Datasets:

Modalities:
Text
Formats:
parquet
Sub-tasks:
extractive-qa
Languages:
Russian
ArXiv:
Libraries:
Datasets
pandas
License:
albertvillanova HF staff commited on
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Delete legacy dataset_infos.json

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  1. dataset_infos.json +0 -1
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- {"sberquad": {"description": "Sber Question Answering Dataset (SberQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Russian original analogue presented in Sberbank Data Science Journey 2017.\n", "citation": "@article{Efimov_2020,\n title={SberQuAD \u2013 Russian Reading Comprehension Dataset: Description and Analysis},\n ISBN={9783030582197},\n ISSN={1611-3349},\n url={http://dx.doi.org/10.1007/978-3-030-58219-7_1},\n DOI={10.1007/978-3-030-58219-7_1},\n journal={Experimental IR Meets Multilinguality, Multimodality, and Interaction},\n publisher={Springer International Publishing},\n author={Efimov, Pavel and Chertok, Andrey and Boytsov, Leonid and Braslavski, Pavel},\n year={2020},\n pages={3\u201315}\n}\n ", "homepage": "", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answers": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "answer_start": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": [{"task": "question-answering-extractive", "question_column": "question", "context_column": "context", "answers_column": "answers"}], "builder_name": "sberquad", "config_name": "sberquad", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 71631661, "num_examples": 45328, "dataset_name": "sberquad"}, "validation": {"name": "validation", "num_bytes": 7972977, "num_examples": 5036, "dataset_name": "sberquad"}, "test": {"name": "test", "num_bytes": 36397848, "num_examples": 23936, "dataset_name": "sberquad"}}, "download_checksums": {"https://sc.link/PNWl": {"num_bytes": 38616884, "checksum": "861b55219f1549139e64b2eed325b54ce9c9c63b792a2c2b3cfbec997aa3d88e"}, "https://sc.link/W6oX": {"num_bytes": 8807953, "checksum": "247bede36a27f076f607117632f39eedb9bb1d80c34d93bbfaeda71fd30fd382"}, "https://sc.link/VOn9": {"num_bytes": 18622439, "checksum": "7793d389208271a76ab38a5dba5cebc98e72f45e99196a99e14b5a37c401c66f"}}, "download_size": 66047276, "post_processing_size": null, "dataset_size": 116002486, "size_in_bytes": 182049762}}