alana89

alana89/TabSTAR

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Model Documentation

TabSTAR Logo

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Install

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

bash
pip install tabstar

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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))

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

Repository: alanarazi7/TabSTAR

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

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.

Files & Weights

Filename	Size	Action
model.safetensors	0.18 GB