AISE-TUDelft/Capybara
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How to use AISE-TUDelft/BinT5-NoFunName with Transformers:
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("AISE-TUDelft/BinT5-NoFunName")
model = AutoModelForSeq2SeqLM.from_pretrained("AISE-TUDelft/BinT5-NoFunName")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 |
@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}
}