CodeBERT

CodeBERT is a code-based natural language processing (NLP) system developed by researchers at Microsoft and the University of Washington in Seattle. The model is designed to understand natural language like a human would. It is able to understand code and programming languages as well as natural language, allowing programmers to use natural language to interact with software and databases.

CodeBERT is based on the popular open source natural language processing (NLP) system BERT, which uses a series of Transformer layers to understand the relationships between words in a sentence. CodeBERT extends this technology by adding support for code completion and understanding of code blocks. This allows the model to make predictions about what code should be written next, as well as provide natural language explanations for the code it has written.

CodeBERT is built on a novel mixture of NLP, computer vision, and programming language analysis techniques. The system is trained on millions of lines of code, allowing it to learn to complete code blocks written in languages like C#, Java, and Python. It has been demonstrated to be over 30% better at code completion than existing code completion models.

The resulting system has the potential to be a powerful tool for programming, allowing developers to quickly write code and troubleshoot problems without having to know the intricacies of the language they are using. CodeBERT could also be used to create new applications that use natural language processing to understand and interact with code.