Datasets:
Multilinguality:
multilingual
Size Categories:
100K<n<1M
Language Creators:
found
Annotations Creators:
other
Source Datasets:
extended|other
Tags:
discourse-mode-classification
paraphrase-identification
cross-lingual-similarity
headline-classification
License:
File size: 46,465 Bytes
067fa9b 9a3f635 067fa9b 479e755 067fa9b 479e755 067fa9b 479e755 067fa9b 8778d83 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b 2851143 067fa9b |
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 |
"""The IndicGLUE benchmark."""
import csv
import json
import textwrap
import pandas as pd
import datasets
_INDIC_GLUE_CITATION = """\
@inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},
author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},
year={2020},
booktitle={Findings of EMNLP},
}
"""
_INDIC_GLUE_DESCRIPTION = """\
IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide
variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.
"""
_DESCRIPTIONS = {
"wnli": textwrap.dedent(
"""
The Winograd Schema Challenge (Levesque et al., 2011) is a reading comprehension task
in which a system must read a sentence with a pronoun and select the referent of that pronoun from
a list of choices. The examples are manually constructed to foil simple statistical methods: Each
one is contingent on contextual information provided by a single word or phrase in the sentence.
To convert the problem into sentence pair classification, we construct sentence pairs by replacing
the ambiguous pronoun with each possible referent. The task is to predict if the sentence with the
pronoun substituted is entailed by the original sentence. We use a small evaluation set consisting of
new examples derived from fiction books that was shared privately by the authors of the original
corpus. While the included training set is balanced between two classes, the test set is imbalanced
between them (65% not entailment). Also, due to a data quirk, the development set is adversarial:
hypotheses are sometimes shared between training and development examples, so if a model memorizes the
training examples, they will predict the wrong label on corresponding development set
example. As with QNLI, each example is evaluated separately, so there is not a systematic correspondence
between a model's score on this task and its score on the unconverted original task. We
call converted dataset WNLI (Winograd NLI). This dataset is translated and publicly released for 3
Indian languages by AI4Bharat.
"""
),
"copa": textwrap.dedent(
"""
The Choice Of Plausible Alternatives (COPA) evaluation provides researchers with a tool for assessing
progress in open-domain commonsense causal reasoning. COPA consists of 1000 questions, split equally
into development and test sets of 500 questions each. Each question is composed of a premise and two
alternatives, where the task is to select the alternative that more plausibly has a causal relation
with the premise. The correct alternative is randomized so that the expected performance of randomly
guessing is 50%. This dataset is translated and publicly released for 3 languages by AI4Bharat.
"""
),
"sna": textwrap.dedent(
"""
This dataset is a collection of Bengali News articles. The dataset is used for classifying articles into
6 different classes namely national, international, state, kolkata, entertainment and sports.
"""
),
"csqa": textwrap.dedent(
"""
Given a text with an entity randomly masked, the task is to predict that masked entity from a list of 4
candidate entities. The dataset contains around 239k examples across 11 languages.
"""
),
"wstp": textwrap.dedent(
"""
Predict the correct title for a Wikipedia section from a given list of four candidate titles.
The dataset has 400k examples across 11 Indian languages.
"""
),
"inltkh": textwrap.dedent(
"""
Obtained from inltk project. The corpus is a collection of headlines tagged with their news category.
Available for langauges: gu, ml, mr and ta.
"""
),
"bbca": textwrap.dedent(
"""
This release consists of 4335 Hindi documents with tags from the BBC Hindi News website.
"""
),
"cvit-mkb-clsr": textwrap.dedent(
"""
CVIT Maan ki Baat Dataset - Given a sentence in language $L_1$ the task is to retrieve its translation
from a set of candidate sentences in language $L_2$.
The dataset contains around 39k parallel sentence pairs across 8 Indian languages.
"""
),
"iitp-mr": textwrap.dedent(
"""
IIT Patna Product Reviews: Sentiment analysis corpus for product reviews posted in Hindi.
"""
),
"iitp-pr": textwrap.dedent(
"""
IIT Patna Product Reviews: Sentiment analysis corpus for product reviews posted in Hindi.
"""
),
"actsa-sc": textwrap.dedent(
"""
ACTSA Corpus: Sentiment analysis corpus for Telugu sentences.
"""
),
"md": textwrap.dedent(
"""
The Hindi Discourse Analysis dataset is a corpus for analyzing discourse modes present in its sentences.
It contains sentences from stories written by 11 famous authors from the 20th Century. 4-5 stories by
each author have been selected which were available in the public domain resulting in a collection of 53 stories.
Most of these short stories were originally written in Hindi but some of them were written in other Indian languages
and later translated to Hindi.
"""
),
"wiki-ner": textwrap.dedent(
"""
The WikiANN dataset (Pan et al. 2017) is a dataset with NER annotations for PER, ORG and LOC. It has been constructed using
the linked entities in Wikipedia pages for 282 different languages including Danish.
"""
),
}
_CITATIONS = {
"wnli": textwrap.dedent(
"""
@inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},
author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},
year={2020},
booktitle={Findings of EMNLP},
}
@inproceedings{Levesque2011TheWS,
title={The Winograd Schema Challenge},
author={H. Levesque and E. Davis and L. Morgenstern},
booktitle={KR},
year={2011}
}
"""
),
"copa": textwrap.dedent(
"""
@inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},
author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},
year={2020},
booktitle={Findings of EMNLP},
}
@inproceedings{Gordon2011SemEval2012T7,
title={SemEval-2012 Task 7: Choice of Plausible Alternatives: An Evaluation of Commonsense Causal Reasoning},
author={Andrew S. Gordon and Zornitsa Kozareva and Melissa Roemmele},
booktitle={SemEval@NAACL-HLT},
year={2011}
}
"""
),
"sna": textwrap.dedent(
"""
https://www.kaggle.com/csoham/classification-bengali-news-articles-indicnlp
"""
),
"csqa": textwrap.dedent(
"""
@inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},
author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},
year={2020},
booktitle={Findings of EMNLP},
}
"""
),
"wstp": textwrap.dedent(
"""
@inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},
author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},
year={2020},
booktitle={Findings of EMNLP},
}
"""
),
"inltkh": textwrap.dedent(
"""
https://github.com/goru001/inltk
"""
),
"bbca": textwrap.dedent(
"""
https://github.com/NirantK/hindi2vec/releases/tag/bbc-hindi-v0.1
"""
),
"cvit-mkb-clsr": textwrap.dedent(
"""
@inproceedings{siripragada-etal-2020-multilingual,
title = "A Multilingual Parallel Corpora Collection Effort for {I}ndian Languages",
author = "Siripragada, Shashank and
Philip, Jerin and
Namboodiri, Vinay P. and
Jawahar, C V",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://www.aclweb.org/anthology/2020.lrec-1.462",
pages = "3743--3751",
abstract = "We present sentence aligned parallel corpora across 10 Indian Languages - Hindi, Telugu, Tamil, Malayalam, Gujarati, Urdu, Bengali, Oriya, Marathi, Punjabi, and English - many of which are categorized as low resource. The corpora are compiled from online sources which have content shared across languages. The corpora presented significantly extends present resources that are either not large enough or are restricted to a specific domain (such as health). We also provide a separate test corpus compiled from an independent online source that can be independently used for validating the performance in 10 Indian languages. Alongside, we report on the methods of constructing such corpora using tools enabled by recent advances in machine translation and cross-lingual retrieval using deep neural network based methods.",
language = "English",
ISBN = "979-10-95546-34-4",
}
"""
),
"iitp-mr": textwrap.dedent(
"""
@inproceedings{akhtar-etal-2016-hybrid,
title = "A Hybrid Deep Learning Architecture for Sentiment Analysis",
author = "Akhtar, Md Shad and
Kumar, Ayush and
Ekbal, Asif and
Bhattacharyya, Pushpak",
booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://www.aclweb.org/anthology/C16-1047",
pages = "482--493",
abstract = "In this paper, we propose a novel hybrid deep learning archtecture which is highly efficient for sentiment analysis in resource-poor languages. We learn sentiment embedded vectors from the Convolutional Neural Network (CNN). These are augmented to a set of optimized features selected through a multi-objective optimization (MOO) framework. The sentiment augmented optimized vector obtained at the end is used for the training of SVM for sentiment classification. We evaluate our proposed approach for coarse-grained (i.e. sentence level) as well as fine-grained (i.e. aspect level) sentiment analysis on four Hindi datasets covering varying domains. In order to show that our proposed method is generic in nature we also evaluate it on two benchmark English datasets. Evaluation shows that the results of the proposed method are consistent across all the datasets and often outperforms the state-of-art systems. To the best of our knowledge, this is the very first attempt where such a deep learning model is used for less-resourced languages such as Hindi.",
}
"""
),
"iitp-pr": textwrap.dedent(
"""
@inproceedings{akhtar-etal-2016-hybrid,
title = "A Hybrid Deep Learning Architecture for Sentiment Analysis",
author = "Akhtar, Md Shad and
Kumar, Ayush and
Ekbal, Asif and
Bhattacharyya, Pushpak",
booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://www.aclweb.org/anthology/C16-1047",
pages = "482--493",
abstract = "In this paper, we propose a novel hybrid deep learning archtecture which is highly efficient for sentiment analysis in resource-poor languages. We learn sentiment embedded vectors from the Convolutional Neural Network (CNN). These are augmented to a set of optimized features selected through a multi-objective optimization (MOO) framework. The sentiment augmented optimized vector obtained at the end is used for the training of SVM for sentiment classification. We evaluate our proposed approach for coarse-grained (i.e. sentence level) as well as fine-grained (i.e. aspect level) sentiment analysis on four Hindi datasets covering varying domains. In order to show that our proposed method is generic in nature we also evaluate it on two benchmark English datasets. Evaluation shows that the results of the proposed method are consistent across all the datasets and often outperforms the state-of-art systems. To the best of our knowledge, this is the very first attempt where such a deep learning model is used for less-resourced languages such as Hindi.",
}
"""
),
"actsa-sc": textwrap.dedent(
"""
@inproceedings{mukku-mamidi-2017-actsa,
title = "{ACTSA}: Annotated Corpus for {T}elugu Sentiment Analysis",
author = "Mukku, Sandeep Sricharan and
Mamidi, Radhika",
booktitle = "Proceedings of the First Workshop on Building Linguistically Generalizable {NLP} Systems",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W17-5408",
doi = "10.18653/v1/W17-5408",
pages = "54--58",
abstract = "Sentiment analysis deals with the task of determining the polarity of a document or sentence and has received a lot of attention in recent years for the English language. With the rapid growth of social media these days, a lot of data is available in regional languages besides English. Telugu is one such regional language with abundant data available in social media, but it{'}s hard to find a labelled data of sentences for Telugu Sentiment Analysis. In this paper, we describe an effort to build a gold-standard annotated corpus of Telugu sentences to support Telugu Sentiment Analysis. The corpus, named ACTSA (Annotated Corpus for Telugu Sentiment Analysis) has a collection of Telugu sentences taken from different sources which were then pre-processed and manually annotated by native Telugu speakers using our annotation guidelines. In total, we have annotated 5457 sentences, which makes our corpus the largest resource currently available. The corpus and the annotation guidelines are made publicly available.",
}
"""
),
"md": textwrap.dedent(
"""
@inproceedings{Dhanwal2020AnAD,
title={An Annotated Dataset of Discourse Modes in Hindi Stories},
author={Swapnil Dhanwal and Hritwik Dutta and Hitesh Nankani and Nilay Shrivastava and Y. Kumar and Junyi Jessy Li and Debanjan Mahata and Rakesh Gosangi and Haimin Zhang and R. R. Shah and Amanda Stent},
booktitle={LREC},
year={2020}
}
"""
),
"wiki-ner": textwrap.dedent(
"""
@inproceedings{pan-etal-2017-cross,
title = "Cross-lingual Name Tagging and Linking for 282 Languages",
author = "Pan, Xiaoman and
Zhang, Boliang and
May, Jonathan and
Nothman, Joel and
Knight, Kevin and
Ji, Heng",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P17-1178",
doi = "10.18653/v1/P17-1178",
pages = "1946--1958",
abstract = "The ambitious goal of this work is to develop a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia. Given a document in any of these languages, our framework is able to identify name mentions, assign a coarse-grained or fine-grained type to each mention, and link it to an English Knowledge Base (KB) if it is linkable. We achieve this goal by performing a series of new KB mining methods: generating {``}silver-standard{''} annotations by transferring annotations from English to other languages through cross-lingual links and KB properties, refining annotations through self-training and topic selection, deriving language-specific morphology features from anchor links, and mining word translation pairs from cross-lingual links. Both name tagging and linking results for 282 languages are promising on Wikipedia data and on-Wikipedia data.",
}
"""
),
}
_TEXT_FEATURES = {
"wnli": {"hypothesis": "sentence1", "premise": "sentence2"},
"copa": {"premise": "premise", "choice1": "choice1", "choice2": "choice2", "question": "question"},
"sna": {"text": "text"},
"csqa": {"question": "question", "answer": "answer", "category": "category", "title": "title"},
"wstp": {
"sectionText": "sectionText",
"correctTitle": "correctTitle",
"titleA": "titleA",
"titleB": "titleB",
"titleC": "titleC",
"titleD": "titleD",
"url": "url",
},
"inltkh": {"text": "text"},
"bbca": {"label": "label", "text": "text"},
"cvit-mkb-clsr": {"sentence1": "sentence1", "sentence2": "sentence2"},
"iitp-mr": {"text": "text"},
"iitp-pr": {"text": "text"},
"actsa-sc": {"text": "text"},
"md": {"sentence": "sentence", "discourse_mode": "discourse_mode"},
"wiki-ner": {},
}
_DATA_URLS = {
"wnli": "https://ai4b-public-nlu-nlg.objectstore.e2enetworks.net/IndicGLUE/wnli-translated.tar.gz",
"copa": "https://ai4b-public-nlu-nlg.objectstore.e2enetworks.net/IndicGLUE/copa-translated.tar.gz",
"sna": "https://ai4b-public-nlu-nlg.objectstore.e2enetworks.net/IndicGLUE/soham-articles.tar.gz",
"csqa": "https://ai4b-public-nlu-nlg.objectstore.e2enetworks.net/IndicGLUE/wiki-cloze.tar.gz",
"wstp": "https://ai4b-public-nlu-nlg.objectstore.e2enetworks.net/IndicGLUE/wiki-section-titles.tar.gz",
"inltkh": "https://ai4b-public-nlu-nlg.objectstore.e2enetworks.net/IndicGLUE/inltk-headlines.tar.gz",
"bbca": "https://ai4b-public-nlu-nlg.objectstore.e2enetworks.net/IndicGLUE/bbc-articles.tar.gz",
"cvit-mkb-clsr": "https://ai4b-public-nlu-nlg.objectstore.e2enetworks.net/IndicGLUE/cvit-mkb.tar.gz",
"iitp-mr": "https://ai4b-public-nlu-nlg.objectstore.e2enetworks.net/IndicGLUE/iitp-movie-reviews.tar.gz",
"iitp-pr": "https://ai4b-public-nlu-nlg.objectstore.e2enetworks.net/IndicGLUE/iitp-product-reviews.tar.gz",
"actsa-sc": "https://ai4b-public-nlu-nlg.objectstore.e2enetworks.net/IndicGLUE/actsa.tar.gz",
"md": "https://ai4b-public-nlu-nlg.objectstore.e2enetworks.net/IndicGLUE/midas-discourse.tar.gz",
"wiki-ner": "https://ai4b-public-nlu-nlg.objectstore.e2enetworks.net/IndicGLUE/wikiann-ner.tar.gz",
}
_URLS = {
"wnli": "https://ai4bharat.iitm.ac.in/indic-glue",
"copa": "https://ai4bharat.iitm.ac.in/indic-glue",
"sna": "https://ai4bharat.iitm.ac.in/indic-glue",
"csqa": "https://ai4bharat.iitm.ac.in/indic-glue",
"wstp": "https://ai4bharat.iitm.ac.in/indic-glue",
"inltkh": "https://ai4bharat.iitm.ac.in/indic-glue",
"bbca": "https://ai4bharat.iitm.ac.in/indic-glue",
"cvit-mkb-clsr": "https://ai4bharat.iitm.ac.in/indic-glue",
"iitp-mr": "https://ai4bharat.iitm.ac.in/indic-glue",
"iitp-pr": "https://ai4bharat.iitm.ac.in/indic-glue",
"actsa-sc": "https://ai4bharat.iitm.ac.in/indic-glue",
"md": "https://ai4bharat.iitm.ac.in/indic-glue",
"wiki-ner": "https://ai4bharat.iitm.ac.in/indic-glue",
}
_INDIC_GLUE_URL = "https://ai4bharat.iitm.ac.in/indic-glue"
_WNLI_LANGS = ["en", "hi", "gu", "mr"]
_COPA_LANGS = ["en", "hi", "gu", "mr"]
_SNA_LANGS = ["bn"]
_CSQA_LANGS = ["as", "bn", "gu", "hi", "kn", "ml", "mr", "or", "pa", "ta", "te"]
_WSTP_LANGS = ["as", "bn", "gu", "hi", "kn", "ml", "mr", "or", "pa", "ta", "te"]
_iNLTKH_LANGS = ["gu", "ml", "mr", "ta", "te"]
_BBCA_LANGS = ["hi"]
_CVIT_MKB_CLSR = ["en-bn", "en-gu", "en-hi", "en-ml", "en-mr", "en-or", "en-ta", "en-te", "en-ur"]
_IITP_MR_LANGS = ["hi"]
_IITP_PR_LANGS = ["hi"]
_ACTSA_LANGS = ["te"]
_MD_LANGS = ["hi"]
_WIKI_NER_LANGS = ["as", "bn", "gu", "hi", "kn", "ml", "mr", "or", "pa", "ta", "te"]
_NAMES = []
for lang in _WNLI_LANGS:
_NAMES.append(f"wnli.{lang}")
for lang in _COPA_LANGS:
_NAMES.append(f"copa.{lang}")
for lang in _SNA_LANGS:
_NAMES.append(f"sna.{lang}")
for lang in _CSQA_LANGS:
_NAMES.append(f"csqa.{lang}")
for lang in _WSTP_LANGS:
_NAMES.append(f"wstp.{lang}")
for lang in _iNLTKH_LANGS:
_NAMES.append(f"inltkh.{lang}")
for lang in _BBCA_LANGS:
_NAMES.append(f"bbca.{lang}")
for lang in _CVIT_MKB_CLSR:
_NAMES.append(f"cvit-mkb-clsr.{lang}")
for lang in _IITP_MR_LANGS:
_NAMES.append(f"iitp-mr.{lang}")
for lang in _IITP_PR_LANGS:
_NAMES.append(f"iitp-pr.{lang}")
for lang in _ACTSA_LANGS:
_NAMES.append(f"actsa-sc.{lang}")
for lang in _MD_LANGS:
_NAMES.append(f"md.{lang}")
for lang in _WIKI_NER_LANGS:
_NAMES.append(f"wiki-ner.{lang}")
class IndicGlueConfig(datasets.BuilderConfig):
"""BuilderConfig for IndicGLUE."""
def __init__(self, data_url, citation, url, text_features, **kwargs):
"""
Args:
data_url: `string`, url to download the zip file from.
citation: `string`, citation for the data set.
url: `string`, url for information about the data set.
text_features: `dict[string, string]`, map from the name of the feature
dict for each text field to the name of the column in the csv/json file
**kwargs: keyword arguments forwarded to super.
"""
super(IndicGlueConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
self.data_url = data_url
self.citation = citation
self.url = url
self.text_features = text_features
class IndicGlue(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
IndicGlueConfig(
name=name,
description=_DESCRIPTIONS[name.split(".")[0]],
text_features=_TEXT_FEATURES[name.split(".")[0]],
data_url=_DATA_URLS[name.split(".")[0]],
citation=_CITATIONS[name.split(".")[0]],
url=_URLS[name.split(".")[0]],
)
for name in _NAMES
]
def _info(self):
features = {text_feature: datasets.Value("string") for text_feature in self.config.text_features.keys()}
if self.config.name.startswith("copa"):
features["label"] = datasets.Value("int32")
if self.config.name.startswith("sna"):
features["label"] = datasets.features.ClassLabel(
names=["kolkata", "state", "national", "sports", "entertainment", "international"]
)
if self.config.name.startswith("inltkh"):
features["label"] = datasets.features.ClassLabel(
names=[
"entertainment",
"business",
"tech",
"sports",
"state",
"spirituality",
"tamil-cinema",
"positive",
"negative",
"neutral",
]
)
if self.config.name.startswith("iitp"):
features["label"] = datasets.features.ClassLabel(names=["negative", "neutral", "positive"])
if self.config.name.startswith("wnli"):
features["label"] = datasets.features.ClassLabel(names=["not_entailment", "entailment", "None"])
if self.config.name.startswith("actsa"):
features["label"] = datasets.features.ClassLabel(names=["positive", "negative"])
if self.config.name.startswith("csqa"):
features["options"] = datasets.features.Sequence(datasets.Value("string"))
features["out_of_context_options"] = datasets.features.Sequence(datasets.Value("string"))
if self.config.name.startswith("md"):
features["story_number"] = datasets.Value("int32")
features["id"] = datasets.Value("int32")
if self.config.name.startswith("wiki-ner"):
features["tokens"] = datasets.features.Sequence(datasets.Value("string"))
features["ner_tags"] = datasets.features.Sequence(
datasets.features.ClassLabel(names=["B-LOC", "B-ORG", "B-PER", "I-LOC", "I-ORG", "I-PER", "O"])
)
features["additional_info"] = datasets.features.Sequence(
datasets.features.Sequence(datasets.Value("string"))
)
return datasets.DatasetInfo(
description=_INDIC_GLUE_DESCRIPTION + "\n" + self.config.description,
features=datasets.Features(features),
homepage=self.config.url,
citation=_INDIC_GLUE_CITATION + "\n" + self.config.citation,
)
def _split_generators(self, dl_manager):
if self.config.name.startswith("wnli"):
archive = dl_manager.download(self.config.data_url)
task_name = self._get_task_name_from_data_url(self.config.data_url)
dl_dir = task_name + "/" + self.config.name.split(".")[1]
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"datafile": dl_dir + "/" + "train.csv",
"split": datasets.Split.TRAIN,
"key": "train-split",
"files": dl_manager.iter_archive(archive),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"datafile": dl_dir + "/" + "dev.csv",
"split": datasets.Split.VALIDATION,
"key": "val-split",
"files": dl_manager.iter_archive(archive),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"datafile": dl_dir + "/" + "test.csv",
"split": datasets.Split.TEST,
"key": "test-split",
"files": dl_manager.iter_archive(archive),
},
),
]
if self.config.name.startswith("copa"):
archive = dl_manager.download(self.config.data_url)
task_name = self._get_task_name_from_data_url(self.config.data_url)
dl_dir = task_name + "/" + self.config.name.split(".")[1]
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"datafile": dl_dir + "/" + "train.jsonl",
"split": datasets.Split.TRAIN,
"key": "train-split",
"files": dl_manager.iter_archive(archive),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"datafile": dl_dir + "/" + "val.jsonl",
"split": datasets.Split.VALIDATION,
"key": "val-split",
"files": dl_manager.iter_archive(archive),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"datafile": dl_dir + "/" + "test.jsonl",
"split": datasets.Split.TEST,
"key": "test-split",
"files": dl_manager.iter_archive(archive),
},
),
]
if self.config.name.startswith("sna"):
archive = dl_manager.download(self.config.data_url)
task_name = self._get_task_name_from_data_url(self.config.data_url)
dl_dir = task_name + "/" + self.config.name.split(".")[1]
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"datafile": dl_dir + "/" + "bn-train.csv",
"split": datasets.Split.TRAIN,
"files": dl_manager.iter_archive(archive),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"datafile": dl_dir + "/" + "bn-valid.csv",
"split": datasets.Split.VALIDATION,
"files": dl_manager.iter_archive(archive),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"datafile": dl_dir + "/" + "bn-test.csv",
"split": datasets.Split.TEST,
"files": dl_manager.iter_archive(archive),
},
),
]
if self.config.name.startswith("csqa"):
archive = dl_manager.download(self.config.data_url)
task_name = self._get_task_name_from_data_url(self.config.data_url)
dl_dir = task_name
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"datafile": dl_dir + "/" + f"{self.config.name.split('.')[1]}.json",
"split": datasets.Split.TEST,
"files": dl_manager.iter_archive(archive),
},
)
]
if self.config.name.startswith("wstp"):
archive = dl_manager.download(self.config.data_url)
task_name = self._get_task_name_from_data_url(self.config.data_url)
dl_dir = task_name + "/" + self.config.name.split(".")[1]
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"datafile": dl_dir + "/" + f"{self.config.name.split('.')[1]}-train.json",
"split": datasets.Split.TRAIN,
"files": dl_manager.iter_archive(archive),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"datafile": dl_dir + "/" + f"{self.config.name.split('.')[1]}-valid.json",
"split": datasets.Split.VALIDATION,
"files": dl_manager.iter_archive(archive),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"datafile": dl_dir + "/" + f"{self.config.name.split('.')[1]}-test.json",
"split": datasets.Split.TEST,
"files": dl_manager.iter_archive(archive),
},
),
]
if (
self.config.name.startswith("inltkh")
or self.config.name.startswith("iitp")
or self.config.name.startswith("actsa")
):
archive = dl_manager.download(self.config.data_url)
task_name = self._get_task_name_from_data_url(self.config.data_url)
dl_dir = task_name + "/" + self.config.name.split(".")[1]
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"datafile": dl_dir + "/" + f"{self.config.name.split('.')[1]}-train.csv",
"split": datasets.Split.TRAIN,
"files": dl_manager.iter_archive(archive),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"datafile": dl_dir + "/" + f"{self.config.name.split('.')[1]}-valid.csv",
"split": datasets.Split.VALIDATION,
"files": dl_manager.iter_archive(archive),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"datafile": dl_dir + "/" + f"{self.config.name.split('.')[1]}-test.csv",
"split": datasets.Split.TEST,
"files": dl_manager.iter_archive(archive),
},
),
]
if self.config.name.startswith("bbca"):
archive = dl_manager.download(self.config.data_url)
task_name = self._get_task_name_from_data_url(self.config.data_url)
dl_dir = task_name + "/" + self.config.name.split(".")[1]
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"datafile": dl_dir + "/" + f"{self.config.name.split('.')[1]}-train.csv",
"split": datasets.Split.TRAIN,
"files": dl_manager.iter_archive(archive),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"datafile": dl_dir + "/" + f"{self.config.name.split('.')[1]}-test.csv",
"split": datasets.Split.TEST,
"files": dl_manager.iter_archive(archive),
},
),
]
if self.config.name.startswith("cvit"):
archive = dl_manager.download(self.config.data_url)
task_name = self._get_task_name_from_data_url(self.config.data_url)
dl_dir = task_name + "/" + self.config.name.split(".")[1]
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"datafile": None,
"src": dl_dir + "/" + f"mkb.{self.config.name.split('.')[1].split('-')[0]}",
"tgt": dl_dir + "/" + f"mkb.{self.config.name.split('.')[1].split('-')[1]}",
"split": datasets.Split.TEST,
"files": dl_manager.iter_archive(archive),
},
)
]
if self.config.name.startswith("md"):
archive = dl_manager.download(self.config.data_url)
task_name = self._get_task_name_from_data_url(self.config.data_url)
dl_dir = task_name + "/" + self.config.name.split(".")[1]
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"datafile": dl_dir + "/" + "train.json",
"split": datasets.Split.TRAIN,
"files": dl_manager.iter_archive(archive),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"datafile": dl_dir + "/" + "val.json",
"split": datasets.Split.VALIDATION,
"files": dl_manager.iter_archive(archive),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"datafile": dl_dir + "/" + "test.json",
"split": datasets.Split.TEST,
"files": dl_manager.iter_archive(archive),
},
),
]
if self.config.name.startswith("wiki-ner"):
archive = dl_manager.download(self.config.data_url)
task_name = self._get_task_name_from_data_url(self.config.data_url)
dl_dir = task_name + "/" + self.config.name.split(".")[1]
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"datafile": dl_dir + "/" + f"{self.config.name.split('.')[1]}-train.txt",
"split": datasets.Split.TRAIN,
"files": dl_manager.iter_archive(archive),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"datafile": dl_dir + "/" + f"{self.config.name.split('.')[1]}-valid.txt",
"split": datasets.Split.VALIDATION,
"files": dl_manager.iter_archive(archive),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"datafile": dl_dir + "/" + f"{self.config.name.split('.')[1]}-test.txt",
"split": datasets.Split.TEST,
"files": dl_manager.iter_archive(archive),
},
),
]
def _generate_examples(self, **args):
"""Yields examples."""
filepath = args["datafile"]
files = args["files"]
if self.config.name.startswith("wnli"):
if args["key"] == "test-split":
for path, f in files:
if path == filepath:
data = csv.DictReader((line.decode("utf-8") for line in f))
for id_, row in enumerate(data):
yield id_, {"hypothesis": row["sentence1"], "premise": row["sentence2"], "label": "None"}
break
else:
for path, f in files:
if path == filepath:
data = csv.DictReader((line.decode("utf-8") for line in f))
for id_, row in enumerate(data):
label = "entailment" if row["label"] else "not_entailment"
yield id_, {
"hypothesis": row["sentence1"],
"premise": row["sentence2"],
"label": label,
}
break
if self.config.name.startswith("copa"):
if args["key"] == "test-split":
for path, f in files:
if path == filepath:
lines = f.readlines()
data = map(lambda l: json.loads(l), lines)
data = list(data)
for id_, row in enumerate(data):
yield id_, {
"premise": row["premise"],
"choice1": row["choice1"],
"choice2": row["choice2"],
"question": row["question"],
"label": 0,
}
break
else:
for path, f in files:
if path == filepath:
lines = f.readlines()
data = map(lambda l: json.loads(l), lines)
data = list(data)
for id_, row in enumerate(data):
yield id_, {
"premise": row["premise"],
"choice1": row["choice1"],
"choice2": row["choice2"],
"question": row["question"],
"label": row["label"],
}
break
if self.config.name.startswith("sna"):
for path, f in files:
if path == filepath:
df = pd.read_csv(f, names=["label", "text"])
for id_, row in df.iterrows():
yield id_, {"text": row["text"], "label": row["label"]}
break
if self.config.name.startswith("csqa"):
for path, f in files:
if path == filepath:
data = json.load(f)
df = pd.DataFrame(data["cloze_data"])
df["out_of_context_options"].loc[df["out_of_context_options"].isnull()] = (
df["out_of_context_options"].loc[df["out_of_context_options"].isnull()].apply(lambda x: [])
)
for id_, row in df.iterrows():
yield id_, {
"question": row["question"],
"answer": row["answer"],
"category": row["category"],
"title": row["title"],
"out_of_context_options": row["out_of_context_options"],
"options": row["options"],
}
break
if self.config.name.startswith("wstp"):
for path, f in files:
if path == filepath:
df = pd.read_json(f)
for id_, row in df.iterrows():
yield id_, {
"sectionText": row["sectionText"],
"correctTitle": row["correctTitle"],
"titleA": row["titleA"],
"titleB": row["titleB"],
"titleC": row["titleC"],
"titleD": row["titleD"],
"url": row["url"],
}
break
if (
self.config.name.startswith("inltkh")
or self.config.name.startswith("bbca")
or self.config.name.startswith("iitp")
):
for path, f in files:
if path == filepath:
df = pd.read_csv(f, names=["label", "text"])
for id_, row in df.iterrows():
yield id_, {"text": row["text"], "label": row["label"]}
break
if self.config.name.startswith("actsa"):
for path, f in files:
if path == filepath:
df = pd.read_csv(f, names=["label", "text"])
for id_, row in df.iterrows():
label = "positive" if row["label"] else "negative"
yield id_, {"text": row["text"], "label": label}
break
if self.config.name.startswith("cvit"):
source = args["src"]
target = args["tgt"]
src, tgt = None, None
for path, f in files:
if path == source:
src = f.read().decode("utf-8").splitlines()
elif path == target:
tgt = f.read().decode("utf-8").splitlines()
if src is not None and tgt is not None:
for id_, row in enumerate(zip(src, tgt)):
yield id_, {"sentence1": row[0], "sentence2": row[1]}
break
if self.config.name.startswith("md"):
for path, f in files:
if path == filepath:
df = pd.read_json(f)
for id_, row in df.iterrows():
yield id_, {
"story_number": row["Story_no"],
"sentence": row["Sentence"],
"discourse_mode": row["Discourse Mode"],
"id": row["id"],
}
break
if self.config.name.startswith("wiki-ner"):
for path, f in files:
if path == filepath:
data = f.read().decode("utf-8").splitlines()
tokens = []
labels = []
infos = []
for id_, row in enumerate(data):
row = row.split()
if len(row) == 0:
yield id_, {"tokens": tokens, "ner_tags": labels, "additional_info": infos}
tokens = []
labels = []
infos = []
continue
tokens.append(row[0])
labels.append(row[-1])
infos.append(row[1:-1])
break
def _get_task_name_from_data_url(self, data_url):
return data_url.split("/")[-1].split(".")[0]
|