CodeRankEmbed
| Property | Value |
|---|---|
| Parameter Count | 137M |
| Model Type | Bi-encoder |
| Context Length | 8192 tokens |
| Base Model | Snowflake/snowflake-arctic-embed-m-long |
What is CodeRankEmbed?
CodeRankEmbed is a specialized bi-encoder model designed for efficient code retrieval tasks. Built by cornstack, it represents a significant advancement in code search technology, achieving state-of-the-art performance on multiple benchmarks. The model demonstrates impressive metrics with 77.9 MRR on CSN and 60.1 NDCG@10 on CoIR, surpassing both open-source and proprietary alternatives.
Implementation Details
The model utilizes a shared-weight architecture between text and code encoders, built upon the Arctic-Embed-M-Long foundation. It's been fine-tuned using contrastive learning with InfoNCE loss on the extensive CoRNStack dataset, comprising 21 million high-quality examples.
- Built on Arctic-Embed-M-Long architecture
- Supports extended context length of 8,192 tokens
- Implements sentence-transformers library
- Requires specific task instruction prefix for queries
Core Capabilities
- Superior code retrieval performance compared to larger models
- Efficient processing of long code sequences
- Compatible with sentence-transformers ecosystem
- Can be combined with CodeRankLLM for enhanced results
Frequently Asked Questions
Q: What makes this model unique?
CodeRankEmbed stands out for achieving superior performance with a relatively compact 137M parameter count, outperforming even larger models like CodeSage-Large (1.3B parameters). It's particularly notable for maintaining high accuracy while supporting an extended context length of 8,192 tokens.
Q: What are the recommended use cases?
The model is specifically designed for code retrieval tasks, making it ideal for code search engines, documentation linking, and code reference systems. It requires the specific query prefix "Represent this query for searching relevant code" for optimal performance.
