Code for CodeT5: a new code-aware pre-trained encoder-decoder model.

Overview

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

This is the official PyTorch implementation for the following EMNLP 2021 paper from Salesforce Research:

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

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

CodeT5 demo

Updates

Sep 24, 2021

CodeT5 is now in hugginface!

You can simply load the model (CodeT5-small and CodeT5-base) and do the inference:

from transformers import RobertaTokenizer, T5ForConditionalGeneration

tokenizer = RobertaTokenizer.from_pretrained('Salesforce/codet5-base')
model = T5ForConditionalGeneration.from_pretrained('Salesforce/codet5-base')

text = "def greet(user): print(f'hello <extra_id_0>!')"
input_ids = tokenizer(text, return_tensors="pt").input_ids

# simply generate one code span
generated_ids = model.generate(input_ids, max_length=8)
print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
# this prints "{user.username}"

Introduction

This repo provides the code for reproducing the experiments in CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation. CodeT5 is a new pre-trained encoder-decoder model for programming languages, which is pre-trained on 8.35M functions in 8 programming languages (Python, Java, JavaScript, PHP, Ruby, Go, C, and C#). In total, it achieves state-of-the-art results on 14 sub-tasks in a code intelligence benchmark - CodeXGLUE.

Paper link: https://arxiv.org/abs/2109.00859

Blog link: https://blog.einstein.ai/codet5/

The code currently includes two pre-trained checkpoints (CodeT5-small and CodeT5-base) and scripts to fine-tine them on 4 generation tasks (code summarization, code generation, translation, and refinement) plus 2 understanding tasks (code defect detection and clone detection) in CodeXGLUE.

In practice, CodeT5 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 for Apex developers:

  • 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.

Table of Contents

  1. Citation
  2. License
  3. Dependency
  4. Download
  5. Fine-tuning
  6. Get Involved

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={Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021},
    year={2021},
}

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.

Dependency

  • Pytorch 1.7.1
  • tensorboard 2.4.1
  • transformers 4.6.1
  • tree-sitter 0.2.2

Download

Instructions to download:

pip install gsutil

gsutil -m cp -r "gs://sfr-codet5-data-research/data/" .

mkdir pretrained_models; cd pretrained_models
gsutil -m cp -r \
  "gs://sfr-codet5-data-research/pretrained_models/codet5_small" \
  "gs://sfr-codet5-data-research/pretrained_models/codet5_base" \
  .

The repository structure will look like the following after the download:

├── CODE_OF_CONDUCT.md
├── README.md
├── SECURITY.md
├── codet5.gif
├── configs.py
├── models.py
├── run_clone.py
├── run_gen.py
├── utils.py
├── _utils.py
├── LICENSE.txt
├── data
│   ├── clone
│   ├── concode
│   ├── defect
│   ├── refine
│   │   ├── medium
│   │   └── small
│   ├── summarize
│   │   ├── go
│   │   ├── java
│   │   ├── javascript
│   │   ├── php
│   │   ├── python
│   │   └── ruby
│   └── translate
├── evaluator
│   ├── bleu.py
│   ├── smooth_bleu.py
│   └── CodeBLEU
├── pretrained_models
│   ├── codet5_base
│   └── codet5_small
├── sh
│   ├── exp_with_args.sh
│   ├── run_exp.py
│   ├── results
│   ├── saved_models
│   └── tensorboard
└── tokenizer
    └── salesforce
        ├── codet5-merges.txt
        └── codet5-vocab.json    

Fine-tuning

Go to sh folder, set the WORKDIR in exp_with_args.sh to be your downloaded CodeT5 repository path.

You can use run_exp.py to run a broad set of experiments by simply passing the model_tag, task, and sub_task arguments. In total, we support four models (i.e., ['roberta', 'codebert', 'codet5_small', 'codet5_base']) and six tasks (i.e., ['summarize', 'concode', 'translate', 'refine', 'defect', 'clone']). For each task, we use the sub_task to specify which specific datasets to fine-tine on.

For example, if you want to run CodeT5-base model on the code summarization task for Ruby, you can simply run:

python run_exp.py --model_tag codet5_base --task summarize --sub_task ruby

Besides, you can specify:

model_dir: where to save fine-tuning checkpoints
res_dir: where to save the performance results 
summary_dir: where to save the training curves
data_num: how many data instances to use, the default -1 is for using the full data
gpu: the index of the GPU to use in the cluster

You can also revise the suggested arguments here and refer to the argument flags in configs.py for the full available options. The saved training curves in summary_dir can be visualized using tensorboard.

Get Involved

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

Owner
Salesforce
A variety of vendor agnostic projects which power Salesforce
Salesforce
Pytorch NLP library based on FastAI

Quick NLP Quick NLP is a deep learning nlp library inspired by the fast.ai library It follows the same api as fastai and extends it allowing for quick

Agis pof 283 Nov 21, 2022
A desktop GUI providing an audio interface for GPT3.

Jabberwocky neil_degrasse_tyson_with_audio.mp4 Project Description This GUI provides an audio interface to GPT-3. My main goal was to provide a conven

16 Nov 27, 2022
Minimal GUI for accessing the Watson Text to Speech service.

Description Minimal graphical application for accessing the Watson Text to Speech service. Requirements Python 3 plus all dependencies listed in requi

Moritz Maxeiner 1 Oct 22, 2021
A Semi-Intelligent ChatBot filled with statistical and economical data for the Premier League.

MONEYBALL - ChatBot Module: 4006CEM, Class: B, Group: 5 Contributors: Jonas Djondo Roshan Kc Cole Samson Daniel Rodrigues Ihteshaam Naseer Kind remind

Jonas Djondo 1 Nov 18, 2021
Natural Language Processing at EDHEC, 2022

Natural Language Processing Here you will find the teaching materials for the "Natural Language Processing" course at EDHEC Business School, 2022 What

1 Feb 04, 2022
Code for our ACL 2021 paper - ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer

ConSERT Code for our ACL 2021 paper - ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer Requirements torch==1.6.0

Yan Yuanmeng 478 Dec 25, 2022
Implementation of Natural Language Code Search in the project CodeBERT: A Pre-Trained Model for Programming and Natural Languages.

CodeBERT-Implementation In this repo we have replicated the paper CodeBERT: A Pre-Trained Model for Programming and Natural Languages. We are interest

Tanuj Sur 4 Jul 01, 2022
Finally decent dictionaries based on Wiktionary for your beloved eBook reader.

eBook Reader Dictionaries Finally, decent dictionaries based on Wiktionary for your beloved eBook reader. Dictionaries Catalan 🚧 Ελληνικά (help welco

Mickaël Schoentgen 163 Dec 31, 2022
Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.

Tensor2Tensor Tensor2Tensor, or T2T for short, is a library of deep learning models and datasets designed to make deep learning more accessible and ac

12.9k Jan 07, 2023
Ongoing research training transformer language models at scale, including: BERT & GPT-2

Megatron (1 and 2) is a large, powerful transformer developed by the Applied Deep Learning Research team at NVIDIA.

NVIDIA Corporation 3.5k Dec 30, 2022
RIDE automatically creates the package and boilerplate OOP Python node scripts as per your needs

RIDE: ROS IDE RIDE automatically creates the package and boilerplate OOP Python code for nodes as per your needs (RIDE is not an IDE, but even ROS isn

Jash Mota 20 Jul 14, 2022
ThinkTwice: A Two-Stage Method for Long-Text Machine Reading Comprehension

ThinkTwice ThinkTwice is a retriever-reader architecture for solving long-text machine reading comprehension. It is based on the paper: ThinkTwice: A

Walle 4 Aug 06, 2021
Princeton NLP's pre-training library based on fairseq with DeepSpeed kernel integration 🚃

This repository provides a library for efficient training of masked language models (MLM), built with fairseq. We fork fairseq to give researchers mor

Princeton Natural Language Processing 92 Dec 27, 2022
Pipeline for fast building text classification TF-IDF + LogReg baselines.

Text Classification Baseline Pipeline for fast building text classification TF-IDF + LogReg baselines. Usage Instead of writing custom code for specif

Dani El-Ayyass 57 Dec 07, 2022
This is the offline-training-pipeline for our project.

offline-training-pipeline This is the offline-training-pipeline for our project. We adopt the offline training and online prediction Machine Learning

0 Apr 22, 2022
a CTF web challenge about making screenshots

screenshotter (web) A CTF web challenge about making screenshots. It is inspired by a bug found in real life. The challenge was created by @LiveOverfl

219 Jan 02, 2023
基于GRU网络的句子判断程序/A program based on GRU network for judging sentences

SentencesJudger SentencesJudger 是一个基于GRU神经网络的句子判断程序,基本的功能是判断文章中的某一句话是否为一个优美的句子。 English 如何使用SentencesJudger 确认Python运行环境 安装pyTorch与LTP python3 -m pip

8 Mar 24, 2022
TensorFlow code and pre-trained models for BERT

BERT ***** New March 11th, 2020: Smaller BERT Models ***** This is a release of 24 smaller BERT models (English only, uncased, trained with WordPiece

Google Research 32.9k Jan 08, 2023
Leon is an open-source personal assistant who can live on your server.

Leon Your open-source personal assistant. Website :: Documentation :: Roadmap :: Contributing :: Story 👋 Introduction Leon is an open-source personal

Leon AI 11.7k Dec 30, 2022