| --- |
| license: mit |
| --- |
| |
| This is the 3 MB compressed version of GraphCodeBERT that has been fine-tuned for the Vulnerability Prediction task using [Devign](https://sites.google.com/view/devign) 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: |
| ``` |
| @inproceedings{shi2022compressing, |
| author = {Shi, Jieke and Yang, Zhou and Xu, Bowen and Kang, Hong Jin and Lo, David}, |
| title = {Compressing Pre-Trained Models of Code into 3 MB}, |
| year = {2023}, |
| isbn = {9781450394758}, |
| publisher = {Association for Computing Machinery}, |
| address = {New York, NY, USA}, |
| url = {https://doi.org/10.1145/3551349.3556964}, |
| doi = {10.1145/3551349.3556964}, |
| booktitle = {Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering}, |
| articleno = {24}, |
| numpages = {12}, |
| keywords = {Pre-Trained Models, Model Compression, Genetic Algorithm}, |
| location = {Rochester, MI, USA}, |
| series = {ASE '22} |
| } |
| ``` |
