Datasets:
lccc

Fine-Grained Tasks: dialogue-generation
Languages: Chinese
Multilinguality: monolingual
Size Categories: 10M<n<100M
Language Creators: other
Annotations Creators: other
Source Datasets: original
Licenses: mit
lccc / lccc.py
Quentin Lhoest
Release: 2.3.0
8b1f8cf
# 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),
}