Datasets:

Tasks:
Other
Languages:
Chinese
Multilinguality:
monolingual
Size Categories:
1M<n<10M
Language Creators:
found
Annotations Creators:
found
Source Datasets:
original
ArXiv:
Tags:
judgement-prediction
License:
albertvillanova HF staff commited on
Commit
b8d09e8
1 Parent(s): 0288b0a

Delete legacy dataset_infos.json

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  1. dataset_infos.json +0 -101
dataset_infos.json DELETED
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- {
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- "description": "In this paper, we introduce Chinese AI and Law challenge dataset (CAIL2018),\nthe first large-scale Chinese legal dataset for judgment prediction. CAIL contains more than 2.6 million\ncriminal cases published by the Supreme People's Court of China, which are several times larger than other\ndatasets in existing works on judgment prediction. Moreover, the annotations of judgment results are more\ndetailed and rich. It consists of applicable law articles, charges, and prison terms, which are expected\nto be inferred according to the fact descriptions of cases. For comparison, we implement several conventional\ntext classification baselines for judgment prediction and experimental results show that it is still a\nchallenge for current models to predict the judgment results of legal cases, especially on prison terms.\nTo help the researchers make improvements on legal judgment prediction.\n",
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- "citation": "@misc{xiao2018cail2018,\n title={CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction},\n author={Chaojun Xiao and Haoxi Zhong and Zhipeng Guo and Cunchao Tu and Zhiyuan Liu and Maosong Sun and Yansong Feng and Xianpei Han and Zhen Hu and Heng Wang and Jianfeng Xu},\n year={2018},\n eprint={1807.02478},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n",
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- "homepage": "https://arxiv.org/abs/1807.02478",
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