SFR-Embedding-Code-400M_R
| Property | Value |
|---|---|
| Model Size | 400M parameters |
| Author | Salesforce Research |
| Performance | 61.9 NDCG@10 on CoIR |
| Paper | arXiv:2411.12644 |
| License | Research purposes only |
What is SFR-Embedding-Code-400M_R?
SFR-Embedding-Code-400M_R is a cutting-edge code embedding model developed by Salesforce Research, designed specifically for multilingual and multi-task code retrieval. As part of the SFR-Embedding model family, it represents a significant advancement in code understanding and retrieval capabilities, demonstrating superior performance compared to various open-source alternatives.
Implementation Details
The model can be easily implemented using either the Transformers library or Sentence Transformers (>=2.7.0). It supports a maximum sequence length of 8192 tokens and provides normalized embeddings for accurate similarity scoring between code snippets and natural language queries.
- Built on advanced transformer architecture
- Supports both code and text embeddings
- Optimized for multilingual code understanding
- Implements efficient similarity scoring
Core Capabilities
- Code-to-code similarity analysis
- Natural language to code retrieval
- Multilingual code understanding
- High-performance embedding generation
- Efficient retrieval across multiple programming languages
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its balanced performance and efficiency, achieving 61.9 NDCG@10 on the CoIR benchmark while maintaining a relatively compact 400M parameter size. It's specifically optimized for code-related tasks and supports multiple programming languages.
Q: What are the recommended use cases?
The model is ideal for research purposes in code retrieval, code similarity search, and code-to-text matching applications. However, it's important to note that it's released for research purposes only and requires careful evaluation for specific use cases, particularly in high-risk scenarios.
