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--- |
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license: apache-2.0 |
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datasets: |
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- AISE-TUDelft/Capybara |
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tags: |
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- code |
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--- |
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# BinT5 |
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- **Repository: https://github.com/AISE-TUDelft/Capybara-BinT5** |
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- **Paper: https://huggingface.co/papers/2301.01701** |
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- **Point of Contact: https://huggingface.co/aalkaswan** |
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- **Raw Data: https://zenodo.org/records/7229913** |
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BinT5 is a Binary Code Summarization model, the base models are [CodeT5]() and fine-tuned with [Capybara](). |
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We offer 5 variations of the model: |
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| Name | Training Data | |
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|-----------------------------------------------------|------------------------------------------------------| |
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| [BinT5-C](https://huggingface.co/AISE-TUDelft/BinT5-C) | C Source | |
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| [BinT5-Decom](https://huggingface.co/AISE-TUDelft/BinT5-Decom) | Decompiled C Binaries | |
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| [BinT5-Stripped](https://huggingface.co/AISE-TUDelft/BinT5-Stripped) | Stripped Decompiled C Binaries | |
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| [BinT5-Demi](https://huggingface.co/AISE-TUDelft/BinT5-Demi) | Demi-stripped Decompiled C Binaries | |
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| [BinT5-NoFunName](https://huggingface.co/AISE-TUDelft/BinT5-NoFunName) | Decompiled C Binaries with the Function Name removed | |
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### Citation Information |
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``` |
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@inproceedings{alkaswan2023extending, |
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title={Extending Source Code Pre-Trained Language Models to Summarise Decompiled Binaries}, |
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author={Al-Kaswan, Ali and Ahmed, Toufique and Izadi, Maliheh and Sawant, Anand Ashok and Devanbu, Premkumar and van Deursen, Arie}, |
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booktitle={2023 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)}, |
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pages={260--271}, |
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year={2023}, |
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organization={IEEE} |
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} |
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``` |
