Efficient Code Embeddings from Code Generation Models

• URL: http://arxiv.org/abs/2508.21290v1
• Date: Fri, 29 Aug 2025 01:18:15 GMT
• Title: Efficient Code Embeddings from Code Generation Models
• Authors: Daria Kryvosheieva, Saba Sturua, Michael Günther, Scott Martens, Han Xiao,
• Abstract summary: jina-code-embeddings is a novel code embedding model suite designed to retrieve code from natural language queries.<n>It makes innovative use of an autoregressive backbone pre-trained on both text and code, generating embeddings via last-token pooling.
• Score: 4.830460511410865
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: jina-code-embeddings is a novel code embedding model suite designed to retrieve code from natural language queries, perform technical question-answering, and identify semantically similar code snippets across programming languages. It makes innovative use of an autoregressive backbone pre-trained on both text and code, generating embeddings via last-token pooling. We outline the training recipe and demonstrate state-of-the-art performance despite the relatively small size of the models, validating this approach to code embedding model construction.

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