File size: 72,435 Bytes
546448e |
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 |
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "8JqpxyBueqTH",
"outputId": "6c2c3908-9067-496c-ad64-74f21895232a"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Building wheel for flashtext (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting git+https://github.com/boudinfl/pke.git\n",
" Cloning https://github.com/boudinfl/pke.git to /tmp/pip-req-build-s0vst_dk\n",
" Running command git clone -q https://github.com/boudinfl/pke.git /tmp/pip-req-build-s0vst_dk\n",
"Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (from pke==2.0.0) (3.7)\n",
"Requirement already satisfied: networkx in /usr/local/lib/python3.7/dist-packages (from pke==2.0.0) (2.6.3)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from pke==2.0.0) (1.21.6)\n",
"Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from pke==2.0.0) (1.7.3)\n",
"Collecting sklearn\n",
" Downloading sklearn-0.0.post1.tar.gz (3.6 kB)\n",
"Collecting unidecode\n",
" Downloading Unidecode-1.3.6-py3-none-any.whl (235 kB)\n",
"\u001b[K |████████████████████████████████| 235 kB 6.2 MB/s \n",
"\u001b[?25hRequirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from pke==2.0.0) (0.16.0)\n",
"Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from pke==2.0.0) (1.2.0)\n",
"Requirement already satisfied: spacy>=3.2.3 in /usr/local/lib/python3.7/dist-packages (from pke==2.0.0) (3.4.3)\n",
"Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (2.0.7)\n",
"Requirement already satisfied: typing-extensions<4.2.0,>=3.7.4 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (4.1.1)\n",
"Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (1.0.3)\n",
"Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (57.4.0)\n",
"Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.10 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (3.0.10)\n",
"Requirement already satisfied: wasabi<1.1.0,>=0.9.1 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (0.10.1)\n",
"Requirement already satisfied: typer<0.8.0,>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (0.7.0)\n",
"Requirement already satisfied: thinc<8.2.0,>=8.1.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (8.1.5)\n",
"Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (2.4.5)\n",
"Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (3.0.8)\n",
"Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (4.64.1)\n",
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (21.3)\n",
"Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (1.0.9)\n",
"Requirement already satisfied: pathy>=0.3.5 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (0.8.1)\n",
"Requirement already satisfied: pydantic!=1.8,!=1.8.1,<1.11.0,>=1.7.4 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (1.10.2)\n",
"Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (2.23.0)\n",
"Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (3.3.0)\n",
"Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (2.0.8)\n",
"Requirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from spacy>=3.2.3->pke==2.0.0) (2.11.3)\n",
"Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from catalogue<2.1.0,>=2.0.6->spacy>=3.2.3->pke==2.0.0) (3.10.0)\n",
"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->spacy>=3.2.3->pke==2.0.0) (3.0.9)\n",
"Requirement already satisfied: smart-open<6.0.0,>=5.2.1 in /usr/local/lib/python3.7/dist-packages (from pathy>=0.3.5->spacy>=3.2.3->pke==2.0.0) (5.2.1)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=3.2.3->pke==2.0.0) (2.10)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=3.2.3->pke==2.0.0) (2022.9.24)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=3.2.3->pke==2.0.0) (3.0.4)\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<3.0.0,>=2.13.0->spacy>=3.2.3->pke==2.0.0) (1.24.3)\n",
"Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.7/dist-packages (from thinc<8.2.0,>=8.1.0->spacy>=3.2.3->pke==2.0.0) (0.0.3)\n",
"Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.7/dist-packages (from thinc<8.2.0,>=8.1.0->spacy>=3.2.3->pke==2.0.0) (0.7.9)\n",
"Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.7/dist-packages (from typer<0.8.0,>=0.3.0->spacy>=3.2.3->pke==2.0.0) (7.1.2)\n",
"Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->spacy>=3.2.3->pke==2.0.0) (2.0.1)\n",
"Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.7/dist-packages (from nltk->pke==2.0.0) (2022.6.2)\n",
"Building wheels for collected packages: pke, sklearn\n",
" Building wheel for pke (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for pke: filename=pke-2.0.0-py3-none-any.whl size=6160276 sha256=6967c9216d570e0bbc7bab2c16f5f1810ecd62dcc9fad636e26ff35edbab3a68\n",
" Stored in directory: /tmp/pip-ephem-wheel-cache-_mu5g7sn/wheels/fa/b3/09/612ee93bf3ee4164bcd5783e742942cdfc892a86039d3e0a33\n",
" Building wheel for sklearn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for sklearn: filename=sklearn-0.0.post1-py3-none-any.whl size=2344 sha256=47f5287c3e5d1518e0617e1db17d093069e553338d6c0e359aa70352e6c78d66\n",
" Stored in directory: /root/.cache/pip/wheels/42/56/cc/4a8bf86613aafd5b7f1b310477667c1fca5c51c3ae4124a003\n",
"Successfully built pke sklearn\n",
"Installing collected packages: unidecode, sklearn, pke\n",
"Successfully installed pke-2.0.0 sklearn-0.0.post1 unidecode-1.3.6\n"
]
}
],
"source": [
"!pip install --quiet flashtext==2.7\n",
"!pip install git+https://github.com/boudinfl/pke.git\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "am3XUlr5evYK"
},
"outputs": [],
"source": [
"!pip install --quiet transformers==4.8.1\n",
"!pip install --quiet sentencepiece==0.1.95\n",
"!pip install --quiet textwrap3==0.9.2\n",
"!pip install gradio"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "mhwpLyuBfFUK",
"outputId": "dc6f4900-429d-4815-c98c-b8625efcbe7b"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[?25l\r\u001b[K |███████▊ | 10 kB 27.7 MB/s eta 0:00:01\r\u001b[K |███████████████▌ | 20 kB 34.6 MB/s eta 0:00:01\r\u001b[K |███████████████████████▏ | 30 kB 15.4 MB/s eta 0:00:01\r\u001b[K |███████████████████████████████ | 40 kB 6.6 MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 42 kB 955 kB/s \n",
"\u001b[?25h"
]
}
],
"source": [
"!pip install --quiet strsim==0.0.3\n",
"!pip install --quiet sense2vec==2.0.0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "NcNXz17EfQLJ",
"outputId": "c90851f7-e320-48e3-d994-fcc5c174c636"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[?25l\r\u001b[K |▏ | 10 kB 10.5 MB/s eta 0:00:01\r\u001b[K |▍ | 20 kB 7.8 MB/s eta 0:00:01\r\u001b[K |▋ | 30 kB 11.1 MB/s eta 0:00:01\r\u001b[K |▉ | 40 kB 6.3 MB/s eta 0:00:01\r\u001b[K |█ | 51 kB 6.3 MB/s eta 0:00:01\r\u001b[K |█▎ | 61 kB 7.4 MB/s eta 0:00:01\r\u001b[K |█▌ | 71 kB 7.9 MB/s eta 0:00:01\r\u001b[K |█▊ | 81 kB 8.7 MB/s eta 0:00:01\r\u001b[K |█▉ | 92 kB 8.7 MB/s eta 0:00:01\r\u001b[K |██ | 102 kB 7.5 MB/s eta 0:00:01\r\u001b[K |██▎ | 112 kB 7.5 MB/s eta 0:00:01\r\u001b[K |██▌ | 122 kB 7.5 MB/s eta 0:00:01\r\u001b[K |██▊ | 133 kB 7.5 MB/s eta 0:00:01\r\u001b[K |███ | 143 kB 7.5 MB/s eta 0:00:01\r\u001b[K |███▏ | 153 kB 7.5 MB/s eta 0:00:01\r\u001b[K |███▍ | 163 kB 7.5 MB/s eta 0:00:01\r\u001b[K |███▌ | 174 kB 7.5 MB/s eta 0:00:01\r\u001b[K |███▊ | 184 kB 7.5 MB/s eta 0:00:01\r\u001b[K |████ | 194 kB 7.5 MB/s eta 0:00:01\r\u001b[K |████▏ | 204 kB 7.5 MB/s eta 0:00:01\r\u001b[K |████▍ | 215 kB 7.5 MB/s eta 0:00:01\r\u001b[K |████▋ | 225 kB 7.5 MB/s eta 0:00:01\r\u001b[K |████▉ | 235 kB 7.5 MB/s eta 0:00:01\r\u001b[K |█████ | 245 kB 7.5 MB/s eta 0:00:01\r\u001b[K |█████▎ | 256 kB 7.5 MB/s eta 0:00:01\r\u001b[K |█████▍ | 266 kB 7.5 MB/s eta 0:00:01\r\u001b[K |█████▋ | 276 kB 7.5 MB/s eta 0:00:01\r\u001b[K |█████▉ | 286 kB 7.5 MB/s eta 0:00:01\r\u001b[K |██████ | 296 kB 7.5 MB/s eta 0:00:01\r\u001b[K |██████▎ | 307 kB 7.5 MB/s eta 0:00:01\r\u001b[K |██████▌ | 317 kB 7.5 MB/s eta 0:00:01\r\u001b[K |██████▊ | 327 kB 7.5 MB/s eta 0:00:01\r\u001b[K |███████ | 337 kB 7.5 MB/s eta 0:00:01\r\u001b[K |███████ | 348 kB 7.5 MB/s eta 0:00:01\r\u001b[K |███████▎ | 358 kB 7.5 MB/s eta 0:00:01\r\u001b[K |███████▌ | 368 kB 7.5 MB/s eta 0:00:01\r\u001b[K |███████▊ | 378 kB 7.5 MB/s eta 0:00:01\r\u001b[K |████████ | 389 kB 7.5 MB/s eta 0:00:01\r\u001b[K |████████▏ | 399 kB 7.5 MB/s eta 0:00:01\r\u001b[K |████████▍ | 409 kB 7.5 MB/s eta 0:00:01\r\u001b[K |████████▋ | 419 kB 7.5 MB/s eta 0:00:01\r\u001b[K |████████▊ | 430 kB 7.5 MB/s eta 0:00:01\r\u001b[K |█████████ | 440 kB 7.5 MB/s eta 0:00:01\r\u001b[K |█████████▏ | 450 kB 7.5 MB/s eta 0:00:01\r\u001b[K |█████████▍ | 460 kB 7.5 MB/s eta 0:00:01\r\u001b[K |█████████▋ | 471 kB 7.5 MB/s eta 0:00:01\r\u001b[K |█████████▉ | 481 kB 7.5 MB/s eta 0:00:01\r\u001b[K |██████████ | 491 kB 7.5 MB/s eta 0:00:01\r\u001b[K |██████████▎ | 501 kB 7.5 MB/s eta 0:00:01\r\u001b[K |██████████▌ | 512 kB 7.5 MB/s eta 0:00:01\r\u001b[K |██████████▋ | 522 kB 7.5 MB/s eta 0:00:01\r\u001b[K |██████████▉ | 532 kB 7.5 MB/s eta 0:00:01\r\u001b[K |███████████ | 542 kB 7.5 MB/s eta 0:00:01\r\u001b[K |███████████▎ | 552 kB 7.5 MB/s eta 0:00:01\r\u001b[K |███████████▌ | 563 kB 7.5 MB/s eta 0:00:01\r\u001b[K |███████████▊ | 573 kB 7.5 MB/s eta 0:00:01\r\u001b[K |████████████ | 583 kB 7.5 MB/s eta 0:00:01\r\u001b[K |████████████▏ | 593 kB 7.5 MB/s eta 0:00:01\r\u001b[K |████████████▎ | 604 kB 7.5 MB/s eta 0:00:01\r\u001b[K |████████████▌ | 614 kB 7.5 MB/s eta 0:00:01\r\u001b[K |████████████▊ | 624 kB 7.5 MB/s eta 0:00:01\r\u001b[K |█████████████ | 634 kB 7.5 MB/s eta 0:00:01\r\u001b[K |█████████████▏ | 645 kB 7.5 MB/s eta 0:00:01\r\u001b[K |█████████████▍ | 655 kB 7.5 MB/s eta 0:00:01\r\u001b[K |█████████████▋ | 665 kB 7.5 MB/s eta 0:00:01\r\u001b[K |█████████████▉ | 675 kB 7.5 MB/s eta 0:00:01\r\u001b[K |██████████████ | 686 kB 7.5 MB/s eta 0:00:01\r\u001b[K |██████████████▏ | 696 kB 7.5 MB/s eta 0:00:01\r\u001b[K |██████████████▍ | 706 kB 7.5 MB/s eta 0:00:01\r\u001b[K |██████████████▋ | 716 kB 7.5 MB/s eta 0:00:01\r\u001b[K |██████████████▉ | 727 kB 7.5 MB/s eta 0:00:01\r\u001b[K |███████████████ | 737 kB 7.5 MB/s eta 0:00:01\r\u001b[K |███████████████▎ | 747 kB 7.5 MB/s eta 0:00:01\r\u001b[K |███████████████▌ | 757 kB 7.5 MB/s eta 0:00:01\r\u001b[K |███████████████▊ | 768 kB 7.5 MB/s eta 0:00:01\r\u001b[K |███████████████▉ | 778 kB 7.5 MB/s eta 0:00:01\r\u001b[K |████████████████ | 788 kB 7.5 MB/s eta 0:00:01\r\u001b[K |████████████████▎ | 798 kB 7.5 MB/s eta 0:00:01\r\u001b[K |████████████████▌ | 808 kB 7.5 MB/s eta 0:00:01\r\u001b[K |████████████████▊ | 819 kB 7.5 MB/s eta 0:00:01\r\u001b[K |█████████████████ | 829 kB 7.5 MB/s eta 0:00:01\r\u001b[K |█████████████████▏ | 839 kB 7.5 MB/s eta 0:00:01\r\u001b[K |█████████████████▍ | 849 kB 7.5 MB/s eta 0:00:01\r\u001b[K |█████████████████▌ | 860 kB 7.5 MB/s eta 0:00:01\r\u001b[K |█████████████████▊ | 870 kB 7.5 MB/s eta 0:00:01\r\u001b[K |██████████████████ | 880 kB 7.5 MB/s eta 0:00:01\r\u001b[K |██████████████████▏ | 890 kB 7.5 MB/s eta 0:00:01\r\u001b[K |██████████████████▍ | 901 kB 7.5 MB/s eta 0:00:01\r\u001b[K |██████████████████▋ | 911 kB 7.5 MB/s eta 0:00:01\r\u001b[K |██████████████████▉ | 921 kB 7.5 MB/s eta 0:00:01\r\u001b[K |███████████████████ | 931 kB 7.5 MB/s eta 0:00:01\r\u001b[K |███████████████████▎ | 942 kB 7.5 MB/s eta 0:00:01\r\u001b[K |███████████████████▍ | 952 kB 7.5 MB/s eta 0:00:01\r\u001b[K |███████████████████▋ | 962 kB 7.5 MB/s eta 0:00:01\r\u001b[K |███████████████████▉ | 972 kB 7.5 MB/s eta 0:00:01\r\u001b[K |████████████████████ | 983 kB 7.5 MB/s eta 0:00:01\r\u001b[K |████████████████████▎ | 993 kB 7.5 MB/s eta 0:00:01\r\u001b[K |████████████████████▌ | 1.0 MB 7.5 MB/s eta 0:00:01\r\u001b[K |████████████████████▊ | 1.0 MB 7.5 MB/s eta 0:00:01\r\u001b[K |█████████████████████ | 1.0 MB 7.5 MB/s eta 0:00:01\r\u001b[K |█████████████████████ | 1.0 MB 7.5 MB/s eta 0:00:01\r\u001b[K |█████████████████████▎ | 1.0 MB 7.5 MB/s eta 0:00:01\r\u001b[K |█████████████████████▌ | 1.1 MB 7.5 MB/s eta 0:00:01\r\u001b[K |█████████████████████▊ | 1.1 MB 7.5 MB/s eta 0:00:01\r\u001b[K |██████████████████████ | 1.1 MB 7.5 MB/s eta 0:00:01\r\u001b[K |██████████████████████▏ | 1.1 MB 7.5 MB/s eta 0:00:01\r\u001b[K |██████████████████████▍ | 1.1 MB 7.5 MB/s eta 0:00:01\r\u001b[K |██████████████████████▋ | 1.1 MB 7.5 MB/s eta 0:00:01\r\u001b[K |██████████████████████▊ | 1.1 MB 7.5 MB/s eta 0:00:01\r\u001b[K |███████████████████████ | 1.1 MB 7.5 MB/s eta 0:00:01\r\u001b[K |███████████████████████▏ | 1.1 MB 7.5 MB/s eta 0:00:01\r\u001b[K |███████████████████████▍ | 1.1 MB 7.5 MB/s eta 0:00:01\r\u001b[K |███████████████████████▋ | 1.2 MB 7.5 MB/s eta 0:00:01\r\u001b[K |███████████████████████▉ | 1.2 MB 7.5 MB/s eta 0:00:01\r\u001b[K |████████████████████████ | 1.2 MB 7.5 MB/s eta 0:00:01\r\u001b[K |████████████████████████▎ | 1.2 MB 7.5 MB/s eta 0:00:01\r\u001b[K |████████████████████████▌ | 1.2 MB 7.5 MB/s eta 0:00:01\r\u001b[K |████████████████████████▋ | 1.2 MB 7.5 MB/s eta 0:00:01\r\u001b[K |████████████████████████▉ | 1.2 MB 7.5 MB/s eta 0:00:01\r\u001b[K |█████████████████████████ | 1.2 MB 7.5 MB/s eta 0:00:01\r\u001b[K |█████████████████████████▎ | 1.2 MB 7.5 MB/s eta 0:00:01\r\u001b[K |█████████████████████████▌ | 1.2 MB 7.5 MB/s eta 0:00:01\r\u001b[K |█████████████████████████▊ | 1.3 MB 7.5 MB/s eta 0:00:01\r\u001b[K |██████████████████████████ | 1.3 MB 7.5 MB/s eta 0:00:01\r\u001b[K |██████████████████████████▏ | 1.3 MB 7.5 MB/s eta 0:00:01\r\u001b[K |██████████████████████████▎ | 1.3 MB 7.5 MB/s eta 0:00:01\r\u001b[K |██████████████████████████▌ | 1.3 MB 7.5 MB/s eta 0:00:01\r\u001b[K |██████████████████████████▊ | 1.3 MB 7.5 MB/s eta 0:00:01\r\u001b[K |███████████████████████████ | 1.3 MB 7.5 MB/s eta 0:00:01\r\u001b[K |███████████████████████████▏ | 1.3 MB 7.5 MB/s eta 0:00:01\r\u001b[K |███████████████████████████▍ | 1.3 MB 7.5 MB/s eta 0:00:01\r\u001b[K |███████████████████████████▋ | 1.4 MB 7.5 MB/s eta 0:00:01\r\u001b[K |███████████████████████████▉ | 1.4 MB 7.5 MB/s eta 0:00:01\r\u001b[K |████████████████████████████ | 1.4 MB 7.5 MB/s eta 0:00:01\r\u001b[K |████████████████████████████▏ | 1.4 MB 7.5 MB/s eta 0:00:01\r\u001b[K |████████████████████████████▍ | 1.4 MB 7.5 MB/s eta 0:00:01\r\u001b[K |████████████████████████████▋ | 1.4 MB 7.5 MB/s eta 0:00:01\r\u001b[K |████████████████████████████▉ | 1.4 MB 7.5 MB/s eta 0:00:01\r\u001b[K |█████████████████████████████ | 1.4 MB 7.5 MB/s eta 0:00:01\r\u001b[K |█████████████████████████████▎ | 1.4 MB 7.5 MB/s eta 0:00:01\r\u001b[K |█████████████████████████████▌ | 1.4 MB 7.5 MB/s eta 0:00:01\r\u001b[K |█████████████████████████████▊ | 1.5 MB 7.5 MB/s eta 0:00:01\r\u001b[K |█████████████████████████████▉ | 1.5 MB 7.5 MB/s eta 0:00:01\r\u001b[K |██████████████████████████████ | 1.5 MB 7.5 MB/s eta 0:00:01\r\u001b[K |██████████████████████████████▎ | 1.5 MB 7.5 MB/s eta 0:00:01\r\u001b[K |██████████████████████████████▌ | 1.5 MB 7.5 MB/s eta 0:00:01\r\u001b[K |██████████████████████████████▊ | 1.5 MB 7.5 MB/s eta 0:00:01\r\u001b[K |███████████████████████████████ | 1.5 MB 7.5 MB/s eta 0:00:01\r\u001b[K |███████████████████████████████▏| 1.5 MB 7.5 MB/s eta 0:00:01\r\u001b[K |███████████████████████████████▍| 1.5 MB 7.5 MB/s eta 0:00:01\r\u001b[K |███████████████████████████████▌| 1.5 MB 7.5 MB/s eta 0:00:01\r\u001b[K |███████████████████████████████▊| 1.6 MB 7.5 MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 1.6 MB 7.5 MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 1.6 MB 7.5 MB/s \n",
"\u001b[?25htime: 506 µs (started: 2022-11-24 06:06:09 +00:00)\n"
]
}
],
"source": [
"!pip install --quiet ipython-autotime\n",
"%load_ext autotime"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "Bijc_hfbfUwp",
"outputId": "54a7f895-8f08-452d-8f3a-8e5310a1aa6c"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[K |████████████████████████████████| 85 kB 3.9 MB/s \n",
"\u001b[K |████████████████████████████████| 182 kB 49.1 MB/s \n",
"\u001b[K |████████████████████████████████| 5.5 MB 54.9 MB/s \n",
"\u001b[K |████████████████████████████████| 7.6 MB 55.0 MB/s \n",
"\u001b[?25h Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"time: 10.4 s (started: 2022-11-24 06:06:09 +00:00)\n"
]
}
],
"source": [
"!pip install --quiet sentence-transformers==2.2.2"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bmVx9L0yfgvR"
},
"source": [
"The below code restarts the colab notebook. Once it is restarted continue from next section and no need to run this section (installation) again."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "uPO9U__1fZWh",
"outputId": "31e8d745-2a88-4bd6-f136-55cd2147ee3f"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"time: 556 µs (started: 2022-11-24 06:06:20 +00:00)\n"
]
}
],
"source": [
"# import os\n",
"# os.kill(os.getpid(), 9)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "POh2_zvgrk0h"
},
"source": [
"## Example 1"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VJP4CDBBrnNY"
},
"source": [
"Text taken from: \n",
"https://gadgets.ndtv.com/internet/news/dogecoin-price-rally-surge-elon-musk-tweet-twitter-working-developers-improve-transaction-efficiency-2442120"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "P_jlw7MUfjOp",
"outputId": "fd3e08da-3595-445d-941f-2c8047e34f08"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Elon Musk has shown again he can influence the digital currency market with just his tweets. After saying that his electric vehicle-making company\n",
"Tesla will not accept payments in Bitcoin because of environmental concerns, he tweeted that he was working with developers of Dogecoin to improve\n",
"system transaction efficiency. Following the two distinct statements from him, the world's largest cryptocurrency hit a two-month low, while Dogecoin\n",
"rallied by about 20 percent. The SpaceX CEO has in recent months often tweeted in support of Dogecoin, but rarely for Bitcoin. In a recent tweet,\n",
"Musk put out a statement from Tesla that it was “concerned” about the rapidly increasing use of fossil fuels for Bitcoin (price in India) mining and\n",
"transaction, and hence was suspending vehicle purchases using the cryptocurrency. A day later he again tweeted saying, “To be clear, I strongly\n",
"believe in crypto, but it can't drive a massive increase in fossil fuel use, especially coal”. It triggered a downward spiral for Bitcoin value but\n",
"the cryptocurrency has stabilised since. A number of Twitter users welcomed Musk's statement. One of them said it's time people started realising\n",
"that Dogecoin “is here to stay” and another referred to Musk's previous assertion that crypto could become the world's future currency.\n",
"\n",
"\n",
"time: 18.8 ms (started: 2022-11-24 06:06:20 +00:00)\n"
]
}
],
"source": [
"from textwrap3 import wrap\n",
"\n",
"text = \"\"\"Elon Musk has shown again he can influence the digital currency market with just his tweets. After saying that his electric vehicle-making company\n",
"Tesla will not accept payments in Bitcoin because of environmental concerns, he tweeted that he was working with developers of Dogecoin to improve\n",
"system transaction efficiency. Following the two distinct statements from him, the world's largest cryptocurrency hit a two-month low, while Dogecoin\n",
"rallied by about 20 percent. The SpaceX CEO has in recent months often tweeted in support of Dogecoin, but rarely for Bitcoin. In a recent tweet,\n",
"Musk put out a statement from Tesla that it was “concerned” about the rapidly increasing use of fossil fuels for Bitcoin (price in India) mining and\n",
"transaction, and hence was suspending vehicle purchases using the cryptocurrency. A day later he again tweeted saying, “To be clear, I strongly\n",
"believe in crypto, but it can't drive a massive increase in fossil fuel use, especially coal”. It triggered a downward spiral for Bitcoin value but\n",
"the cryptocurrency has stabilised since. A number of Twitter users welcomed Musk's statement. One of them said it's time people started realising\n",
"that Dogecoin “is here to stay” and another referred to Musk's previous assertion that crypto could become the world's future currency.\"\"\"\n",
"\n",
"for wrp in wrap(text, 150):\n",
" print (wrp)\n",
"print (\"\\n\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ShPNEZz8u7s6"
},
"source": [
"# **Summarization with T5**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true,
"referenced_widgets": [
"c9c2e5d5824345f780befcf11d6ff946",
"c39b4e7e424d4f64a8fb25495f8c7026",
"543714c7a41a4429a57a069bc2eca1dc"
]
},
"id": "H1eIU521rrn5",
"outputId": "d3bb1402-1cba-4881-b05f-b8e24bb19278"
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c9c2e5d5824345f780befcf11d6ff946",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading: 0%| | 0.00/1.20k [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c39b4e7e424d4f64a8fb25495f8c7026",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading: 0%| | 0.00/892M [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "543714c7a41a4429a57a069bc2eca1dc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading: 0%| | 0.00/792k [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/transformers/models/t5/tokenization_t5.py:174: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.\n",
"For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.\n",
"- Be aware that you SHOULD NOT rely on t5-base automatically truncating your input to 512 when padding/encoding.\n",
"- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.\n",
"- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.\n",
" FutureWarning,\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"time: 30.6 s (started: 2022-11-24 06:06:20 +00:00)\n"
]
}
],
"source": [
"import torch\n",
"from transformers import T5ForConditionalGeneration,T5Tokenizer\n",
"summary_model = T5ForConditionalGeneration.from_pretrained('t5-base')\n",
"summary_tokenizer = T5Tokenizer.from_pretrained('t5-base')\n",
"\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"summary_model = summary_model.to(device)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "8mVsjMPTu-bj",
"outputId": "e0ac198d-4625-4f8f-a2fd-9968c0a5a72d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"time: 1.03 ms (started: 2022-11-24 06:06:50 +00:00)\n"
]
}
],
"source": [
"import random\n",
"import numpy as np\n",
"\n",
"def set_seed(seed: int):\n",
" random.seed(seed)\n",
" np.random.seed(seed)\n",
" torch.manual_seed(seed)\n",
" torch.cuda.manual_seed_all(seed)\n",
"\n",
"set_seed(42)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "Gh2Xc5JRvQDp",
"outputId": "c1198166-2a2b-4571-b831-3ed1a8705c9e"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package punkt to /root/nltk_data...\n",
"[nltk_data] Unzipping tokenizers/punkt.zip.\n",
"[nltk_data] Downloading package brown to /root/nltk_data...\n",
"[nltk_data] Unzipping corpora/brown.zip.\n",
"[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"original Text >>\n",
"Elon Musk has shown again he can influence the digital currency market with just his tweets. After saying that his electric vehicle-making company\n",
"Tesla will not accept payments in Bitcoin because of environmental concerns, he tweeted that he was working with developers of Dogecoin to improve\n",
"system transaction efficiency. Following the two distinct statements from him, the world's largest cryptocurrency hit a two-month low, while Dogecoin\n",
"rallied by about 20 percent. The SpaceX CEO has in recent months often tweeted in support of Dogecoin, but rarely for Bitcoin. In a recent tweet,\n",
"Musk put out a statement from Tesla that it was “concerned” about the rapidly increasing use of fossil fuels for Bitcoin (price in India) mining and\n",
"transaction, and hence was suspending vehicle purchases using the cryptocurrency. A day later he again tweeted saying, “To be clear, I strongly\n",
"believe in crypto, but it can't drive a massive increase in fossil fuel use, especially coal”. It triggered a downward spiral for Bitcoin value but\n",
"the cryptocurrency has stabilised since. A number of Twitter users welcomed Musk's statement. One of them said it's time people started realising\n",
"that Dogecoin “is here to stay” and another referred to Musk's previous assertion that crypto could become the world's future currency.\n",
"\n",
"\n",
"Summarized Text >>\n",
"Musk tweeted that his electric vehicle-making company tesla will not accept payments in bitcoin because of environmental concerns. He also said that\n",
"the company was working with developers of dogecoin to improve system transaction efficiency. The world's largest cryptocurrency hit a two-month low,\n",
"while doge coin rallied by about 20 percent. Musk has in recent months often tweeted in support of crypto, but rarely for bitcoin.\n",
"\n",
"\n",
"time: 6.14 s (started: 2022-11-24 06:06:50 +00:00)\n"
]
}
],
"source": [
"import nltk\n",
"nltk.download('punkt')\n",
"nltk.download('brown')\n",
"nltk.download('wordnet')\n",
"from nltk.corpus import wordnet as wn\n",
"from nltk.tokenize import sent_tokenize\n",
"\n",
"def postprocesstext (content):\n",
" final=\"\"\n",
" for sent in sent_tokenize(content):\n",
" sent = sent.capitalize()\n",
" final = final +\" \"+sent\n",
" return final\n",
"\n",
"\n",
"def summarizer(text,model,tokenizer):\n",
" text = text.strip().replace(\"\\n\",\" \")\n",
" text = \"summarize: \"+text\n",
" # print (text)\n",
" max_len = 512\n",
" encoding = tokenizer.encode_plus(text,max_length=max_len, pad_to_max_length=False,truncation=True, return_tensors=\"pt\").to(device)\n",
"\n",
" input_ids, attention_mask = encoding[\"input_ids\"], encoding[\"attention_mask\"]\n",
"\n",
" outs = model.generate(input_ids=input_ids,\n",
" attention_mask=attention_mask,\n",
" early_stopping=True,\n",
" num_beams=3,\n",
" num_return_sequences=1,\n",
" no_repeat_ngram_size=2,\n",
" min_length = 75,\n",
" max_length=300)\n",
"\n",
"\n",
" dec = [tokenizer.decode(ids,skip_special_tokens=True) for ids in outs]\n",
" summary = dec[0]\n",
" summary = postprocesstext(summary)\n",
" summary= summary.strip()\n",
"\n",
" return summary\n",
"\n",
"\n",
"summarized_text = summarizer(text,summary_model,summary_tokenizer)\n",
"\n",
"\n",
"print (\"\\noriginal Text >>\")\n",
"for wrp in wrap(text, 150):\n",
" print (wrp)\n",
"print (\"\\n\")\n",
"print (\"Summarized Text >>\")\n",
"for wrp in wrap(summarized_text, 150):\n",
" print (wrp)\n",
"print (\"\\n\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JvBHu5eXv_wp"
},
"source": [
"# **Answer Span Extraction (Keywords and Noun Phrases)**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "84DxJGFn4MfD",
"outputId": "27c39b58-dcaa-4b92-ff9e-0da292be34d9"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
"[nltk_data] Unzipping corpora/stopwords.zip.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"time: 8.23 s (started: 2022-11-24 06:06:56 +00:00)\n"
]
}
],
"source": [
"import nltk\n",
"nltk.download('stopwords')\n",
"from nltk.corpus import stopwords\n",
"import string\n",
"import pke\n",
"import traceback\n",
"\n",
"def get_nouns_multipartite(content):\n",
" out=[]\n",
" try:\n",
" extractor = pke.unsupervised.MultipartiteRank()\n",
" extractor.load_document(input=content,language='en')\n",
" # not contain punctuation marks or stopwords as candidates.\n",
" pos = {'PROPN','NOUN'}\n",
" #pos = {'PROPN','NOUN'}\n",
" stoplist = list(string.punctuation)\n",
" stoplist += ['-lrb-', '-rrb-', '-lcb-', '-rcb-', '-lsb-', '-rsb-']\n",
" stoplist += stopwords.words('english')\n",
" # extractor.candidate_selection(pos=pos, stoplist=stoplist)\n",
" extractor.candidate_selection(pos=pos)\n",
" # 4. build the Multipartite graph and rank candidates using random walk,\n",
" # alpha controls the weight adjustment mechanism, see TopicRank for\n",
" # threshold/method parameters.\n",
" extractor.candidate_weighting(alpha=1.1,\n",
" threshold=0.75,\n",
" method='average')\n",
" keyphrases = extractor.get_n_best(n=15)\n",
" \n",
"\n",
" for val in keyphrases:\n",
" out.append(val[0])\n",
" except:\n",
" out = []\n",
" traceback.print_exc()\n",
"\n",
" return out"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "E8LNRzDVwDbp",
"outputId": "c2ae2bda-8250-4e82-ed71-d10568251e68"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"keywords unsummarized: ['elon musk', 'dogecoin', 'bitcoin', 'statements', 'use', 'cryptocurrency', 'tesla', 'tweets', 'musk', 'system transaction efficiency', 'currency market', 'world', 'price', 'payments', 'company']\n",
"keywords_found in summarized: ['world', 'dogecoin', 'musk', 'cryptocurrency', 'system transaction efficiency', 'payments', 'company', 'bitcoin', 'tesla']\n",
"['dogecoin', 'bitcoin', 'cryptocurrency', 'tesla', 'musk', 'system transaction efficiency', 'world', 'payments', 'company']\n",
"time: 785 ms (started: 2022-11-24 06:07:05 +00:00)\n"
]
}
],
"source": [
"from flashtext import KeywordProcessor\n",
"\n",
"\n",
"def get_keywords(originaltext,summarytext):\n",
" keywords = get_nouns_multipartite(originaltext)\n",
" print (\"keywords unsummarized: \",keywords)\n",
" keyword_processor = KeywordProcessor()\n",
" for keyword in keywords:\n",
" keyword_processor.add_keyword(keyword)\n",
"\n",
" keywords_found = keyword_processor.extract_keywords(summarytext)\n",
" keywords_found = list(set(keywords_found))\n",
" print (\"keywords_found in summarized: \",keywords_found)\n",
"\n",
" important_keywords =[]\n",
" for keyword in keywords:\n",
" if keyword in keywords_found:\n",
" important_keywords.append(keyword)\n",
"\n",
" return important_keywords[:10]\n",
"\n",
"\n",
"imp_keywords = get_keywords(text,summarized_text)\n",
"print (imp_keywords)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true,
"referenced_widgets": [
"24334ddee9f74d3c82a575f0edbc8720",
"c884156893794fa6bad4171a9aacbd2f",
"2f0d8bf7b60a423383ae6ab2469106eb",
"70c932999b0f4dcda0525b9a81ceabf3",
"7897cc69283d475694042ed9cbc6e92c"
]
},
"id": "m44RM44OwGzR",
"outputId": "ca45cae8-a813-4425-9adc-3d8e0f886324"
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "24334ddee9f74d3c82a575f0edbc8720",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading: 0%| | 0.00/1.21k [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c884156893794fa6bad4171a9aacbd2f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading: 0%| | 0.00/892M [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2f0d8bf7b60a423383ae6ab2469106eb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading: 0%| | 0.00/792k [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "70c932999b0f4dcda0525b9a81ceabf3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading: 0%| | 0.00/1.79k [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7897cc69283d475694042ed9cbc6e92c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading: 0%| | 0.00/1.86k [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"time: 35.2 s (started: 2022-11-24 06:07:05 +00:00)\n"
]
}
],
"source": [
"question_model = T5ForConditionalGeneration.from_pretrained('ramsrigouthamg/t5_squad_v1')\n",
"question_tokenizer = T5Tokenizer.from_pretrained('ramsrigouthamg/t5_squad_v1')\n",
"question_model = question_model.to(device)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "1usLabLu5DUB",
"outputId": "69d364b6-ee46-46d2-ee22-19b1fe5b2411"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Musk tweeted that his electric vehicle-making company tesla will not accept payments in bitcoin because of environmental concerns. He also said that\n",
"the company was working with developers of dogecoin to improve system transaction efficiency. The world's largest cryptocurrency hit a two-month low,\n",
"while doge coin rallied by about 20 percent. Musk has in recent months often tweeted in support of crypto, but rarely for bitcoin.\n",
"\n",
"\n",
"What did Musk say he was working with to improve system transaction efficiency?\n",
"Dogecoin\n",
"\n",
"\n",
"What cryptocurrency did Musk rarely tweet about?\n",
"Bitcoin\n",
"\n",
"\n",
"What has Musk often tweeted in support of?\n",
"Cryptocurrency\n",
"\n",
"\n",
"What company did Musk say would not accept bitcoin payments?\n",
"Tesla\n",
"\n",
"\n",
"Who said tesla would not accept bitcoin payments?\n",
"Musk\n",
"\n",
"\n",
"What did Musk want to improve with dogecoin?\n",
"System transaction efficiency\n",
"\n",
"\n",
"What is the largest cryptocurrency?\n",
"World\n",
"\n",
"\n",
"What did Musk say his company would not accept in bitcoin?\n",
"Payments\n",
"\n",
"\n",
"What did Musk say was working with dogecoin developers?\n",
"Company\n",
"\n",
"\n",
"time: 2.78 s (started: 2022-11-24 06:07:41 +00:00)\n"
]
}
],
"source": [
"def get_question(context,answer,model,tokenizer):\n",
" text = \"context: {} answer: {}\".format(context,answer)\n",
" encoding = tokenizer.encode_plus(text,max_length=384, pad_to_max_length=False,truncation=True, return_tensors=\"pt\").to(device)\n",
" input_ids, attention_mask = encoding[\"input_ids\"], encoding[\"attention_mask\"]\n",
"\n",
" outs = model.generate(input_ids=input_ids,\n",
" attention_mask=attention_mask,\n",
" early_stopping=True,\n",
" num_beams=5,\n",
" num_return_sequences=1,\n",
" no_repeat_ngram_size=2,\n",
" max_length=72)\n",
"\n",
"\n",
" dec = [tokenizer.decode(ids,skip_special_tokens=True) for ids in outs]\n",
"\n",
"\n",
" Question = dec[0].replace(\"question:\",\"\")\n",
" Question= Question.strip()\n",
" return Question\n",
"\n",
"\n",
"\n",
"for wrp in wrap(summarized_text, 150):\n",
" print (wrp)\n",
"print (\"\\n\")\n",
"\n",
"for answer in imp_keywords:\n",
" ques = get_question(summarized_text,answer,question_model,question_tokenizer)\n",
" print (ques)\n",
" print (answer.capitalize())\n",
" print (\"\\n\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "4kEuH__G6oDK",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 740
},
"outputId": "8a8b7911-1e79-403e-9601-6f7221fc8bd7"
},
"outputs": [
{
"metadata": {
"tags": null
},
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/gradio/inputs.py:27: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
" \"Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\",\n",
"/usr/local/lib/python3.7/dist-packages/gradio/deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
" warnings.warn(value)\n",
"/usr/local/lib/python3.7/dist-packages/gradio/deprecation.py:40: UserWarning: `numeric` parameter is deprecated, and it has no effect\n",
" warnings.warn(value)\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stdout",
"output_type": "stream",
"text": [
"Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
"Note: opening Chrome Inspector may crash demo inside Colab notebooks.\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"application/javascript": [
"(async (port, path, width, height, cache, element) => {\n",
" if (!google.colab.kernel.accessAllowed && !cache) {\n",
" return;\n",
" }\n",
" element.appendChild(document.createTextNode(''));\n",
" const url = await google.colab.kernel.proxyPort(port, {cache});\n",
"\n",
" const external_link = document.createElement('div');\n",
" external_link.innerHTML = `\n",
" <div style=\"font-family: monospace; margin-bottom: 0.5rem\">\n",
" Running on <a href=${new URL(path, url).toString()} target=\"_blank\">\n",
" https://localhost:${port}${path}\n",
" </a>\n",
" </div>\n",
" `;\n",
" element.appendChild(external_link);\n",
"\n",
" const iframe = document.createElement('iframe');\n",
" iframe.src = new URL(path, url).toString();\n",
" iframe.height = height;\n",
" iframe.allow = \"autoplay; camera; microphone; clipboard-read; clipboard-write;\"\n",
" iframe.width = width;\n",
" iframe.style.border = 0;\n",
" element.appendChild(iframe);\n",
" })(7860, \"/\", \"100%\", 500, false, window.element)"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import gradio as gr\n",
"\n",
"context = gr.inputs.Textbox(lines=10, placeholder=\"Enter paragraph/content here...\")\n",
"output = gr.outputs.HTML( label=\"Question and Answers\")\n",
"\n",
"\n",
"def generate_question(context):\n",
" summary_text = summarizer(context,summary_model,summary_tokenizer)\n",
" for wrp in wrap(summary_text, 150):\n",
" print (wrp)\n",
" np = get_keywords(context,summary_text)\n",
" print (\"\\n\\nNoun phrases\",np)\n",
" output=\"\"\n",
" for answer in np:\n",
" ques = get_question(summary_text,answer,question_model,question_tokenizer)\n",
" # output= output + ques + \"\\n\" + \"Ans: \"+answer.capitalize() + \"\\n\\n\"\n",
" output = output + \"<b style='color:blue;'>\" + ques + \"</b>\"\n",
" output = output + \"<br>\"\n",
" output = output + \"<b style='color:green;'>\" + \"Ans: \" +answer.capitalize()+ \"</b>\"\n",
" output = output + \"<br>\"\n",
"\n",
" summary =\"Summary: \"+ summary_text\n",
" for answer in np:\n",
" summary = summary.replace(answer,\"<b>\"+answer+\"</b>\")\n",
" summary = summary.replace(answer.capitalize(),\"<b>\"+answer.capitalize()+\"</b>\")\n",
" output = output + \"<p>\"+summary+\"</p>\"\n",
" \n",
" return output\n",
"\n",
"iface = gr.Interface(\n",
" fn=generate_question, \n",
" inputs=context, \n",
" outputs=output)\n",
"iface.launch(debug=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dNmJx7QNfLcy"
},
"source": [
"# **Filter keywords with Maximum marginal Relevance**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "zPBj-IUL7L8x"
},
"outputs": [],
"source": [
"!wget https://github.com/explosion/sense2vec/releases/download/v1.0.0/s2v_reddit_2015_md.tar.gz\n",
"!tar -xvf s2v_reddit_2015_md.tar.gz"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "s5RI3fk9fOOz"
},
"outputs": [],
"source": [
"import numpy as np\n",
"from sense2vec import Sense2Vec\n",
"s2v = Sense2Vec().from_disk('s2v_old')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "J2y3unpvfo1y"
},
"outputs": [],
"source": [
"from sentence_transformers import SentenceTransformer\n",
"# paraphrase-distilroberta-base-v1\n",
"sentence_transformer_model = SentenceTransformer('msmarco-distilbert-base-v3')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "pvfmhuWVfsJb"
},
"outputs": [],
"source": [
"from similarity.normalized_levenshtein import NormalizedLevenshtein\n",
"normalized_levenshtein = NormalizedLevenshtein()\n",
"\n",
"def filter_same_sense_words(original,wordlist):\n",
" filtered_words=[]\n",
" base_sense =original.split('|')[1] \n",
" print (base_sense)\n",
" for eachword in wordlist:\n",
" if eachword[0].split('|')[1] == base_sense:\n",
" filtered_words.append(eachword[0].split('|')[0].replace(\"_\", \" \").title().strip())\n",
" return filtered_words\n",
"\n",
"def get_highest_similarity_score(wordlist,wrd):\n",
" score=[]\n",
" for each in wordlist:\n",
" score.append(normalized_levenshtein.similarity(each.lower(),wrd.lower()))\n",
" return max(score)\n",
"\n",
"def sense2vec_get_words(word,s2v,topn,question):\n",
" output = []\n",
" print (\"word \",word)\n",
" try:\n",
" sense = s2v.get_best_sense(word, senses= [\"NOUN\", \"PERSON\",\"PRODUCT\",\"LOC\",\"ORG\",\"EVENT\",\"NORP\",\"WORK OF ART\",\"FAC\",\"GPE\",\"NUM\",\"FACILITY\"])\n",
" most_similar = s2v.most_similar(sense, n=topn)\n",
" # print (most_similar)\n",
" output = filter_same_sense_words(sense,most_similar)\n",
" print (\"Similar \",output)\n",
" except:\n",
" output =[]\n",
"\n",
" threshold = 0.6\n",
" final=[word]\n",
" checklist =question.split()\n",
" for x in output:\n",
" if get_highest_similarity_score(final,x)<threshold and x not in final and x not in checklist:\n",
" final.append(x)\n",
" \n",
" return final[1:]\n",
"\n",
"def mmr(doc_embedding, word_embeddings, words, top_n, lambda_param):\n",
"\n",
" # Extract similarity within words, and between words and the document\n",
" word_doc_similarity = cosine_similarity(word_embeddings, doc_embedding)\n",
" word_similarity = cosine_similarity(word_embeddings)\n",
"\n",
" # Initialize candidates and already choose best keyword/keyphrase\n",
" keywords_idx = [np.argmax(word_doc_similarity)]\n",
" candidates_idx = [i for i in range(len(words)) if i != keywords_idx[0]]\n",
"\n",
" for _ in range(top_n - 1):\n",
" # Extract similarities within candidates and\n",
" # between candidates and selected keywords/phrases\n",
" candidate_similarities = word_doc_similarity[candidates_idx, :]\n",
" target_similarities = np.max(word_similarity[candidates_idx][:, keywords_idx], axis=1)\n",
"\n",
" # Calculate MMR\n",
" mmr = (lambda_param) * candidate_similarities - (1-lambda_param) * target_similarities.reshape(-1, 1)\n",
" mmr_idx = candidates_idx[np.argmax(mmr)]\n",
"\n",
" # Update keywords & candidates\n",
" keywords_idx.append(mmr_idx)\n",
" candidates_idx.remove(mmr_idx)\n",
"\n",
" return [words[idx] for idx in keywords_idx]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "UCN0-kXEfxwy"
},
"outputs": [],
"source": [
"from collections import OrderedDict\n",
"from sklearn.metrics.pairwise import cosine_similarity\n",
"import nltk\n",
"nltk.download('omw-1.4')\n",
"\n",
"def get_distractors_wordnet(word):\n",
" distractors=[]\n",
" try:\n",
" syn = wn.synsets(word,'n')[0]\n",
" \n",
" word= word.lower()\n",
" orig_word = word\n",
" if len(word.split())>0:\n",
" word = word.replace(\" \",\"_\")\n",
" hypernym = syn.hypernyms()\n",
" if len(hypernym) == 0: \n",
" return distractors\n",
" for item in hypernym[0].hyponyms():\n",
" name = item.lemmas()[0].name()\n",
" #print (\"name \",name, \" word\",orig_word)\n",
" if name == orig_word:\n",
" continue\n",
" name = name.replace(\"_\",\" \")\n",
" name = \" \".join(w.capitalize() for w in name.split())\n",
" if name is not None and name not in distractors:\n",
" distractors.append(name)\n",
" except:\n",
" print (\"Wordnet distractors not found\")\n",
" return distractors\n",
"\n",
"def get_distractors (word,origsentence,sense2vecmodel,sentencemodel,top_n,lambdaval):\n",
" distractors = sense2vec_get_words(word,sense2vecmodel,top_n,origsentence)\n",
" print (\"distractors \",distractors)\n",
" if len(distractors) ==0:\n",
" return distractors\n",
" distractors_new = [word.capitalize()]\n",
" distractors_new.extend(distractors)\n",
" # print (\"distractors_new .. \",distractors_new)\n",
"\n",
" embedding_sentence = origsentence+ \" \"+word.capitalize()\n",
" # embedding_sentence = word\n",
" keyword_embedding = sentencemodel.encode([embedding_sentence])\n",
" distractor_embeddings = sentencemodel.encode(distractors_new)\n",
"\n",
" # filtered_keywords = mmr(keyword_embedding, distractor_embeddings,distractors,4,0.7)\n",
" max_keywords = min(len(distractors_new),5)\n",
" filtered_keywords = mmr(keyword_embedding, distractor_embeddings,distractors_new,max_keywords,lambdaval)\n",
" # filtered_keywords = filtered_keywords[1:]\n",
" final = [word.capitalize()]\n",
" for wrd in filtered_keywords:\n",
" if wrd.lower() !=word.lower():\n",
" final.append(wrd.capitalize())\n",
" final = final[1:]\n",
" return final\n",
"\n",
"sent = \"What cryptocurrency did Musk rarely tweet about?\"\n",
"keyword = \"Bitcoin\"\n",
"\n",
"# sent = \"What did Musk say he was working with to improve system transaction efficiency?\"\n",
"# keyword= \"Dogecoin\"\n",
"\n",
"\n",
"# sent = \"What company did Musk say would not accept bitcoin payments?\"\n",
"# keyword= \"Tesla\"\n",
"\n",
"\n",
"# sent = \"What has Musk often tweeted in support of?\"\n",
"# keyword = \"Cryptocurrency\"\n",
"\n",
"print (get_distractors(keyword,sent,s2v,sentence_transformer_model,40,0.2))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "s2FX-mGdf08p"
},
"outputs": [],
"source": [
"get_distractors_wordnet('lion')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "vgvffLecf4Cq"
},
"outputs": [],
"source": [
"import gradio as gr\n",
"\n",
"context = gr.inputs.Textbox(lines=10, placeholder=\"Enter paragraph/content here...\")\n",
"output = gr.outputs.HTML( label=\"Question and Answers\")\n",
"radiobutton = gr.inputs.Radio([\"Wordnet\", \"Sense2Vec\"])\n",
"\n",
"def generate_question(context,radiobutton):\n",
" summary_text = summarizer(context,summary_model,summary_tokenizer)\n",
" for wrp in wrap(summary_text, 100):\n",
" print (wrp)\n",
" # np = getnounphrases(summary_text,sentence_transformer_model,3)\n",
" np = get_keywords(context,summary_text)\n",
" print (\"\\n\\nNoun phrases\",np)\n",
" output=\"\"\n",
" for answer in np:\n",
" ques = get_question(summary_text,answer,question_model,question_tokenizer)\n",
" if radiobutton==\"Wordnet\":\n",
" distractors = get_distractors_wordnet(answer)\n",
" else:\n",
" distractors = get_distractors(answer.capitalize(),ques,s2v,sentence_transformer_model,40,0.2)\n",
" # output= output + ques + \"\\n\" + \"Ans: \"+answer.capitalize() + \"\\n\\n\"\n",
" output = output + \"<b style='color:blue;'>\" + ques + \"</b>\"\n",
" output = output + \"<br>\"\n",
" output = output + \"<b style='color:green;'>\" + \"Ans: \" +answer.capitalize()+ \"</b>\"+\"<br>\"\n",
" if len(distractors)>0:\n",
" for distractor in distractors[:4]:\n",
" output = output + \"<b style='color:brown;'>\" + distractor+ \"</b>\"+\"<br>\"\n",
" output = output + \"<br>\"\n",
"\n",
" summary =\"Summary: \"+ summary_text\n",
" for answer in np:\n",
" summary = summary.replace(answer,\"<b>\"+answer+\"</b>\" + \"<br>\")\n",
" summary = summary.replace(answer.capitalize(),\"<b>\"+answer.capitalize()+\"</b>\")\n",
" output = output + \"<p>\"+summary+\"</p>\"\n",
" output = output + \"<br>\"\n",
" return output\n",
"\n",
"\n",
"iface = gr.Interface(\n",
" fn=generate_question, \n",
" inputs=[context,radiobutton], \n",
" outputs=output)\n",
"iface.launch(debug=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "EhKGhA1ff7Hi"
},
"outputs": [],
"source": [
"import requests\n",
"\n",
"url = \"https://question-answer.p.rapidapi.com/question-answer\"\n",
"\n",
"querystring = {\"question\":\"What are some tips to starting up your own small business?\"}\n",
"\n",
"headers = {\n",
"\t\"X-RapidAPI-Key\": \"SIGN-UP-FOR-KEY\",\n",
"\t\"X-RapidAPI-Host\": \"question-answer.p.rapidapi.com\"\n",
"}\n",
"\n",
"response = requests.request(\"GET\", url, headers=headers, params=querystring)\n",
"\n",
"print(response.text)"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"provenance": []
},
"gpuClass": "standard",
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
} |