TabFM is a 400M-parameter foundation model for tabular data that provides calibrated zero-shot predictions through in-context learning, achieving top performance on 51 benchmark datasets without task-specific tuning. It uses synthetic data from structural causal models and includes extensions like multi-view feature expansion and LLM-guided data processing to improve results.
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3 source votesReal observations only. History before source connection is not reconstructed.
Suitable for tasks requiring quick predictions on new tabular datasets without extensive retraining.
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3 days ago