CodeCSE: A Simple Multilingual Model for Code and Comment Sentence Embeddings
<|reference_start|>CodeCSE: A Simple Multilingual Model for Code and Comment Sentence Embeddings: 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<|reference_end|>
@article{varkey2024codecse:,
    title={CodeCSE: A Simple Multilingual Model for Code and Comment Sentence
  Embeddings},
    author={Anthony Varkey, Siyuan Jiang, Weijing Huang},
    journal={arXiv preprint arXiv:2407.06360},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.06360},
    primaryClass={cs.SE}
}