task_categories:conversational size_categories:10K<n<100K language:zh language:en license:apache-2.0 arxiv:2305.14994 region:us
---
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: 106344832.26735915
    num_examples: 8848
  - name: zh
    num_bytes: 101753322.73345818
    num_examples: 9597
  download_size: 86625605
  dataset_size: 208098155.00081733
task_categories:
- conversational
language:
- zh
- en
arxiv: https://arxiv.org/abs/2305.14994
size_categories:
- 10K<n<100K
---
# Dataset Card for RefGPT-Code-bg

## 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-bg** is the "bug fixing" 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]