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pipeline_tag: tabular-regression |
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# TabPFN v2: A Tabular Foundation Model |
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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. |
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## Installation |
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```bash |
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pip install tabpfn |
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``` |
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## Model Details |
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- **Developed by:** Prior Labs |
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- **Model type:** Transformer-based foundation model for tabular data |
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- **License:** TBD |
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- **Paper:** Published in Nature (January 2024) |
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- **Repository:** [GitHub - priorlabs/tabpfn](https://github.com/priorlabs/tabpfn) |
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### Citation |
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TBD |
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## Quick Start |
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```python |
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from tabpfn import TabPFNRegressor |
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# Initialize model |
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regressor = TabPFNRegressor() |
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regressor.fit(X_train, y_train) |
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predictions = regressor.predict(X_test) |
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``` |
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## Technical Requirements |
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- Python ≥ 3.9 |
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- PyTorch ≥ 2.1 |
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- scikit-learn ≥ 1.0 |
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- Hardware: 16GB+ RAM, CPU (GPU optional) |
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## Limitations |
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- Not designed for very large datasets |
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- Not suitable for non-tabular data formats |
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## Resources |
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- **Documentation:** https://priorlabs.ai/docs |
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- **Source:** https://github.com/priorlabs/tabpfn |
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- **Paper:** https://doi.org/10.1038/s41586-024-08328-6 |
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### Team |
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- Noah Hollmann |
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- Samuel Müller |
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- Lennart Purucker |
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- Arjun Krishnakumar |
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- Max Körfer |
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- Shi Bin Hoo |
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- Robin Tibor Schirrmeister |
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- Frank Hutter |
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- Eddie Bergman |
