Upload README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- AISE-TUDelft/Capybara
+tags:
+- code
+---
+
+# 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](https://huggingface.co/AISE-TUDelft/BinT5-C)         | C Source                                             |
+| [BinT5-Decom](https://huggingface.co/AISE-TUDelft/BinT5-Decom)     | Decompiled C Binaries                                |
+| [BinT5-Stripped](https://huggingface.co/AISE-TUDelft/BinT5-Stripped)  | Stripped Decompiled C Binaries                       |
+| [BinT5-Demi](https://huggingface.co/AISE-TUDelft/BinT5-Demi)      | Demi-stripped Decompiled C Binaries                  |
+| [BinT5-NoFunName](https://huggingface.co/AISE-TUDelft/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}
+}
+```