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# nl2bash-custom
nl2bash-custom is a custom dataset used to fine-tune Large Language Models for Bash Code Generation. Fine tune the Code-Llamma family of LLMs (7b, 13b, 70b) for best results.
The dataset is created by reformatting and reshiffling of 2 original datasets
- [nl2bash by TelinaTool](https://github.com/TellinaTool/nl2bash)
- [NLC2CMD by Magnum Reasearch Group](https://github.com/magnumresearchgroup/Magnum-NLC2CMD)
## Dataset Structure
- `train.json`: Training split.
- `dev.json`: Development split.
- `test.json`: Test split.
## Usage
```python
from datasets import load_dataset
dataset = load_dataset("AnishJoshi/nl2bash-custom")
```
## Features
- 'srno': Serial number of the input-output pair
- 'nl_command': The natural language input/command
- 'bash_code': Corresponding bash code
## References
@inproceedings{LinWZE2018:NL2Bash,
author = {Xi Victoria Lin and Chenglong Wang and Luke Zettlemoyer and Michael D. Ernst},
title = {NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System},
booktitle = {Proceedings of the Eleventh International Conference on Language Resources
and Evaluation {LREC} 2018, Miyazaki (Japan), 7-12 May, 2018.},
year = {2018}
}
@article{Fu2021ATransform,
title={A Transformer-based Approach for Translating Natural Language to Bash Commands},
author={Quchen Fu and Zhongwei Teng and Jules White and Douglas C. Schmidt},
journal={2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)},
year={2021},
pages={1241-1244}
}
@article{fu2023nl2cmd,
title={NL2CMD: An Updated Workflow for Natural Language to Bash Commands Translation},
author={Fu, Quchen and Teng, Zhongwei and Georgaklis, Marco and White, Jules and Schmidt, Douglas C},
journal={Journal of Machine Learning Theory, Applications and Practice},
pages={45--82},
year={2023}
}
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