Datasets:
lccc

	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	"""
	LCCC: Large-scale Cleaned Chinese Conversation corpus (LCCC) is a large corpus of Chinese conversations.
	A rigorous data cleaning pipeline is designed to ensure the quality of the corpus.
	This pipeline involves a set of rules and several classifier-based filters.
	Noises such as offensive or sensitive words, special symbols, emojis,
	grammatically incorrect sentences, and incoherent conversations are filtered.
	"""

	import json
	import os

	import datasets


	# BibTeX citation
	_CITATION = """\
	@inproceedings{wang2020chinese,
	title={A Large-Scale Chinese Short-Text Conversation Dataset},
	author={Wang, Yida and Ke, Pei and Zheng, Yinhe and Huang, Kaili and Jiang, Yong and Zhu, Xiaoyan and Huang, Minlie},
	booktitle={NLPCC},
	year={2020},
	url={https://arxiv.org/abs/2008.03946}
	}
	"""

	# Description of the dataset here
	_DESCRIPTION = """\
	LCCC: Large-scale Cleaned Chinese Conversation corpus (LCCC) is a large corpus of Chinese conversations.
	A rigorous data cleaning pipeline is designed to ensure the quality of the corpus.
	This pipeline involves a set of rules and several classifier-based filters.
	Noises such as offensive or sensitive words, special symbols, emojis,
	grammatically incorrect sentences, and incoherent conversations are filtered.
	"""

	_HOMEPAGE = "https://github.com/thu-coai/CDial-GPT"
	_LICENSE = "MIT"
	_URLS = {
	"large": "https://huggingface.co/datasets/silver/lccc/resolve/main/lccc_large.jsonl.gz",
	"base": {
	"train": "https://huggingface.co/datasets/silver/lccc/resolve/main/lccc_base_train.jsonl.gz",
	"valid": "https://huggingface.co/datasets/silver/lccc/resolve/main/lccc_base_valid.jsonl.gz",
	"test": "https://huggingface.co/datasets/silver/lccc/resolve/main/lccc_base_test.jsonl.gz",
	},
	}


	class LCCC(datasets.GeneratorBasedBuilder):
	"""Large-scale Cleaned Chinese Conversation corpus."""

	VERSION = datasets.Version("1.0.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="large", version=VERSION, description="The large version of LCCC"),
	datasets.BuilderConfig(name="base", version=VERSION, description="The base version of LCCC"),
	]

	def _info(self):
	features = datasets.Features(
	{
	"dialog": [datasets.Value("string")],
	}
	)
	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# This defines the different columns of the dataset and their types
	features=features, # Here we define them above because they are different between the two configurations
	# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
	# specify them. They'll be used if as_supervised=True in builder.as_dataset.
	# supervised_keys=("sentence", "label"),
	# Homepage of the dataset for documentation
	homepage=_HOMEPAGE,
	# License for the dataset if available
	license=_LICENSE,
	# Citation for the dataset
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	urls = _URLS[self.config.name]
	downloaded_data = dl_manager.download_and_extract(urls)
	if self.config.name == "large":
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": os.path.join(downloaded_data),
	},
	)
	]
	elif self.config.name == "base":
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": os.path.join(downloaded_data["train"]),
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"filepath": os.path.join(downloaded_data["test"])},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"filepath": os.path.join(downloaded_data["valid"]),
	},
	),
	]

	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, filepath):
	with open(filepath, encoding="utf-8") as f:
	for key, row in enumerate(f):
	row = row.strip()
	if row:
	yield key, {
	"dialog": json.loads(row),
	}