CodeSage-Large
| Property | Value |
|---|---|
| Model Size | 1.3B parameters |
| License | Apache 2.0 |
| Paper | Code Representation Learning At Scale |
| Supported Languages | 9 (Python, Java, JavaScript, TypeScript, C, C#, Go, PHP, Ruby) |
What is CodeSage-Large?
CodeSage-Large is an advanced code embedding model designed for comprehensive source code understanding tasks. Developed by researchers including Dejiao Zhang and Wasi Uddin Ahmad, it represents a significant advancement in code representation learning, utilizing a sophisticated encoder architecture that produces 2048-dimensional embeddings.
Implementation Details
The model employs a two-phase training approach: initial training using masked language modeling (MLM) on code data, followed by training on bimodal text-code pair data. It utilizes the Starcoder tokenizer and can be easily implemented using the Transformers library.
- Encoder architecture with 1.3B parameters
- Produces 2048-dimensional embeddings
- Requires EOS token addition for optimal performance
- Compatible with PyTorch framework
Core Capabilities
- Multi-language code understanding across 9 programming languages
- High-dimensional code representation generation
- Efficient code embedding extraction
- Support for various source code understanding tasks
Frequently Asked Questions
Q: What makes this model unique?
CodeSage-Large stands out for its comprehensive training on the Stack dataset and its ability to generate high-quality 2048-dimensional embeddings across multiple programming languages. The two-phase training approach combining MLM and bimodal text-code pair learning makes it particularly effective for code understanding tasks.
Q: What are the recommended use cases?
The model is ideal for tasks requiring deep code understanding, including code similarity analysis, code search, and code-to-code translation. It's particularly suitable for applications requiring sophisticated code representation across multiple programming languages.
