Natgen

🚀 NatGen Pretrained Model

A pretrained model for NatGen: Generative Pre - training by “Naturalizing” Source Code, which can be applied in code generation, code translation, and bug fixing.

🚀 Quick Start

To load the model, you can use the following code:

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("saikatc/NatGen")
model = AutoModelForSeq2SeqLM.from_pretrained("saikatc/NatGen")

📚 Documentation

Model Information

Paper and Resources

This model is based on the research paper: NatGen: Generative Pre - Training by “Naturalizing” Source Code. You can access the code and slide related to this project.

📄 License

This project is licensed under the MIT license.

📖 Citation

If you use this model in your research, please cite the following paper:

@inproceedings{chakraborty2022natgen,
    author = {Chakraborty, Saikat and Ahmed, Toufique and Ding, Yangruibo and Devanbu, Premkumar T. and Ray, Baishakhi},
    title = {NatGen: Generative Pre-Training by “Naturalizing” Source Code},
    year = {2022},
    isbn = {9781450394130},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3540250.3549162},
    doi = {10.1145/3540250.3549162},
    booktitle = {Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering},
    pages = {18–30},
    numpages = {13},
    keywords = {Neural Network, Semantic Preserving Transformation, Source Code Transformer, Source Code Pre-training},
    location = {Singapore, Singapore},
    series = {ESEC/FSE 2022}
}