dataset_infos.json

{"default": {"description": "Cosmos QA is a large-scale dataset of 35.6K problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. It focuses on reading between the lines over a diverse collection of people's everyday narratives, asking questions concerning on the likely causes or effects of events that require reasoning beyond the exact text spans in the context\n", "citation": "@inproceedings{huang-etal-2019-cosmos,\n    title = \"Cosmos {QA}: Machine Reading Comprehension with Contextual Commonsense Reasoning\",\n    author = \"Huang, Lifu  and\n      Le Bras, Ronan  and\n      Bhagavatula, Chandra  and\n      Choi, Yejin\",\n    booktitle = \"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)\",\n    month = nov,\n    year = \"2019\",\n    address = \"Hong Kong, China\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/D19-1243\",\n    doi = \"10.18653/v1/D19-1243\",\n    pages = \"2391--2401\",\n}\n", "homepage": "https://wilburone.github.io/cosmos/", "license": "CC BY 4.0", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answer0": {"dtype": "string", "id": null, "_type": "Value"}, "answer1": {"dtype": "string", "id": null, "_type": "Value"}, "answer2": {"dtype": "string", "id": null, "_type": "Value"}, "answer3": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "cosmos_qa", "config_name": "default", "version": {"version_str": "0.1.0", "description": null, "major": 0, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 17159918, "num_examples": 25262, "dataset_name": "cosmos_qa"}, "test": {"name": "test", "num_bytes": 5121479, "num_examples": 6963, "dataset_name": "cosmos_qa"}, "validation": {"name": "validation", "num_bytes": 2186987, "num_examples": 2985, "dataset_name": "cosmos_qa"}}, "download_checksums": {"https://github.com/wilburOne/cosmosqa/raw/master/data/train.csv": {"num_bytes": 16660449, "checksum": "d8d5ca1f9f6534b6530550718591af89372d976a8fc419360fab4158dee4d0b2"}, "https://github.com/wilburOne/cosmosqa/raw/master/data/test.jsonl": {"num_bytes": 5610681, "checksum": "70005196dc2588b95de34f1657b25e2c1a4810cfe55b5bb0c0e15580c37b3ed0"}, "https://github.com/wilburOne/cosmosqa/raw/master/data/valid.csv": {"num_bytes": 2128345, "checksum": "a6a94fc1463ca82bb10f98ef68ed535405e6f5c36e044ff8e136b5c19dea63f3"}}, "download_size": 24399475, "post_processing_size": null, "dataset_size": 24468384, "size_in_bytes": 48867859}}