CodeBERT-Java
| Property | Value |
|---|---|
| Author | neulab |
| Downloads | 203,680 |
| Paper | CodeBERTScore Paper |
| Tags | Fill-Mask, Transformers, PyTorch, RoBERTa |
What is codebert-java?
CodeBERT-Java is a specialized variant of the microsoft/codebert-base-mlm model, specifically trained on Java code from the codeparrot/github-code-clean dataset. This model has undergone extensive training for 1,000,000 steps with a batch size of 32, focusing on masked language modeling tasks for Java code understanding and evaluation.
Implementation Details
The model is built on the RoBERTa architecture and is primarily designed for use in CodeBERTScore, a novel method for evaluating code generation. It leverages transformer-based architecture to understand and process Java code contexts effectively.
- Trained on clean Java code from GitHub
- 1,000,000 training steps with batch size of 32
- Optimized for masked language modeling
- Built on microsoft/codebert-base-mlm architecture
Core Capabilities
- Code evaluation using CodeBERTScore methodology
- Masked language modeling for Java code
- Code understanding and analysis
- Integration with PyTorch framework
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for Java code understanding and evaluation, making it particularly effective for CodeBERTScore applications and Java-specific code analysis tasks.
Q: What are the recommended use cases?
The primary use case is within the CodeBERTScore framework for evaluating code generation, but it can also be applied to other Java code analysis tasks, masked language modeling, and code understanding applications.
