---
base_model:
- intfloat/e5-small-v2
license: cc-by-4.0
pipeline_tag: tabular-classification
---


<p align="center">
  <img src="https://raw.githubusercontent.com/alanarazi7/TabSTAR/main/figures/tabstar_logo.png" alt="TabSTAR Logo" width="50%">
</p>

---

## Install

To fit a pretrained TabSTAR model to your own dataset, install the package:

```bash
pip install tabstar
```

---

## Quickstart Example

```python
from importlib.resources import files
import pandas as pd
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split

from tabstar.tabstar_model import TabSTARClassifier

csv_path = files("tabstar").joinpath("resources", "imdb.csv")
x = pd.read_csv(csv_path)
y = x.pop('Genre_is_Drama')
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1)
# 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))
```

---

# 📚 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.

---

## 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.