TabPFN-3.5 — Tabular Foundation Model

by hugging-appsco.huggingface.hugging-apps/tabpfn-3-5-demo

  • Remote endpoint
  • 6 tools

Tools

  • tabpfn_3_5_demo_on_dataset_change Update the target-column dropdown after the data source changes. If a user passes a file as an input, use the upload_file_to_gradio tool, if present, to upload the file to the gradio app and create a Gradio File Input. Then use the returned path as the input to the tool
  • tabpfn_3_5_demo_on_dataset_change_1 Update the target-column dropdown after the data source changes. If a user passes a file as an input, use the upload_file_to_gradio tool, if present, to upload the file to the gradio app and create a Gradio File Input. Then use the returned path as the input to the tool
  • tabpfn_3_5_demo_peek_rows Show the first 5 rows of the selected dataset. If a user passes a file as an input, use the upload_file_to_gradio tool, if present, to upload the file to the gradio app and create a Gradio File Input. Then use the returned path as the input to the tool
  • tabpfn_3_5_demo_peek_rows_1 Show the first 5 rows of the selected dataset. If a user passes a file as an input, use the upload_file_to_gradio tool, if present, to upload the file to the gradio app and create a Gradio File Input. Then use the returned path as the input to the tool
  • tabpfn_3_5_demo_peek_rows_2 Show the first 5 rows of the selected dataset. If a user passes a file as an input, use the upload_file_to_gradio tool, if present, to upload the file to the gradio app and create a Gradio File Input. Then use the returned path as the input to the tool
  • tabpfn_3_5_demo_run_tabpfn Train TabPFN-3.5 on your tabular data and evaluate on a held-out split. Upload a CSV or select a built-in dataset, pick the target column, and TabPFN-3.5 fits in a single forward pass - no gradient descent and no hyperparameters. Returns test-split predictions with per-class probabilities (classification) or predicted values (regression), a confusion matrix (classification), and headline metrics. If a user passes a file as an input, use the upload_file_to_gradio tool, if present, to upload the file to the gradio app and create a Gradio File Input. Then use the returned path as the input to the tool

As the server's own listing gives them. We never call a tool.