📚 TabSTAR: A Foundation Tabular Model With Semantically Target-Aware Representations
To fit a pretrained TabSTAR model to your own dataset, install the package using:
pip install tabstarYou can quickly get started with TabSTAR using the following example.
from importlib.resources import files
import pandas as pd
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)
# tabstar.save("my_model_path.pkl")
# tabstar = TabSTARClassifier.load("my_model_path.pkl")
# y_pred = tabstar.predict(x_test)
metric = tabstar.score(X=x_test, y=y_test)
print(f"AUC: {metric:.4f}")For paper replication, TabSTAR evaluation on benchmarks, custom pretraining or research purposes, see:
If you use TabSTAR in your work, please cite:
@article{arazi2025tabstarf,
title = {TabSTAR: A Foundation Tabular Model With Semantically Target-Aware Representations},
author = {Alan Arazi and Eilam Shapira and Roi Reichart},
journal = {arXiv preprint arXiv:2505.18125},
year = {2025},
}MIT © Alan Arazi et al.