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google-research/tabfm
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Using TabFM, write a Python script that loads a CSV with 200 rows of customer data, uses the TabFM classifier to predict churn risk, and prints the predictions for 10 new rows.
Using TabFM with scikit-learn, show me how to create a regressor that predicts house prices from square footage and neighborhood, using 50 example rows as in-context training data.
Install TabFM and set it up with PyTorch as the backend, then load the pre-trained weights and run a classification task on a tabular dataset with mixed numerical and categorical columns.
Using TabFM, compare predictions on the same dataset using both the JAX and PyTorch backends to see if results differ.
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