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--- |
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license: mit |
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--- |
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This is the 25 MB compressed version of GraphCodeBERT that has been fine-tuned for the Clone Detection task using [BigCloneBench](https://github.com/clonebench/BigCloneBench.git) dataset. |
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The compression is based on our ASE 2022 paper named ["**Compressing Pre-trained Models of Code into 3 MB**"](https://arxiv.org/abs/2208.07120). |
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If you are interested in using this model, please check our **GitHub repository: https://github.com/soarsmu/Compressor.git**. If you use the model or any code from our repo in your paper, please kindly cite: |
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``` |
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@inproceedings{shi2022compressing, |
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author = {Shi, Jieke and Yang, Zhou and Xu, Bowen and Kang, Hong Jin and Lo, David}, |
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title = {Compressing Pre-Trained Models of Code into 3 MB}, |
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year = {2023}, |
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isbn = {9781450394758}, |
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publisher = {Association for Computing Machinery}, |
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address = {New York, NY, USA}, |
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url = {https://doi.org/10.1145/3551349.3556964}, |
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doi = {10.1145/3551349.3556964}, |
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booktitle = {Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering}, |
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articleno = {24}, |
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numpages = {12}, |
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keywords = {Pre-Trained Models, Model Compression, Genetic Algorithm}, |
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location = {Rochester, MI, USA}, |
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series = {ASE '22} |
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} |
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``` |
