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 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 Quick Start 📚 For detailed usage examples and best practices, check out: Interactive Colab Tutorial 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
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