Datasets:
cail2018

	import json
	import os

	import datasets


	_CITATION = """\
	@misc{xiao2018cail2018,
	title={CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction},
	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},
	year={2018},
	eprint={1807.02478},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	"""

	_DESCRIPTION = """\
	In this paper, we introduce Chinese AI and Law challenge dataset (CAIL2018),
	the first large-scale Chinese legal dataset for judgment prediction. CAIL contains more than 2.6 million
	criminal cases published by the Supreme People's Court of China, which are several times larger than other
	datasets in existing works on judgment prediction. Moreover, the annotations of judgment results are more
	detailed and rich. It consists of applicable law articles, charges, and prison terms, which are expected
	to be inferred according to the fact descriptions of cases. For comparison, we implement several conventional
	text classification baselines for judgment prediction and experimental results show that it is still a
	challenge for current models to predict the judgment results of legal cases, especially on prison terms.
	To help the researchers make improvements on legal judgment prediction.
	"""
	_URL = "https://cail.oss-cn-qingdao.aliyuncs.com/CAIL2018_ALL_DATA.zip"


	class Cail2018(datasets.GeneratorBasedBuilder):
	VERSION = datasets.Version("1.0.0")

	def _info(self):
	features = datasets.Features(
	{
	"fact": datasets.Value("string"),
	"relevant_articles": datasets.Sequence(datasets.Value("int32")),
	"accusation": datasets.Sequence(datasets.Value("string")),
	"punish_of_money": datasets.Value("float"),
	"criminals": datasets.Sequence(datasets.Value("string")),
	"death_penalty": datasets.Value("bool"),
	"imprisonment": datasets.Value("float"),
	"life_imprisonment": datasets.Value("bool"),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	homepage="https://arxiv.org/abs/1807.02478",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""

	dl_dir = dl_manager.download_and_extract(_URL)

	return [
	datasets.SplitGenerator(
	name=datasets.Split("exercise_contest_train"),
	gen_kwargs={
	"filepath": os.path.join(dl_dir, "final_all_data/exercise_contest/data_train.json"),
	"split": "exercise_contest_train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split("exercise_contest_valid"),
	gen_kwargs={
	"filepath": os.path.join(dl_dir, "final_all_data/exercise_contest/data_valid.json"),
	"split": "exercise_contest_valid",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split("exercise_contest_test"),
	gen_kwargs={
	"filepath": os.path.join(dl_dir, "final_all_data/exercise_contest/data_test.json"),
	"split": "exercise_contest_test",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split("first_stage_train"),
	gen_kwargs={
	"filepath": os.path.join(dl_dir, "final_all_data/first_stage/train.json"),
	"split": "first_stage_train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split("first_stage_test"),
	gen_kwargs={
	"filepath": os.path.join(dl_dir, "final_all_data/first_stage/test.json"),
	"split": "first_stage_test",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split("final_test"),
	gen_kwargs={"filepath": os.path.join(dl_dir, "final_all_data/final_test.json"), "split": "final_test"},
	),
	]

	def _generate_examples(self, filepath, split):
	"""Yields examples."""
	with open(filepath, encoding="utf-8") as f:
	for idx, row in enumerate(f):
	data = json.loads(row)
	yield idx, {
	"fact": data["fact"],
	"relevant_articles": data["meta"]["relevant_articles"],
	"accusation": data["meta"]["accusation"],
	"punish_of_money": data["meta"]["punish_of_money"],
	"criminals": data["meta"]["criminals"],
	"death_penalty": data["meta"]["term_of_imprisonment"]["death_penalty"],
	"imprisonment": data["meta"]["term_of_imprisonment"]["imprisonment"],
	"life_imprisonment": data["meta"]["term_of_imprisonment"]["life_imprisonment"],
	}