cainstudios / codet5

Home of CodeT5: Open Code LLMs for Code Understanding and Generation

CodeT5 and CodeT5+

Official research release for CodeT5 and CodeT5+ models for Code Understanding and Generation from Salesforce Research, which are introduced by the following papers:

Title: CodeT5+: Open Code Large Language Models for Code Understanding and Generation

Authors: Yue Wang*, Hung Le*, Akhilesh Deepak Gotmare, Nghi D.Q. Bui, Junnan Li, Steven C.H. Hoi (* indicates equal contribution)

Title: CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation

Authors: Yue Wang, Weishi Wang , Shafiq Joty, Steven C.H. Hoi

In practice, CodeT5 and CodeT5+ models can be deployed as an AI-powered coding assistant to boost the productivity of software developers. At Salesforce, we build an AI coding assistant demo using CodeT5 as a VS Code plugin to provide three capabilities:

• Text-to-code generation: generate code based on the natural language description.
• Code autocompletion: complete the whole function of code given the target function name.
• Code summarization: generate the summary of a function in natural language description.

What's New: 🎉

May 2023

CodeT5+ paper and models are released！🔥
paper | code | model | blog

Sep 2022

Our CodeRL paper has been accepted to NeurIPS 2022!
paper | code | blog

July 2022

We release two large-sized CodeT5 checkpoints at HuggingFace: Salesforce/codet5-large and Salesforce/codet5-large-ntp-py, which are introduced by the CodeRL paper.

Oct 2021

We release fine-tuned checkpoints for all the downstream tasks covered in the paper. Besides, we release a CodeT5-base fine-tuned checkpoint (Salesforce/codet5-base-multi-sum) for multilingual code summarization.

Sep, 2021

CodeT5 paper accepted to EMNLP 2021 and models are released!
paper | code | model | model card | blog

Citation

If you find this code to be useful for your research, please consider citing:

@inproceedings{
    wang2021codet5,
    title={CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation}, 
    author={Yue Wang, Weishi Wang, Shafiq Joty, Steven C.H. Hoi},
    booktitle={EMNLP},
    year={2021},
}

@inproceedings{
    le2022coderl,
    title={CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning},
    author={Le, Hung and Wang, Yue and Gotmare, Akhilesh Deepak and Savarese, Silvio and Hoi, Steven C. H.},
    booktitle={NeurIPS},
    year={2022}
}

@article{
    wang2023codet5plus,
    title={CodeT5+: Open Code Large Language Models for Code Understanding and Generation},
    author={Wang, Yue and Le, Hung and Gotmare, Akhilesh Deepak and Bui, Nghi D.Q. and Li, Junnan and Hoi, Steven C. H.},
    journal={arXiv preprint},
    year={2023}
}

License

The code is released under the BSD-3 License (see LICENSE.txt for details), but we also ask that users respect the following:

This software should not be used to promote or profit from:

violence, hate, and division,

environmental destruction,

abuse of human rights, or

the destruction of people's physical and mental health.

We encourage users of this software to tell us about the applications in which they are putting it to use by emailing [email protected], and to use appropriate documentation when developing high-stakes applications of this model.

Get Involved

Please create a GitHub issue if you have any questions, suggestions, requests or bug-reports. We welcome PRs!