---
title: LCCC
canonical_url: "https://www.modelscope.cn/datasets/OmniData/LCCC"
md_url: "https://www.modelscope.cn/datasets/OmniData/LCCC.md"
repository: OmniData/LCCC
last_updated: 2024-07-08
license: "[LCCC Custom]"
storage_size: "939 MB"
domain:
  - publishDate
  - labelTypes
  - paperUrl
  - publishUrl
  - displayName
  - publisher
  - taskTypes
tasks:
  - 2020
  - "Chinese Corpus"
  - "https://arxiv.org/pdf/2008.03946v2.pdf"
  - "https://github.com/thu-coai/CDial-GPT"
  - "LCCC (Large-scale Cleaned Chinese Conversation corpus)"
  - "Samsung Research China"
  - "Short Text Conversation"
downloads: 3489
stars: 10
---

# LCCC

> LCCC - OmniData 在 ModelScope 开源的数据集。displayName: LCCC (Large-scale Cleaned Chinese Conversation corpus) labelTypes: Chinese Corpus license: LCCC Custom paperUrl: https://arxiv.org/pdf/2008.03946v2.pdf publishDate: "2020" publishUrl:…

OmniData/LCCC 是 ModelScope 魔搭社区上的2020、Chinese Corpus、https://arxiv.org/pdf/2008.03946v2.pdf数据集，涉及 publishDate、labelTypes、paperUrl 领域，存储大小 939 MB，采用 [LCCC Custom] 许可。

- **Repository**: OmniData/LCCC
- **License**: [LCCC Custom]
- **Tasks**: 2020, Chinese Corpus, https://arxiv.org/pdf/2008.03946v2.pdf, https://github.com/thu-coai/CDial-GPT, LCCC (Large-scale Cleaned Chinese Conversation corpus), Samsung Research China, Short Text Conversation
- **Domain**: publishDate, labelTypes, paperUrl, publishUrl, displayName, publisher, taskTypes
- **Storage size**: 939 MB
- **Downloads**: 3489
- **Stars**: 10
- **Last updated**: 2024-07-08

Source: https://www.modelscope.cn/datasets/OmniData/LCCC

---

displayName: LCCC (Large-scale Cleaned Chinese Conversation corpus)
labelTypes:
- Chinese Corpus
license:
- LCCC Custom
paperUrl: https://arxiv.org/pdf/2008.03946v2.pdf
publishDate: "2020"
publishUrl: https://github.com/thu-coai/CDial-GPT
publisher:
- Tsinghua University
- Samsung Research China
tags:
- Sensitive Words
- Special Symbols
taskTypes:
- Dialogue Generation
- Short Text Conversation

---
# 数据集介绍
  ## 简介
  我们提出了一个大型清洁汉语会话语料库（LCCC），其中包含：LCCC-base 和 LCCC-large。为了保证语料库的质量，设计了严格的数据清洗流水线。该管道涉及一组规则和几个基于分类器的过滤器。诸如攻击性或敏感词、特殊符号、表情符号、语法错误的句子和不连贯的对话等噪音都会被过滤掉。
  ## 引文
  ```
@inproceedings{wang2020large,
  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={CCF International Conference on Natural Language Processing and Chinese Computing},
  pages={91--103},
  year={2020},
  organization={Springer}
}
```
  
## Download dataset
:modelscope-code[]{type="git"}