| language: | |
| - multilingual | |
| tags: | |
| - code | |
| - sentence embedding | |
| license: mit | |
| datasets: | |
| - CodeSearchNet | |
| pipeline_tag: feature-extraction | |
| # Model Card for CodeCSE | |
| A simple pre-trained model for code and comment sentence embeddings using contrastive learning. This model was pretrained using [CodeSearchNet](https://huggingface.co./datasets/code_search_net). | |
| Please [**clone the CodeCSE repository**](https://github.com/emu-se/CodeCSE) to get `GraphCodeBERTForCL` and other dependencies to use this pretrained model. https://github.com/emu-se/CodeCSE | |
| Detailed instructions are listed in the repository's README.md. Overall, you will need: | |
| 1. GraphCodeBERT (CodeCSE uses GraphCodeBERT's input format for code) | |
| 2. GraphCodeBERTForCL defined in [codecse/codecse](https://github.com/emu-se/CodeCSE/tree/main/codecse/codecse) | |
| ## Inference example | |
| NL input example: example_nl.json | |
| ```json | |
| { | |
| "original_string": "", | |
| "docstring_tokens": ["Save", "model", "to", "a", "pickle", "located", "at", "path"], | |
| "url": "https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/deepq/deepq.py#L55-L72" | |
| } | |
| ``` | |
| Code snippet to get the embedding of an NL document ([link to complete code](https://github.com/emu-se/CodeCSE/blob/a04a025c7048204bdfd908fe259fafc55e2df169/inference.py#L105)): | |
| ``` | |
| nl_json = load_example("example_nl.json") | |
| batch = prepare_inputs(nl_json, tokenizer, args) | |
| nl_inputs = batch[3] | |
| with torch.no_grad(): | |
| nl_vec = model(input_ids=nl_inputs, sent_emb="nl")[1] | |
| ``` |