  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
