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arXiv · 2407.06360

CodeCSE: A Simple Multilingual Model for Code and Comment Sentence Embeddings

Anthony Varkey·Siyuan Jiang·Weijing Huang
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Abstract

Pretrained language models for code token embeddings are used in code search, code clone detection, and other code-related tasks. Similarly, code function embeddings are useful in such tasks. However, there are no out-of-box models for function embeddings in the current literature. So, this paper proposes CodeCSE, a contrastive learning model that learns embeddings for functions and their descriptions in one space. We evaluated CodeCSE using code search. CodeCSE's multi-lingual zero-shot approach is as efficient as the models finetuned from GraphCodeBERT for specific languages. CodeCSE is open source at https://github.com/emu-se/codecse and the pretrained model is available at the HuggingFace public hub: https://huggingface.co/sjiang1/codecse

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Anthony Varkey, Siyuan Jiang, Weijing Huang. 2024-07-08. CodeCSE: A Simple Multilingual Model for Code and Comment Sentence Embeddings. https://arxiv.org/abs/2407.06360

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