Mutonix/RefGPT-Code-cr

---
license: apache-2.0
dataset_info:
features:
- name: dialogue
dtype: string
- name: reference
dtype: string
- name: language
dtype: string
- name: type
dtype: string
splits:
- name: en
num_bytes: 165025559.5254741
num_examples: 14119
- name: zh
num_bytes: 157858797.9941188
num_examples: 15288
download_size: 136112295
dataset_size: 322884357.5195929
task_categories:
- conversational
language:
- zh
- en
arxiv: https://arxiv.org/abs/2305.14994
size_categories:
- 10K<n<100K
---
# Dataset Card for RefGPT-Code-cr

## Dataset Description

- **Homepage:**
- **Repository:** [https://github.com/ziliwangnlp/RefGPT](https://github.com/ziliwangnlp/RefGPT)
- **Paper:** [https://arxiv.org/abs/2305.14994](https://arxiv.org/abs/2305.14994)
- **Leaderboard:**
- **Point of Contact:**

### Dataset Summary
<p align="center">
<a href="https://arxiv.org/abs/2305.14994"><b>[Paper] RefGPT</b></a> |
<a href="https://github.com/ziliwangnlp/RefGPT"><b>[Github] RefGPT</b></a>
</p>

RefGPT-Code is a dataset containing 76k multi-turn dialogues about programming with 37k English and 39k Chinese, which has covered most aspects of code usage scenarios and multiple types of programming languages. Both the English version and Chinese version use the public GitHub dataset on Google BiqQuery with no overlap in these two languages. RefGPT-Code has derived various ways of leveraging the program code as the reference to enable different scenarios. We consider three perspectives of code discussion, code creation and bug fixing in RefGPT-Code.

**RefGPT-Code-cr** is the "code creation" subset.

### Supported Tasks and Leaderboards

Chatbot instruction finetuning

### Languages

Chinese, English

## Dataset Structure

### Data Instances

[More Information Needed]

### Data Fields

[More Information Needed]

### Data Splits

[More Information Needed]

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

Please pay attention that RefGPT Datasets, including RefGPT-Fact and RefGPT-Code, have not undergone manual verification, and as such, their security cannot be strictly guaranteed. Users should be aware that they are responsible for the results generated using this data.

### Discussion of Biases

As the datasets RefGPT-Fact and RefGPT-Code are collected by using the references like Wikipedia and Github repositories, it can not be avoided that the reference itself has factual errors, typos, or bugs and malicious code if it is from Github repositories. The datasets may also reflect the biases of the selected references and GPT-3.5/GPT-4 model

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

[More Information Needed]

### Citation Information

```bibtex
@misc{yang2023refgpt,
title={RefGPT: Reference -> Truthful & Customized Dialogues Generation by GPTs and for GPTs},
author={Dongjie Yang and Ruifeng Yuan and YuanTao Fan and YiFei Yang and Zili Wang and Shusen Wang and Hai Zhao},
year={2023},
eprint={2305.14994},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```

### Contributions

[More Information Needed]

原始信息汇总

数据集概述

数据集名称

• RefGPT-Code-cr

数据集描述

• RefGPT-Code-cr 是 "code creation" 子集，包含关于编程的多轮对话数据。

数据集内容

• 包含76k多轮对话，涉及编程的多个方面和多种编程语言。
• 分为英语和中文两个版本，英语部分有37k对话，中文部分有39k对话。

数据集特征

• dialogue: 字符串类型
• reference: 字符串类型
• language: 字符串类型
• type: 字符串类型

数据集分割

• en: 14119个示例，总大小165025559.5254741字节
• zh: 15288个示例，总大小157858797.9941188字节

数据集大小

• 下载大小: 136112295字节
• 数据集总大小: 322884357.5195929字节

语言

• 中文
• 英文

任务类别

• 对话式

许可证

• Apache-2.0

数据集限制

• 数据集未经过手动验证，安全性无法严格保证。
• 数据可能包含参考资料中的事实错误、拼写错误或恶意代码。
• 数据可能反映所选参考资料和GPT-3.5/GPT-4模型的偏见。