CodeBERT: A Pre-Trained Model for Programming and Natural Languages

Code

• github.com/graykode/commit-autosuggestions
  pytorch★ 389
• github.com/sakirinn/llm4cvd
  pytorch★ 40
• github.com/zhangzwwww/dietcode
  pytorch★ 20
• github.com/zhangzwwww/dietcodebert
  pytorch★ 20
• github.com/zfj1998/CodeBert-Code2Text
  pytorch★ 14
• github.com/JasonZhu-WHU/CoderAssistant
  none★ 5
• github.com/aminatadjer/test
  pytorch★ 0

Abstract

We present CodeBERT, a bimodal pre-trained model for programming language (PL) and nat-ural language (NL). CodeBERT learns general-purpose representations that support downstream NL-PL applications such as natural language codesearch, code documentation generation, etc. We develop CodeBERT with Transformer-based neural architecture, and train it with a hybrid objective function that incorporates the pre-training task of replaced token detection, which is to detect plausible alternatives sampled from generators. This enables us to utilize both bimodal data of NL-PL pairs and unimodal data, where the former provides input tokens for model training while the latter helps to learn better generators. We evaluate CodeBERT on two NL-PL applications by fine-tuning model parameters. Results show that CodeBERT achieves state-of-the-art performance on both natural language code search and code documentation generation tasks. Furthermore, to investigate what type of knowledge is learned in CodeBERT, we construct a dataset for NL-PL probing, and evaluate in a zero-shot setting where parameters of pre-trained models are fixed. Results show that CodeBERT performs better than previous pre-trained models on NL-PL probing.

Tasks

Benchmark Results

Reproductions