb216fa076e7d475d4a/sacremoses-0.0.45-py3-none-any.whl (895kB)\n",
"\u001b[K |████████████████████████████████| 901kB 30.0MB/s \n",
"\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (1.1.5)\n",
"Collecting mock\n",
" Downloading https://files.pythonhosted.org/packages/5c/03/b7e605db4a57c0f6fba744b11ef3ddf4ddebcada35022927a2b5fc623fdf/mock-4.0.3-py3-none-any.whl\n",
"Collecting sacrebleu\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/7e/57/0c7ca4e31a126189dab99c19951910bd081dea5bbd25f24b77107750eae7/sacrebleu-1.5.1-py3-none-any.whl (54kB)\n",
"\u001b[K |████████████████████████████████| 61kB 9.1MB/s \n",
"\u001b[?25hCollecting tensorboardX\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/07/84/46421bd3e0e89a92682b1a38b40efc22dafb6d8e3d947e4ceefd4a5fabc7/tensorboardX-2.2-py2.py3-none-any.whl (120kB)\n",
"\u001b[K |████████████████████████████████| 122kB 58.2MB/s \n",
"\u001b[?25hRequirement already satisfied: pyarrow in /usr/local/lib/python3.7/dist-packages (3.0.0)\n",
"Collecting indic-nlp-library\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/84/d4/495bb43b88a2a6d04b09c29fc5115f24872af74cd8317fe84026abd4ddb1/indic_nlp_library-0.81-py3-none-any.whl (40kB)\n",
"\u001b[K |████████████████████████████████| 40kB 6.3MB/s \n",
"\u001b[?25hRequirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses) (1.0.1)\n",
"Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses) (7.1.2)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses) (1.15.0)\n",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from sacremoses) (4.41.1)\n",
"Requirement already satisfied: regex in /usr/local/lib/python3.7/dist-packages (from sacremoses) (2019.12.20)\n",
"Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.7/dist-packages (from pandas) (1.19.5)\n",
"Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (2.8.1)\n",
"Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas) (2018.9)\n",
"Collecting portalocker==2.0.0\n",
" Downloading https://files.pythonhosted.org/packages/89/a6/3814b7107e0788040870e8825eebf214d72166adf656ba7d4bf14759a06a/portalocker-2.0.0-py2.py3-none-any.whl\n",
"Requirement already satisfied: protobuf>=3.8.0 in /usr/local/lib/python3.7/dist-packages (from tensorboardX) (3.12.4)\n",
"Collecting sphinx-rtd-theme\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/ac/24/2475e8f83519b54b2148d4a56eb1111f9cec630d088c3ffc214492c12107/sphinx_rtd_theme-0.5.2-py2.py3-none-any.whl (9.1MB)\n",
"\u001b[K |████████████████████████████████| 9.2MB 41.6MB/s \n",
"\u001b[?25hCollecting sphinx-argparse\n",
" Downloading https://files.pythonhosted.org/packages/06/2b/dfad6a1831c3aeeae25d8d3d417224684befbf45e10c7f2141631616a6ed/sphinx-argparse-0.2.5.tar.gz\n",
"Collecting morfessor\n",
" Downloading https://files.pythonhosted.org/packages/39/e6/7afea30be2ee4d29ce9de0fa53acbb033163615f849515c0b1956ad074ee/Morfessor-2.0.6-py3-none-any.whl\n",
"Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from protobuf>=3.8.0->tensorboardX) (57.0.0)\n",
"Requirement already satisfied: sphinx in /usr/local/lib/python3.7/dist-packages (from sphinx-rtd-theme->indic-nlp-library) (1.8.5)\n",
"Collecting docutils<0.17\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/81/44/8a15e45ffa96e6cf82956dd8d7af9e666357e16b0d93b253903475ee947f/docutils-0.16-py2.py3-none-any.whl (548kB)\n",
"\u001b[K |████████████████████████████████| 552kB 33.3MB/s \n",
"\u001b[?25hRequirement already satisfied: Pygments>=2.0 in /usr/local/lib/python3.7/dist-packages (from sphinx->sphinx-rtd-theme->indic-nlp-library) (2.6.1)\n",
"Requirement already satisfied: requests>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from sphinx->sphinx-rtd-theme->indic-nlp-library) (2.23.0)\n",
"Requirement already satisfied: babel!=2.0,>=1.3 in /usr/local/lib/python3.7/dist-packages (from sphinx->sphinx-rtd-theme->indic-nlp-library) (2.9.1)\n",
"Requirement already satisfied: Jinja2>=2.3 in /usr/local/lib/python3.7/dist-packages (from sphinx->sphinx-rtd-theme->indic-nlp-library) (2.11.3)\n",
"Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from sphinx->sphinx-rtd-theme->indic-nlp-library) (20.9)\n",
"Requirement already satisfied: sphinxcontrib-websupport in /usr/local/lib/python3.7/dist-packages (from sphinx->sphinx-rtd-theme->indic-nlp-library) (1.2.4)\n",
"Requirement already satisfied: imagesize in /usr/local/lib/python3.7/dist-packages (from sphinx->sphinx-rtd-theme->indic-nlp-library) (1.2.0)\n",
"Requirement already satisfied: snowballstemmer>=1.1 in /usr/local/lib/python3.7/dist-packages (from sphinx->sphinx-rtd-theme->indic-nlp-library) (2.1.0)\n",
"Requirement already satisfied: alabaster<0.8,>=0.7 in /usr/local/lib/python3.7/dist-packages (from sphinx->sphinx-rtd-theme->indic-nlp-library) (0.7.12)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.0.0->sphinx->sphinx-rtd-theme->indic-nlp-library) (2020.12.5)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.0.0->sphinx->sphinx-rtd-theme->indic-nlp-library) (3.0.4)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.0.0->sphinx->sphinx-rtd-theme->indic-nlp-library) (2.10)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.0.0->sphinx->sphinx-rtd-theme->indic-nlp-library) (1.24.3)\n",
"Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from Jinja2>=2.3->sphinx->sphinx-rtd-theme->indic-nlp-library) (2.0.1)\n",
"Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->sphinx->sphinx-rtd-theme->indic-nlp-library) (2.4.7)\n",
"Requirement already satisfied: sphinxcontrib-serializinghtml in /usr/local/lib/python3.7/dist-packages (from sphinxcontrib-websupport->sphinx->sphinx-rtd-theme->indic-nlp-library) (1.1.4)\n",
"Building wheels for collected packages: sphinx-argparse\n",
" Building wheel for sphinx-argparse (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for sphinx-argparse: filename=sphinx_argparse-0.2.5-cp37-none-any.whl size=11552 sha256=16adb2732e7fea31509536176157766068ca67667ad9ad00a5ee3b15bdec2d18\n",
" Stored in directory: /root/.cache/pip/wheels/2a/18/1b/4990a1859da4edc77ab312bc2986c08d2733fb5713d06e44f5\n",
"Successfully built sphinx-argparse\n",
"\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n",
"Installing collected packages: sacremoses, mock, portalocker, sacrebleu, tensorboardX, docutils, sphinx-rtd-theme, sphinx-argparse, morfessor, indic-nlp-library\n",
" Found existing installation: docutils 0.17.1\n",
" Uninstalling docutils-0.17.1:\n",
" Successfully uninstalled docutils-0.17.1\n",
"Successfully installed docutils-0.16 indic-nlp-library-0.81 mock-4.0.3 morfessor-2.0.6 portalocker-2.0.0 sacrebleu-1.5.1 sacremoses-0.0.45 sphinx-argparse-0.2.5 sphinx-rtd-theme-0.5.2 tensorboardX-2.2\n",
"Cloning into 'fairseq'...\n",
"remote: Enumerating objects: 28243, done.\u001b[K\n",
"remote: Counting objects: 100% (62/62), done.\u001b[K\n",
"remote: Compressing objects: 100% (39/39), done.\u001b[K\n",
"remote: Total 28243 (delta 29), reused 44 (delta 22), pack-reused 28181\u001b[K\n",
"Receiving objects: 100% (28243/28243), 11.81 MiB | 24.38 MiB/s, done.\n",
"Resolving deltas: 100% (21225/21225), done.\n",
"/content/finetuning/fairseq\n",
"Obtaining file:///content/finetuning/fairseq\n",
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
"Collecting omegaconf<2.1\n",
" Downloading https://files.pythonhosted.org/packages/d0/eb/9d63ce09dd8aa85767c65668d5414958ea29648a0eec80a4a7d311ec2684/omegaconf-2.0.6-py3-none-any.whl\n",
"Requirement already satisfied: numpy; python_version >= \"3.7\" in /usr/local/lib/python3.7/dist-packages (from fairseq==1.0.0a0+2fd9d8a) (1.19.5)\n",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from fairseq==1.0.0a0+2fd9d8a) (4.41.1)\n",
"Collecting hydra-core<1.1\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/52/e3/fbd70dd0d3ce4d1d75c22d56c0c9f895cfa7ed6587a9ffb821d6812d6a60/hydra_core-1.0.6-py3-none-any.whl (123kB)\n",
"\u001b[K |████████████████████████████████| 133kB 32.0MB/s \n",
"\u001b[?25hRequirement already satisfied: regex in /usr/local/lib/python3.7/dist-packages (from fairseq==1.0.0a0+2fd9d8a) (2019.12.20)\n",
"Requirement already satisfied: cffi in /usr/local/lib/python3.7/dist-packages (from fairseq==1.0.0a0+2fd9d8a) (1.14.5)\n",
"Requirement already satisfied: sacrebleu>=1.4.12 in /usr/local/lib/python3.7/dist-packages (from fairseq==1.0.0a0+2fd9d8a) (1.5.1)\n",
"Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from fairseq==1.0.0a0+2fd9d8a) (1.8.1+cu101)\n",
"Requirement already satisfied: cython in /usr/local/lib/python3.7/dist-packages (from fairseq==1.0.0a0+2fd9d8a) (0.29.23)\n",
"Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from omegaconf<2.1->fairseq==1.0.0a0+2fd9d8a) (3.7.4.3)\n",
"Collecting PyYAML>=5.1.*\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/7a/a5/393c087efdc78091afa2af9f1378762f9821c9c1d7a22c5753fb5ac5f97a/PyYAML-5.4.1-cp37-cp37m-manylinux1_x86_64.whl (636kB)\n",
"\u001b[K |████████████████████████████████| 645kB 31.7MB/s \n",
"\u001b[?25hRequirement already satisfied: importlib-resources; python_version < \"3.9\" in /usr/local/lib/python3.7/dist-packages (from hydra-core<1.1->fairseq==1.0.0a0+2fd9d8a) (5.1.3)\n",
"Collecting antlr4-python3-runtime==4.8\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/56/02/789a0bddf9c9b31b14c3e79ec22b9656185a803dc31c15f006f9855ece0d/antlr4-python3-runtime-4.8.tar.gz (112kB)\n",
"\u001b[K |████████████████████████████████| 112kB 53.4MB/s \n",
"\u001b[?25hRequirement already satisfied: pycparser in /usr/local/lib/python3.7/dist-packages (from cffi->fairseq==1.0.0a0+2fd9d8a) (2.20)\n",
"Requirement already satisfied: portalocker==2.0.0 in /usr/local/lib/python3.7/dist-packages (from sacrebleu>=1.4.12->fairseq==1.0.0a0+2fd9d8a) (2.0.0)\n",
"Requirement already satisfied: zipp>=0.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-resources; python_version < \"3.9\"->hydra-core<1.1->fairseq==1.0.0a0+2fd9d8a) (3.4.1)\n",
"Building wheels for collected packages: antlr4-python3-runtime\n",
" Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.8-cp37-none-any.whl size=141231 sha256=5e816253108c1c7a8687228b17c910230fee3243ba77f5567a8b08f7c1a5a101\n",
" Stored in directory: /root/.cache/pip/wheels/e3/e2/fa/b78480b448b8579ddf393bebd3f47ee23aa84c89b6a78285c8\n",
"Successfully built antlr4-python3-runtime\n",
"Installing collected packages: PyYAML, omegaconf, antlr4-python3-runtime, hydra-core, fairseq\n",
" Found existing installation: PyYAML 3.13\n",
" Uninstalling PyYAML-3.13:\n",
" Successfully uninstalled PyYAML-3.13\n",
" Running setup.py develop for fairseq\n",
"Successfully installed PyYAML-5.4.1 antlr4-python3-runtime-4.8 fairseq hydra-core-1.0.6 omegaconf-2.0.6\n",
"/content/finetuning\n"
]
}
],
"source": [
"! sudo apt install tree\n",
"\n",
"# Install the necessary libraries\n",
"!pip install sacremoses pandas mock sacrebleu tensorboardX pyarrow indic-nlp-library\n",
"# Install fairseq from source\n",
"!git clone https://github.com/pytorch/fairseq.git\n",
"%cd fairseq\n",
"# !git checkout da9eaba12d82b9bfc1442f0e2c6fc1b895f4d35d\n",
"!pip install --editable ./\n",
"%cd .."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "oD2EHQdqEH70",
"outputId": "0b988dde-9da3-487c-a393-510fbcae92f3"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2021-06-09 18:47:20-- https://storage.googleapis.com/samanantar-public/V0.2/models/indic-en.zip\n",
"Resolving storage.googleapis.com (storage.googleapis.com)... 172.253.62.128, 172.253.115.128, 172.253.122.128, ...\n",
"Connecting to storage.googleapis.com (storage.googleapis.com)|172.253.62.128|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 4551079075 (4.2G) [application/zip]\n",
"Saving to: ‘indic-en.zip’\n",
"\n",
"indic-en.zip 100%[===================>] 4.24G 61.3MB/s in 56s \n",
"\n",
"2021-06-09 18:48:16 (77.9 MB/s) - ‘indic-en.zip’ saved [4551079075/4551079075]\n",
"\n",
"Archive: indic-en.zip\n",
" creating: indic-en/\n",
" creating: indic-en/vocab/\n",
" inflating: indic-en/vocab/bpe_codes.32k.SRC \n",
" inflating: indic-en/vocab/vocab.SRC \n",
" inflating: indic-en/vocab/vocab.TGT \n",
" inflating: indic-en/vocab/bpe_codes.32k.TGT \n",
" creating: indic-en/final_bin/\n",
" inflating: indic-en/final_bin/dict.TGT.txt \n",
" inflating: indic-en/final_bin/dict.SRC.txt \n",
" creating: indic-en/model/\n",
" inflating: indic-en/model/checkpoint_best.pt \n"
]
}
],
"source": [
"# download the indictrans model\n",
"\n",
"\n",
"# downloading the en-indic model\n",
"# this will contain:\n",
"# en-indic/\n",
"# ├── final_bin # contains fairseq dictionaries (we will use this to binarize the new finetuning data)\n",
"# │ ├── dict.SRC.txt\n",
"# │ └── dict.TGT.txt\n",
"# ├── model # contains model checkpoint(s)\n",
"# │ └── checkpoint_best.pt\n",
"# └── vocab # contains bpes for src and tgt (since we train seperate vocabularies) generated with subword_nmt (we will use this bpes to convert finetuning data to subwords)\n",
"# ├── bpe_codes.32k.SRC\n",
"# ├── bpe_codes.32k.TGT\n",
"# ├── vocab.SRC\n",
"# └── vocab.TGT\n",
"\n",
"\n",
"\n",
"!wget https://storage.googleapis.com/samanantar-public/V0.3/models/indic-en.zip\n",
"!unzip indic-en.zip\n",
"\n",
"# if you want to finetune indic-en models, use the link below\n",
"\n",
"# !wget https://storage.googleapis.com/samanantar-public/V0.3/models/en-indic.zip\n",
"# !unzip en-indic.zip\n",
"\n",
"# if you want to finetune indic-indic models, use the link below\n",
"\n",
"# !wget https://storage.googleapis.com/samanantar-public/V0.3/models/m2m.zip\n",
"# !unzip m2m.zip\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "lj7XNBuwE0OV",
"outputId": "98b3a156-c205-4f1b-de79-f1d640555349"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2021-06-09 18:50:23-- http://lotus.kuee.kyoto-u.ac.jp/WAT/indic-multilingual/indic_wat_2021.tar.gz\n",
"Resolving lotus.kuee.kyoto-u.ac.jp (lotus.kuee.kyoto-u.ac.jp)... 130.54.208.131\n",
"Connecting to lotus.kuee.kyoto-u.ac.jp (lotus.kuee.kyoto-u.ac.jp)|130.54.208.131|:80... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 777928004 (742M) [application/x-gzip]\n",
"Saving to: ‘indic_wat_2021.tar.gz’\n",
"\n",
"indic_wat_2021.tar. 100%[===================>] 741.89M 13.6MB/s in 57s \n",
"\n",
"2021-06-09 18:51:20 (13.1 MB/s) - ‘indic_wat_2021.tar.gz’ saved [777928004/777928004]\n",
"\n",
"finalrepo/\n",
"finalrepo/README\n",
"finalrepo/dev/\n",
"finalrepo/dev/dev.mr\n",
"finalrepo/dev/dev.kn\n",
"finalrepo/dev/dev.gu\n",
"finalrepo/dev/dev.ta\n",
"finalrepo/dev/dev.bn\n",
"finalrepo/dev/dev.pa\n",
"finalrepo/dev/dev.ml\n",
"finalrepo/dev/dev.or\n",
"finalrepo/dev/dev.en\n",
"finalrepo/dev/dev.hi\n",
"finalrepo/dev/dev.te\n",
"finalrepo/train/\n",
"finalrepo/train/zeroshotcorpstats\n",
"finalrepo/train/opensubtitles/\n",
"finalrepo/train/opensubtitles/en-ta/\n",
"finalrepo/train/opensubtitles/en-ta/train.ta\n",
"finalrepo/train/opensubtitles/en-ta/train.en\n",
"finalrepo/train/opensubtitles/en-te/\n",
"finalrepo/train/opensubtitles/en-te/train.te\n",
"finalrepo/train/opensubtitles/en-te/train.en\n",
"finalrepo/train/opensubtitles/en-ml/\n",
"finalrepo/train/opensubtitles/en-ml/train.ml\n",
"finalrepo/train/opensubtitles/en-ml/train.en\n",
"finalrepo/train/opensubtitles/en-bn/\n",
"finalrepo/train/opensubtitles/en-bn/train.bn\n",
"finalrepo/train/opensubtitles/en-bn/train.en\n",
"finalrepo/train/opensubtitles/en-hi/\n",
"finalrepo/train/opensubtitles/en-hi/train.hi\n",
"finalrepo/train/opensubtitles/en-hi/train.en\n",
"finalrepo/train/cvit-pib/\n",
"finalrepo/train/cvit-pib/en-ta/\n",
"finalrepo/train/cvit-pib/en-ta/train.ta\n",
"finalrepo/train/cvit-pib/en-ta/train.en\n",
"finalrepo/train/cvit-pib/en-te/\n",
"finalrepo/train/cvit-pib/en-te/train.te\n",
"finalrepo/train/cvit-pib/en-te/train.en\n",
"finalrepo/train/cvit-pib/en-or/\n",
"finalrepo/train/cvit-pib/en-or/train.or\n",
"finalrepo/train/cvit-pib/en-or/train.en\n",
"finalrepo/train/cvit-pib/en-ml/\n",
"finalrepo/train/cvit-pib/en-ml/train.ml\n",
"finalrepo/train/cvit-pib/en-ml/train.en\n",
"finalrepo/train/cvit-pib/en-bn/\n",
"finalrepo/train/cvit-pib/en-bn/train.bn\n",
"finalrepo/train/cvit-pib/en-bn/train.en\n",
"finalrepo/train/cvit-pib/en-gu/\n",
"finalrepo/train/cvit-pib/en-gu/train.en\n",
"finalrepo/train/cvit-pib/en-gu/train.gu\n",
"finalrepo/train/cvit-pib/en-mr/\n",
"finalrepo/train/cvit-pib/en-mr/train.mr\n",
"finalrepo/train/cvit-pib/en-mr/train.en\n",
"finalrepo/train/cvit-pib/en-pa/\n",
"finalrepo/train/cvit-pib/en-pa/train.pa\n",
"finalrepo/train/cvit-pib/en-pa/train.en\n",
"finalrepo/train/cvit-pib/en-hi/\n",
"finalrepo/train/cvit-pib/en-hi/train.hi\n",
"finalrepo/train/cvit-pib/en-hi/train.en\n",
"finalrepo/train/bibleuedin/\n",
"finalrepo/train/bibleuedin/en-te/\n",
"finalrepo/train/bibleuedin/en-te/train.te\n",
"finalrepo/train/bibleuedin/en-te/train.en\n",
"finalrepo/train/bibleuedin/en-ml/\n",
"finalrepo/train/bibleuedin/en-ml/train.ml\n",
"finalrepo/train/bibleuedin/en-ml/train.en\n",
"finalrepo/train/bibleuedin/en-gu/\n",
"finalrepo/train/bibleuedin/en-gu/train.en\n",
"finalrepo/train/bibleuedin/en-gu/train.gu\n",
"finalrepo/train/bibleuedin/en-mr/\n",
"finalrepo/train/bibleuedin/en-mr/train.mr\n",
"finalrepo/train/bibleuedin/en-mr/train.en\n",
"finalrepo/train/bibleuedin/en-hi/\n",
"finalrepo/train/bibleuedin/en-hi/train.hi\n",
"finalrepo/train/bibleuedin/en-hi/train.en\n",
"finalrepo/train/bibleuedin/en-kn/\n",
"finalrepo/train/bibleuedin/en-kn/train.kn\n",
"finalrepo/train/bibleuedin/en-kn/train.en\n",
"finalrepo/train/iitb/\n",
"finalrepo/train/iitb/en-hi/\n",
"finalrepo/train/iitb/en-hi/train.hi\n",
"finalrepo/train/iitb/en-hi/train.en\n",
"finalrepo/train/wikimatrix/\n",
"finalrepo/train/wikimatrix/en-ta/\n",
"finalrepo/train/wikimatrix/en-ta/train.ta\n",
"finalrepo/train/wikimatrix/en-ta/train.en\n",
"finalrepo/train/wikimatrix/en-te/\n",
"finalrepo/train/wikimatrix/en-te/train.te\n",
"finalrepo/train/wikimatrix/en-te/train.en\n",
"finalrepo/train/wikimatrix/en-ml/\n",
"finalrepo/train/wikimatrix/en-ml/train.ml\n",
"finalrepo/train/wikimatrix/en-ml/train.en\n",
"finalrepo/train/wikimatrix/en-bn/\n",
"finalrepo/train/wikimatrix/en-bn/train.bn\n",
"finalrepo/train/wikimatrix/en-bn/train.en\n",
"finalrepo/train/wikimatrix/en-mr/\n",
"finalrepo/train/wikimatrix/en-mr/train.mr\n",
"finalrepo/train/wikimatrix/en-mr/train.en\n",
"finalrepo/train/wikimatrix/en-hi/\n",
"finalrepo/train/wikimatrix/en-hi/train.hi\n",
"finalrepo/train/wikimatrix/en-hi/train.en\n",
"finalrepo/train/alt/\n",
"finalrepo/train/alt/en-bn/\n",
"finalrepo/train/alt/en-bn/train.bn\n",
"finalrepo/train/alt/en-bn/train.en\n",
"finalrepo/train/alt/en-hi/\n",
"finalrepo/train/alt/en-hi/train.hi\n",
"finalrepo/train/alt/en-hi/train.en\n",
"finalrepo/train/pmi/\n",
"finalrepo/train/pmi/en-ta/\n",
"finalrepo/train/pmi/en-ta/train.ta\n",
"finalrepo/train/pmi/en-ta/train.en\n",
"finalrepo/train/pmi/en-te/\n",
"finalrepo/train/pmi/en-te/train.te\n",
"finalrepo/train/pmi/en-te/train.en\n",
"finalrepo/train/pmi/en-or/\n",
"finalrepo/train/pmi/en-or/train.or\n",
"finalrepo/train/pmi/en-or/train.en\n",
"finalrepo/train/pmi/en-ml/\n",
"finalrepo/train/pmi/en-ml/train.ml\n",
"finalrepo/train/pmi/en-ml/train.en\n",
"finalrepo/train/pmi/en-bn/\n",
"finalrepo/train/pmi/en-bn/train.bn\n",
"finalrepo/train/pmi/en-bn/train.en\n",
"finalrepo/train/pmi/en-gu/\n",
"finalrepo/train/pmi/en-gu/train.en\n",
"finalrepo/train/pmi/en-gu/train.gu\n",
"finalrepo/train/pmi/en-mr/\n",
"finalrepo/train/pmi/en-mr/train.mr\n",
"finalrepo/train/pmi/en-mr/train.en\n",
"finalrepo/train/pmi/en-pa/\n",
"finalrepo/train/pmi/en-pa/train.pa\n",
"finalrepo/train/pmi/en-pa/train.en\n",
"finalrepo/train/pmi/en-hi/\n",
"finalrepo/train/pmi/en-hi/train.hi\n",
"finalrepo/train/pmi/en-hi/train.en\n",
"finalrepo/train/pmi/en-kn/\n",
"finalrepo/train/pmi/en-kn/train.kn\n",
"finalrepo/train/pmi/en-kn/train.en\n",
"finalrepo/train/wikititles/\n",
"finalrepo/train/wikititles/en-ta/\n",
"finalrepo/train/wikititles/en-ta/train.ta\n",
"finalrepo/train/wikititles/en-ta/train.en\n",
"finalrepo/train/wikititles/en-gu/\n",
"finalrepo/train/wikititles/en-gu/train.en\n",
"finalrepo/train/wikititles/en-gu/train.gu\n",
"finalrepo/train/mtenglish2odia/\n",
"finalrepo/train/mtenglish2odia/en-or/\n",
"finalrepo/train/mtenglish2odia/en-or/train.or\n",
"finalrepo/train/mtenglish2odia/en-or/train.en\n",
"finalrepo/train/urst/\n",
"finalrepo/train/urst/en-gu/\n",
"finalrepo/train/urst/en-gu/train.en\n",
"finalrepo/train/urst/en-gu/train.gu\n",
"finalrepo/train/jw/\n",
"finalrepo/train/jw/en-ta/\n",
"finalrepo/train/jw/en-ta/train.ta\n",
"finalrepo/train/jw/en-ta/train.en\n",
"finalrepo/train/jw/en-te/\n",
"finalrepo/train/jw/en-te/train.te\n",
"finalrepo/train/jw/en-te/train.en\n",
"finalrepo/train/jw/en-ml/\n",
"finalrepo/train/jw/en-ml/train.ml\n",
"finalrepo/train/jw/en-ml/train.en\n",
"finalrepo/train/jw/en-bn/\n",
"finalrepo/train/jw/en-bn/train.bn\n",
"finalrepo/train/jw/en-bn/train.en\n",
"finalrepo/train/jw/en-gu/\n",
"finalrepo/train/jw/en-gu/train.en\n",
"finalrepo/train/jw/en-gu/train.gu\n",
"finalrepo/train/jw/en-mr/\n",
"finalrepo/train/jw/en-mr/train.mr\n",
"finalrepo/train/jw/en-mr/train.en\n",
"finalrepo/train/jw/en-pa/\n",
"finalrepo/train/jw/en-pa/train.pa\n",
"finalrepo/train/jw/en-pa/train.en\n",
"finalrepo/train/jw/en-hi/\n",
"finalrepo/train/jw/en-hi/train.hi\n",
"finalrepo/train/jw/en-hi/train.en\n",
"finalrepo/train/jw/en-kn/\n",
"finalrepo/train/jw/en-kn/train.kn\n",
"finalrepo/train/jw/en-kn/train.en\n",
"finalrepo/train/nlpc/\n",
"finalrepo/train/nlpc/en-ta/\n",
"finalrepo/train/nlpc/en-ta/train.ta\n",
"finalrepo/train/nlpc/en-ta/train.en\n",
"finalrepo/train/get_zero_shot_pairs.py\n",
"finalrepo/train/ufal/\n",
"finalrepo/train/ufal/en-ta/\n",
"finalrepo/train/ufal/en-ta/train.ta\n",
"finalrepo/train/ufal/en-ta/train.en\n",
"finalrepo/train/odiencorp/\n",
"finalrepo/train/odiencorp/en-or/\n",
"finalrepo/train/odiencorp/en-or/train.or\n",
"finalrepo/train/odiencorp/en-or/train.en\n",
"finalrepo/train/tanzil/\n",
"finalrepo/train/tanzil/en-ta/\n",
"finalrepo/train/tanzil/en-ta/train.ta\n",
"finalrepo/train/tanzil/en-ta/train.en\n",
"finalrepo/train/tanzil/en-ml/\n",
"finalrepo/train/tanzil/en-ml/train.ml\n",
"finalrepo/train/tanzil/en-ml/train.en\n",
"finalrepo/train/tanzil/en-bn/\n",
"finalrepo/train/tanzil/en-bn/train.bn\n",
"finalrepo/train/tanzil/en-bn/train.en\n",
"finalrepo/train/tanzil/en-hi/\n",
"finalrepo/train/tanzil/en-hi/train.hi\n",
"finalrepo/train/tanzil/en-hi/train.en\n",
"finalrepo/train/ted2020/\n",
"finalrepo/train/ted2020/en-ta/\n",
"finalrepo/train/ted2020/en-ta/train.ta\n",
"finalrepo/train/ted2020/en-ta/train.en\n",
"finalrepo/train/ted2020/en-te/\n",
"finalrepo/train/ted2020/en-te/train.te\n",
"finalrepo/train/ted2020/en-te/train.en\n",
"finalrepo/train/ted2020/en-ml/\n",
"finalrepo/train/ted2020/en-ml/train.ml\n",
"finalrepo/train/ted2020/en-ml/train.en\n",
"finalrepo/train/ted2020/en-bn/\n",
"finalrepo/train/ted2020/en-bn/train.bn\n",
"finalrepo/train/ted2020/en-bn/train.en\n",
"finalrepo/train/ted2020/en-gu/\n",
"finalrepo/train/ted2020/en-gu/train.en\n",
"finalrepo/train/ted2020/en-gu/train.gu\n",
"finalrepo/train/ted2020/en-mr/\n",
"finalrepo/train/ted2020/en-mr/train.mr\n",
"finalrepo/train/ted2020/en-mr/train.en\n",
"finalrepo/train/ted2020/en-pa/\n",
"finalrepo/train/ted2020/en-pa/train.pa\n",
"finalrepo/train/ted2020/en-pa/train.en\n",
"finalrepo/train/ted2020/en-hi/\n",
"finalrepo/train/ted2020/en-hi/train.hi\n",
"finalrepo/train/ted2020/en-hi/train.en\n",
"finalrepo/train/ted2020/en-kn/\n",
"finalrepo/train/ted2020/en-kn/train.kn\n",
"finalrepo/train/ted2020/en-kn/train.en\n",
"finalrepo/test/\n",
"finalrepo/test/test.gu\n",
"finalrepo/test/test.fm.prob\n",
"finalrepo/test/test.kn\n",
"finalrepo/test/test.ta\n",
"finalrepo/test/cached_lm_test.en\n",
"finalrepo/test/test.pa\n",
"finalrepo/test/test.bn\n",
"finalrepo/test/test.hi\n",
"finalrepo/test/test.ml\n",
"finalrepo/test/test.or\n",
"finalrepo/test/test.mr\n",
"finalrepo/test/test.en\n",
"finalrepo/test/test.te\n"
]
}
],
"source": [
"# In this example, we will finetuning on cvit-pib corpus which is part of the WAT2021 training dataset.\n",
"\n",
"# Lets first download the full wat2021 training data (cvit-pib is a part of this big training set)\n",
"# ***Note***: See the next section to mine for mining indic to indic data from english centric WAT data. This dataset can be used to finetune indic2indic model\n",
"!wget http://lotus.kuee.kyoto-u.ac.jp/WAT/indic-multilingual/indic_wat_2021.tar.gz\n",
"!tar -xzvf indic_wat_2021.tar.gz\n",
"# all train sets will now be in wat2021/train\n",
"!mv finalrepo wat2021"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "BSoZDR3fHpUk",
"outputId": "11bd057b-d1b0-45b8-feac-85b3e900104e"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mkdir: cannot create directory ‘wat2021-indic2indic’: File exists\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r 0%| | 0/2 [00:00<?, ?it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"bn hi\n",
"bn gu\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r 50%|█████ | 1/2 [03:46<03:46, 226.18s/it]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"hi gu\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 2/2 [06:05<00:00, 182.80s/it]\n"
]
}
],
"source": [
"# this cell is for mining indic to indic data from a english centric corpus. This data can then be used to our finetune indic2indic model\n",
"\n",
"# Mining Indic to Indic pairs from english centric corpus\n",
"# The `extract_non_english_pairs` in `scripts/extract_non_english_pairs.py` can be used to mine indic to indic pairs from english centric corpus.\n",
"\n",
"# As described in the paper (section 2.5) , we use a very strict deduplication criterion to avoid the creation of very similar parallel sentences. \n",
"# For example, if an en sentence is aligned to M hi sentences and N ta sentences, then we would get MN hi-ta pairs. However, these pairs would be very similar and not contribute much to the training process. \n",
"# Hence, we retain only 1 randomly chosen pair out of these MN pairs.\n",
"\n",
"!mkdir wat2021-indic2indic\n",
"\n",
"from indicTrans.scripts.extract_non_english_pairs import extract_non_english_pairs\n",
"\n",
"\"\"\"\n",
"extract_non_english_pairs(indir, outdir, LANGS)\n",
"\n",
" Extracts non-english pair parallel corpora\n",
" indir: contains english centric data in the following form:\n",
" - directory named en-xx for language xx\n",
" - each directory contains a train.en and train.xx\n",
" outdir: output directory to store mined data for each pair.\n",
" One directory is created for each pair.\n",
" LANGS: list of languages in the corpus (other than English).\n",
" The language codes must correspond to the ones used in the\n",
" files and directories in indir. Prefarably, sort the languages\n",
" in this list in alphabetic order. outdir will contain data for xx-yy,\n",
" but not for yy-xx, so it will be convenient to have this list in sorted order.\n",
"\"\"\"\n",
"# here we are using three langs to mine bn-hi, hi-gu and gu-bn pairs from wat2021/cvit-pib en-X data\n",
"# you should see the following files after running the code below\n",
"# wat2021-indic2indic\n",
"# ├── bn-gu\n",
"# │ ├── train.bn\n",
"# │ └── train.gu\n",
"# ├── bn-hi\n",
"# │ ├── train.bn\n",
"# │ └── train.hi\n",
"# └── hi-gu\n",
"# ├── train.gu\n",
"# └── train.hi\n",
"\n",
"# NOTE: Make sure to dedup the output text files and remove any overlaps with test sets before finetuning\n",
"# Both of the above are implemented in scripts/remove_train_devtest_overlaps.py -> remove_train_devtest_overlaps(train_dir, devtest_dir, many2many=True)\n",
"\n",
"extract_non_english_pairs('wat2021/train/cvit-pib', 'wat2021-indic2indic', ['bn', 'hi', 'gu'])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ys_QURP3Sx7G",
"outputId": "d41f5baa-e700-4e07-93cd-b23b08122dc5"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/content/finetuning/indicTrans\n"
]
}
],
"source": [
"# wat2021\n",
"# ├── dev # contains Wat2021 dev data\n",
"# │ ├── dev.bn\n",
"# │ ├── dev.en\n",
"# │ ├── dev.gu\n",
"# │ ├── dev.hi\n",
"# │ ├── dev.kn\n",
"# │ ├── dev.ml\n",
"# │ ├── dev.mr\n",
"# │ ├── dev.or\n",
"# │ ├── dev.pa\n",
"# │ ├── dev.ta\n",
"# │ └── dev.te\n",
"# ├── README\n",
"# ├── test # contains Wat2021 test data\n",
"# │ ├── test.bn\n",
"# │ ├── test.en\n",
"# │ ├── test.gu\n",
"# │ ├── test.hi\n",
"# │ ├── test.kn\n",
"# │ ├── test.ml\n",
"# │ ├── test.mr\n",
"# │ ├── test.or\n",
"# │ ├── test.pa\n",
"# │ ├── test.ta\n",
"# │ └── test.te\n",
"# └── train # contains WAT2021 train data which has lot of corpuses (alt, bible, Jw300, etc)\n",
"# ├── alt/\n",
"# ├── bibleuedin/\n",
"# ├── iitb/\n",
"# ├── jw/\n",
"# ├── mtenglish2odia/\n",
"# ├── nlpc/\n",
"# ├── odiencorp/\n",
"# ├── opensubtitles/\n",
"# ├── pmi/\n",
"# ├── tanzil/\n",
"# ├── ted2020/\n",
"# ├── ufal/\n",
"# ├── urst/\n",
"# ├── wikimatrix/\n",
"# ├── wikititles/\n",
"# └── cvit-pib \n",
"# ├── en-bn # within a train corpus folder the files are arranged in {src_lang}-{tgt_lang}/train.{src_lang}, train.{tgt_lang}\n",
"# │ ├── train.bn\n",
"# │ └── train.en\n",
"# ├── en-gu\n",
"# │ ├── train.en\n",
"# │ └── train.gu\n",
"# ├── en-hi\n",
"# │ ├── train.en\n",
"# │ └── train.hi\n",
"# ├── en-ml\n",
"# │ ├── train.en\n",
"# │ └── train.ml\n",
"# ├── en-mr\n",
"# │ ├── train.en\n",
"# │ └── train.mr\n",
"# ├── en-or\n",
"# │ ├── train.en\n",
"# │ └── train.or\n",
"# ├── en-pa\n",
"# │ ├── train.en\n",
"# │ └── train.pa\n",
"# ├── en-ta\n",
"# │ ├── train.en\n",
"# │ └── train.ta\n",
"# └── en-te\n",
"# ├── train.en\n",
"# └── train.te\n",
"\n",
"\n",
"\n",
"# instead of using all the data for this example, we will mainly use the cvit-pib corpus from wat2021 train set\n",
"# for dev and test set, we will use the dev and test provided by wat2021\n",
"\n",
"# In case, you want to finetune on all these corpuses, you would need to merge all the training data into one folder and remove duplicate train sentence pairs.\n",
"# To do this, refer to this gist: https://gist.github.com/gowtham1997/2524f8e9559cff586d1f935e621fc598\n",
"\n",
"\n",
"# copy everything to a dataset folder\n",
"!mkdir -p dataset/train\n",
"! cp -r wat2021/train/cvit-pib/* dataset/train\n",
"! cp -r wat2021/dev dataset\n",
"! cp -r wat2021/test dataset\n",
"\n",
"\n",
"# lets cd to indicTrans\n",
"%cd indicTrans"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "8yPTbM_clKfI",
"outputId": "d4459da6-3e0b-45c8-f291-d6761e536284"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"../dataset\n"
]
},
{
"data": {
"text/plain": []
},
"execution_count": 7,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"source": [
"%%shell\n",
"\n",
"exp_dir=../dataset\n",
"src_lang=en\n",
"tgt_lang=indic\n",
"\n",
"# change this to indic-en, if you have downloaded the indic-en dir or m2m if you have downloaded the indic2indic model\n",
"download_dir=../en-indic\n",
"\n",
"train_data_dir=$exp_dir/train\n",
"dev_data_dir=$exp_dir/dev\n",
"test_data_dir=$exp_dir/test\n",
"echo $exp_dir\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "NhwUXyYVXrOY",
"outputId": "9ddb06dd-3fcc-4d4c-a4ec-131a9f4ea220"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running experiment ../dataset on en to indic\n",
"Applying normalization and script conversion for train bn\n",
"100% 91985/91985 [00:25<00:00, 3582.55it/s]\n",
"100% 91985/91985 [00:14<00:00, 6330.85it/s]\n",
"Number of sentences in train bn: 91985\n",
"Applying normalization and script conversion for dev bn\n",
"100% 1000/1000 [00:00<00:00, 1593.70it/s]\n",
"100% 1000/1000 [00:00<00:00, 7232.26it/s]\n",
"Number of sentences in dev bn: 1000\n",
"Applying normalization and script conversion for test bn\n",
"100% 2390/2390 [00:00<00:00, 2874.03it/s]\n",
"100% 2390/2390 [00:00<00:00, 6727.65it/s]\n",
"Number of sentences in test bn: 2390\n",
"Applying normalization and script conversion for train hi\n",
"100% 266545/266545 [01:15<00:00, 3546.17it/s]\n",
"100% 266545/266545 [00:45<00:00, 5913.09it/s]\n",
"Number of sentences in train hi: 266545\n",
"Applying normalization and script conversion for dev hi\n",
"100% 1000/1000 [00:00<00:00, 1666.49it/s]\n",
"100% 1000/1000 [00:00<00:00, 5857.08it/s]\n",
"Number of sentences in dev hi: 1000\n",
"Applying normalization and script conversion for test hi\n",
"100% 2390/2390 [00:00<00:00, 2928.00it/s]\n",
"100% 2390/2390 [00:00<00:00, 6789.39it/s]\n",
"Number of sentences in test hi: 2390\n",
"Applying normalization and script conversion for train gu\n",
"100% 58264/58264 [00:15<00:00, 3688.72it/s]\n",
"100% 58264/58264 [00:09<00:00, 6391.97it/s]\n",
"Number of sentences in train gu: 58264\n",
"Applying normalization and script conversion for dev gu\n",
"100% 1000/1000 [00:00<00:00, 1670.01it/s]\n",
"100% 1000/1000 [00:00<00:00, 6530.46it/s]\n",
"Number of sentences in dev gu: 1000\n",
"Applying normalization and script conversion for test gu\n",
"100% 2390/2390 [00:00<00:00, 2884.69it/s]\n",
"100% 2390/2390 [00:00<00:00, 6099.24it/s]\n",
"Number of sentences in test gu: 2390\n",
"Applying normalization and script conversion for train ml\n",
"100% 43087/43087 [00:12<00:00, 3589.89it/s]\n",
"100% 43087/43087 [00:07<00:00, 5968.67it/s]\n",
"Number of sentences in train ml: 43087\n",
"Applying normalization and script conversion for dev ml\n",
"100% 1000/1000 [00:00<00:00, 1691.23it/s]\n",
"100% 1000/1000 [00:00<00:00, 6090.55it/s]\n",
"Number of sentences in dev ml: 1000\n",
"Applying normalization and script conversion for test ml\n",
"100% 2390/2390 [00:00<00:00, 2961.81it/s]\n",
"100% 2390/2390 [00:00<00:00, 6878.08it/s]\n",
"Number of sentences in test ml: 2390\n",
"Applying normalization and script conversion for train mr\n",
"100% 114220/114220 [00:30<00:00, 3773.79it/s]\n",
"100% 114220/114220 [00:17<00:00, 6513.13it/s]\n",
"Number of sentences in train mr: 114220\n",
"Applying normalization and script conversion for dev mr\n",
"100% 1000/1000 [00:00<00:00, 1671.69it/s]\n",
"100% 1000/1000 [00:00<00:00, 5737.54it/s]\n",
"Number of sentences in dev mr: 1000\n",
"Applying normalization and script conversion for test mr\n",
"100% 2390/2390 [00:00<00:00, 2959.82it/s]\n",
"100% 2390/2390 [00:00<00:00, 6393.52it/s]\n",
"Number of sentences in test mr: 2390\n",
"Applying normalization and script conversion for train or\n",
"100% 94494/94494 [00:24<00:00, 3912.66it/s]\n",
"100% 94494/94494 [00:13<00:00, 6919.45it/s]\n",
"Number of sentences in train or: 94494\n",
"Applying normalization and script conversion for dev or\n",
"100% 1000/1000 [00:00<00:00, 1680.80it/s]\n",
"100% 1000/1000 [00:00<00:00, 5797.35it/s]\n",
"Number of sentences in dev or: 1000\n",
"Applying normalization and script conversion for test or\n",
"100% 2390/2390 [00:00<00:00, 2978.67it/s]\n",
"100% 2390/2390 [00:00<00:00, 6787.01it/s]\n",
"Number of sentences in test or: 2390\n",
"Applying normalization and script conversion for train pa\n",
"100% 101092/101092 [00:26<00:00, 3826.32it/s]\n",
"100% 101092/101092 [00:15<00:00, 6425.22it/s]\n",
"Number of sentences in train pa: 101092\n",
"Applying normalization and script conversion for dev pa\n",
"100% 1000/1000 [00:00<00:00, 1667.88it/s]\n",
"100% 1000/1000 [00:00<00:00, 6182.50it/s]\n",
"Number of sentences in dev pa: 1000\n",
"Applying normalization and script conversion for test pa\n",
"100% 2390/2390 [00:00<00:00, 2993.56it/s]\n",
"100% 2390/2390 [00:00<00:00, 8002.74it/s]\n",
"Number of sentences in test pa: 2390\n",
"Applying normalization and script conversion for train ta\n",
"100% 115968/115968 [00:30<00:00, 3838.68it/s]\n",
"100% 115968/115968 [00:19<00:00, 5805.14it/s]\n",
"Number of sentences in train ta: 115968\n",
"Applying normalization and script conversion for dev ta\n",
"100% 1000/1000 [00:00<00:00, 1659.50it/s]\n",
"100% 1000/1000 [00:00<00:00, 6223.34it/s]\n",
"Number of sentences in dev ta: 1000\n",
"Applying normalization and script conversion for test ta\n",
"100% 2390/2390 [00:00<00:00, 3046.92it/s]\n",
"100% 2390/2390 [00:00<00:00, 6047.32it/s]\n",
"Number of sentences in test ta: 2390\n",
"Applying normalization and script conversion for train te\n",
"100% 44720/44720 [00:12<00:00, 3524.75it/s]\n",
"100% 44720/44720 [00:07<00:00, 6016.25it/s]\n",
"Number of sentences in train te: 44720\n",
"Applying normalization and script conversion for dev te\n",
"100% 1000/1000 [00:00<00:00, 1673.03it/s]\n",
"100% 1000/1000 [00:00<00:00, 6102.16it/s]\n",
"Number of sentences in dev te: 1000\n",
"Applying normalization and script conversion for test te\n",
"100% 2390/2390 [00:00<00:00, 2960.42it/s]\n",
"100% 2390/2390 [00:00<00:00, 7440.37it/s]\n",
"Number of sentences in test te: 2390\n",
"\n",
"../dataset/data/train.SRC\n",
"../dataset/data/train.TGT\n",
" 0% 0/11 [00:00<?, ?it/s]src: en, tgt:as\n",
"src: en, tgt:bn\n",
"../dataset/norm/en-bn/train.en\n",
"../dataset/norm/en-bn/train.bn\n",
"src: en, tgt:gu\n",
"../dataset/norm/en-gu/train.en\n",
"../dataset/norm/en-gu/train.gu\n",
" 27% 3/11 [00:00<00:00, 28.98it/s]src: en, tgt:hi\n",
"../dataset/norm/en-hi/train.en\n",
"../dataset/norm/en-hi/train.hi\n",
" 36% 4/11 [00:00<00:01, 6.87it/s]src: en, tgt:kn\n",
"src: en, tgt:ml\n",
"../dataset/norm/en-ml/train.en\n",
"../dataset/norm/en-ml/train.ml\n",
"src: en, tgt:mr\n",
"../dataset/norm/en-mr/train.en\n",
"../dataset/norm/en-mr/train.mr\n",
" 64% 7/11 [00:00<00:00, 8.15it/s]src: en, tgt:or\n",
"../dataset/norm/en-or/train.en\n",
"../dataset/norm/en-or/train.or\n",
" 73% 8/11 [00:00<00:00, 8.13it/s]src: en, tgt:pa\n",
"../dataset/norm/en-pa/train.en\n",
"../dataset/norm/en-pa/train.pa\n",
" 82% 9/11 [00:01<00:00, 6.74it/s]src: en, tgt:ta\n",
"../dataset/norm/en-ta/train.en\n",
"../dataset/norm/en-ta/train.ta\n",
" 91% 10/11 [00:01<00:00, 5.53it/s]src: en, tgt:te\n",
"../dataset/norm/en-te/train.en\n",
"../dataset/norm/en-te/train.te\n",
"100% 11/11 [00:01<00:00, 7.52it/s]\n",
" 0% 0/11 [00:00<?, ?it/s]src: en, tgt:as\n",
"src: en, tgt:bn\n",
"../dataset/norm/en-bn/train.en\n",
"src: en, tgt:gu\n",
"../dataset/norm/en-gu/train.en\n",
"src: en, tgt:hi\n",
"../dataset/norm/en-hi/train.en\n",
" 36% 4/11 [00:00<00:00, 31.79it/s]src: en, tgt:kn\n",
"src: en, tgt:ml\n",
"../dataset/norm/en-ml/train.en\n",
"src: en, tgt:mr\n",
"../dataset/norm/en-mr/train.en\n",
"src: en, tgt:or\n",
"../dataset/norm/en-or/train.en\n",
"src: en, tgt:pa\n",
"../dataset/norm/en-pa/train.en\n",
" 82% 9/11 [00:00<00:00, 35.57it/s]src: en, tgt:ta\n",
"../dataset/norm/en-ta/train.en\n",
"src: en, tgt:te\n",
"../dataset/norm/en-te/train.en\n",
"100% 11/11 [00:00<00:00, 39.26it/s]\n",
"\n",
"../dataset/data/dev.SRC\n",
"../dataset/data/dev.TGT\n",
" 0% 0/11 [00:00<?, ?it/s]src: en, tgt:as\n",
"src: en, tgt:bn\n",
"../dataset/norm/en-bn/dev.en\n",
"../dataset/norm/en-bn/dev.bn\n",
"src: en, tgt:gu\n",
"../dataset/norm/en-gu/dev.en\n",
"../dataset/norm/en-gu/dev.gu\n",
"src: en, tgt:hi\n",
"../dataset/norm/en-hi/dev.en\n",
"../dataset/norm/en-hi/dev.hi\n",
"src: en, tgt:kn\n",
"src: en, tgt:ml\n",
"../dataset/norm/en-ml/dev.en\n",
"../dataset/norm/en-ml/dev.ml\n",
"src: en, tgt:mr\n",
"../dataset/norm/en-mr/dev.en\n",
"../dataset/norm/en-mr/dev.mr\n",
"src: en, tgt:or\n",
"../dataset/norm/en-or/dev.en\n",
"../dataset/norm/en-or/dev.or\n",
"src: en, tgt:pa\n",
"../dataset/norm/en-pa/dev.en\n",
"../dataset/norm/en-pa/dev.pa\n",
"src: en, tgt:ta\n",
"../dataset/norm/en-ta/dev.en\n",
"../dataset/norm/en-ta/dev.ta\n",
"src: en, tgt:te\n",
"../dataset/norm/en-te/dev.en\n",
"../dataset/norm/en-te/dev.te\n",
"100% 11/11 [00:00<00:00, 108.87it/s]\n",
" 0% 0/11 [00:00<?, ?it/s]src: en, tgt:as\n",
"src: en, tgt:bn\n",
"../dataset/norm/en-bn/dev.en\n",
"src: en, tgt:gu\n",
"../dataset/norm/en-gu/dev.en\n",
"src: en, tgt:hi\n",
"../dataset/norm/en-hi/dev.en\n",
"src: en, tgt:kn\n",
"src: en, tgt:ml\n",
"../dataset/norm/en-ml/dev.en\n",
"src: en, tgt:mr\n",
"../dataset/norm/en-mr/dev.en\n",
"src: en, tgt:or\n",
"../dataset/norm/en-or/dev.en\n",
"src: en, tgt:pa\n",
"../dataset/norm/en-pa/dev.en\n",
"src: en, tgt:ta\n",
"../dataset/norm/en-ta/dev.en\n",
"src: en, tgt:te\n",
"../dataset/norm/en-te/dev.en\n",
"100% 11/11 [00:00<00:00, 3176.85it/s]\n",
"\n",
"../dataset/data/test.SRC\n",
"../dataset/data/test.TGT\n",
" 0% 0/11 [00:00<?, ?it/s]src: en, tgt:as\n",
"src: en, tgt:bn\n",
"../dataset/norm/en-bn/test.en\n",
"../dataset/norm/en-bn/test.bn\n",
"src: en, tgt:gu\n",
"../dataset/norm/en-gu/test.en\n",
"../dataset/norm/en-gu/test.gu\n",
"src: en, tgt:hi\n",
"../dataset/norm/en-hi/test.en\n",
"../dataset/norm/en-hi/test.hi\n",
"src: en, tgt:kn\n",
"src: en, tgt:ml\n",
"../dataset/norm/en-ml/test.en\n",
"../dataset/norm/en-ml/test.ml\n",
"src: en, tgt:mr\n",
"../dataset/norm/en-mr/test.en\n",
"../dataset/norm/en-mr/test.mr\n",
"src: en, tgt:or\n",
"../dataset/norm/en-or/test.en\n",
"../dataset/norm/en-or/test.or\n",
"src: en, tgt:pa\n",
"../dataset/norm/en-pa/test.en\n",
"../dataset/norm/en-pa/test.pa\n",
"src: en, tgt:ta\n",
"../dataset/norm/en-ta/test.en\n",
"../dataset/norm/en-ta/test.ta\n",
"src: en, tgt:te\n",
"../dataset/norm/en-te/test.en\n",
"../dataset/norm/en-te/test.te\n",
"100% 11/11 [00:00<00:00, 105.59it/s]\n",
" 0% 0/11 [00:00<?, ?it/s]src: en, tgt:as\n",
"src: en, tgt:bn\n",
"../dataset/norm/en-bn/test.en\n",
"src: en, tgt:gu\n",
"../dataset/norm/en-gu/test.en\n",
"src: en, tgt:hi\n",
"../dataset/norm/en-hi/test.en\n",
"src: en, tgt:kn\n",
"src: en, tgt:ml\n",
"../dataset/norm/en-ml/test.en\n",
"src: en, tgt:mr\n",
"../dataset/norm/en-mr/test.en\n",
"src: en, tgt:or\n",
"../dataset/norm/en-or/test.en\n",
"src: en, tgt:pa\n",
"../dataset/norm/en-pa/test.en\n",
"src: en, tgt:ta\n",
"../dataset/norm/en-ta/test.en\n",
"src: en, tgt:te\n",
"../dataset/norm/en-te/test.en\n",
"100% 11/11 [00:00<00:00, 1584.11it/s]\n",
"Applying bpe to the new finetuning data\n",
"train\n",
"Apply to SRC corpus\n",
"subword-nmt/subword_nmt/apply_bpe.py:444: UserWarning: In parallel mode, the input cannot be STDIN. Using 1 processor instead.\n",
" warnings.warn(\"In parallel mode, the input cannot be STDIN. Using 1 processor instead.\")\n",
"Apply to TGT corpus\n",
"subword-nmt/subword_nmt/apply_bpe.py:444: UserWarning: In parallel mode, the input cannot be STDIN. Using 1 processor instead.\n",
" warnings.warn(\"In parallel mode, the input cannot be STDIN. Using 1 processor instead.\")\n",
"dev\n",
"Apply to SRC corpus\n",
"subword-nmt/subword_nmt/apply_bpe.py:444: UserWarning: In parallel mode, the input cannot be STDIN. Using 1 processor instead.\n",
" warnings.warn(\"In parallel mode, the input cannot be STDIN. Using 1 processor instead.\")\n",
"Apply to TGT corpus\n",
"subword-nmt/subword_nmt/apply_bpe.py:444: UserWarning: In parallel mode, the input cannot be STDIN. Using 1 processor instead.\n",
" warnings.warn(\"In parallel mode, the input cannot be STDIN. Using 1 processor instead.\")\n",
"test\n",
"Apply to SRC corpus\n",
"subword-nmt/subword_nmt/apply_bpe.py:444: UserWarning: In parallel mode, the input cannot be STDIN. Using 1 processor instead.\n",
" warnings.warn(\"In parallel mode, the input cannot be STDIN. Using 1 processor instead.\")\n",
"Apply to TGT corpus\n",
"subword-nmt/subword_nmt/apply_bpe.py:444: UserWarning: In parallel mode, the input cannot be STDIN. Using 1 processor instead.\n",
" warnings.warn(\"In parallel mode, the input cannot be STDIN. Using 1 processor instead.\")\n",
"Adding language tags\n",
"930375it [00:06, 134771.06it/s]\n",
"9000it [00:00, 170578.75it/s]\n",
"21510it [00:00, 171968.15it/s]\n",
"Binarizing data. This will take some time depending on the size of finetuning data\n",
"2021-05-09 14:01:33 | INFO | fairseq_cli.preprocess | Namespace(align_suffix=None, alignfile=None, all_gather_list_size=16384, azureml_logging=False, bf16=False, bpe=None, cpu=False, criterion='cross_entropy', dataset_impl='mmap', destdir='../dataset/final_bin', empty_cache_freq=0, fp16=False, fp16_init_scale=128, fp16_no_flatten_grads=False, fp16_scale_tolerance=0.0, fp16_scale_window=None, joined_dictionary=False, log_file=None, log_format=None, log_interval=100, lr_scheduler='fixed', memory_efficient_bf16=False, memory_efficient_fp16=False, min_loss_scale=0.0001, model_parallel_size=1, no_progress_bar=False, nwordssrc=-1, nwordstgt=-1, only_source=False, optimizer=None, padding_factor=8, plasma_path='/tmp/plasma', profile=False, quantization_config_path=None, reset_logging=False, scoring='bleu', seed=1, source_lang='SRC', srcdict='../en-indic/final_bin/dict.SRC.txt', suppress_crashes=False, target_lang='TGT', task='translation', tensorboard_logdir=None, testpref='../dataset/final/test', tgtdict='../en-indic/final_bin/dict.TGT.txt', threshold_loss_scale=None, thresholdsrc=5, thresholdtgt=5, tokenizer=None, tpu=False, trainpref='../dataset/final/train', use_plasma_view=False, user_dir=None, validpref='../dataset/final/dev', wandb_project=None, workers=2)\n",
"2021-05-09 14:01:33 | INFO | fairseq_cli.preprocess | [SRC] Dictionary: 32104 types\n",
"2021-05-09 14:03:48 | INFO | fairseq_cli.preprocess | [SRC] ../dataset/final/train.SRC: 930375 sents, 31481494 tokens, 0.0% replaced by <unk>\n",
"2021-05-09 14:03:48 | INFO | fairseq_cli.preprocess | [SRC] Dictionary: 32104 types\n",
"2021-05-09 14:03:49 | INFO | fairseq_cli.preprocess | [SRC] ../dataset/final/dev.SRC: 9000 sents, 200619 tokens, 0.117% replaced by <unk>\n",
"2021-05-09 14:03:49 | INFO | fairseq_cli.preprocess | [SRC] Dictionary: 32104 types\n",
"2021-05-09 14:03:51 | INFO | fairseq_cli.preprocess | [SRC] ../dataset/final/test.SRC: 21510 sents, 471564 tokens, 0.155% replaced by <unk>\n",
"2021-05-09 14:03:51 | INFO | fairseq_cli.preprocess | [TGT] Dictionary: 35848 types\n",
"2021-05-09 14:07:06 | INFO | fairseq_cli.preprocess | [TGT] ../dataset/final/train.TGT: 930375 sents, 35902065 tokens, 0.318% replaced by <unk>\n",
"2021-05-09 14:07:06 | INFO | fairseq_cli.preprocess | [TGT] Dictionary: 35848 types\n",
"2021-05-09 14:07:07 | INFO | fairseq_cli.preprocess | [TGT] ../dataset/final/dev.TGT: 9000 sents, 224623 tokens, 0.631% replaced by <unk>\n",
"2021-05-09 14:07:07 | INFO | fairseq_cli.preprocess | [TGT] Dictionary: 35848 types\n",
"2021-05-09 14:07:11 | INFO | fairseq_cli.preprocess | [TGT] ../dataset/final/test.TGT: 21510 sents, 526380 tokens, 0.57% replaced by <unk>\n",
"2021-05-09 14:07:11 | INFO | fairseq_cli.preprocess | Wrote preprocessed data to ../dataset/final_bin\n"
]
},
{
"data": {
"text/plain": []
},
"execution_count": 9,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"source": [
"# all the data preparation happens in this cell\n",
"%%shell\n",
"\n",
"exp_dir=../dataset\n",
"src_lang=en\n",
"tgt_lang=indic\n",
"\n",
"# change this to indic-en, if you have downloaded the indic-en dir or m2m if you have downloaded the indic2indic model\n",
"download_dir=../en-indic\n",
"\n",
"train_data_dir=$exp_dir/train\n",
"dev_data_dir=$exp_dir/dev\n",
"test_data_dir=$exp_dir/test\n",
"\n",
"\n",
"echo \"Running experiment ${exp_dir} on ${src_lang} to ${tgt_lang}\"\n",
"\n",
"\n",
"train_processed_dir=$exp_dir/data\n",
"devtest_processed_dir=$exp_dir/data\n",
"\n",
"out_data_dir=$exp_dir/final_bin\n",
"\n",
"mkdir -p $train_processed_dir\n",
"mkdir -p $devtest_processed_dir\n",
"mkdir -p $out_data_dir\n",
"\n",
"# indic languages.\n",
"# cvit-pib corpus does not have as (assamese) and kn (kannada), hence its not part of this list\n",
"langs=(bn hi gu ml mr or pa ta te)\n",
"\n",
"for lang in ${langs[@]};do\n",
"\tif [ $src_lang == en ]; then\n",
"\t\ttgt_lang=$lang\n",
"\telse\n",
"\t\tsrc_lang=$lang\n",
"\tfi\n",
"\n",
"\ttrain_norm_dir=$exp_dir/norm/$src_lang-$tgt_lang\n",
"\tdevtest_norm_dir=$exp_dir/norm/$src_lang-$tgt_lang\n",
"\tmkdir -p $train_norm_dir\n",
"\tmkdir -p $devtest_norm_dir\n",
"\n",
"\n",
" # preprocessing pretokenizes the input (we use moses tokenizer for en and indicnlp lib for indic languages)\n",
" # after pretokenization, we use indicnlp to transliterate all the indic data to devnagiri script\n",
"\n",
"\t# train preprocessing\n",
"\ttrain_infname_src=$train_data_dir/en-${lang}/train.$src_lang\n",
"\ttrain_infname_tgt=$train_data_dir/en-${lang}/train.$tgt_lang\n",
"\ttrain_outfname_src=$train_norm_dir/train.$src_lang\n",
"\ttrain_outfname_tgt=$train_norm_dir/train.$tgt_lang\n",
"\techo \"Applying normalization and script conversion for train $lang\"\n",
"\tinput_size=`python scripts/preprocess_translate.py $train_infname_src $train_outfname_src $src_lang true`\n",
"\tinput_size=`python scripts/preprocess_translate.py $train_infname_tgt $train_outfname_tgt $tgt_lang true`\n",
"\techo \"Number of sentences in train $lang: $input_size\"\n",
"\n",
"\t# dev preprocessing\n",
"\tdev_infname_src=$dev_data_dir/dev.$src_lang\n",
"\tdev_infname_tgt=$dev_data_dir/dev.$tgt_lang\n",
"\tdev_outfname_src=$devtest_norm_dir/dev.$src_lang\n",
"\tdev_outfname_tgt=$devtest_norm_dir/dev.$tgt_lang\n",
"\techo \"Applying normalization and script conversion for dev $lang\"\n",
"\tinput_size=`python scripts/preprocess_translate.py $dev_infname_src $dev_outfname_src $src_lang true`\n",
"\tinput_size=`python scripts/preprocess_translate.py $dev_infname_tgt $dev_outfname_tgt $tgt_lang true`\n",
"\techo \"Number of sentences in dev $lang: $input_size\"\n",
"\n",
"\t# test preprocessing\n",
"\ttest_infname_src=$test_data_dir/test.$src_lang\n",
"\ttest_infname_tgt=$test_data_dir/test.$tgt_lang\n",
"\ttest_outfname_src=$devtest_norm_dir/test.$src_lang\n",
"\ttest_outfname_tgt=$devtest_norm_dir/test.$tgt_lang\n",
"\techo \"Applying normalization and script conversion for test $lang\"\n",
"\tinput_size=`python scripts/preprocess_translate.py $test_infname_src $test_outfname_src $src_lang true`\n",
"\tinput_size=`python scripts/preprocess_translate.py $test_infname_tgt $test_outfname_tgt $tgt_lang true`\n",
"\techo \"Number of sentences in test $lang: $input_size\"\n",
"done\n",
"\n",
"\n",
"\n",
"\n",
"# Now that we have preprocessed all the data, we can now merge these different text files into one\n",
"# ie. for en-as, we have train.en and corresponding train.as, similarly for en-bn, we have train.en and corresponding train.bn\n",
"# now we will concatenate all this into en-X where train.SRC will have all the en (src) training data and train.TGT will have all the concatenated indic lang data\n",
"\n",
"python scripts/concat_joint_data.py $exp_dir/norm $exp_dir/data $src_lang $tgt_lang 'train'\n",
"python scripts/concat_joint_data.py $exp_dir/norm $exp_dir/data $src_lang $tgt_lang 'dev'\n",
"python scripts/concat_joint_data.py $exp_dir/norm $exp_dir/data $src_lang $tgt_lang 'test'\n",
"\n",
"# use the vocab from downloaded dir\n",
"cp -r $download_dir/vocab $exp_dir\n",
"\n",
"\n",
"echo \"Applying bpe to the new finetuning data\"\n",
"bash apply_single_bpe_traindevtest_notag.sh $exp_dir\n",
"\n",
"mkdir -p $exp_dir/final\n",
"\n",
"# We also add special tags to indicate the source and target language in the inputs\n",
"# Eg: to translate a sentence from english to hindi , the input would be __src__en__ __tgt__hi__ <en bpe tokens>\n",
"\n",
"echo \"Adding language tags\"\n",
"python scripts/add_joint_tags_translate.py $exp_dir 'train'\n",
"python scripts/add_joint_tags_translate.py $exp_dir 'dev'\n",
"python scripts/add_joint_tags_translate.py $exp_dir 'test'\n",
"\n",
"\n",
"\n",
"data_dir=$exp_dir/final\n",
"out_data_dir=$exp_dir/final_bin\n",
"\n",
"rm -rf $out_data_dir\n",
"\n",
"# binarizing the new data (train, dev and test) using dictionary from the download dir\n",
"\n",
" num_workers=`python -c \"import multiprocessing; print(multiprocessing.cpu_count())\"`\n",
"\n",
"data_dir=$exp_dir/final\n",
"out_data_dir=$exp_dir/final_bin\n",
"\n",
"# rm -rf $out_data_dir\n",
"\n",
"echo \"Binarizing data. This will take some time depending on the size of finetuning data\"\n",
"fairseq-preprocess --source-lang SRC --target-lang TGT \\\n",
" --trainpref $data_dir/train --validpref $data_dir/dev --testpref $data_dir/test \\\n",
" --destdir $out_data_dir --workers $num_workers \\\n",
" --srcdict $download_dir/final_bin/dict.SRC.txt --tgtdict $download_dir/final_bin/dict.TGT.txt --thresholdtgt 5 --thresholdsrc 5 "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "iz6tzbe2tcs7",
"outputId": "6705e2d6-b5cb-4810-c833-6a1370d3fce4"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2021-05-09 14:29:11 | INFO | fairseq_cli.train | {'_name': None, 'common': {'_name': None, 'no_progress_bar': False, 'log_interval': 100, 'log_format': None, 'log_file': None, 'tensorboard_logdir': '../dataset/tensorboard-wandb', 'wandb_project': None, 'azureml_logging': False, 'seed': 1, 'cpu': False, 'tpu': False, 'bf16': False, 'memory_efficient_bf16': False, 'fp16': True, 'memory_efficient_fp16': False, 'fp16_no_flatten_grads': False, 'fp16_init_scale': 128, 'fp16_scale_window': None, 'fp16_scale_tolerance': 0.0, 'min_loss_scale': 0.0001, 'threshold_loss_scale': None, 'user_dir': 'model_configs', 'empty_cache_freq': 0, 'all_gather_list_size': 16384, 'model_parallel_size': 1, 'quantization_config_path': None, 'profile': False, 'reset_logging': False, 'suppress_crashes': False, 'use_plasma_view': False, 'plasma_path': '/tmp/plasma'}, 'common_eval': {'_name': None, 'path': None, 'post_process': None, 'quiet': False, 'model_overrides': '{}', 'results_path': None}, 'distributed_training': {'_name': None, 'distributed_world_size': 1, 'distributed_rank': 0, 'distributed_backend': 'nccl', 'distributed_init_method': None, 'distributed_port': -1, 'device_id': 0, 'distributed_no_spawn': False, 'ddp_backend': 'pytorch_ddp', 'ddp_comm_hook': 'none', 'bucket_cap_mb': 25, 'fix_batches_to_gpus': False, 'find_unused_parameters': False, 'fast_stat_sync': False, 'heartbeat_timeout': -1, 'broadcast_buffers': False, 'slowmo_momentum': None, 'slowmo_algorithm': 'LocalSGD', 'localsgd_frequency': 3, 'nprocs_per_node': 1, 'pipeline_model_parallel': False, 'pipeline_balance': None, 'pipeline_devices': None, 'pipeline_chunks': 0, 'pipeline_encoder_balance': None, 'pipeline_encoder_devices': None, 'pipeline_decoder_balance': None, 'pipeline_decoder_devices': None, 'pipeline_checkpoint': 'never', 'zero_sharding': 'none', 'fp16': True, 'memory_efficient_fp16': False, 'tpu': False, 'no_reshard_after_forward': False, 'fp32_reduce_scatter': False, 'cpu_offload': False, 'distributed_num_procs': 1}, 'dataset': {'_name': None, 'num_workers': 1, 'skip_invalid_size_inputs_valid_test': True, 'max_tokens': 256, 'batch_size': None, 'required_batch_size_multiple': 8, 'required_seq_len_multiple': 1, 'dataset_impl': None, 'data_buffer_size': 10, 'train_subset': 'train', 'valid_subset': 'valid', 'validate_interval': 1, 'validate_interval_updates': 0, 'validate_after_updates': 0, 'fixed_validation_seed': None, 'disable_validation': False, 'max_tokens_valid': 256, 'batch_size_valid': None, 'max_valid_steps': None, 'curriculum': 0, 'gen_subset': 'test', 'num_shards': 1, 'shard_id': 0}, 'optimization': {'_name': None, 'max_epoch': 0, 'max_update': 1000, 'stop_time_hours': 0.0, 'clip_norm': 1.0, 'sentence_avg': False, 'update_freq': [2], 'lr': [3e-05], 'stop_min_lr': -1.0, 'use_bmuf': False}, 'checkpoint': {'_name': None, 'save_dir': '../dataset/model', 'restore_file': '../en-indic/model/checkpoint_best.pt', 'finetune_from_model': None, 'reset_dataloader': True, 'reset_lr_scheduler': True, 'reset_meters': True, 'reset_optimizer': True, 'optimizer_overrides': '{}', 'save_interval': 1, 'save_interval_updates': 0, 'keep_interval_updates': -1, 'keep_interval_updates_pattern': -1, 'keep_last_epochs': 5, 'keep_best_checkpoints': -1, 'no_save': False, 'no_epoch_checkpoints': False, 'no_last_checkpoints': False, 'no_save_optimizer_state': False, 'best_checkpoint_metric': 'loss', 'maximize_best_checkpoint_metric': False, 'patience': 5, 'checkpoint_suffix': '', 'checkpoint_shard_count': 1, 'load_checkpoint_on_all_dp_ranks': False, 'write_checkpoints_asynchronously': False, 'model_parallel_size': 1}, 'bmuf': {'_name': None, 'block_lr': 1.0, 'block_momentum': 0.875, 'global_sync_iter': 50, 'warmup_iterations': 500, 'use_nbm': False, 'average_sync': False, 'distributed_world_size': 1}, 'generation': {'_name': None, 'beam': 5, 'nbest': 1, 'max_len_a': 0.0, 'max_len_b': 200, 'min_len': 1, 'match_source_len': False, 'unnormalized': False, 'no_early_stop': False, 'no_beamable_mm': False, 'lenpen': 1.0, 'unkpen': 0.0, 'replace_unk': None, 'sacrebleu': False, 'score_reference': False, 'prefix_size': 0, 'no_repeat_ngram_size': 0, 'sampling': False, 'sampling_topk': -1, 'sampling_topp': -1.0, 'constraints': None, 'temperature': 1.0, 'diverse_beam_groups': -1, 'diverse_beam_strength': 0.5, 'diversity_rate': -1.0, 'print_alignment': None, 'print_step': False, 'lm_path': None, 'lm_weight': 0.0, 'iter_decode_eos_penalty': 0.0, 'iter_decode_max_iter': 10, 'iter_decode_force_max_iter': False, 'iter_decode_with_beam': 1, 'iter_decode_with_external_reranker': False, 'retain_iter_history': False, 'retain_dropout': False, 'retain_dropout_modules': None, 'decoding_format': None, 'no_seed_provided': False}, 'eval_lm': {'_name': None, 'output_word_probs': False, 'output_word_stats': False, 'context_window': 0, 'softmax_batch': 9223372036854775807}, 'interactive': {'_name': None, 'buffer_size': 0, 'input': '-'}, 'model': Namespace(_name='transformer_4x', activation_dropout=0.0, activation_fn='relu', adam_betas='(0.9, 0.98)', adam_eps=1e-08, adaptive_input=False, adaptive_softmax_cutoff=None, adaptive_softmax_dropout=0, all_gather_list_size=16384, arch='transformer_4x', attention_dropout=0.0, azureml_logging=False, batch_size=None, batch_size_valid=None, best_checkpoint_metric='loss', bf16=False, bpe=None, broadcast_buffers=False, bucket_cap_mb=25, checkpoint_activations=False, checkpoint_shard_count=1, checkpoint_suffix='', clip_norm=1.0, cpu=False, cpu_offload=False, criterion='label_smoothed_cross_entropy', cross_self_attention=False, curriculum=0, data='../dataset/final_bin', data_buffer_size=10, dataset_impl=None, ddp_backend='pytorch_ddp', ddp_comm_hook='none', decoder_attention_heads=16, decoder_embed_dim=1536, decoder_embed_path=None, decoder_ffn_embed_dim=4096, decoder_input_dim=1536, decoder_layerdrop=0, decoder_layers=6, decoder_layers_to_keep=None, decoder_learned_pos=False, decoder_normalize_before=False, decoder_output_dim=1536, device_id=0, disable_validation=False, distributed_backend='nccl', distributed_init_method=None, distributed_no_spawn=False, distributed_port=-1, distributed_rank=0, distributed_world_size=1, dropout=0.2, empty_cache_freq=0, encoder_attention_heads=16, encoder_embed_dim=1536, encoder_embed_path=None, encoder_ffn_embed_dim=4096, encoder_layerdrop=0, encoder_layers=6, encoder_layers_to_keep=None, encoder_learned_pos=False, encoder_normalize_before=False, eos=2, eval_bleu=False, eval_bleu_args='{}', eval_bleu_detok='space', eval_bleu_detok_args='{}', eval_bleu_print_samples=False, eval_bleu_remove_bpe=None, eval_tokenized_bleu=False, fast_stat_sync=False, find_unused_parameters=False, finetune_from_model=None, fix_batches_to_gpus=False, fixed_validation_seed=None, fp16=True, fp16_init_scale=128, fp16_no_flatten_grads=False, fp16_scale_tolerance=0.0, fp16_scale_window=None, fp32_reduce_scatter=False, gen_subset='test', heartbeat_timeout=-1, ignore_prefix_size=0, keep_best_checkpoints=-1, keep_interval_updates=-1, keep_interval_updates_pattern=-1, keep_last_epochs=5, label_smoothing=0.1, layernorm_embedding=False, left_pad_source=True, left_pad_target=False, load_alignments=False, load_checkpoint_on_all_dp_ranks=False, localsgd_frequency=3, log_file=None, log_format=None, log_interval=100, lr=[3e-05], lr_scheduler='inverse_sqrt', max_epoch=0, max_source_positions=210, max_target_positions=210, max_tokens=256, max_tokens_valid=256, max_update=1000, max_valid_steps=None, maximize_best_checkpoint_metric=False, memory_efficient_bf16=False, memory_efficient_fp16=False, min_loss_scale=0.0001, min_params_to_wrap=100000000, model_parallel_size=1, no_cross_attention=False, no_epoch_checkpoints=False, no_last_checkpoints=False, no_progress_bar=False, no_reshard_after_forward=False, no_save=False, no_save_optimizer_state=False, no_scale_embedding=False, no_seed_provided=False, no_token_positional_embeddings=False, nprocs_per_node=1, num_batch_buckets=0, num_shards=1, num_workers=1, offload_activations=False, optimizer='adam', optimizer_overrides='{}', pad=1, patience=5, pipeline_balance=None, pipeline_checkpoint='never', pipeline_chunks=0, pipeline_decoder_balance=None, pipeline_decoder_devices=None, pipeline_devices=None, pipeline_encoder_balance=None, pipeline_encoder_devices=None, pipeline_model_parallel=False, plasma_path='/tmp/plasma', profile=False, quant_noise_pq=0, quant_noise_pq_block_size=8, quant_noise_scalar=0, quantization_config_path=None, report_accuracy=False, required_batch_size_multiple=8, required_seq_len_multiple=1, reset_dataloader=True, reset_logging=False, reset_lr_scheduler=True, reset_meters=True, reset_optimizer=True, restore_file='../en-indic/model/checkpoint_best.pt', save_dir='../dataset/model', save_interval=1, save_interval_updates=0, scoring='bleu', seed=1, sentence_avg=False, shard_id=0, share_all_embeddings=False, share_decoder_input_output_embed=False, skip_invalid_size_inputs_valid_test=True, slowmo_algorithm='LocalSGD', slowmo_momentum=None, source_lang='SRC', stop_min_lr=-1.0, stop_time_hours=0, suppress_crashes=False, target_lang='TGT', task='translation', tensorboard_logdir='../dataset/tensorboard-wandb', threshold_loss_scale=None, tie_adaptive_weights=False, tokenizer=None, tpu=False, train_subset='train', truncate_source=False, unk=3, update_freq=[2], upsample_primary=-1, use_bmuf=False, use_old_adam=False, use_plasma_view=False, user_dir='model_configs', valid_subset='valid', validate_after_updates=0, validate_interval=1, validate_interval_updates=0, wandb_project=None, warmup_init_lr=1e-07, warmup_updates=4000, weight_decay=0.0, write_checkpoints_asynchronously=False, zero_sharding='none'), 'task': {'_name': 'translation', 'data': '../dataset/final_bin', 'source_lang': 'SRC', 'target_lang': 'TGT', 'load_alignments': False, 'left_pad_source': True, 'left_pad_target': False, 'max_source_positions': 210, 'max_target_positions': 210, 'upsample_primary': -1, 'truncate_source': False, 'num_batch_buckets': 0, 'train_subset': 'train', 'dataset_impl': None, 'required_seq_len_multiple': 1, 'eval_bleu': False, 'eval_bleu_args': '{}', 'eval_bleu_detok': 'space', 'eval_bleu_detok_args': '{}', 'eval_tokenized_bleu': False, 'eval_bleu_remove_bpe': None, 'eval_bleu_print_samples': False}, 'criterion': {'_name': 'label_smoothed_cross_entropy', 'label_smoothing': 0.1, 'report_accuracy': False, 'ignore_prefix_size': 0, 'sentence_avg': False}, 'optimizer': {'_name': 'adam', 'adam_betas': '(0.9, 0.98)', 'adam_eps': 1e-08, 'weight_decay': 0.0, 'use_old_adam': False, 'tpu': False, 'lr': [3e-05]}, 'lr_scheduler': {'_name': 'inverse_sqrt', 'warmup_updates': 4000, 'warmup_init_lr': 1e-07, 'lr': [3e-05]}, 'scoring': {'_name': 'bleu', 'pad': 1, 'eos': 2, 'unk': 3}, 'bpe': None, 'tokenizer': None}\n",
"2021-05-09 14:29:11 | INFO | fairseq.tasks.translation | [SRC] dictionary: 32104 types\n",
"2021-05-09 14:29:11 | INFO | fairseq.tasks.translation | [TGT] dictionary: 35848 types\n",
"2021-05-09 14:29:19 | INFO | fairseq_cli.train | TransformerModel(\n",
" (encoder): TransformerEncoder(\n",
" (dropout_module): FairseqDropout()\n",
" (embed_tokens): Embedding(32104, 1536, padding_idx=1)\n",
" (embed_positions): SinusoidalPositionalEmbedding()\n",
" (layers): ModuleList(\n",
" (0): TransformerEncoderLayer(\n",
" (self_attn): MultiheadAttention(\n",
" (dropout_module): FairseqDropout()\n",
" (k_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (v_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (q_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (out_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" )\n",
" (self_attn_layer_norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
" (dropout_module): FairseqDropout()\n",
" (activation_dropout_module): FairseqDropout()\n",
" (fc1): Linear(in_features=1536, out_features=4096, bias=True)\n",
" (fc2): Linear(in_features=4096, out_features=1536, bias=True)\n",
" (final_layer_norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
" )\n",
" (1): TransformerEncoderLayer(\n",
" (self_attn): MultiheadAttention(\n",
" (dropout_module): FairseqDropout()\n",
" (k_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (v_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (q_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (out_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" )\n",
" (self_attn_layer_norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
" (dropout_module): FairseqDropout()\n",
" (activation_dropout_module): FairseqDropout()\n",
" (fc1): Linear(in_features=1536, out_features=4096, bias=True)\n",
" (fc2): Linear(in_features=4096, out_features=1536, bias=True)\n",
" (final_layer_norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
" )\n",
" (2): TransformerEncoderLayer(\n",
" (self_attn): MultiheadAttention(\n",
" (dropout_module): FairseqDropout()\n",
" (k_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (v_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (q_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (out_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" )\n",
" (self_attn_layer_norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
" (dropout_module): FairseqDropout()\n",
" (activation_dropout_module): FairseqDropout()\n",
" (fc1): Linear(in_features=1536, out_features=4096, bias=True)\n",
" (fc2): Linear(in_features=4096, out_features=1536, bias=True)\n",
" (final_layer_norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
" )\n",
" (3): TransformerEncoderLayer(\n",
" (self_attn): MultiheadAttention(\n",
" (dropout_module): FairseqDropout()\n",
" (k_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (v_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (q_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (out_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" )\n",
" (self_attn_layer_norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
" (dropout_module): FairseqDropout()\n",
" (activation_dropout_module): FairseqDropout()\n",
" (fc1): Linear(in_features=1536, out_features=4096, bias=True)\n",
" (fc2): Linear(in_features=4096, out_features=1536, bias=True)\n",
" (final_layer_norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
" )\n",
" (4): TransformerEncoderLayer(\n",
" (self_attn): MultiheadAttention(\n",
" (dropout_module): FairseqDropout()\n",
" (k_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (v_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (q_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (out_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" )\n",
" (self_attn_layer_norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
" (dropout_module): FairseqDropout()\n",
" (activation_dropout_module): FairseqDropout()\n",
" (fc1): Linear(in_features=1536, out_features=4096, bias=True)\n",
" (fc2): Linear(in_features=4096, out_features=1536, bias=True)\n",
" (final_layer_norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
" )\n",
" (5): TransformerEncoderLayer(\n",
" (self_attn): MultiheadAttention(\n",
" (dropout_module): FairseqDropout()\n",
" (k_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (v_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (q_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (out_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" )\n",
" (self_attn_layer_norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
" (dropout_module): FairseqDropout()\n",
" (activation_dropout_module): FairseqDropout()\n",
" (fc1): Linear(in_features=1536, out_features=4096, bias=True)\n",
" (fc2): Linear(in_features=4096, out_features=1536, bias=True)\n",
" (final_layer_norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
" )\n",
" )\n",
" )\n",
" (decoder): TransformerDecoder(\n",
" (dropout_module): FairseqDropout()\n",
" (embed_tokens): Embedding(35848, 1536, padding_idx=1)\n",
" (embed_positions): SinusoidalPositionalEmbedding()\n",
" (layers): ModuleList(\n",
" (0): TransformerDecoderLayer(\n",
" (dropout_module): FairseqDropout()\n",
" (self_attn): MultiheadAttention(\n",
" (dropout_module): FairseqDropout()\n",
" (k_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (v_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (q_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (out_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" )\n",
" (activation_dropout_module): FairseqDropout()\n",
" (self_attn_layer_norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
" (encoder_attn): MultiheadAttention(\n",
" (dropout_module): FairseqDropout()\n",
" (k_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (v_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (q_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (out_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" )\n",
" (encoder_attn_layer_norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
" (fc1): Linear(in_features=1536, out_features=4096, bias=True)\n",
" (fc2): Linear(in_features=4096, out_features=1536, bias=True)\n",
" (final_layer_norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
" )\n",
" (1): TransformerDecoderLayer(\n",
" (dropout_module): FairseqDropout()\n",
" (self_attn): MultiheadAttention(\n",
" (dropout_module): FairseqDropout()\n",
" (k_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (v_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (q_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (out_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" )\n",
" (activation_dropout_module): FairseqDropout()\n",
" (self_attn_layer_norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
" (encoder_attn): MultiheadAttention(\n",
" (dropout_module): FairseqDropout()\n",
" (k_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (v_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (q_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (out_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" )\n",
" (encoder_attn_layer_norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
" (fc1): Linear(in_features=1536, out_features=4096, bias=True)\n",
" (fc2): Linear(in_features=4096, out_features=1536, bias=True)\n",
" (final_layer_norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
" )\n",
" (2): TransformerDecoderLayer(\n",
" (dropout_module): FairseqDropout()\n",
" (self_attn): MultiheadAttention(\n",
" (dropout_module): FairseqDropout()\n",
" (k_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (v_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (q_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (out_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" )\n",
" (activation_dropout_module): FairseqDropout()\n",
" (self_attn_layer_norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
" (encoder_attn): MultiheadAttention(\n",
" (dropout_module): FairseqDropout()\n",
" (k_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (v_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (q_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (out_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" )\n",
" (encoder_attn_layer_norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
" (fc1): Linear(in_features=1536, out_features=4096, bias=True)\n",
" (fc2): Linear(in_features=4096, out_features=1536, bias=True)\n",
" (final_layer_norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
" )\n",
" (3): TransformerDecoderLayer(\n",
" (dropout_module): FairseqDropout()\n",
" (self_attn): MultiheadAttention(\n",
" (dropout_module): FairseqDropout()\n",
" (k_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (v_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (q_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (out_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" )\n",
" (activation_dropout_module): FairseqDropout()\n",
" (self_attn_layer_norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
" (encoder_attn): MultiheadAttention(\n",
" (dropout_module): FairseqDropout()\n",
" (k_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (v_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (q_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (out_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" )\n",
" (encoder_attn_layer_norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
" (fc1): Linear(in_features=1536, out_features=4096, bias=True)\n",
" (fc2): Linear(in_features=4096, out_features=1536, bias=True)\n",
" (final_layer_norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
" )\n",
" (4): TransformerDecoderLayer(\n",
" (dropout_module): FairseqDropout()\n",
" (self_attn): MultiheadAttention(\n",
" (dropout_module): FairseqDropout()\n",
" (k_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (v_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (q_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (out_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" )\n",
" (activation_dropout_module): FairseqDropout()\n",
" (self_attn_layer_norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
" (encoder_attn): MultiheadAttention(\n",
" (dropout_module): FairseqDropout()\n",
" (k_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (v_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (q_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (out_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" )\n",
" (encoder_attn_layer_norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
" (fc1): Linear(in_features=1536, out_features=4096, bias=True)\n",
" (fc2): Linear(in_features=4096, out_features=1536, bias=True)\n",
" (final_layer_norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
" )\n",
" (5): TransformerDecoderLayer(\n",
" (dropout_module): FairseqDropout()\n",
" (self_attn): MultiheadAttention(\n",
" (dropout_module): FairseqDropout()\n",
" (k_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (v_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (q_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (out_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" )\n",
" (activation_dropout_module): FairseqDropout()\n",
" (self_attn_layer_norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
" (encoder_attn): MultiheadAttention(\n",
" (dropout_module): FairseqDropout()\n",
" (k_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (v_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (q_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" (out_proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
" )\n",
" (encoder_attn_layer_norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
" (fc1): Linear(in_features=1536, out_features=4096, bias=True)\n",
" (fc2): Linear(in_features=4096, out_features=1536, bias=True)\n",
" (final_layer_norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
" )\n",
" )\n",
" (output_projection): Linear(in_features=1536, out_features=35848, bias=False)\n",
" )\n",
")\n",
"2021-05-09 14:29:19 | INFO | fairseq_cli.train | task: TranslationTask\n",
"2021-05-09 14:29:19 | INFO | fairseq_cli.train | model: TransformerModel\n",
"2021-05-09 14:29:19 | INFO | fairseq_cli.train | criterion: LabelSmoothedCrossEntropyCriterion\n",
"2021-05-09 14:29:19 | INFO | fairseq_cli.train | num. shared model params: 480,571,392 (num. trained: 480,571,392)\n",
"2021-05-09 14:29:19 | INFO | fairseq_cli.train | num. expert model params: 0 (num. trained: 0)\n",
"2021-05-09 14:29:19 | INFO | fairseq.data.data_utils | loaded 9,000 examples from: ../dataset/final_bin/valid.SRC-TGT.SRC\n",
"2021-05-09 14:29:19 | INFO | fairseq.data.data_utils | loaded 9,000 examples from: ../dataset/final_bin/valid.SRC-TGT.TGT\n",
"2021-05-09 14:29:19 | INFO | fairseq.tasks.translation | ../dataset/final_bin valid SRC-TGT 9000 examples\n",
"2021-05-09 14:29:21 | INFO | fairseq.utils | ***********************CUDA enviroments for all 1 workers***********************\n",
"2021-05-09 14:29:21 | INFO | fairseq.utils | rank 0: capabilities = 3.7 ; total memory = 11.173 GB ; name = Tesla K80 \n",
"2021-05-09 14:29:21 | INFO | fairseq.utils | ***********************CUDA enviroments for all 1 workers***********************\n",
"2021-05-09 14:29:21 | INFO | fairseq_cli.train | training on 1 devices (GPUs/TPUs)\n",
"2021-05-09 14:29:21 | INFO | fairseq_cli.train | max tokens per device = 256 and max sentences per device = None\n",
"2021-05-09 14:29:21 | INFO | fairseq.trainer | Preparing to load checkpoint ../en-indic/model/checkpoint_best.pt\n",
"tcmalloc: large alloc 1922285568 bytes == 0x55e01c93a000 @ 0x7f8579074b6b 0x7f8579094379 0x7f851797e25e 0x7f851797f9d2 0x7f85559a8e7d 0x7f85665a3120 0x7f85661e1bd9 0x55df57c868a8 0x55df57cf9fd5 0x55df57cf47ad 0x55df57c873ea 0x55df57cf53b5 0x55df57cf47ad 0x55df57c87003 0x55df57c86b09 0x55df57dce28d 0x55df57d3d1db 0x55df57c85bb1 0x55df57d76fed 0x55df57cf9988 0x55df57cf47ad 0x55df57bc6e2c 0x55df57cf6bb5 0x55df57cf44ae 0x55df57c873ea 0x55df57cf632a 0x55df57cf44ae 0x55df57c873ea 0x55df57cf632a 0x55df57cf44ae 0x55df57c873ea\n",
"tcmalloc: large alloc 1922285568 bytes == 0x55e08f276000 @ 0x7f8579074b6b 0x7f8579094379 0x7f851797e25e 0x7f851797f9d2 0x7f85559a8e7d 0x7f85665a3120 0x7f85661e1bd9 0x55df57c868a8 0x55df57cf9fd5 0x55df57cf47ad 0x55df57c873ea 0x55df57cf53b5 0x55df57cf47ad 0x55df57c87003 0x55df57c86b09 0x55df57dce28d 0x55df57d3d1db 0x55df57c85bb1 0x55df57d76fed 0x55df57cf9988 0x55df57cf47ad 0x55df57bc6e2c 0x55df57cf6bb5 0x55df57cf44ae 0x55df57c873ea 0x55df57cf632a 0x55df57cf44ae 0x55df57c873ea 0x55df57cf632a 0x55df57cf44ae 0x55df57c873ea\n",
"2021-05-09 14:32:01 | INFO | fairseq.trainer | NOTE: your device does NOT support faster training with --fp16, please switch to FP32 which is likely to be faster\n",
"2021-05-09 14:32:01 | INFO | fairseq.trainer | Loaded checkpoint ../en-indic/model/checkpoint_best.pt (epoch 20 @ 0 updates)\n",
"2021-05-09 14:32:01 | INFO | fairseq.trainer | loading train data for epoch 1\n",
"2021-05-09 14:32:01 | INFO | fairseq.data.data_utils | loaded 930,375 examples from: ../dataset/final_bin/train.SRC-TGT.SRC\n",
"2021-05-09 14:32:01 | INFO | fairseq.data.data_utils | loaded 930,375 examples from: ../dataset/final_bin/train.SRC-TGT.TGT\n",
"2021-05-09 14:32:01 | INFO | fairseq.tasks.translation | ../dataset/final_bin train SRC-TGT 930375 examples\n",
"2021-05-09 14:32:01 | WARNING | fairseq.tasks.fairseq_task | 1,647 samples have invalid sizes and will be skipped, max_positions=(210, 210), first few sample ids=[865604, 927195, 465934, 204968, 865293, 859052, 1713, 672173, 858328, 286278]\n",
"epoch 001: 0% 0/86283 [00:00<?, ?it/s]2021-05-09 14:32:02 | INFO | fairseq.trainer | begin training epoch 1\n",
"2021-05-09 14:32:02 | INFO | fairseq_cli.train | Start iterating over samples\n",
"2021-05-09 14:32:04 | WARNING | fairseq.trainer | OOM: Ran out of memory with exception: CUDA out of memory. Tried to allocate 1.79 GiB (GPU 0; 11.17 GiB total capacity; 8.96 GiB already allocated; 1.66 GiB free; 9.08 GiB reserved in total by PyTorch)\n",
"2021-05-09 14:32:04 | WARNING | fairseq.trainer | |===========================================================================|\n",
"| PyTorch CUDA memory summary, device ID 0 |\n",
"|---------------------------------------------------------------------------|\n",
"| CUDA OOMs: 1 | cudaMalloc retries: 1 |\n",
"|===========================================================================|\n",
"| Metric | Cur Usage | Peak Usage | Tot Alloc | Tot Freed |\n",
"|---------------------------------------------------------------------------|\n",
"| Allocated memory | 9176 MB | 9176 MB | 11221 MB | 2044 MB |\n",
"| from large pool | 9174 MB | 9174 MB | 10487 MB | 1312 MB |\n",
"| from small pool | 2 MB | 122 MB | 734 MB | 732 MB |\n",
"|---------------------------------------------------------------------------|\n",
"| Active memory | 9176 MB | 9176 MB | 11221 MB | 2044 MB |\n",
"| from large pool | 9174 MB | 9174 MB | 10487 MB | 1312 MB |\n",
"| from small pool | 2 MB | 122 MB | 734 MB | 732 MB |\n",
"|---------------------------------------------------------------------------|\n",
"| GPU reserved memory | 9298 MB | 9298 MB | 9666 MB | 376832 KB |\n",
"| from large pool | 9258 MB | 9258 MB | 9484 MB | 231424 KB |\n",
"| from small pool | 40 MB | 136 MB | 182 MB | 145408 KB |\n",
"|---------------------------------------------------------------------------|\n",
"| Non-releasable memory | 124264 KB | 136495 KB | 2155 MB | 2034 MB |\n",
"| from large pool | 85648 KB | 97880 KB | 1308 MB | 1225 MB |\n",
"| from small pool | 38616 KB | 38616 KB | 846 MB | 809 MB |\n",
"|---------------------------------------------------------------------------|\n",
"| Allocations | 507 | 811 | 2952 | 2445 |\n",
"| from large pool | 202 | 228 | 407 | 205 |\n",
"| from small pool | 305 | 587 | 2545 | 2240 |\n",
"|---------------------------------------------------------------------------|\n",
"| Active allocs | 507 | 811 | 2952 | 2445 |\n",
"| from large pool | 202 | 228 | 407 | 205 |\n",
"| from small pool | 305 | 587 | 2545 | 2240 |\n",
"|---------------------------------------------------------------------------|\n",
"| GPU reserved segments | 113 | 164 | 189 | 76 |\n",
"| from large pool | 93 | 96 | 98 | 5 |\n",
"| from small pool | 20 | 68 | 91 | 71 |\n",
"|---------------------------------------------------------------------------|\n",
"| Non-releasable allocs | 77 | 96 | 1365 | 1288 |\n",
"| from large pool | 39 | 40 | 167 | 128 |\n",
"| from small pool | 38 | 78 | 1198 | 1160 |\n",
"|===========================================================================|\n",
"\n",
"2021-05-09 14:32:04 | ERROR | fairseq.trainer | OOM during optimization, irrecoverable\n",
"Traceback (most recent call last):\n",
" File \"/usr/local/bin/fairseq-train\", line 33, in <module>\n",
" sys.exit(load_entry_point('fairseq', 'console_scripts', 'fairseq-train')())\n",
" File \"/content/finetuning/fairseq/fairseq_cli/train.py\", line 496, in cli_main\n",
" distributed_utils.call_main(cfg, main)\n",
" File \"/content/finetuning/fairseq/fairseq/distributed/utils.py\", line 369, in call_main\n",
" main(cfg, **kwargs)\n",
" File \"/content/finetuning/fairseq/fairseq_cli/train.py\", line 173, in main\n",
" valid_losses, should_stop = train(cfg, trainer, task, epoch_itr)\n",
" File \"/usr/lib/python3.7/contextlib.py\", line 74, in inner\n",
" return func(*args, **kwds)\n",
" File \"/content/finetuning/fairseq/fairseq_cli/train.py\", line 284, in train\n",
" log_output = trainer.train_step(samples)\n",
" File \"/usr/lib/python3.7/contextlib.py\", line 74, in inner\n",
" return func(*args, **kwds)\n",
" File \"/content/finetuning/fairseq/fairseq/trainer.py\", line 810, in train_step\n",
" raise e\n",
" File \"/content/finetuning/fairseq/fairseq/trainer.py\", line 782, in train_step\n",
" self.optimizer, model=self.model, update_num=self.get_num_updates()\n",
" File \"/content/finetuning/fairseq/fairseq/tasks/fairseq_task.py\", line 489, in optimizer_step\n",
" optimizer.step()\n",
" File \"/content/finetuning/fairseq/fairseq/optim/fp16_optimizer.py\", line 213, in step\n",
" self.fp32_optimizer.step(closure, groups=groups)\n",
" File \"/content/finetuning/fairseq/fairseq/optim/fairseq_optimizer.py\", line 127, in step\n",
" self.optimizer.step(closure)\n",
" File \"/usr/local/lib/python3.7/dist-packages/torch/optim/optimizer.py\", line 89, in wrapper\n",
" return func(*args, **kwargs)\n",
" File \"/content/finetuning/fairseq/fairseq/optim/adam.py\", line 210, in step\n",
" denom = exp_avg_sq.sqrt().add_(group[\"eps\"])\n",
"RuntimeError: CUDA out of memory. Tried to allocate 1.79 GiB (GPU 0; 11.17 GiB total capacity; 8.96 GiB already allocated; 1.66 GiB free; 9.08 GiB reserved in total by PyTorch)\n"
]
}
],
"source": [
"# Finetuning the model\n",
"\n",
"# pls refer to fairseq documentaion to know more about each of these options (https://fairseq.readthedocs.io/en/latest/command_line_tools.html)\n",
"\n",
"\n",
"# some notable args:\n",
"# --max-update=1000 -> for this example, to demonstrate how to finetune we are only training for 1000 steps. You should increase this when finetuning\n",
"# --arch=transformer_4x -> we use a custom transformer model and name it transformer_4x (4 times the parameter size of transformer base)\n",
"# --user_dir -> we define the custom transformer arch in model_configs folder and pass it as an argument to user_dir for fairseq to register this architechture\n",
"# --lr -> learning rate. From our limited experiments, we find that lower learning rates like 3e-5 works best for finetuning.\n",
"# --restore-file -> reload the pretrained checkpoint and start training from here (change this path for indic-en. Currently its is set to en-indic)\n",
"# --reset-* -> reset and not use lr scheduler, dataloader, optimizer etc of the older checkpoint\n",
"# --max_tokns -> this is max tokens per batch\n",
"\n",
"\n",
"!( fairseq-train ../dataset/final_bin \\\n",
"--max-source-positions=210 \\\n",
"--max-target-positions=210 \\\n",
"--max-update=1000 \\\n",
"--save-interval=1 \\\n",
"--arch=transformer_4x \\\n",
"--criterion=label_smoothed_cross_entropy \\\n",
"--source-lang=SRC \\\n",
"--lr-scheduler=inverse_sqrt \\\n",
"--target-lang=TGT \\\n",
"--label-smoothing=0.1 \\\n",
"--optimizer adam \\\n",
"--adam-betas \"(0.9, 0.98)\" \\\n",
"--clip-norm 1.0 \\\n",
"--warmup-init-lr 1e-07 \\\n",
"--warmup-updates 4000 \\\n",
"--dropout 0.2 \\\n",
"--tensorboard-logdir ../dataset/tensorboard-wandb \\\n",
"--save-dir ../dataset/model \\\n",
"--keep-last-epochs 5 \\\n",
"--patience 5 \\\n",
"--skip-invalid-size-inputs-valid-test \\\n",
"--fp16 \\\n",
"--user-dir model_configs \\\n",
"--update-freq=2 \\\n",
"--distributed-world-size 1 \\\n",
"--max-tokens 256 \\\n",
"--lr 3e-5 \\\n",
"--restore-file ../en-indic/model/checkpoint_best.pt \\\n",
"--reset-lr-scheduler \\\n",
"--reset-meters \\\n",
"--reset-dataloader \\\n",
"--reset-optimizer)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "tpPsT1e7vuO9"
},
"outputs": [],
"source": [
"# To test the models after training, you can use joint_translate.sh\n",
"\n",
"\n",
"\n",
"# joint_translate takes src_file, output_fname, src_lang, tgt_lang, model_folder as inputs\n",
"# src_file -> input text file to be translated\n",
"# output_fname -> name of the output file (will get created) containing the model predictions\n",
"# src_lang -> source lang code of the input text ( in this case we are using en-indic model and hence src_lang would be 'en')\n",
"# tgt_lang -> target lang code of the input text ( tgt lang for en-indic model would be any of the 11 indic langs we trained on:\n",
"# as, bn, hi, gu, kn, ml, mr, or, pa, ta, te)\n",
"# supported languages are:\n",
"# as - assamese, bn - bengali, gu - gujarathi, hi - hindi, kn - kannada, \n",
"# ml - malayalam, mr - marathi, or - oriya, pa - punjabi, ta - tamil, te - telugu\n",
"\n",
"# model_dir -> the directory containing the model and the vocab files\n",
"\n",
"# Note: if the translation is taking a lot of time, please tune the buffer_size and batch_size parameter for fairseq-interactive defined inside this joint_translate script\n",
"\n",
"\n",
"# here we are translating the english sentences to hindi\n",
"!bash joint_translate.sh $exp_dir/test/test.en en_hi_outputs.txt 'en' 'hi' $exp_dir"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "bPqneByPxilN"
},
"outputs": [],
"source": [
"# to compute bleu scores for the predicitions with a reference file, use the following command\n",
"# arguments:\n",
"# pred_fname: file that contains model predictions\n",
"# ref_fname: file that contains references\n",
"# src_lang and tgt_lang : the source and target language\n",
"\n",
"bash compute_bleu.sh en_hi_outputs.txt $exp_dir/test/test.hi 'en' 'hi'\n"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [],
"include_colab_link": true,
"name": "indicTrans_Finetuning.ipynb",
"provenance": []
},
"interpreter": {
"hash": "3c7d4130300118f0c7487d576c6841c0dbbdeec039e1e658ac9b107412a09af0"
},
"kernelspec": {
"display_name": "Python 3.7.7 64-bit",
"name": "python3"
},
"language_info": {
"name": "python",
"version": ""
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|