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TabPFN

TabPFN is a transformer based foundation model for tabular data, distributed as an open source Python package (pip install tabpfn) alongside a hosted client for GPU free inference. Rather than being retrained per dataset, it performs in context learning: a fitted classifier or regressor conditions on training examples directly to predict on new data, handling missing values without preprocessing and without needing feature scaling or one hot encoding. The current default checkpoint, TabPFN-3, supports datasets up to 1,000,000 rows by 200 features, or up to 1,000 rows by 20,000 features, with earlier checkpoints (TabPFN-2, TabPFN-2.5, TabPFN-2.6) selectable for compatibility. The project includes classifier and regressor APIs, batch prediction utilities, and offline model download scripts.
New Structured_data Gen 7
Released: May 12, 2026

Overview

TabPFN is a tabular foundation model built by Prior Labs for classification and regression on structured datasets. It uses in-context learning to predict on new data without per-dataset training. The current default, TabPFN-3, supports datasets up to 1,000,000 rows and 200 features. It runs locally as an open source Python package with PyTorch and CUDA support, or via a hosted client.

About Prior Labs

Prior Labs is a Freiburg, Germany-based AI lab and pioneer of Tabular Foundation Models — pre-trained models for state-of-the-art predictions on structured data, no tuning needed. Its model TabPFN, published in Nature, is used in finance, healthcare, industrials, and energy. In May 2026, SAP agreed to acquire Prior Labs, planning to invest over €1B into it over four years while it operates independently.

Industry: Artificial Intelligence
Location: Freiburg im Breisgau, Baden-Württemberg, DE
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Last updated: July 1, 2026
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