Tabular Classification
FlexTab
tabular
foundation-model
deep-learning
in-context
tabular-regression
tabular-matching
tabular-anomaly-detection
tabular-multi-table
tabular-clustering
Instructions to use SAP/FlexTab with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- FlexTab
How to use SAP/FlexTab with FlexTab:
pip install git+https://github.com/SAP-samples/flextab
# Run a classification task from sklearn.datasets import load_breast_cancer from sklearn.metrics import accuracy_score from sklearn.model_selection import train_test_split from flextab import FlexTabClassifier # Load sample data X, y = load_breast_cancer(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42) # Initialize a classifier, 8k context and 8-fold bagging gives best performance, reduce if running out of memory clf = FlexTabClassifier(max_context_size=8192, bagging=8) clf.fit(X_train, y_train) # Predict probabilities prediction_probabilities = clf.predict_proba(X_test) # Predict labels predictions = clf.predict(X_test) print("Accuracy", accuracy_score(y_test, predictions))# Run a regression task from sklearn.datasets import fetch_openml from sklearn.metrics import r2_score from sklearn.model_selection import train_test_split from flextab import FlexTabRegressor # Load sample data df = fetch_openml(data_id=531, as_frame=True) X = df.data y = df.target.astype(float) # Train-test split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42) # Initialize the regressor, 8k context and 8-fold bagging gives best performance, reduce if running out of memory regressor = FlexTabRegressor(max_context_size=8192, bagging=8) regressor.fit(X_train, y_train) # Predict on the test set predictions = regressor.predict(X_test) r2 = r2_score(y_test, predictions) print("R² Score:", r2)# Run a matching task from sklearn.metrics import accuracy_score, roc_auc_score from flextab import FlexTabMatcher from flextab.utils.test_utils import load_febrl4 left_train, left_test, right_train, right_test, y_train, y_test = load_febrl4() matcher = FlexTabMatcher(max_context_size=8192, bagging=1) matcher.fit(left_train, right_train, y_train) predictions = matcher.predict(left_test, right_test) print(f'Accuracy {accuracy_score(y_test, predictions):.2%}') # Probabilities (e.g. for thresholding or AUROC): # probas = matcher.predict_proba_matching(left_test, right_test) # print(f'AUROC {roc_auc_score(y_test, probas[:, 1]):.2%}') - Notebooks
- Google Colab
- Kaggle
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# FlexTab
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[](https://openreview.net/forum?id=fOph6xxdyP) [](https://api.reuse.software/info/github.com/SAP-samples/flextab)
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If you wish to contribute code, offer fixes or improvements, please send a pull request. Due to legal reasons, contributors will be asked to accept a DCO when they create the first pull request to this project. This happens in an automated fashion during the submission process. SAP uses [the standard DCO text of the Linux Foundation](https://developercertificate.org/).
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## License
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Copyright (c) 2026 SAP SE or an SAP affiliate company. All rights reserved. This project is licensed under the Apache Software License, version 2.0 except as noted otherwise in the [LICENSE](LICENSE) file.
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---
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license: apache-2.0
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pipeline_tag: tabular-classification
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tags:
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- tabular
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- foundation-model
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- deep-learning
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- in-context
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- flextab
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- tabular-classification
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- tabular-regression
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- tabular-matching
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---
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# FlexTab
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[](https://openreview.net/forum?id=fOph6xxdyP) [](https://api.reuse.software/info/github.com/SAP-samples/flextab)
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If you wish to contribute code, offer fixes or improvements, please send a pull request. Due to legal reasons, contributors will be asked to accept a DCO when they create the first pull request to this project. This happens in an automated fashion during the submission process. SAP uses [the standard DCO text of the Linux Foundation](https://developercertificate.org/).
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## License
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Copyright (c) 2026 SAP SE or an SAP affiliate company. All rights reserved. This project is licensed under the Apache Software License, version 2.0 except as noted otherwise in the [LICENSE](LICENSE) file.
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