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--- |
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base_model: |
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- intfloat/e5-small-v2 |
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license: cc-by-4.0 |
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pipeline_tag: tabular-classification |
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--- |
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<p align="center"> |
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<img src="https://raw.githubusercontent.com/alanarazi7/TabSTAR/main/figures/tabstar_logo.png" alt="TabSTAR Logo" width="50%"> |
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</p> |
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--- |
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## Install |
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To fit a pretrained TabSTAR model to your own dataset, install the package: |
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```bash |
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pip install tabstar |
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``` |
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--- |
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## Quickstart Example |
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```python |
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from importlib.resources import files |
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import pandas as pd |
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from sklearn.metrics import classification_report |
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from sklearn.model_selection import train_test_split |
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from tabstar.tabstar_model import TabSTARClassifier |
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csv_path = files("tabstar").joinpath("resources", "imdb.csv") |
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x = pd.read_csv(csv_path) |
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y = x.pop('Genre_is_Drama') |
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x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1) |
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# For regression tasks, replace `TabSTARClassifier` with `TabSTARRegressor`. |
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tabstar = TabSTARClassifier() |
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tabstar.fit(x_train, y_train) |
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y_pred = tabstar.predict(x_test) |
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print(classification_report(y_test, y_pred)) |
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``` |
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--- |
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# 📚 TabSTAR: A Foundation Tabular Model With Semantically Target-Aware Representations |
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**Repository:** [alanarazi7/TabSTAR](https://github.com/alanarazi7/TabSTAR) |
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**Paper:** [TabSTAR: A Foundation Tabular Model With Semantically Target-Aware Representations](https://arxiv.org/abs/2505.18125) |
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**License:** MIT © Alan Arazi et al. |
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--- |
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## Abstract |
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> While deep learning has achieved remarkable success across many domains, it |
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> has historically underperformed on tabular learning tasks, which remain |
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> dominated by gradient boosting decision trees (GBDTs). However, recent |
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> advancements are paving the way for Tabular Foundation Models, which can |
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> leverage real-world knowledge and generalize across diverse datasets, |
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> particularly when the data contains free-text. Although incorporating language |
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> model capabilities into tabular tasks has been explored, most existing methods |
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> utilize static, target-agnostic textual representations, limiting their |
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> effectiveness. We introduce TabSTAR: a Foundation Tabular Model with |
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> Semantically Target-Aware Representations. TabSTAR is designed to enable |
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> transfer learning on tabular data with textual features, with an architecture |
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> free of dataset-specific parameters. It unfreezes a pretrained text encoder and |
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> takes as input target tokens, which provide the model with the context needed |
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> to learn task-specific embeddings. TabSTAR achieves state-of-the-art |
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> performance for both medium- and large-sized datasets across known benchmarks |
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> of classification tasks with text features, and its pretraining phase exhibits |
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> scaling laws in the number of datasets, offering a pathway for further |
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> performance improvements. |
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