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dataset_infos.json
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dataset_infos.json
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{"v1.0": {"description": "\nCommon Sense Explanations (CoS-E) allows for training language models to\nautomatically generate explanations that can be used during training and\ninference in a novel Commonsense Auto-Generated Explanation (CAGE) framework.\n", "citation": "\n@inproceedings{rajani2019explain,\n title = \"Explain Yourself! Leveraging Language models for Commonsense Reasoning\",\n author = \"Rajani, Nazneen Fatema and\n McCann, Bryan and\n Xiong, Caiming and\n Socher, Richard\",\n year=\"2019\",\n booktitle = \"Proceedings of the 2019 Conference of the Association for Computational Linguistics (ACL2019)\",\n url =\"https://arxiv.org/abs/1906.02361\"\n}\n", "homepage": "https://github.com/salesforce/cos-e", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "choices": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "abstractive_explanation": {"dtype": "string", "id": null, "_type": "Value"}, "extractive_explanation": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "cos_e", "config_name": "v1.0", "version": {"version_str": "1.0.0", "description": "", "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2077517, "num_examples": 7610, "dataset_name": "cos_e"}, "validation": {"name": "validation", "num_bytes": 261887, "num_examples": 950, "dataset_name": "cos_e"}}, "download_checksums": {"https://raw.githubusercontent.com/salesforce/cos-e/master/data/v1.0/train_rand_split.jsonl": {"num_bytes": 2160200, "checksum": "1989ce97e24d8572113d6a18f44e0f11ee9d206fb9bf9a1133937645583e697e"}, "https://raw.githubusercontent.com/salesforce/cos-e/master/data/v1.0/dev_rand_split.jsonl": {"num_bytes": 268531, "checksum": "790dd2a8492e7f3b51ded04116de603115b7acaded32ea84f6a7101f9d571ac1"}, "https://raw.githubusercontent.com/salesforce/cos-e/master/data/v1.0/test_rand_split_no_answers.jsonl": {"num_bytes": 250752, "checksum": "b9c3d1319667ea1569be6f7b3ed0546bd8222d2f3a759f928307343a0282e190"}, "https://raw.githubusercontent.com/salesforce/cos-e/master/data/v1.0/cose_dev_v1.0_processed.jsonl": {"num_bytes": 182444, "checksum": "ab7b8ac91bca1a6ba798816af6aca703a739f576c919360ddc376d9d3046be53"}, "https://raw.githubusercontent.com/salesforce/cos-e/master/data/v1.0/cose_train_v1.0_processed.jsonl": {"num_bytes": 1433393, "checksum": "df9f83ac4891f38e0771470858d5f1c4b5bb08fee5c53f38f9df9b3d3675ea74"}}, "download_size": 4295320, "dataset_size": 2339404, "size_in_bytes": 6634724}, "v1.11": {"description": "\nCommon Sense Explanations (CoS-E) allows for training language models to\nautomatically generate explanations that can be used during training and\ninference in a novel Commonsense Auto-Generated Explanation (CAGE) framework.\n", "citation": "\n@inproceedings{rajani2019explain,\n title = \"Explain Yourself! 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