| --- |
| license: apache-2.0 |
| tags: |
| - codet5 |
| datasets: |
| - code_search_net |
| inference: false |
| --- |
| |
| # CodeT5 (small-sized model) |
|
|
| Pre-trained CodeT5 model. It was introduced in the paper [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models |
| for Code Understanding and Generation](https://arxiv.org/abs/2109.00859) by Yue Wang, Weishi Wang, Shafiq Joty, Steven C.H. Hoi and first released in [this repository](https://github.com/salesforce/CodeT5). |
|
|
| Disclaimer: The team releasing CodeT5 did not write a model card for this model so this model card has been written by the Hugging Face team (more specifically, [nielsr](https://huggingface.co/nielsr)). |
|
|
| ## Model description |
|
|
| From the abstract: |
|
|
| "We present CodeT5, a unified pre-trained encoder-decoder Transformer model that better leverages the code semantics conveyed from the developer-assigned identifiers. Our model employs a unified framework to seamlessly support both code understanding and generation tasks and allows for multi-task learning. Besides, we propose a novel identifier-aware pre-training task that enables the model to distinguish which code tokens are identifiers and to recover them when they are masked. Furthermore, we propose to exploit the user-written code comments with a bimodal dual generation task for better NL-PL alignment. Comprehensive experiments show that CodeT5 significantly outperforms prior methods on understanding tasks such as code defect detection and clone detection, and generation tasks across various directions including PL-NL, NL-PL, and PL-PL. Further analysis reveals that our model can better capture semantic information from code." |
|
|
| ## Intended uses & limitations |
|
|
| This repository contains the pre-trained model only, so you can use this model for masked span prediction, as shown in the code example below. However, the main use of this model is to fine-tune it for a downstream task of interest, such as: |
| * code summarization |
| * code generation |
| * code translation |
| * code refinement |
| * code defect detection |
| * code clone detection. |
|
|
| See the [model hub](https://huggingface.co/models?search=salesforce/codet) to look for fine-tuned versions on a task that interests you. |
|
|
| ### How to use |
|
|
| Here is how to use this model: |
|
|
| ```python |
| from transformers import RobertaTokenizer, T5ForConditionalGeneration |
| |
| tokenizer = RobertaTokenizer.from_pretrained('Salesforce/codet5-small') |
| model = T5ForConditionalGeneration.from_pretrained('Salesforce/codet5-small') |
| |
| 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=10) |
| print(tokenizer.decode(generated_ids[0], skip_special_tokens=True)) |
| # this prints "user: {user.name}" |
| ``` |
|
|
| ## Training data |
|
|
| The CodeT5 model was pretrained on CodeSearchNet [Husain et al., 2019](https://arxiv.org/abs/1909.09436). Additionally, the authors collected two datasets of C/CSharp from [BigQuery1](https://console.cloud.google.com/marketplace/details/github/github-repos) to ensure that all downstream tasks have overlapped programming languages with the pre-training data. In total, around 8.35 million instances are used for pretraining. |
|
|
| ## Training procedure |
|
|
| ### Preprocessing |
|
|
| This model uses a code-specific BPE (Byte-Pair Encoding) tokenizer. One can prepare text (or code) for the model using RobertaTokenizer, with the files from this repository. |
|
|
| ## Evaluation results |
|
|
| For evaluation results on several downstream benchmarks, we refer to the paper. |
|
|
| ### BibTeX entry and citation info |
|
|
| ```bibtex |
| @misc{wang2021codet5, |
| title={CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation}, |
| author={Yue Wang and Weishi Wang and Shafiq Joty and Steven C. H. Hoi}, |
| year={2021}, |
| eprint={2109.00859}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL} |
| } |
| ``` |
