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
@article{arxiv.2407.06360,
title = {CodeCSE: A Simple Multilingual Model for Code and Comment Sentence Embeddings},
author = {Anthony Varkey and Siyuan Jiang and Weijing Huang},
journal= {arXiv preprint arXiv:2407.06360},
year = {2024}
}