For regression tasks, replace TabSTARClassifier with TabSTARRegressor.

tabstar = TabSTARClassifier() tabstar.fit(x_train, y_train) y_pred = tabstar.predict(x_test) print(classification_report(y_test, y_pred))

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# 📚 TabSTAR: A Foundation Tabular Model With Semantically Target-Aware Representations

**Repository:** [alanarazi7/TabSTAR](https://github.com/alanarazi7/TabSTAR)

**Paper:** [TabSTAR: A Foundation Tabular Model With Semantically Target-Aware Representations](https://arxiv.org/abs/2505.18125)

**License:** MIT © Alan Arazi et al.

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## Abstract

> While deep learning has achieved remarkable success across many domains, it
> has historically underperformed on tabular learning tasks, which remain
> dominated by gradient boosting decision trees (GBDTs). However, recent
> advancements are paving the way for Tabular Foundation Models, which can
> leverage real-world knowledge and generalize across diverse datasets,
> particularly when the data contains free-text. Although incorporating language
> model capabilities into tabular tasks has been explored, most existing methods
> utilize static, target-agnostic textual representations, limiting their
> effectiveness. We introduce TabSTAR: a Foundation Tabular Model with
> Semantically Target-Aware Representations. TabSTAR is designed to enable
> transfer learning on tabular data with textual features, with an architecture
> free of dataset-specific parameters. It unfreezes a pretrained text encoder and
> takes as input target tokens, which provide the model with the context needed
> to learn task-specific embeddings. TabSTAR achieves state-of-the-art
> performance for both medium- and large-sized datasets across known benchmarks
> of classification tasks with text features, and its pretraining phase exhibits
> scaling laws in the number of datasets, offering a pathway for further
> performance improvements.