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---
license: mit
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: method
dtype: string
- name: clean_method
dtype: string
- name: doc
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- name: comment
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struct:
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dtype: string
- name: path
dtype: string
- name: license
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dtype: int64
- name: imports
sequence: string
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sequence: string
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splits:
- name: train
num_bytes: 5373034269
num_examples: 2916582
download_size: 2492962682
dataset_size: 5373034269
tags:
- code-generation
pretty_name: 'CodeGen4Libs '
size_categories:
- 1M<n<10M
---
# Dataset Card for FudanSELab CodeGen4Libs Code Retrieval Library
## Dataset Description
- **Repository:** [GitHub Repository](https://github.com/FudanSELab/codegen4libs)
- **Paper:** [CodeGen4Libs: A Two-stage Approach for Library-oriented Code Generation](https://mingwei-liu.github.io/publication/2023-08-18-ase-CodeGen4Libs)
### Dataset Summary
This dataset is the code retrieval library used in the ASE2023 paper titled ["CodeGen4Libs: A Two-stage Approach for Library-oriented Code Generation"](https://mingwei-liu.github.io/publication/2023-08-18-ase-CodeGen4Libs).
## Additional Information
### Citation Information
```
@inproceedings{ase2023codegen4libs,
author = {Mingwei Liu and Tianyong Yang and Yiling Lou and Xueying Du and Ying Wang and and Xin Peng},
title = {{CodeGen4Libs}: A Two-stage Approach for Library-oriented Code Generation},
booktitle = {38th {IEEE/ACM} International Conference on Automated Software Engineering,
{ASE} 2023, Kirchberg, Luxembourg, September 11-15, 2023},
pages = {0--0},
publisher = {{IEEE}},
year = {2023},
}
```