What is CodeBERT?
CodeBERT is a pre-trained language model for programming languages, developed by Microsoft Research. It is designed to understand both natural language and programming code, making it useful for tasks such as code summarization, code translation, and code completion. CodeBERT is based on the popular BERT architecture, which is widely used for natural language processing tasks. By pretraining the model on a large corpus of code and natural language data, CodeBERT is able to learn the syntactic and semantic patterns of programming languages, making it an effective tool for various code-related tasks.
Example Applications of CodeBERT
Code summarization: Generating human-readable summaries of code snippets to aid in code comprehension and documentation.
Code translation: Converting code from one programming language to another, such as translating Python code to Java.
Code completion: Suggesting code snippets to complete partially-written code, improving developer productivity and reducing the likelihood of errors.
Resources to Learn More About CodeBERT
CodeBERT: A Pre-Trained Model for Programming and Natural Languages, the original paper that introduced CodeBERT.
Microsoft’s CodeBERT GitHub repository, which includes code and pretrained models.