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---
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license: mit
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---
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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 CodeBERT 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:
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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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```
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