数据集:
cos_e
任务:
问答子任务:
open-domain-qa语言:
en计算机处理:
monolingual大小:
10K<n<100K语言创建人:
crowdsourced批注创建人:
crowdsourced源数据集:
extended|commonsense_qa预印本库:
arxiv:1906.02361许可:
license:unknownCommon Sense Explanations (CoS-E) allows for training language models to automatically generate explanations that can be used during training and inference in a novel Commonsense Auto-Generated Explanation (CAGE) framework.
An example of 'train' looks as follows.
{ "abstractive_explanation": "this is open-ended", "answer": "b", "choices": ["a", "b", "c"], "extractive_explanation": "this is selected train", "id": "42", "question": "question goes here." }v1.11
An example of 'train' looks as follows.
{ "abstractive_explanation": "this is open-ended", "answer": "b", "choices": ["a", "b", "c"], "extractive_explanation": "this is selected train", "id": "42", "question": "question goes here." }
The data fields are the same among all splits.
v1.0name | train | validation |
---|---|---|
v1.0 | 7610 | 950 |
v1.11 | 9741 | 1221 |
Unknown.
@inproceedings{rajani2019explain, title = "Explain Yourself! Leveraging Language models for Commonsense Reasoning", author = "Rajani, Nazneen Fatema and McCann, Bryan and Xiong, Caiming and Socher, Richard", year="2019", booktitle = "Proceedings of the 2019 Conference of the Association for Computational Linguistics (ACL2019)", url ="https://arxiv.org/abs/1906.02361" }
Thanks to @lewtun , @thomwolf , @mariamabarham , @patrickvonplaten , @albertvillanova , @lhoestq for adding this dataset.