New jina-code-embeddings models achieve SOTA performance

A new suite of code embedding models, jina-code-embeddings, has been developed to facilitate code retrieval from natural language queries, technical question-answering, and identification of similar code snippets across programming languages. These models utilize an autoregressive backbone trained on both text and code, generating embeddings through last-token pooling. Despite their relatively small size, the models demonstrate state-of-the-art performance, validating the approach to constructing effective code embedding models. AI

IMPACT This research introduces a new method for code embedding that could improve code search and understanding for developers.

RANK_REASON This is a research paper detailing a new model suite. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• Han Xiao
• Hugging Face
• jina-code-embeddings

• paper
• model release

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