dataset_infos.json

{"wnli.en": {"description": "    IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide\n    variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.\n\n\nThe Winograd Schema Challenge (Levesque et al., 2011) is a reading comprehension task\nin which a system must read a sentence with a pronoun and select the referent of that pronoun from\na list of choices. The examples are manually constructed to foil simple statistical methods: Each\none is contingent on contextual information provided by a single word or phrase in the sentence.\nTo convert the problem into sentence pair classification, we construct sentence pairs by replacing\nthe ambiguous pronoun with each possible referent. The task is to predict if the sentence with the\npronoun substituted is entailed by the original sentence. We use a small evaluation set consisting of\nnew examples derived from fiction books that was shared privately by the authors of the original\ncorpus. While the included training set is balanced between two classes, the test set is imbalanced\nbetween them (65% not entailment). Also, due to a data quirk, the development set is adversarial:\nhypotheses are sometimes shared between training and development examples, so if a model memorizes the\ntraining examples, they will predict the wrong label on corresponding development set\nexample. As with QNLI, each example is evaluated separately, so there is not a systematic correspondence\nbetween a model's score on this task and its score on the unconverted original task. We\ncall converted dataset WNLI (Winograd NLI). This dataset is translated and publicly released for 3\nIndian languages by AI4Bharat.\n", "citation": "    @inproceedings{kakwani2020indicnlpsuite,\n    title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\n    author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\n    year={2020},\n    booktitle={Findings of EMNLP},\n}\n\n\n@inproceedings{kakwani2020indicnlpsuite,\ntitle={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\nauthor={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\nyear={2020},\nbooktitle={Findings of EMNLP},\n}\n@inproceedings{Levesque2011TheWS,\ntitle={The Winograd Schema Challenge},\nauthor={H. Levesque and E. Davis and L. Morgenstern},\nbooktitle={KR},\nyear={2011}\n}\n", "homepage": "https://ai4bharat.iitm.ac.in/indic-glue", "license": "", "features": {"hypothesis": {"dtype": "string", "id": null, "_type": "Value"}, "premise": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["not_entailment", "entailment", "None"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "indic_glue", "config_name": "wnli.en", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 104577, "num_examples": 635, "dataset_name": "indic_glue"}, "validation": {"name": "validation", "num_bytes": 11886, "num_examples": 71, "dataset_name": "indic_glue"}, "test": {"name": "test", "num_bytes": 37305, "num_examples": 146, "dataset_name": "indic_glue"}}, "download_checksums": {"https://ai4b-public-nlu-nlg.objectstore.e2enetworks.net/IndicGLUE/wnli-translated.tar.gz": {"num_bytes": 591249, "checksum": "7babf4a8250bf727e6cd55a4c5e1d4564f01317e91adea45f8eb57b9887b048b"}}, "download_size": 591249, "post_processing_size": null, "dataset_size": 153768, "size_in_bytes": 745017}, "wnli.hi": {"description": "    IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide\n    variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.\n\n\nThe Winograd Schema Challenge (Levesque et al., 2011) is a reading comprehension task\nin which a system must read a sentence with a pronoun and select the referent of that pronoun from\na list of choices. The examples are manually constructed to foil simple statistical methods: Each\none is contingent on contextual information provided by a single word or phrase in the sentence.\nTo convert the problem into sentence pair classification, we construct sentence pairs by replacing\nthe ambiguous pronoun with each possible referent. The task is to predict if the sentence with the\npronoun substituted is entailed by the original sentence. We use a small evaluation set consisting of\nnew examples derived from fiction books that was shared privately by the authors of the original\ncorpus. While the included training set is balanced between two classes, the test set is imbalanced\nbetween them (65% not entailment). Also, due to a data quirk, the development set is adversarial:\nhypotheses are sometimes shared between training and development examples, so if a model memorizes the\ntraining examples, they will predict the wrong label on corresponding development set\nexample. As with QNLI, each example is evaluated separately, so there is not a systematic correspondence\nbetween a model's score on this task and its score on the unconverted original task. We\ncall converted dataset WNLI (Winograd NLI). This dataset is translated and publicly released for 3\nIndian languages by AI4Bharat.\n", "citation": "    @inproceedings{kakwani2020indicnlpsuite,\n    title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\n    author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\n    year={2020},\n    booktitle={Findings of EMNLP},\n}\n\n\n@inproceedings{kakwani2020indicnlpsuite,\ntitle={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\nauthor={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\nyear={2020},\nbooktitle={Findings of EMNLP},\n}\n@inproceedings{Levesque2011TheWS,\ntitle={The Winograd Schema Challenge},\nauthor={H. Levesque and E. Davis and L. Morgenstern},\nbooktitle={KR},\nyear={2011}\n}\n", "homepage": "https://ai4bharat.iitm.ac.in/indic-glue", "license": "", "features": {"hypothesis": {"dtype": "string", "id": null, "_type": "Value"}, "premise": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["not_entailment", "entailment", "None"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "indic_glue", "config_name": "wnli.hi", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 253342, "num_examples": 635, "dataset_name": "indic_glue"}, "validation": {"name": "validation", "num_bytes": 28684, "num_examples": 71, "dataset_name": "indic_glue"}, "test": {"name": "test", "num_bytes": 90831, "num_examples": 146, "dataset_name": "indic_glue"}}, "download_checksums": {"https://ai4b-public-nlu-nlg.objectstore.e2enetworks.net/IndicGLUE/wnli-translated.tar.gz": {"num_bytes": 591249, "checksum": "7babf4a8250bf727e6cd55a4c5e1d4564f01317e91adea45f8eb57b9887b048b"}}, "download_size": 591249, "post_processing_size": null, "dataset_size": 372857, "size_in_bytes": 964106}, "wnli.gu": {"description": "    IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide\n    variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.\n\n\nThe Winograd Schema Challenge (Levesque et al., 2011) is a reading comprehension task\nin which a system must read a sentence with a pronoun and select the referent of that pronoun from\na list of choices. The examples are manually constructed to foil simple statistical methods: Each\none is contingent on contextual information provided by a single word or phrase in the sentence.\nTo convert the problem into sentence pair classification, we construct sentence pairs by replacing\nthe ambiguous pronoun with each possible referent. The task is to predict if the sentence with the\npronoun substituted is entailed by the original sentence. We use a small evaluation set consisting of\nnew examples derived from fiction books that was shared privately by the authors of the original\ncorpus. While the included training set is balanced between two classes, the test set is imbalanced\nbetween them (65% not entailment). Also, due to a data quirk, the development set is adversarial:\nhypotheses are sometimes shared between training and development examples, so if a model memorizes the\ntraining examples, they will predict the wrong label on corresponding development set\nexample. As with QNLI, each example is evaluated separately, so there is not a systematic correspondence\nbetween a model's score on this task and its score on the unconverted original task. We\ncall converted dataset WNLI (Winograd NLI). This dataset is translated and publicly released for 3\nIndian languages by AI4Bharat.\n", "citation": "    @inproceedings{kakwani2020indicnlpsuite,\n    title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\n    author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\n    year={2020},\n    booktitle={Findings of EMNLP},\n}\n\n\n@inproceedings{kakwani2020indicnlpsuite,\ntitle={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\nauthor={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\nyear={2020},\nbooktitle={Findings of EMNLP},\n}\n@inproceedings{Levesque2011TheWS,\ntitle={The Winograd Schema Challenge},\nauthor={H. Levesque and E. Davis and L. Morgenstern},\nbooktitle={KR},\nyear={2011}\n}\n", "homepage": "https://ai4bharat.iitm.ac.in/indic-glue", "license": "", "features": {"hypothesis": {"dtype": "string", "id": null, "_type": "Value"}, "premise": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["not_entailment", "entailment", "None"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "indic_glue", "config_name": "wnli.gu", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 251562, "num_examples": 635, "dataset_name": "indic_glue"}, "validation": {"name": "validation", "num_bytes": 28183, "num_examples": 71, "dataset_name": "indic_glue"}, "test": {"name": "test", "num_bytes": 94586, "num_examples": 146, "dataset_name": "indic_glue"}}, "download_checksums": {"https://ai4b-public-nlu-nlg.objectstore.e2enetworks.net/IndicGLUE/wnli-translated.tar.gz": {"num_bytes": 591249, "checksum": "7babf4a8250bf727e6cd55a4c5e1d4564f01317e91adea45f8eb57b9887b048b"}}, "download_size": 591249, "post_processing_size": null, "dataset_size": 374331, "size_in_bytes": 965580}, "wnli.mr": {"description": "    IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide\n    variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.\n\n\nThe Winograd Schema Challenge (Levesque et al., 2011) is a reading comprehension task\nin which a system must read a sentence with a pronoun and select the referent of that pronoun from\na list of choices. The examples are manually constructed to foil simple statistical methods: Each\none is contingent on contextual information provided by a single word or phrase in the sentence.\nTo convert the problem into sentence pair classification, we construct sentence pairs by replacing\nthe ambiguous pronoun with each possible referent. The task is to predict if the sentence with the\npronoun substituted is entailed by the original sentence. We use a small evaluation set consisting of\nnew examples derived from fiction books that was shared privately by the authors of the original\ncorpus. While the included training set is balanced between two classes, the test set is imbalanced\nbetween them (65% not entailment). Also, due to a data quirk, the development set is adversarial:\nhypotheses are sometimes shared between training and development examples, so if a model memorizes the\ntraining examples, they will predict the wrong label on corresponding development set\nexample. As with QNLI, each example is evaluated separately, so there is not a systematic correspondence\nbetween a model's score on this task and its score on the unconverted original task. We\ncall converted dataset WNLI (Winograd NLI). This dataset is translated and publicly released for 3\nIndian languages by AI4Bharat.\n", "citation": "    @inproceedings{kakwani2020indicnlpsuite,\n    title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\n    author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\n    year={2020},\n    booktitle={Findings of EMNLP},\n}\n\n\n@inproceedings{kakwani2020indicnlpsuite,\ntitle={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\nauthor={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\nyear={2020},\nbooktitle={Findings of EMNLP},\n}\n@inproceedings{Levesque2011TheWS,\ntitle={The Winograd Schema Challenge},\nauthor={H. Levesque and E. Davis and L. Morgenstern},\nbooktitle={KR},\nyear={2011}\n}\n", "homepage": "https://ai4bharat.iitm.ac.in/indic-glue", "license": "", "features": {"hypothesis": {"dtype": "string", "id": null, "_type": "Value"}, "premise": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["not_entailment", "entailment", "None"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "indic_glue", "config_name": "wnli.mr", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 256657, "num_examples": 635, "dataset_name": "indic_glue"}, "validation": {"name": "validation", "num_bytes": 29226, "num_examples": 71, "dataset_name": "indic_glue"}, "test": {"name": "test", "num_bytes": 97136, "num_examples": 146, "dataset_name": "indic_glue"}}, "download_checksums": {"https://ai4b-public-nlu-nlg.objectstore.e2enetworks.net/IndicGLUE/wnli-translated.tar.gz": {"num_bytes": 591249, "checksum": "7babf4a8250bf727e6cd55a4c5e1d4564f01317e91adea45f8eb57b9887b048b"}}, "download_size": 591249, "post_processing_size": null, "dataset_size": 383019, "size_in_bytes": 974268}, "copa.en": {"description": "    IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide\n    variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.\n\n\nThe Choice Of Plausible Alternatives (COPA) evaluation provides researchers with a tool for assessing\nprogress in open-domain commonsense causal reasoning. COPA consists of 1000 questions, split equally\ninto development and test sets of 500 questions each. Each question is composed of a premise and two\nalternatives, where the task is to select the alternative that more plausibly has a causal relation\nwith the premise. The correct alternative is randomized so that the expected performance of randomly\nguessing is 50%. This dataset is translated and publicly released for 3 languages by AI4Bharat.\n", "citation": "    @inproceedings{kakwani2020indicnlpsuite,\n    title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\n    author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\n    year={2020},\n    booktitle={Findings of EMNLP},\n}\n\n\n@inproceedings{kakwani2020indicnlpsuite,\ntitle={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\nauthor={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. 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This dataset is translated and publicly released for 3 languages by AI4Bharat.\n", "citation": "    @inproceedings{kakwani2020indicnlpsuite,\n    title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\n    author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\n    year={2020},\n    booktitle={Findings of EMNLP},\n}\n\n\n@inproceedings{kakwani2020indicnlpsuite,\ntitle={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\nauthor={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. 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This dataset is translated and publicly released for 3 languages by AI4Bharat.\n", "citation": "    @inproceedings{kakwani2020indicnlpsuite,\n    title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\n    author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\n    year={2020},\n    booktitle={Findings of EMNLP},\n}\n\n\n@inproceedings{kakwani2020indicnlpsuite,\ntitle={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\nauthor={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. 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It contains a wide\n    variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.\n\n\nACTSA Corpus: Sentiment analysis corpus for Telugu sentences.\n", "citation": "    @inproceedings{kakwani2020indicnlpsuite,\n    title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\n    author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\n    year={2020},\n    booktitle={Findings of EMNLP},\n}\n\n\n    @inproceedings{mukku-mamidi-2017-actsa,\n    title = \"{ACTSA}: Annotated Corpus for {T}elugu Sentiment Analysis\",\n    author = \"Mukku, Sandeep Sricharan  and\n    Mamidi, Radhika\",\n    booktitle = \"Proceedings of the First Workshop on Building Linguistically Generalizable {NLP} Systems\",\n    month = sep,\n    year = \"2017\",\n    address = \"Copenhagen, Denmark\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W17-5408\",\n    doi = \"10.18653/v1/W17-5408\",\n    pages = \"54--58\",\n    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.\",\n}\n", "homepage": "https://ai4bharat.iitm.ac.in/indic-glue", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["positive", "negative"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "indic_glue", "config_name": "actsa-sc.te", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1370911, "num_examples": 4328, "dataset_name": "indic_glue"}, "validation": {"name": "validation", "num_bytes": 166093, "num_examples": 541, "dataset_name": "indic_glue"}, "test": {"name": "test", "num_bytes": 168295, "num_examples": 541, "dataset_name": "indic_glue"}}, "download_checksums": {"https://ai4b-public-nlu-nlg.objectstore.e2enetworks.net/IndicGLUE/actsa.tar.gz": {"num_bytes": 378882, "checksum": "013a7df570095bd085fb491dabd71c92f285a2cdde718d676c5a7cd16210559b"}}, "download_size": 378882, "post_processing_size": null, "dataset_size": 1705299, "size_in_bytes": 2084181}, "md.hi": {"description": "    IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide\n    variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.\n\n\nThe Hindi Discourse Analysis dataset is a corpus for analyzing discourse modes present in its sentences.\nIt contains sentences from stories written by 11 famous authors from the 20th Century. 4-5 stories by\neach author have been selected which were available in the public domain resulting in a collection of 53 stories.\nMost of these short stories were originally written in Hindi but some of them were written in other Indian languages\nand later translated to Hindi.\n", "citation": "    @inproceedings{kakwani2020indicnlpsuite,\n    title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\n    author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\n    year={2020},\n    booktitle={Findings of EMNLP},\n}\n\n\n@inproceedings{Dhanwal2020AnAD,\ntitle={An Annotated Dataset of Discourse Modes in Hindi Stories},\nauthor={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},\nbooktitle={LREC},\nyear={2020}\n}\n", "homepage": "https://ai4bharat.iitm.ac.in/indic-glue", "license": "", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "discourse_mode": {"dtype": "string", "id": null, "_type": "Value"}, "story_number": {"dtype": "int32", "id": null, "_type": "Value"}, "id": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "indic_glue", "config_name": "md.hi", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1672117, "num_examples": 7974, "dataset_name": "indic_glue"}, "validation": {"name": "validation", "num_bytes": 211195, "num_examples": 997, "dataset_name": "indic_glue"}, "test": {"name": "test", "num_bytes": 210183, "num_examples": 997, "dataset_name": "indic_glue"}}, "download_checksums": {"https://ai4b-public-nlu-nlg.objectstore.e2enetworks.net/IndicGLUE/midas-discourse.tar.gz": {"num_bytes": 1048441, "checksum": "37ccd01a16525edce4faf9b6e4a4aca08e7e171f83bcc2ef9a86660c5feecd07"}}, "download_size": 1048441, "post_processing_size": null, "dataset_size": 2093495, "size_in_bytes": 3141936}, "wiki-ner.as": {"description": "    IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide\n    variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.\n\n\nThe WikiANN dataset (Pan et al. 2017) is a dataset with NER annotations for PER, ORG and LOC. It has been constructed using\nthe linked entities in Wikipedia pages for 282 different languages including Danish.\n", "citation": "    @inproceedings{kakwani2020indicnlpsuite,\n    title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\n    author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\n    year={2020},\n    booktitle={Findings of EMNLP},\n}\n\n\n    @inproceedings{pan-etal-2017-cross,\n    title = \"Cross-lingual Name Tagging and Linking for 282 Languages\",\n    author = \"Pan, Xiaoman  and\n    Zhang, Boliang  and\n    May, Jonathan  and\n    Nothman, Joel  and\n    Knight, Kevin  and\n    Ji, Heng\",\n    booktitle = \"Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",\n    month = jul,\n    year = \"2017\",\n    address = \"Vancouver, Canada\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/P17-1178\",\n    doi = \"10.18653/v1/P17-1178\",\n    pages = \"1946--1958\",\n    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.\",\n}\n", "homepage": "https://ai4bharat.iitm.ac.in/indic-glue", "license": "", "features": {"tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "ner_tags": {"feature": {"num_classes": 7, "names": ["B-LOC", "B-ORG", "B-PER", "I-LOC", "I-ORG", "I-PER", "O"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}, "additional_info": {"feature": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "indic_glue", "config_name": "wiki-ner.as", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 375007, "num_examples": 1021, "dataset_name": "indic_glue"}, "validation": {"name": "validation", "num_bytes": 49336, "num_examples": 157, "dataset_name": "indic_glue"}, "test": {"name": "test", "num_bytes": 50480, "num_examples": 160, "dataset_name": "indic_glue"}}, "download_checksums": {"https://ai4b-public-nlu-nlg.objectstore.e2enetworks.net/IndicGLUE/wikiann-ner.tar.gz": {"num_bytes": 5980272, "checksum": "2bb060394a6816c9a4f3c31976d9819174067cc75b976bc4b00e5c624cd2751d"}}, "download_size": 5980272, "post_processing_size": null, "dataset_size": 474823, "size_in_bytes": 6455095}, "wiki-ner.bn": {"description": "    IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide\n    variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.\n\n\nThe WikiANN dataset (Pan et al. 2017) is a dataset with NER annotations for PER, ORG and LOC. It has been constructed using\nthe linked entities in Wikipedia pages for 282 different languages including Danish.\n", "citation": "    @inproceedings{kakwani2020indicnlpsuite,\n    title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\n    author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\n    year={2020},\n    booktitle={Findings of EMNLP},\n}\n\n\n    @inproceedings{pan-etal-2017-cross,\n    title = \"Cross-lingual Name Tagging and Linking for 282 Languages\",\n    author = \"Pan, Xiaoman  and\n    Zhang, Boliang  and\n    May, Jonathan  and\n    Nothman, Joel  and\n    Knight, Kevin  and\n    Ji, Heng\",\n    booktitle = \"Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",\n    month = jul,\n    year = \"2017\",\n    address = \"Vancouver, Canada\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/P17-1178\",\n    doi = \"10.18653/v1/P17-1178\",\n    pages = \"1946--1958\",\n    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.\",\n}\n", "homepage": "https://ai4bharat.iitm.ac.in/indic-glue", "license": "", "features": {"tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "ner_tags": {"feature": {"num_classes": 7, "names": ["B-LOC", "B-ORG", "B-PER", "I-LOC", "I-ORG", "I-PER", "O"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}, "additional_info": {"feature": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "indic_glue", "config_name": "wiki-ner.bn", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 7502896, "num_examples": 20223, "dataset_name": "indic_glue"}, "validation": {"name": "validation", "num_bytes": 988707, "num_examples": 2985, "dataset_name": "indic_glue"}, "test": {"name": "test", "num_bytes": 985965, "num_examples": 2690, "dataset_name": "indic_glue"}}, "download_checksums": {"https://ai4b-public-nlu-nlg.objectstore.e2enetworks.net/IndicGLUE/wikiann-ner.tar.gz": {"num_bytes": 5980272, "checksum": "2bb060394a6816c9a4f3c31976d9819174067cc75b976bc4b00e5c624cd2751d"}}, "download_size": 5980272, "post_processing_size": null, "dataset_size": 9477568, "size_in_bytes": 15457840}, "wiki-ner.gu": {"description": "    IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide\n    variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.\n\n\nThe WikiANN dataset (Pan et al. 2017) is a dataset with NER annotations for PER, ORG and LOC. It has been constructed using\nthe linked entities in Wikipedia pages for 282 different languages including Danish.\n", "citation": "    @inproceedings{kakwani2020indicnlpsuite,\n    title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\n    author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\n    year={2020},\n    booktitle={Findings of EMNLP},\n}\n\n\n    @inproceedings{pan-etal-2017-cross,\n    title = \"Cross-lingual Name Tagging and Linking for 282 Languages\",\n    author = \"Pan, Xiaoman  and\n    Zhang, Boliang  and\n    May, Jonathan  and\n    Nothman, Joel  and\n    Knight, Kevin  and\n    Ji, Heng\",\n    booktitle = \"Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",\n    month = jul,\n    year = \"2017\",\n    address = \"Vancouver, Canada\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/P17-1178\",\n    doi = \"10.18653/v1/P17-1178\",\n    pages = \"1946--1958\",\n    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.\",\n}\n", "homepage": "https://ai4bharat.iitm.ac.in/indic-glue", "license": "", "features": {"tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "ner_tags": {"feature": {"num_classes": 7, "names": ["B-LOC", "B-ORG", "B-PER", "I-LOC", "I-ORG", "I-PER", "O"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}, "additional_info": {"feature": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "indic_glue", "config_name": "wiki-ner.gu", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1571612, "num_examples": 2343, "dataset_name": "indic_glue"}, "validation": {"name": "validation", "num_bytes": 192828, "num_examples": 297, "dataset_name": "indic_glue"}, "test": {"name": "test", "num_bytes": 197901, "num_examples": 255, "dataset_name": "indic_glue"}}, "download_checksums": {"https://ai4b-public-nlu-nlg.objectstore.e2enetworks.net/IndicGLUE/wikiann-ner.tar.gz": {"num_bytes": 5980272, "checksum": "2bb060394a6816c9a4f3c31976d9819174067cc75b976bc4b00e5c624cd2751d"}}, "download_size": 5980272, "post_processing_size": null, "dataset_size": 1962341, "size_in_bytes": 7942613}, "wiki-ner.hi": {"description": "    IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide\n    variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.\n\n\nThe WikiANN dataset (Pan et al. 2017) is a dataset with NER annotations for PER, ORG and LOC. It has been constructed using\nthe linked entities in Wikipedia pages for 282 different languages including Danish.\n", "citation": "    @inproceedings{kakwani2020indicnlpsuite,\n    title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\n    author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\n    year={2020},\n    booktitle={Findings of EMNLP},\n}\n\n\n    @inproceedings{pan-etal-2017-cross,\n    title = \"Cross-lingual Name Tagging and Linking for 282 Languages\",\n    author = \"Pan, Xiaoman  and\n    Zhang, Boliang  and\n    May, Jonathan  and\n    Nothman, Joel  and\n    Knight, Kevin  and\n    Ji, Heng\",\n    booktitle = \"Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",\n    month = jul,\n    year = \"2017\",\n    address = \"Vancouver, Canada\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/P17-1178\",\n    doi = \"10.18653/v1/P17-1178\",\n    pages = \"1946--1958\",\n    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.\",\n}\n", "homepage": "https://ai4bharat.iitm.ac.in/indic-glue", "license": "", "features": {"tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "ner_tags": {"feature": {"num_classes": 7, "names": ["B-LOC", "B-ORG", "B-PER", "I-LOC", "I-ORG", "I-PER", "O"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}, "additional_info": {"feature": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "indic_glue", "config_name": "wiki-ner.hi", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3762529, "num_examples": 9463, "dataset_name": "indic_glue"}, "validation": {"name": "validation", "num_bytes": 468702, "num_examples": 1114, "dataset_name": "indic_glue"}, "test": {"name": "test", "num_bytes": 475277, "num_examples": 1256, "dataset_name": "indic_glue"}}, "download_checksums": {"https://ai4b-public-nlu-nlg.objectstore.e2enetworks.net/IndicGLUE/wikiann-ner.tar.gz": {"num_bytes": 5980272, "checksum": "2bb060394a6816c9a4f3c31976d9819174067cc75b976bc4b00e5c624cd2751d"}}, "download_size": 5980272, "post_processing_size": null, "dataset_size": 4706508, "size_in_bytes": 10686780}, "wiki-ner.kn": {"description": "    IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide\n    variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.\n\n\nThe WikiANN dataset (Pan et al. 2017) is a dataset with NER annotations for PER, ORG and LOC. It has been constructed using\nthe linked entities in Wikipedia pages for 282 different languages including Danish.\n", "citation": "    @inproceedings{kakwani2020indicnlpsuite,\n    title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\n    author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\n    year={2020},\n    booktitle={Findings of EMNLP},\n}\n\n\n    @inproceedings{pan-etal-2017-cross,\n    title = \"Cross-lingual Name Tagging and Linking for 282 Languages\",\n    author = \"Pan, Xiaoman  and\n    Zhang, Boliang  and\n    May, Jonathan  and\n    Nothman, Joel  and\n    Knight, Kevin  and\n    Ji, Heng\",\n    booktitle = \"Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",\n    month = jul,\n    year = \"2017\",\n    address = \"Vancouver, Canada\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/P17-1178\",\n    doi = \"10.18653/v1/P17-1178\",\n    pages = \"1946--1958\",\n    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. 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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. 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