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. For detailed usage examples and best practices, check out: - Python ≥ 3.9 - PyTorch ≥ 2.1 - scikit-learn ≥ 1.0 This repository hosts the production TabPFN-v2 base checkpoints. Files matching the pattern tabpfn-v2-classifier-finetuned-.ckpt are content-identical aliases of the corresponding base checkpoints (e.g. tabpfn-v2-classifier-finetuned-gn2p4bpt-xp6f0iqb.ckpt is identical to tabpfn-v2-classifier-gn2p4bpt.ckpt; tabpfn-v2-classifier-finetuned-zk73skhh.ckpt is identical to…
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tabpfn
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. Fitting a classifier and predicting looks like this: 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…
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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. Fitting a classifier and predicting looks like this: For more examples (e.g. how to train a regressor), see the github repo: https://github.com/PriorLabs/tabPFN! Developed by Prior Labs. Regression and classification tasks with ≤50 000 samples and ≤2000 features in structured tabular format. - Not suitable for unstructured data (text, images); use API version for textual features. - Not tested for >50 000 samples or > 2000 features. Transformer with TabPFNv2-like alternating attention with…
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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. Fitting a classifier and predicting looks like this: TabPFNRegressor works the same way, off the same checkpoint. For more examples, see the github repo: https://github.com/PriorLabs/TabPFN. To use a specific checkpoint, pass its filename via modelpath: Every checkpoint ships with basic text and datetime column handling enabled (TRANSFORMTEXT, TRANSFORMDATES) and a feature limit of 20,000. Developed by Prior Labs. Regression and…
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TabPFN-2.6 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. Fitting a classifier and predicting looks like this: For more examples (e.g. how to train a regressor), see the github repo: https://github.com/PriorLabs/tabPFN! Developed by Prior Labs. Regression and classification tasks with ≤50 000 samples and ≤2000 features in structured tabular format. - Not suitable for unstructured data (text, images); use API version for textual features. - Not tested for >50 000 samples or > 2000 features. Transformer with TabPFNv2-like alternating attention with…
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