📚 TabSTAR: A Tabular Foundation Model for Tabular Data with Text Fields
🚀 TabSTAR v1.1 is on its way
We are releasing TabSTAR v1.1, a new generation of pretrained TabSTAR models. It ships alongside the original models, in its own module, so existing code keeps working unchanged:
| import | estimators | |
|---|---|---|
| TabSTAR | tabstar.tabstar_model |
TabSTARClassifier, TabSTARRegressor |
| TabSTAR v1.1 | tabstar.tabstarv1p1_model |
TabSTARv1p1Classifier, TabSTARv1p1Regressor |
We are releasing the model first. A detailed writeup of what changed, together with the v1.1 pretraining code, will follow.
Install
To fit a pretrained TabSTAR model to your own dataset, install the package using:
pip install tabstar
Quickstart Example
You 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}")
To use TabSTAR v1.1 instead, swap the import and the class name:
from tabstar.tabstarv1p1_model import TabSTARv1p1Classifier, TabSTARv1p1Regressor
tabstar = TabSTARv1p1Classifier()
tabstar.fit(x_train, y_train)
y_pred = tabstar.predict(x_test)
metric = tabstar.score(X=x_test, y=y_test)
fit, predict, predict_proba, score, save and load behave as they do for the original models.
Model cards: classification, regression.
For paper replication, TabSTAR evaluation on benchmarks, custom pretraining or research purposes, see:
🔗 TabSTAR Research Repository
Citation
If you use TabSTAR in your work, please cite:
@inproceedings{
arazi2025tabstar,
title={Tab{STAR}: A Tabular Foundation Model for Tabular Data with Text Fields},
author={Alan Arazi and Eilam Shapira and Roi Reichart},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
year={2025},
url={https://openreview.net/forum?id=FrXHdcTEzE}
}
License
MIT © Alan Arazi et al.
1 maintainer
Metadata
1 maintainer
Release files for tabstar 1.2.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| tabstar-1.2.1.tar.gz | 184.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| tabstar-1.2.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 382.9 kB
Release files / tabstar-1.2.1.tar.gz
| Download URL | tabstar-1.2.1.tar.gz |
|---|---|
| Size | 184.4 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
170f99e0a66ebdff155af19e5315a6d1e09cf6ae39dbd318d9e8b1f28426f736
|
|
BLAKE2b-256 checksum How to use checksums |
be486c4d6c4194f8979a7fa0d1e5d5034679478674b4c42f91ce5f388b77c48a
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.14.0
|
Release files / tabstar-1.2.1-py3-none-any.whl
| Download URL | tabstar-1.2.1-py3-none-any.whl |
|---|---|
| Size | 198.5 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
48181958c7908692c337c8c2091449b39ca0e6076fe122686e68a4477bb54456
|
|
BLAKE2b-256 checksum How to use checksums |
ef3c14c983a91ee2c93aafce29e4c8888de99a27b1366c318cea71a2821e99c6
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.14.0
|
