README.md

metadata

title: tabBench
emoji: 🧮
colorFrom: blue
colorTo: yellow
sdk: gradio
sdk_version: 5.35.0
app_file: app.py
pinned: false
license: other
python_version: 3.11
models:
  - google/tabfm-1.0.0-pytorch
tags:
  - tabular-classification
  - tabular-regression
  - benchmark
  - leaderboard
  - tabfm

tabBench

A Hugging Face Space for benchmarking Google's TabFM against practical tabular classification and regression baselines.

This Space is linked to google/tabfm-1.0.0-pytorch, so it should appear in the model page's Spaces using this model section after the Space is pushed and indexed by Hugging Face.

The app includes:

• A benchmark catalog with 10+ common tabular tasks, including Titanic-style survival, housing prices, fraud detection, recipe ratings, Halloween candy ranking, and classic sklearn datasets.
• A leaderboard with model metrics, timing, and task-aware ranking.
• Charts for accuracy/F1/AUC or RMSE/MAE/R2.
• Controls for sample size, train/test split, random seed, model selection, chart metrics, and TabFM inclusion.
• A CSV upload flow so visitors can run the same arena on their own dataset.

The built-in catalog uses real open datasets where possible:

• OpenML: Titanic, Ames Housing, Adult Income
• KaggleHub: Credit Card Fraud, Epicurious Recipes, CalCOFI, Szeged Weather, WWII Weather, Montreal Bike Lanes, NYC Bike Crossings, UK Road Safety, KCBS BBQ
• FiveThirtyEight GitHub data: Halloween Candy
• sklearn: California Housing, Iris, Wine, Breast Cancer, Digits, Diabetes

TabFM is loaded through the public Google Research package when available. The app keeps graceful fallbacks so the Space still works on CPU-only or dependency-constrained runtimes. Live TabFM runs are opt-in because the model weights are large and CPU-only inference can be slow.

Model choices include Logistic, Linear Regression, Ridge, Bayesian Ridge, Naive Bayes, RandomForest, ExtraTrees, GradientBoosting, HistGradientBoosting, AdaBoost, KNN, SVC/SVR, XGBoost, LightGBM, Dummy, and optional live TabFM.

TabFM is always represented in the leaderboard: it appears as skipped until Run TabFM live is enabled, then reports live metrics when the run completes.

Operational notes

• The Space is pinned to Python 3.11 because TabFM's upstream package requires Python >= 3.11 and downloads weights from Hugging Face Hub.
• TabFM is loaded with the correct checkpoint for the selected task: model_type="classification" for classification and model_type="regression" for regression.
• Uploaded CSV runs coerce numeric regression targets, drop invalid target rows, and sanitize TabFM feature inputs before fitting.
• The first live TabFM run can take a couple of minutes on CPU Basic while the model dependency and weights are loaded; later runs are faster while the Space remains warm.

Note: TabFM's weights use their own non-commercial license. Review the upstream model license before using this Space commercially.

Links:

• GitHub: https://github.com/google-research/tabfm
• Hugging Face weights: https://huggingface.co/google/tabfm-1.0.0-pytorch

Deploy

hf auth login
hf repo create YOUR_USERNAME/tabBench --type space --space-sdk gradio --exist-ok
hf upload YOUR_USERNAME/tabBench . . --repo-type space \
  --include app.py \
  --include README.md \
  --include requirements.txt \
  --commit-message "Launch tabBench"