--- pipeline_tag: tabular-regression library_name: TabPFN --- # TabPFN v2: A Tabular Foundation Model TabPFN is a transformer-based foundation model for tabular data that leverages prior-data based learning to achieve strong performance on small tabular regression tasks without requiring task-specific training. ## Installation ```bash pip install tabpfn ``` ## Model Details - **Developed by:** Prior Labs - **Model type:** Transformer-based foundation model for tabular data - **License:** [Prior Labs License (Apache 2.0 with additional attribution requirement)](https://priorlabs.ai/tabpfn-license/) - **Paper:** Published in Nature (January 2025) - **Repository:** [GitHub - priorlabs/tabpfn](https://github.com/priorlabs/tabpfn) ### 📚 Citation ```bibtex @article{hollmann2025tabpfn, title={Accurate predictions on small data with a tabular foundation model}, author={Hollmann, Noah and M{\"u}ller, Samuel and Purucker, Lennart and Krishnakumar, Arjun and K{\"o}rfer, Max and Hoo, Shi Bin and Schirrmeister, Robin Tibor and Hutter, Frank}, journal={Nature}, year={2025}, month={01}, day={09}, doi={10.1038/s41586-024-08328-6}, publisher={Springer Nature}, url={https://www.nature.com/articles/s41586-024-08328-6}, } ``` ## Quick Start 📚 For detailed usage examples and best practices, check out: - [Interactive Colab Tutorial](https://tinyurl.com/tabpfn-colab-api) ## Technical Requirements - Python ≥ 3.9 - PyTorch ≥ 2.1 - scikit-learn ≥ 1.0 - Hardware: 16GB+ RAM, CPU (GPU optional) ## Limitations - Not designed for very large datasets - Not suitable for non-tabular data formats ## Resources - **Documentation:** https://priorlabs.ai/docs - **Source:** https://github.com/priorlabs/tabpfn - **Paper:** https://www.nature.com/articles/s41586-024-08328-6 ### Team - Noah Hollmann - Samuel Müller - Lennart Purucker - Arjun Krishnakumar - Max Körfer - Shi Bin Hoo - Robin Tibor Schirrmeister - Frank Hutter - Eddie Bergman - Léo Grinsztajn