CodeBERT-JavaScript

Introduction

CodeBERT-JavaScript is a specialized model based on the microsoft/codebert-base-mlm, trained for masked-language-modeling tasks on JavaScript code. It is designed to function with CodeBERTScore but is adaptable for other models and tasks.

Architecture

The model is a variant of the RoBERTa architecture, leveraging the PyTorch library. It has been specifically fine-tuned on JavaScript code data sourced from the codeparrot/github-code-clean dataset.

Training

The model was trained for 1,000,000 steps with a batch size of 32, focusing on masked-language modeling tasks. The dataset used was curated from GitHub repositories, aiming to enhance the model's understanding and prediction capabilities in JavaScript code contexts.

Guide: Running Locally

1. Environment Setup: Ensure Python and PyTorch are installed. You may need additional libraries such as transformers.
2. Model Download: Access the model via Hugging Face's Model Hub.
3. Utilization: Load the model using the transformers library and integrate it into your application or testing environment.
4. Hardware Suggestion: For optimal performance, especially on larger datasets, consider using cloud-based GPU services like AWS, Google Cloud, or Azure.

License

The model and its associated data are subject to the licensing terms provided by Hugging Face and any related repositories. Always review the specific license agreements for compliance.