codebert base

Introduction

CodeBERT is a pre-trained model developed by Microsoft for programming and natural languages. It leverages bi-modal data, specifically documents and code, to facilitate tasks such as code search and code-to-document generation. The model is based on the Roberta-base architecture and uses a combination of Masked Language Modeling (MLM) and Replaced Token Detection (RTD) for training.

Architecture

CodeBERT is initialized with the Roberta-base architecture. This model architecture is particularly suited for understanding both programming languages and natural languages, making it effective for a variety of code-related tasks.

Training

The training data for CodeBERT comes from the CodeSearchNet corpus, which consists of a large dataset of paired code and documentation. The model is trained using the MLM+RTD objectives, which help in understanding the context and semantics of both code and natural languages.

Guide: Running Locally

1. Installation: Ensure Python is installed, and set up a virtual environment. Install the Hugging Face Transformers library using pip install transformers.
2. Download Model: Use the Hugging Face transformers library to load the CodeBERT model.
3. Scripts: Access the official repository for scripts supporting "code search" and "code-to-document generation".
4. Cloud GPUs: For better performance, especially for training or heavy inference tasks, consider using cloud GPUs from providers like AWS, Google Cloud, or Azure.

License

Please refer to the official GitHub repository for licensing details: CodeBERT GitHub Repository.