interpreto 0.1.0

Interpretability toolbox for LLMs

Explore Interpreto docs »

📚 Table of contents

• 📚 Table of contents
• 🚀 Quick Start
• 📦 What's Included
• 👍 Contributing
• 👀 See Also
• 🙏 Acknowledgments
• 👨‍🎓 Creators
• 🗞️ Citation
• 📝 License

🚀 Quick Start

You can get acquainted with our library with our Getting started tutorial. interpreto can be installed using Pypi:

pip install interpreto

Now that interpreto is installed, here are some basic examples of what you can do with the available modules.

📦 What's Included

TODO: A list or table of methods available

👍 Contributing

Feel free to propose your ideas or come and contribute with us on the Libname toolbox! We have a specific document where we describe in a simple way how to make your first pull request.

👀 See Also

More from the DEEL project:

• Xplique a Python library exclusively dedicated to explaining neural networks.
• deel-lip a Python library for training k-Lipschitz neural networks on TF.
• Influenciae Python toolkit dedicated to computing influence values for the discovery of potentially problematic samples in a dataset.
• deel-torchlip a Python library for training k-Lipschitz neural networks on PyTorch.
• DEEL White paper a summary of the DEEL team on the challenges of certifiable AI and the role of data quality, representativity and explainability for this purpose.

🙏 Acknowledgments

This project received funding from the French ”Investing for the Future – PIA3” program within the Artificial and Natural Intelligence Toulouse Institute (ANITI). The authors gratefully acknowledge the support of the DEEL and the FOR projects.

👨‍🎓 Creators

Interpreto 🪄 is a project of the FOR and the DEEL teams at the IRT Saint-Exupéry in Toulouse, France.

🗞️ Citation

If you use Interpreto 🪄 as part of your workflow in a scientific publication, please consider citing 🗞️ our paper:

TODO bibtex

📝 License

The package is released under MIT license.