Codet5 Large

🚀 CodeT5 (large-size model 770M)

CodeT5 is a family of encoder - decoder language models for code. It aims to enhance code understanding and generation capabilities, providing a powerful tool for developers and researchers in the field of programming.

🚀 Quick Start

This model can be easily loaded using the T5ForConditionalGeneration functionality:

from transformers import AutoTokenizer, T5ForConditionalGeneration
tokenizer = AutoTokenizer.from_pretrained("Salesforce/codet5-large")
model = T5ForConditionalGeneration.from_pretrained("Salesforce/codet5-large")
text = "def greet(user): print(f'hello <extra_id_0>!')"
input_ids = tokenizer(text, return_tensors="pt").input_ids

# simply generate a single sequence
generated_ids = model.generate(input_ids, max_length=8)
print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))

✨ Features

• Powerful Encoder - Decoder Architecture: CodeT5 is a family of encoder - decoder language models for code, which can better understand and generate code.
• Multi - language Support: Pretrained on data in six programming languages (Ruby/JavaScript/Go/Python/Java/PHP).

📚 Documentation

Model description

CodeT5 is a family of encoder - decoder language models for code from the paper: CodeT5: Identifier - aware Unified Pre - trained Encoder - Decoder Models for Code Understanding and Generation by Yue Wang, Weishi Wang, Shafiq Joty, and Steven C.H. Hoi.

The checkpoint included in this repository is denoted as CodeT5 - large (770M), which is introduced by the paper: CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning by Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese, Steven C.H. Hoi.

Training data

CodeT5 - large was pretrained on CodeSearchNet data in six programming languages (Ruby/JavaScript/Go/Python/Java/PHP). See Section 4.1 of the paper for more details.

Training procedure

CodeT5 - large was pretrained using masked span prediction objective for 150 epochs. See Section 4.1 of the paper for more details.

Evaluation results

We validate the effectiveness of this checkpoint pretrained with simplified strategies on CodeXGLUE benchmark. See Appendix A.1 of the paper for more details.

Ethical Considerations

This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high - risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use cases, refer to our AUP and AI AUP.

BibTeX entry and citation info

@inproceedings{CodeT52021,
  author    = {Yue Wang and Weishi Wang and Shafiq R. Joty and Steven C. H. Hoi},
  title     = {CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation},
  booktitle = {EMNLP},
  pages     = {8696--8708},
  publisher = {Association for Computational Linguistics},
  year      = {2021}
}

@article{CodeRL2022
  author    = {Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese, Steven C.H. Hoi},
  title     = {CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning},
  journal   = {arXiv preprint},
  volume    = {abs/2207.01780},
  year      = {2022}
}

Information Table

Important Note

⚠️ Important Note

This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model.

Usage Tip

💡 Usage Tip

We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high - risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use cases, refer to our AUP and AI AUP.