Google Research introduced TabFM, a foundation model built for tabular data. TabFM performs classification and regression without dataset-specific training. Every prediction comes from a single forward pass. The model reframes tabular prediction as an in-context learning problem. It is available now on Hugging Face and GitHub.
TL;DR
TabFM predicts on unseen tables with no training, tuning, or feature engineering.
It reads the full dataset as one prompt, then predicts via in-context learning.
The architecture combines TabPFN-style row/column attention with TabICL-style in-context learning.
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