metadata
pipeline_tag: tabular-regression
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: TBD
- Paper: Published in Nature (January 2024)
- Repository: GitHub - priorlabs/tabpfn
Citation
TBD
Quick Start
from tabpfn import TabPFNRegressor
# Initialize model
regressor = TabPFNRegressor()
regressor.fit(X_train, y_train)
predictions = regressor.predict(X_test)
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://doi.org/10.1038/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