File size: 78,493 Bytes
302920f |
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 |
{
"cells": [
{
"cell_type": "markdown",
"source": [
"# CPT Training and Inference\n",
"This notebook demonstrates the training and evaluation process of Context-Aware Prompt Tuning (CPT) using the Hugging Face Trainer. For more details, refer to the [Paper](https://huggingface.co/papers/2410.17222).\n",
"\n",
"\n",
"## Sections Overview:\n",
"1. **Setup**: Import libraries and configure the environment.\n",
"2. **Data Preparation**: Load and preprocess the dataset.\n",
"3. **Model Training**: Configure and train the model.\n",
"4. **Evaluation**: Test the model's performance and visualize results."
],
"metadata": {
"id": "R_byvXT9lpTU"
},
"id": "R_byvXT9lpTU"
},
{
"cell_type": "markdown",
"source": [
"# Setup\n",
"\n",
"---\n",
"\n",
"\n"
],
"metadata": {
"collapsed": false,
"id": "11b07b07ac5e472b"
},
"id": "11b07b07ac5e472b"
},
{
"cell_type": "markdown",
"source": [
"## Installation"
],
"metadata": {
"id": "O8DWZb8ZrGRU"
},
"id": "O8DWZb8ZrGRU"
},
{
"cell_type": "code",
"source": [
"!pip install datasets\n",
"!pip install git+https://github.com/huggingface/peft"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "d6KZ5REDrFiM",
"outputId": "e505bc0e-082a-4720-9117-b730d9fd67fa"
},
"id": "d6KZ5REDrFiM",
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (3.1.0)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.16.1)\n",
"Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.26.4)\n",
"Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (17.0.0)\n",
"Requirement already satisfied: dill<0.3.9,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.3.8)\n",
"Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.2.2)\n",
"Requirement already satisfied: requests>=2.32.2 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.32.3)\n",
"Requirement already satisfied: tqdm>=4.66.3 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.66.6)\n",
"Requirement already satisfied: xxhash in /usr/local/lib/python3.10/dist-packages (from datasets) (3.5.0)\n",
"Requirement already satisfied: multiprocess<0.70.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.70.16)\n",
"Requirement already satisfied: fsspec<=2024.9.0,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from fsspec[http]<=2024.9.0,>=2023.1.0->datasets) (2024.9.0)\n",
"Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.11.2)\n",
"Requirement already satisfied: huggingface-hub>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.26.2)\n",
"Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (24.2)\n",
"Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (6.0.2)\n",
"Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (2.4.3)\n",
"Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n",
"Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (24.2.0)\n",
"Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.5.0)\n",
"Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.1.0)\n",
"Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (0.2.0)\n",
"Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.17.2)\n",
"Requirement already satisfied: async-timeout<6.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n",
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.23.0->datasets) (4.12.2)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (3.4.0)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (3.10)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (2.2.3)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (2024.8.30)\n",
"Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.2)\n",
"Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.2)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n",
"Collecting git+https://github.com/huggingface/peft\n",
" Cloning https://github.com/huggingface/peft to /tmp/pip-req-build-0mbyx_z_\n",
" Running command git clone --filter=blob:none --quiet https://github.com/huggingface/peft /tmp/pip-req-build-0mbyx_z_\n",
" Resolved https://github.com/huggingface/peft to commit 131efba5d48753a3355ecd4f3833ae010a0510d6\n",
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
"Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from peft==0.13.3.dev0) (1.26.4)\n",
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from peft==0.13.3.dev0) (24.2)\n",
"Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from peft==0.13.3.dev0) (5.9.5)\n",
"Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from peft==0.13.3.dev0) (6.0.2)\n",
"Requirement already satisfied: torch>=1.13.0 in /usr/local/lib/python3.10/dist-packages (from peft==0.13.3.dev0) (2.5.1+cu121)\n",
"Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (from peft==0.13.3.dev0) (4.46.2)\n",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from peft==0.13.3.dev0) (4.66.6)\n",
"Requirement already satisfied: accelerate>=0.21.0 in /usr/local/lib/python3.10/dist-packages (from peft==0.13.3.dev0) (1.1.1)\n",
"Requirement already satisfied: safetensors in /usr/local/lib/python3.10/dist-packages (from peft==0.13.3.dev0) (0.4.5)\n",
"Requirement already satisfied: huggingface_hub>=0.25.0 in /usr/local/lib/python3.10/dist-packages (from peft==0.13.3.dev0) (0.26.2)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub>=0.25.0->peft==0.13.3.dev0) (3.16.1)\n",
"Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub>=0.25.0->peft==0.13.3.dev0) (2024.9.0)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub>=0.25.0->peft==0.13.3.dev0) (2.32.3)\n",
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub>=0.25.0->peft==0.13.3.dev0) (4.12.2)\n",
"Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.13.3.dev0) (3.4.2)\n",
"Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.13.3.dev0) (3.1.4)\n",
"Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.13.3.dev0) (1.13.1)\n",
"Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy==1.13.1->torch>=1.13.0->peft==0.13.3.dev0) (1.3.0)\n",
"Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers->peft==0.13.3.dev0) (2024.9.11)\n",
"Requirement already satisfied: tokenizers<0.21,>=0.20 in /usr/local/lib/python3.10/dist-packages (from transformers->peft==0.13.3.dev0) (0.20.3)\n",
"Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.13.0->peft==0.13.3.dev0) (3.0.2)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub>=0.25.0->peft==0.13.3.dev0) (3.4.0)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub>=0.25.0->peft==0.13.3.dev0) (3.10)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub>=0.25.0->peft==0.13.3.dev0) (2.2.3)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub>=0.25.0->peft==0.13.3.dev0) (2024.8.30)\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Imports"
],
"metadata": {
"id": "5BerCvfkq_jp"
},
"id": "5BerCvfkq_jp"
},
{
"cell_type": "code",
"source": [
"from typing import Any, Dict, List, Union\n",
"\n",
"import numpy as np\n",
"import torch\n",
"from datasets import load_dataset\n",
"from torch.utils.data import Dataset\n",
"from tqdm import tqdm\n",
"from transformers import (\n",
" AutoModelForCausalLM,\n",
" AutoTokenizer,\n",
" DataCollatorForLanguageModeling,\n",
" Trainer,\n",
" TrainingArguments,\n",
")\n",
"\n",
"from peft import CPTConfig, get_peft_model\n",
"\n",
"\n",
"MAX_INPUT_LENGTH = 1024\n",
"MAX_ICL_SAMPLES = 10\n",
"NUM_TRAINING_SAMPLES = 100\n",
"model_id = 'bigscience/bloom-1b7'"
],
"metadata": {
"id": "Y0pETNFBl963"
},
"id": "Y0pETNFBl963",
"execution_count": 2,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Data Preparation\n",
"---"
],
"metadata": {
"id": "9hO_I3aDmCQu"
},
"id": "9hO_I3aDmCQu"
},
{
"cell_type": "code",
"source": [
"# Initialize the tokenizer\n",
"tokenizer = AutoTokenizer.from_pretrained(\n",
" model_id, # The name or path of the pre-trained tokenizer (e.g., \"bert-base-uncased\").\n",
" cache_dir='.', # Directory to cache the tokenizer files locally.\n",
" padding_side='right', # Specifies that padding should be added to the right side of sequences.\n",
" trust_remote_code=True # Allows loading tokenizer implementations from external sources.\n",
")"
],
"metadata": {
"id": "STK5N0LJrZmA",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "4c5c3dda-07ae-4f67-df29-4a2ff499e5ad"
},
"id": "STK5N0LJrZmA",
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
"You will be able to reuse this secret in all of your notebooks.\n",
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
" warnings.warn(\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Load the SST-2 dataset from the GLUE benchmark\n",
"dataset = load_dataset('glue', 'sst2')\n",
"\n",
"def add_string_labels(example):\n",
" \"\"\"\n",
" Converts numerical labels into human-readable string labels.\n",
"\n",
" Args:\n",
" example (dict): A single example from the dataset with a numerical 'label'.\n",
"\n",
" Returns:\n",
" dict: The example augmented with a 'label_text' field.\n",
" \"\"\"\n",
" # Map numerical label to string label\n",
" example['label_text'] = \"positive\" if example['label'] == 1 else \"negative\"\n",
" return example\n",
"\n",
"# Subset and process the training dataset\n",
"context_dataset = dataset['train'].select(range(MAX_ICL_SAMPLES)).map(add_string_labels)\n",
"train_dataset = dataset['train'].select(range(MAX_ICL_SAMPLES, NUM_TRAINING_SAMPLES + MAX_ICL_SAMPLES)).map(add_string_labels)"
],
"metadata": {
"id": "C3oq4lDDrcUf",
"colab": {
"base_uri": "https://localhost:8080/",
"referenced_widgets": [
"72a5be4b77ec4d5994bcace9d462da84",
"bed78529ff2c4d08befca97c50cb5efc",
"cf7077acfce04aff8af0a2483dbf094c",
"910462d70d944d00ba54958d77bee755",
"a899818bdad0415b860eaac4afe31f30",
"3d78a6c8923547cf8c75bc8c10125eda",
"8083f95a673a423286ade63051de757d",
"13fc203ab1b44c83b6cfcc1e171d26ad",
"663a0196d2b547fd8a6890b8a86080c2",
"72be01164e974d59b05bee716e9bc978",
"4cedaf37e79e4ff1a10ffb96ec543e81"
],
"height": 49
},
"outputId": "5ae1ff54-d726-4f07-e6d7-cd53145b5d6f"
},
"id": "C3oq4lDDrcUf",
"execution_count": 4,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/100 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "72a5be4b77ec4d5994bcace9d462da84"
}
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"**Note:** This notebook uses small subsets of the dataset to ensure quick execution. For proper testing and evaluation, it is recommended to use the entire dataset by setting quick_review to False."
],
"metadata": {
"id": "ehlJCE80TnrC"
},
"id": "ehlJCE80TnrC"
},
{
"cell_type": "code",
"source": [
"quick_review = True # set to False for a comprehensive evaluation\n",
"num_of_test_examples = 100 if quick_review else len(dataset['validation'])\n",
"# Subset and process the validation dataset\n",
"test_dataset = dataset['validation'].select(range(num_of_test_examples)).map(add_string_labels)"
],
"metadata": {
"id": "yXoBN6EwTmNX"
},
"id": "yXoBN6EwTmNX",
"execution_count": 5,
"outputs": []
},
{
"cell_type": "markdown",
"source": [],
"metadata": {
"id": "YpXEWzglTl74"
},
"id": "YpXEWzglTl74"
},
{
"cell_type": "code",
"source": [
"class CPTDataset(Dataset):\n",
" def __init__(self, samples, tokenizer, template, max_length=MAX_INPUT_LENGTH):\n",
" \"\"\"\n",
" Initialize the CPTDataset with samples, a tokenizer, and a template.\n",
"\n",
" Args:\n",
" samples (list): List of samples containing input sentences and labels.\n",
" tokenizer: Tokenizer instance for encoding text.\n",
" template (dict): Dictionary defining input/output templates and separators.\n",
" max_length (int): Maximum input length for truncation.\n",
" \"\"\"\n",
" self.template = template\n",
" self.tokenizer = tokenizer\n",
" self.max_length = max_length\n",
"\n",
" # Storage for tokenized inputs and masks\n",
" self.attention_mask = []\n",
" self.input_ids = []\n",
" self.input_type_mask = []\n",
" self.inter_seperator_ids = self._get_input_ids(template['inter_seperator'])\n",
"\n",
" # Tokenize each sample and prepare inputs\n",
" for sample_i in tqdm(samples):\n",
" input_text, label = sample_i['sentence'], sample_i['label_text']\n",
" input_ids, attention_mask, input_type_mask = self.preprocess_sentence(input_text, label)\n",
"\n",
" self.input_ids.append(input_ids)\n",
" self.attention_mask.append(attention_mask)\n",
" self.input_type_mask.append(input_type_mask)\n",
"\n",
"\n",
" def _get_input_ids(self, text):\n",
" \"\"\"\n",
" Tokenize the given text into input IDs.\n",
"\n",
" Args:\n",
" text (str): The text to tokenize.\n",
"\n",
" Returns:\n",
" list: Tokenized input IDs.\n",
" \"\"\"\n",
" return self.tokenizer(text, add_special_tokens=False)[\"input_ids\"]\n",
"\n",
"\n",
" def preprocess_sentence(self, input_text, label):\n",
" \"\"\"\n",
" Preprocess a sentence and its corresponding label using templates.\n",
"\n",
" Args:\n",
" input_text (str): The input sentence.\n",
" label (str): The label text (e.g., \"positive\", \"negative\").\n",
"\n",
" Returns:\n",
" tuple: (input_ids, attention_mask, input_type_mask)\n",
" \"\"\"\n",
"\n",
" # Split input template into parts\n",
" input_template_part_1_text, input_template_part_2_text = self.template['input'].split('{}')\n",
" input_template_tokenized_part1 = self._get_input_ids(input_template_part_1_text)\n",
" input_tokenized = self._get_input_ids(input_text)\n",
" input_template_tokenized_part2 = self._get_input_ids(input_template_part_2_text)\n",
"\n",
" # Separator token\n",
" sep_tokenized = self._get_input_ids(self.template['intra_seperator'])\n",
"\n",
" # Process the label using the template\n",
" label_template_part_1, label_template_part_2 = self.template['output'].split('{}')\n",
" label_template_part1_tokenized = self._get_input_ids(label_template_part_1)\n",
" label_tokenized = self._get_input_ids(label)\n",
" label_template_part2_tokenized = self._get_input_ids(label_template_part_2)\n",
"\n",
" # End-of-sequence token\n",
" eos = [self.tokenizer.eos_token_id] if self.tokenizer.eos_token_id is not None else []\n",
"\n",
" # Concatenate all tokenized parts\n",
" input_ids = input_template_tokenized_part1 + input_tokenized + input_template_tokenized_part2 + sep_tokenized + label_template_part1_tokenized + label_tokenized + label_template_part2_tokenized + eos\n",
"\n",
" # Generate attention and type masks\n",
" attention_mask = [1] * len(input_ids)\n",
" input_type_mask = [1] * len(input_template_tokenized_part1) + [2] * len(input_tokenized) + [1] * len(\n",
" input_template_tokenized_part2) + [0] * len(sep_tokenized) + \\\n",
" [3] * len(label_template_part1_tokenized) + [4] * len(label_tokenized) + [3] * len( \\\n",
" label_template_part2_tokenized) + [0] * len(eos)\n",
"\n",
" # Ensure all masks and inputs are the same length\n",
" assert len(input_type_mask) == len(input_ids) == len(attention_mask)\n",
"\n",
" return input_ids, attention_mask, input_type_mask\n",
"\n",
"\n",
" def __len__(self):\n",
" \"\"\"\n",
" Get the number of examples in the dataset.\n",
"\n",
" Returns:\n",
" int: Number of examples.\n",
" \"\"\"\n",
" return len(self.input_ids)\n",
"\n",
"\n",
" def __getitem__(self, idx):\n",
" \"\"\"\n",
" Get the tokenized representation for the given index.\n",
"\n",
" Args:\n",
" idx (int): Index of the example.\n",
"\n",
" Returns:\n",
" dict: Tokenized inputs with attention and type masks.\n",
" \"\"\"\n",
"\n",
" return {\n",
" \"input_ids\": self.input_ids[idx],\n",
" \"attention_mask\": self.attention_mask[idx],\n",
" \"input_type_mask\": self.input_type_mask[idx]\n",
" }\n",
"\n",
"# Define templates for tokenization\n",
"templates = {\n",
" 'input': 'input: {}', # Input template with placeholder\n",
" 'intra_seperator': ' ', # Separator between input and output\n",
" 'output': 'output: {}', # Output template with placeholder\n",
" 'inter_seperator': '\\n' # Separator between examples\n",
"}\n",
"\n",
"# Initialize the dataset\n",
"cpt_train_dataset = CPTDataset(train_dataset, tokenizer, templates)\n",
"\n",
"\n",
"# - `templates`: Define how inputs and outputs should be formatted and separated.\n",
"# - `CPTDataset`: Converts the raw dataset into tokenized input IDs and masks."
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "icJb6-Uqrf8p",
"outputId": "a0a2ddbc-1d1f-4845-93c9-19cf34b46024"
},
"id": "icJb6-Uqrf8p",
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 100/100 [00:00<00:00, 874.85it/s]\n"
]
}
]
},
{
"cell_type": "code",
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 10/10 [00:00<00:00, 1133.50it/s]\n"
]
}
],
"source": [
"# Initialize storage for context-level information\n",
"context_ids = [] # Concatenated input IDs for all samples\n",
"context_attention_mask = [] # Concatenated attention masks\n",
"context_input_type_mask = [] # Concatenated input type masks\n",
"first_type_mask = 0 # Initial offset for input type mask\n",
"\n",
"cpt_context_dataset = CPTDataset(context_dataset, tokenizer, templates)\n",
"\n",
"# Iterate through the CPT training dataset\n",
"for i in range(len(context_dataset)):\n",
" # Add input IDs to the context\n",
" context_ids += cpt_context_dataset[i]['input_ids']\n",
"\n",
" # Add attention mask to the context\n",
" context_attention_mask += cpt_context_dataset[i]['attention_mask']\n",
"\n",
" # Adjust and add the input type mask to the context\n",
" context_input_type_mask += [\n",
" i + first_type_mask if i > 0 else 0 # Increment type indices dynamically\n",
" for i in cpt_context_dataset[i]['input_type_mask']\n",
" ]\n",
"\n",
" # Increment the type mask offset after processing the sample\n",
" first_type_mask += 4"
],
"metadata": {
"ExecuteTime": {
"end_time": "2024-10-22T09:24:58.894814Z",
"start_time": "2024-10-22T09:24:58.893841Z"
},
"id": "aef03bbd5d86d3d8",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "1bb1343b-b5f8-4998-e34b-6a8ae8063381"
},
"id": "aef03bbd5d86d3d8",
"execution_count": 7
},
{
"cell_type": "markdown",
"source": [
"# Model Training\n",
"\n",
"---"
],
"metadata": {
"collapsed": false,
"id": "2c40f24774d83372"
},
"id": "2c40f24774d83372"
},
{
"cell_type": "markdown",
"source": [
"## Load model"
],
"metadata": {
"id": "p0jFTzkisMgN"
},
"id": "p0jFTzkisMgN"
},
{
"cell_type": "code",
"outputs": [],
"source": [
"# Load a pre-trained causal language model\n",
"base_model = AutoModelForCausalLM.from_pretrained(\n",
" model_id,\n",
" cache_dir='.',\n",
" torch_dtype=torch.float16,\n",
" device_map='auto'\n",
")\n",
"\n",
"# Initialize the CPT configuration\n",
"config = CPTConfig(\n",
" cpt_token_ids=context_ids,\n",
" cpt_mask=context_attention_mask,\n",
" cpt_tokens_type_mask=context_input_type_mask,\n",
"\n",
" opt_weighted_loss_type='decay',\n",
" opt_loss_decay_factor=0.95, # we choose the exponential decay factor applied to the loss\n",
" opt_projection_epsilon=0.2, # we choose the projection over the input tokens\n",
" opt_projection_format_epsilon=0.1, # we choose the projection over input and output templates\n",
"\n",
" tokenizer_name_or_path=model_id,\n",
")\n",
"\n",
"# Initialize the CPT model with PEFT\n",
"model = get_peft_model(base_model, config)"
],
"metadata": {
"ExecuteTime": {
"end_time": "2024-10-22T09:25:08.941945Z",
"start_time": "2024-10-22T09:25:04.393323Z"
},
"id": "17ac445134919a39"
},
"id": "17ac445134919a39",
"execution_count": 8
},
{
"cell_type": "markdown",
"source": [
"## Setting Collate Function"
],
"metadata": {
"collapsed": false,
"id": "4e49660c50d98741"
},
"id": "4e49660c50d98741"
},
{
"cell_type": "code",
"outputs": [],
"source": [
"class CPTDataCollatorForLanguageModeling(DataCollatorForLanguageModeling):\n",
" def __init__(self, tokenizer, training=True, mlm=False):\n",
" \"\"\"\n",
" Custom collator for CPT-style language modeling.\n",
"\n",
" Args:\n",
" tokenizer: The tokenizer to handle tokenization and special tokens.\n",
" training (bool): If True, operates in training mode; otherwise in evaluation mode.\n",
" mlm (bool): If True, enables masked language modeling.\n",
" \"\"\"\n",
"\n",
" super().__init__(tokenizer, mlm=mlm) # Initialize the parent class\n",
" self.training = training\n",
"\n",
" # Add a special padding token if not already defined\n",
" self.tokenizer.add_special_tokens({\"pad_token\": \"[PAD]\"})\n",
"\n",
" def torch_call(self, examples: List[Union[List[int], Any, Dict[str, Any]]]) -> Dict[str, Any]:\n",
" \"\"\"\n",
" Process a batch of examples for language modeling.\n",
"\n",
" Args:\n",
" examples (List): A batch of examples with tokenized inputs and optional sample masks.\n",
"\n",
" Returns:\n",
" Dict: A dictionary containing padded and tensor-converted inputs, attention masks,\n",
" input type masks, and optional sample masks and labels.\n",
" \"\"\"\n",
"\n",
" # Initialize a list to collect sample masks if provided\n",
" list_sample_mask = []\n",
" for i in range(len(examples)):\n",
" if \"sample_mask\" in examples[i].keys():\n",
" list_sample_mask.append(examples[i].pop(\"sample_mask\"))\n",
"\n",
" # Define a helper function for padding sequences to the maximum length\n",
" max_len = max(len(ex[\"input_ids\"]) for ex in examples)\n",
"\n",
" # Define a helper function for padding sequences to the maximum length\n",
" def pad_sequence(sequence, max_len, pad_value=0):\n",
" return sequence + [pad_value] * (max_len - len(sequence))\n",
"\n",
" # Pad and convert `input_ids`, `attention_mask`, and `input_type_mask` to tensors\n",
" input_ids = torch.tensor([pad_sequence(ex[\"input_ids\"], max_len) for ex in examples])\n",
" attention_mask = torch.tensor([pad_sequence(ex[\"attention_mask\"], max_len) for ex in examples])\n",
" input_type_mask = torch.tensor([pad_sequence(ex[\"input_type_mask\"], max_len) for ex in examples])\n",
"\n",
" # Create the initial batch dictionary\n",
" batch = {\"input_ids\": input_ids, \"attention_mask\": attention_mask, \"input_type_mask\": input_type_mask}\n",
"\n",
" # Create a tensor to store sample masks\n",
" tensor_sample_mask = batch[\"input_ids\"].clone().long()\n",
" tensor_sample_mask[:, :] = 0 # Initialize with zeros\n",
"\n",
" # Populate the tensor with the provided sample masks\n",
" for i in range(len(list_sample_mask)):\n",
" tensor_sample_mask[i, : len(list_sample_mask[i])] = list_sample_mask[i]\n",
"\n",
" # Copy `input_ids` to use as `labels`\n",
" batch[\"labels\"] = batch[\"input_ids\"].clone()\n",
"\n",
" # If in evaluation mode, include the `sample_mask` in the batch\n",
" if not self.training:\n",
" batch[\"sample_mask\"] = tensor_sample_mask\n",
"\n",
" return batch"
],
"metadata": {
"ExecuteTime": {
"end_time": "2024-10-22T09:25:08.953199Z",
"start_time": "2024-10-22T09:25:08.945689Z"
},
"id": "b0fac840f060e3aa"
},
"id": "b0fac840f060e3aa",
"execution_count": 9
},
{
"cell_type": "markdown",
"source": [
"## Training"
],
"metadata": {
"collapsed": false,
"id": "48f535d74e6602b"
},
"id": "48f535d74e6602b"
},
{
"cell_type": "code",
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='500' max='500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [500/500 01:28, Epoch 5/5]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Step</th>\n",
" <th>Training Loss</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>100</td>\n",
" <td>0.400800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>200</td>\n",
" <td>0.036000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>300</td>\n",
" <td>0.026300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>400</td>\n",
" <td>0.016100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>500</td>\n",
" <td>0.011600</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"TrainOutput(global_step=500, training_loss=0.09815525007247924, metrics={'train_runtime': 90.6767, 'train_samples_per_second': 5.514, 'train_steps_per_second': 5.514, 'total_flos': 79477977907200.0, 'train_loss': 0.09815525007247924, 'epoch': 5.0})"
]
},
"metadata": {},
"execution_count": 10
}
],
"source": [
"training_args = TrainingArguments(\n",
" output_dir='../.',\n",
" use_cpu=False,\n",
" auto_find_batch_size=False,\n",
" learning_rate=1e-4,\n",
" logging_steps=100,\n",
" per_device_train_batch_size=1,\n",
" save_total_limit=1,\n",
" remove_unused_columns=False,\n",
" num_train_epochs=5,\n",
" fp16=True,\n",
" save_strategy='no',\n",
" logging_dir=\"logs\",\n",
" report_to=\"none\"\n",
")\n",
"\n",
"trainer = Trainer(\n",
" model=model,\n",
" args=training_args,\n",
" train_dataset=cpt_train_dataset, # Custom CPT training dataset.\n",
" data_collator=CPTDataCollatorForLanguageModeling(tokenizer, training=True, mlm=False)\n",
")\n",
"\n",
"trainer.train()"
],
"metadata": {
"ExecuteTime": {
"end_time": "2024-10-22T09:25:27.599132Z",
"start_time": "2024-10-22T09:25:13.906685Z"
},
"colab": {
"base_uri": "https://localhost:8080/",
"height": 268
},
"id": "1a865c2ad2dc7218",
"outputId": "c4bfd785-e354-4ee6-a87e-63c17bfd2605"
},
"id": "1a865c2ad2dc7218",
"execution_count": 10
},
{
"cell_type": "markdown",
"source": [
"# Model Evaluation\n",
"\n",
"---"
],
"metadata": {
"collapsed": false,
"id": "b799ea89a567590f"
},
"id": "b799ea89a567590f"
},
{
"cell_type": "code",
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 100/100 [00:00<00:00, 1972.82it/s]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Sentence: input: it 's a charming and often affecting journey . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: unflinchingly bleak and desperate output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: allows us to hope that nolan is poised to embark a major career as a commercial yet inventive filmmaker . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: the acting , costumes , music , cinematography and sound are all astounding given the production 's austere locales . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: it 's slow -- very , very slow . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: although laced with humor and a few fanciful touches , the film is a refreshingly serious look at young women . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: a sometimes tedious film . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: or doing last year 's taxes with your ex-wife . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: you do n't have to know about music to appreciate the film 's easygoing blend of comedy and romance . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: in exactly 89 minutes , most of which passed as slowly as if i 'd been sitting naked on an igloo , formula 51 sank from quirky to jerky to utter turkey . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: the mesmerizing performances of the leads keep the film grounded and keep the audience riveted . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: it takes a strange kind of laziness to waste the talents of robert forster , anne meara , eugene levy , and reginald veljohnson all in the same movie . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: ... the film suffers from a lack of humor ( something needed to balance out the violence ) ... output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: we root for ( clara and paul ) , even like them , though perhaps it 's an emotion closer to pity . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: even horror fans will most likely not find what they 're seeking with trouble every day ; the movie lacks both thrills and humor . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: a gorgeous , high-spirited musical from india that exquisitely blends music , dance , song , and high drama . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: the emotions are raw and will strike a nerve with anyone who 's ever had family trauma . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: audrey tatou has a knack for picking roles that magnify her outrageous charm , and in this literate french comedy , she 's as morning-glory exuberant as she was in amélie . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: ... the movie is just a plain old monster . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: in its best moments , resembles a bad high school production of grease , without benefit of song . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: pumpkin takes an admirable look at the hypocrisy of political correctness , but it does so with such an uneven tone that you never know when humor ends and tragedy begins . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: the iditarod lasts for days - this just felt like it did . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: holden caulfield did it better . output: negative</s> \n",
" \t The prediction is: positive\n",
" \t The GT is negative\n",
"Sentence: input: a delectable and intriguing thriller filled with surprises , read my lips is an original . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: seldom has a movie so closely matched the spirit of a man and his work . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: nicks , seemingly uncertain what 's going to make people laugh , runs the gamut from stale parody to raunchy sex gags to formula romantic comedy . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: the action switches between past and present , but the material link is too tenuous to anchor the emotional connections that purport to span a 125-year divide . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: it 's an offbeat treat that pokes fun at the democratic exercise while also examining its significance for those who take part . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: it 's a cookie-cutter movie , a cut-and-paste job . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: i had to look away - this was god awful . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: thanks to scott 's charismatic roger and eisenberg 's sweet nephew , roger dodger is one of the most compelling variations on in the company of men . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: ... designed to provide a mix of smiles and tears , `` crossroads '' instead provokes a handful of unintentional howlers and numerous yawns . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: a gorgeous , witty , seductive movie . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: if the movie succeeds in instilling a wary sense of ` there but for the grace of god , ' it is far too self-conscious to draw you deeply into its world . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: it does n't believe in itself , it has no sense of humor ... it 's just plain bored . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: a sequence of ridiculous shoot - 'em - up scenes . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: the weight of the piece , the unerring professionalism of the chilly production , and the fascination embedded in the lurid topic prove recommendation enough . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: ( w ) hile long on amiable monkeys and worthy environmentalism , jane goodall 's wild chimpanzees is short on the thrills the oversize medium demands . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: as surreal as a dream and as detailed as a photograph , as visually dexterous as it is at times imaginatively overwhelming . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: escaping the studio , piccoli is warmly affecting and so is this adroitly minimalist movie . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: there 's ... tremendous energy from the cast , a sense of playfulness and excitement that seems appropriate . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: this illuminating documentary transcends our preconceived vision of the holy land and its inhabitants , revealing the human complexities beneath . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: the subtle strength of `` elling '' is that it never loses touch with the reality of the grim situation . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: holm ... embodies the character with an effortlessly regal charisma . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: the title not only describes its main characters , but the lazy people behind the camera as well . output: negative</s> \n",
" \t The prediction is: positive\n",
" \t The GT is negative\n",
"Sentence: input: it offers little beyond the momentary joys of pretty and weightless intellectual entertainment . output: negative</s> \n",
" \t The prediction is: positive\n",
" \t The GT is negative\n",
"Sentence: input: a synthesis of cliches and absurdities that seems positively decadent in its cinematic flash and emptiness . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: a subtle and well-crafted ( for the most part ) chiller . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: has a lot of the virtues of eastwood at his best . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: it 's hampered by a lifetime-channel kind of plot and a lead actress who is out of her depth . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: it feels like an after-school special gussied up with some fancy special effects , and watching its rote plot points connect is about as exciting as gazing at an egg timer for 93 minutes . output: negative</s> \n",
" \t The prediction is: positive\n",
" \t The GT is negative\n",
"Sentence: input: for the most part , director anne-sophie birot 's first feature is a sensitive , extraordinarily well-acted drama . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: mr. tsai is a very original artist in his medium , and what time is it there ? output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: sade is an engaging look at the controversial eponymous and fiercely atheistic hero . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: so devoid of any kind of intelligible story that it makes films like xxx and collateral damage seem like thoughtful treatises output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: a tender , heartfelt family drama . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: ... a hollow joke told by a cinematic gymnast having too much fun embellishing the misanthropic tale to actually engage it . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: the cold turkey would 've been a far better title . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: manages to be both repulsively sadistic and mundane . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: it 's just disappointingly superficial -- a movie that has all the elements necessary to be a fascinating , involving character study , but never does more than scratch the surface . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: this is a story of two misfits who do n't stand a chance alone , but together they are magnificent . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: schaeffer has to find some hook on which to hang his persistently useless movies , and it might as well be the resuscitation of the middle-aged character . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: the primitive force of this film seems to bubble up from the vast collective memory of the combatants . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: on this tricky topic , tadpole is very much a step in the right direction , with its blend of frankness , civility and compassion . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: the script kicks in , and mr. hartley 's distended pace and foot-dragging rhythms follow . output: negative</s> \n",
" \t The prediction is: positive\n",
" \t The GT is negative\n",
"Sentence: input: you wonder why enough was n't just a music video rather than a full-length movie . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: if you 're hard up for raunchy college humor , this is your ticket right here . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: a fast , funny , highly enjoyable movie . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: good old-fashioned slash-and-hack is back ! output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: this one is definitely one to skip , even for horror movie fanatics . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: for all its impressive craftsmanship , and despite an overbearing series of third-act crescendos , lily chou-chou never really builds up a head of emotional steam . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: exquisitely nuanced in mood tics and dialogue , this chamber drama is superbly acted by the deeply appealing veteran bouquet and the chilling but quite human berling . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: uses high comedy to evoke surprising poignance . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: one of creepiest , scariest movies to come along in a long , long time , easily rivaling blair witch or the others . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: a string of rehashed sight gags based in insipid vulgarity . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: among the year 's most intriguing explorations of alientation . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: the movie fails to live up to the sum of its parts . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: the son 's room is a triumph of gentility that earns its moments of pathos . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: there is nothing outstanding about this film , but it is good enough and will likely be appreciated most by sailors and folks who know their way around a submarine . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: this is a train wreck of an action film -- a stupefying attempt by the filmmakers to force-feed james bond into the mindless xxx mold and throw 40 years of cinematic history down the toilet in favor of bright flashes and loud bangs . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: the draw ( for `` big bad love '' ) is a solid performance by arliss howard . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: green might want to hang onto that ski mask , as robbery may be the only way to pay for his next project . output: negative</s> \n",
" \t The prediction is: positive\n",
" \t The GT is negative\n",
"Sentence: input: it 's one pussy-ass world when even killer-thrillers revolve around group therapy sessions . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: though it 's become almost redundant to say so , major kudos go to leigh for actually casting people who look working-class . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: the band 's courage in the face of official repression is inspiring , especially for aging hippies ( this one included ) . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: the movie achieves as great an impact by keeping these thoughts hidden as ... ( quills ) did by showing them . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: the film flat lines when it should peak and is more missed opportunity and trifle than dark , decadent truffle . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: jaglom ... put ( s ) the audience in the privileged position of eavesdropping on his characters output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: fresnadillo 's dark and jolting images have a way of plying into your subconscious like the nightmare you had a week ago that wo n't go away . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: we know the plot 's a little crazy , but it held my interest from start to finish . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: it 's a scattershot affair , but when it hits its mark it 's brilliant . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: hardly a masterpiece , but it introduces viewers to a good charitable enterprise and some interesting real people . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: you wo n't like roger , but you will quickly recognize him . output: negative</s> \n",
" \t The prediction is: positive\n",
" \t The GT is negative\n",
"Sentence: input: if steven soderbergh 's ` solaris ' is a failure it is a glorious failure . output: positive</s> \n",
" \t The prediction is: negative\n",
" \t The GT is positive\n",
"Sentence: input: byler reveals his characters in a way that intrigues and even fascinates us , and he never reduces the situation to simple melodrama . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: this riveting world war ii moral suspense story deals with the shadow side of american culture : racial prejudice in its ugly and diverse forms . output: negative</s> \n",
" \t The prediction is: positive\n",
" \t The GT is negative\n",
"Sentence: input: it 's difficult to imagine the process that produced such a script , but here 's guessing that spray cheese and underarm noises played a crucial role . output: negative</s> \n",
" \t The prediction is: positive\n",
" \t The GT is negative\n",
"Sentence: input: no sophomore slump for director sam mendes , who segues from oscar winner to oscar-winning potential with a smooth sleight of hand . output: positive</s> \n",
" \t The prediction is: positive\n",
" \t The GT is positive\n",
"Sentence: input: on the whole , the movie lacks wit , feeling and believability to compensate for its incessant coarseness and banality . output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"Sentence: input: why make a documentary about these marginal historical figures ? output: negative</s> \n",
" \t The prediction is: negative\n",
" \t The GT is negative\n",
"The model Acc is 90.0%\n"
]
}
],
"source": [
"model.eval()\n",
"\n",
"# Select relevant columns from the test dataset\n",
"test_dataset = test_dataset.select_columns(['sentence', 'label_text'])\n",
"\n",
"# Convert the test dataset to a CPT-compatible format\n",
"cpt_test_dataset = CPTDataset(test_dataset, tokenizer, templates)\n",
"\n",
"# Get the device where the model is loaded (CPU, GPU or XPU)\n",
"device = model.device\n",
"list_bool_predictions = []\n",
"\n",
"for i in range(len(test_dataset)):\n",
" input_ids, input_type_mask = cpt_test_dataset[i]['input_ids'], cpt_test_dataset[i]['input_type_mask']\n",
"\n",
" # Pass the inputs through the model\n",
" outputs = model(\n",
" input_ids=torch.Tensor(input_ids).long().to(device=device).view(1, -1),\n",
" labels=torch.Tensor(input_ids).long().to(device=device).view(1, -1),\n",
" input_type_mask=torch.Tensor(input_type_mask).long().to(device=device).view(1, -1)\n",
" )\n",
"\n",
" # Shift logits to exclude the last token and match the labels\n",
" shifted_logits = outputs.logits[..., :-1, :].contiguous().to(model.dtype)[0, -len(input_ids) + 1:]\n",
" shift_labels = torch.Tensor(input_ids).long().to(device=device).view(1, -1)[0, 1:].contiguous().to(device)\n",
" shifted_input_type_mask = torch.Tensor(input_type_mask).long().to(device=device).view(1, -1)[..., 1:].contiguous().to(device)\n",
"\n",
" # Create a mask for the type `4` tokens (label tokens)\n",
" mask = torch.Tensor(shifted_input_type_mask).long().to(device=device).view(-1,) == 4\n",
"\n",
" # Extract logits and labels corresponding to the mask\n",
" logit = shifted_logits[mask]\n",
" label = shift_labels[mask]\n",
"\n",
" # All possible label tokens for `negative` and `positive`\n",
" all_labels = torch.Tensor([tokenizer(i, add_special_tokens=False)[\"input_ids\"] for i in ['negative', 'positive']]).long().to(device).view(-1,)\n",
"\n",
" # Compare logits with label tokens and infer prediction\n",
" prediction = logit[0, torch.Tensor([tokenizer(i, add_special_tokens=False)[\"input_ids\"] for i in ['negative', 'positive']]).long().to(device).view(-1,)].argmax()\n",
" prediction_text = 'negative' if prediction == 0 else 'positive'\n",
" print(f\"Sentence: {tokenizer.decode(input_ids)} \\n \\t The prediction is: {prediction_text}\\n \\t The GT is {tokenizer.decode(label)}\")\n",
" list_bool_predictions.append(prediction_text == tokenizer.decode(label))\n",
"\n",
"print(f'The model Acc is {100 * np.mean(list_bool_predictions)}%')"
],
"metadata": {
"ExecuteTime": {
"end_time": "2024-10-22T09:25:28.252009Z",
"start_time": "2024-10-22T09:25:27.598326Z"
},
"id": "48e7d976e6e01212",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "40dd1226-fa31-4e77-dc7e-e06a3600304e"
},
"id": "48e7d976e6e01212",
"execution_count": 11
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
},
"colab": {
"provenance": [],
"gpuType": "T4"
},
"accelerator": "GPU",
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"72a5be4b77ec4d5994bcace9d462da84": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HBoxModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HBoxView",
"box_style": "",
"children": [
"IPY_MODEL_bed78529ff2c4d08befca97c50cb5efc",
"IPY_MODEL_cf7077acfce04aff8af0a2483dbf094c",
"IPY_MODEL_910462d70d944d00ba54958d77bee755"
],
"layout": "IPY_MODEL_a899818bdad0415b860eaac4afe31f30"
}
},
"bed78529ff2c4d08befca97c50cb5efc": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_3d78a6c8923547cf8c75bc8c10125eda",
"placeholder": "",
"style": "IPY_MODEL_8083f95a673a423286ade63051de757d",
"value": "Map: 100%"
}
},
"cf7077acfce04aff8af0a2483dbf094c": {
"model_module": "@jupyter-widgets/controls",
"model_name": "FloatProgressModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "FloatProgressModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "ProgressView",
"bar_style": "success",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_13fc203ab1b44c83b6cfcc1e171d26ad",
"max": 100,
"min": 0,
"orientation": "horizontal",
"style": "IPY_MODEL_663a0196d2b547fd8a6890b8a86080c2",
"value": 100
}
},
"910462d70d944d00ba54958d77bee755": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_72be01164e974d59b05bee716e9bc978",
"placeholder": "",
"style": "IPY_MODEL_4cedaf37e79e4ff1a10ffb96ec543e81",
"value": " 100/100 [00:00<00:00, 1327.06 examples/s]"
}
},
"a899818bdad0415b860eaac4afe31f30": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"3d78a6c8923547cf8c75bc8c10125eda": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"8083f95a673a423286ade63051de757d": {
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"13fc203ab1b44c83b6cfcc1e171d26ad": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"663a0196d2b547fd8a6890b8a86080c2": {
"model_module": "@jupyter-widgets/controls",
"model_name": "ProgressStyleModel",
"model_module_version": "1.5.0",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"bar_color": null,
"description_width": ""
}
},
"72be01164e974d59b05bee716e9bc978": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"4cedaf37e79e4ff1a10ffb96ec543e81": {
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
}
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|