CodeRankEmbed CodeRankEmbed is a 137M bi encoder supporting 8192 context length for code retrieval. It significantly outperforms various open source and proprietary code embedding models on various code retrieval tasks. Check out our blog post and paper for more details! Combine CodeRankEmbed with our re ranker CodeRankLLM for even higher quality code retrieval. Performance Benchmarks Name Parameters CSN (MRR) CoIR (NDCG@10) : : : : : : CodeRankEmbed 137M 77.9 60.1 Arctic Embed M Long 137M 53.4 43.0 CodeSage Small 130M 64.9 54.4 CodeSage Base 356M 68.7 57.5 CodeSage Large 1.3B 71.2 59.4 Jina Code v2 161M 67.2 58.4 CodeT5+ 110M 74.2 45.9 OpenAI Ada 002 110M 71.3 45.6 Voyage Code 002 Unknown 68.5 56.3 We release the scripts to evaluate our model's performance here. Usage Important : the query prompt must include the following task instruction prefix : "Represent this query for searching relevant code" Training We use a bi encoder architecture for CodeRankEmbed , with weights shared between the text and code encoder. The retriever is contrastively fine tuned with InfoNCE loss on a 21 million example high quality dataset we curated called CoRNStack. Our encoder is initialized with Arct…
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