Datasets:
lccc

	---
	annotations_creators:
	- other
	language_creators:
	- other
	language:
	- zh
	license:
	- mit
	multilinguality:
	- monolingual
	paperswithcode_id: lccc
	pretty_name: 'LCCC: Large-scale Cleaned Chinese Conversation corpus'
	size_categories:
	- 10M<n<100M
	source_datasets:
	- original
	task_categories:
	- conversational
	task_ids:
	- dialogue-generation
	dataset_info:
	- config_name: large
	features:
	- name: dialog
	list: string
	splits:
	- name: train
	num_bytes: 1530827965
	num_examples: 12007759
	download_size: 607605643
	dataset_size: 1530827965
	- config_name: base
	features:
	- name: dialog
	list: string
	splits:
	- name: train
	num_bytes: 932634902
	num_examples: 6820506
	- name: test
	num_bytes: 1498216
	num_examples: 10000
	- name: validation
	num_bytes: 2922731
	num_examples: 20000
	download_size: 371475095
	dataset_size: 937055849
	---

	# Dataset Card for LCCC

	## Table of Contents
	- [Dataset Card for LCCC](#dataset-card-for-lccc)
	- [Table of Contents](#table-of-contents)
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Fields](#data-fields)
	- [Data Splits](#data-splits)
	- [Dataset Creation](#dataset-creation)
	- [Curation Rationale](#curation-rationale)
	- [Source Data](#source-data)
	- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
	- [Who are the source language producers?](#who-are-the-source-language-producers)
	- [Annotations](#annotations)
	- [Annotation process](#annotation-process)
	- [Who are the annotators?](#who-are-the-annotators)
	- [Personal and Sensitive Information](#personal-and-sensitive-information)
	- [Considerations for Using the Data](#considerations-for-using-the-data)
	- [Social Impact of Dataset](#social-impact-of-dataset)
	- [Discussion of Biases](#discussion-of-biases)
	- [Other Known Limitations](#other-known-limitations)
	- [Additional Information](#additional-information)
	- [Dataset Curators](#dataset-curators)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)
	- [Contributions](#contributions)

	## Dataset Description

	- Repository: https://github.com/thu-coai/CDial-GPT
	- Paper: https://arxiv.org/abs/2008.03946

	### Dataset Summary

	LCCC: Large-scale Cleaned Chinese Conversation corpus (LCCC) is a large Chinese dialogue corpus originate from Chinese social medias. 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.

	LCCC是一套来自于中文社交媒体的对话数据，我们设计了一套严格的数据过滤流程来确保该数据集中对话数据的质量。这一数据过滤流程中包括一系列手工规则以及若干基于机器学习算法所构建的分类器。我们所过滤掉的噪声包括：脏字脏词、特殊字符、颜表情、语法不通的语句、上下文不相关的对话等。

	### Supported Tasks and Leaderboards

	- dialogue-generation: The dataset can be used to train a model for generating dialogue responses.
	- response-retrieval: The dataset can be used to train a reranker model that can be used to implement a retrieval-based dialogue model.

	### Languages

	LCCC is in Chinese

	LCCC中的对话是中文的

	## Dataset Structure

	### Data Instances

	```json
	{
	"dialog": ["火锅我在重庆成都吃了七八顿火锅", "哈哈哈哈！那我的嘴巴可能要烂掉！", "不会的就是好油腻"]
	}
	```

	### Data Fields

	- `dialog` (list of strings): List of utterances consisting of a dialogue.

	### Data Splits

	We do not provide the offical split for LCCC-large.
	But we provide a split for LCCC-base:

	\|train\|valid\|test\|
	\|---:\|---:\|---:\|
	\|6,820,506 \| 20,000 \| 10,000\|

	## Dataset Creation

	### Curation Rationale

	[Needs More Information]

	### Source Data

	#### Initial Data Collection and Normalization

	[Needs More Information]

	#### Who are the source language producers?

	[Needs More Information]

	### Annotations

	#### Annotation process

	[Needs More Information]

	#### Who are the annotators?

	[Needs More Information]

	### Personal and Sensitive Information

	[Needs More Information]

	## Considerations for Using the Data

	### Social Impact of Dataset

	[Needs More Information]

	### Discussion of Biases

	[Needs More Information]

	### Other Known Limitations

	[Needs More Information]

	## Additional Information

	### Dataset Curators

	[Needs More Information]

	### Licensing Information

	MIT License

	Copyright (c) 2020 lemon234071

	Permission is hereby granted, free of charge, to any person obtaining a copy
	of this software and associated documentation files (the "Software"), to deal
	in the Software without restriction, including without limitation the rights
	to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
	copies of the Software, and to permit persons to whom the Software is
	furnished to do so, subject to the following conditions:

	The above copyright notice and this permission notice shall be included in all
	copies or substantial portions of the Software.

	THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
	IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
	FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
	AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
	LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
	OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
	SOFTWARE.

	### Citation Information

	```bibtex
	@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}
	}
	```

	### Contributions

	Thanks to [Yinhe Zheng](https://github.com/silverriver) for adding this dataset.