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
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)
- Paper: Published in Nature (January 2025)
- Repository: GitHub - priorlabs/tabpfn
📚 Citation
@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:
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