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tabpfn_2_5

Prior-Labs

TabPFN-2.5 is a transformer-based foundation model that uses in-context-learning to solve tabular prediction problems in a forward pass. Inference code can be found at https://github.com/PriorLabs/tabPFN. For more examples (e.g. how to train a regressor), see the github repo: https://github.com/PriorLabs/tabPFN! Regression and classification tasks with ≤50 000 samples and ≤2000 features in structured tabular format.

Tabular & time series

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tabpfn_2_6

Prior-Labs

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Basic profile indexed · More details to follow

Tabular & time series

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tabpfn_3

Prior-Labs

TabPFN-3 is a transformer-based foundation model that uses in-context-learning to solve tabular prediction problems in a forward pass. Inference code can be found at https://github.com/PriorLabs/TabPFN. More details can be found in the Model Report. For more examples (e.g. how to train a regressor), see the github repo: https://github.com/PriorLabs/tabPFN! TabPFN-3 ships with default classification and regression checkpoints, plus a few experimental specialized variants. We recommend starting with the defaults — the variants can be useful in ensembling or HPO setups, or tried manually in the regime they were trained for. Their name suffixes indicate what we expect them to be good at.

Tabular & time series

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tabpfn_3_5

Prior-Labs

TabPFN-3.5 is a transformer-based foundation model that uses in-context learning to solve tabular prediction problems in a forward pass. One checkpoint serves both classification and regression. Inference code can be found at https://github.com/PriorLabs/TabPFN. `TabPFNRegressor` works the same way, off the same checkpoint. For more examples, see the github repo: https://github.com/PriorLabs/TabPFN. Every checkpoint ships with basic text and datetime column handling enabled (`TRANSFORM_TEXT`, `TRANSFORM_DATES`) and a feature limit of 20,000.

Tabular & time series

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TabPFN-v2-clf

Prior-Labs

TabPFN is a transformer-based foundation model for tabular data that leverages prior-data based learning to achieve strong performance on small tabular datasets without requiring task-specific training. - **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 📚 For detailed usage examples and best practices, check out: - Interactive Colab Tutorial

Tabular & time series

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TabPFN-v2-reg

Prior-Labs

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. - **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 📚 For detailed usage examples and best practices, check out: - Interactive Colab Tutorial

Tabular & time series

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Explore AI models 6

tabpfn_2_5

tabpfn_2_6

tabpfn_3

tabpfn_3_5

TabPFN-v2-clf

TabPFN-v2-reg

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