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BinT5-Decom
AISE-TUDelft/BinT5-Decom
BinT5-Decom is a machine learning model from AISE-TUDelft. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
- Repository: https://github.com/AISE-TUDelft/Capybara-BinT5 - Paper: https://huggingface.co/papers/2301.01701 - Point of Contact: https://huggingface.co/aalkaswan - Raw Data: https://zenodo.org/records/7229913
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At a glance
- Library
- transformers
- License
- apache-2.0
- Model type
- t5
- Access
- Public
- Created
- Feb 2, 2024
- Updated
- Feb 2, 2024
- SHA
- 20b6b4b1
Datasets
Model card
From the Hugging Face model README
BinT5
- Repository: https://github.com/AISE-TUDelft/Capybara-BinT5
- Paper: https://huggingface.co/papers/2301.01701
- Point of Contact: https://huggingface.co/aalkaswan
- Raw Data: https://zenodo.org/records/7229913
BinT5 is a Binary Code Summarization model, the base models are CodeT5 and fine-tuned with Capybara.
We offer 5 variations of the model:
| Name | Training Data |
|---|---|
| BinT5-C | C Source |
| BinT5-Decom | Decompiled C Binaries |
| BinT5-Stripped | Stripped Decompiled C Binaries |
| BinT5-Demi | Demi-stripped Decompiled C Binaries |
| BinT5-NoFunName | Decompiled C Binaries with the Function Name removed |
Citation Information
@inproceedings{alkaswan2023extending,
title={Extending Source Code Pre-Trained Language Models to Summarise Decompiled Binaries},
author={Al-Kaswan, Ali and Ahmed, Toufique and Izadi, Maliheh and Sawant, Anand Ashok and Devanbu, Premkumar and van Deursen, Arie},
booktitle={2023 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)},
pages={260--271},
year={2023},
organization={IEEE}
}
- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- Feb 2, 2024
- Updated
- Feb 2, 2024