TabPFN Extensions

Interactive Notebook Tutorial

Installation

# Clone and install the repository
pip install "tabpfn-extensions[all] @ git+https://github.com/PriorLabs/tabpfn-extensions.git"

Available Extensions

• interpretability: Explain TabPFN predictions with SHAP values and feature selection
• many_class: Handle classification problems with more classes than your TabPFN checkpoint supports
• classifier_as_regressor: Use TabPFN's classifier for regression tasks
• unsupervised: Data generation and outlier detection
• embedding: Get TabPFN's internal dense sample embeddings
• tabebm: Data augmentation using TabPFN-based Energy-Based Models
• pval_crt: Statistical feature relevance testing (p-values)
• post_hoc_ensembles (deprecated): AutoTabPFN* — improve performance with model combination via AutoGluon. Scheduled for removal in a future release.
• hpo (deprecated): TunedTabPFN* — automatic hyperparameter tuning for TabPFN via Hyperopt. Scheduled for removal in a future release.

See the Documentation section below for guides, examples, and per-extension READMEs.

Backend Options

Many TabPFN Extensions works with two TabPFN implementations:

1. TabPFN Package — Full PyTorch implementation for local inference:

   pip install tabpfn
   

2. TabPFN Client — Lightweight API client for cloud-based inference:

   pip install tabpfn-client
   

Choose the backend that fits your needs - most extensions work with either option!

Exceptions to this are post_hoc_ensembles (deprecated) and embedding, which only work with the local tabpfn package.

Documentation

Documentation for tabpfn-extensions is spread across several sources. If you are new to the project, the examples are usually the fastest way to get started; for deeper conceptual guides, see the TabPFN Docs pages.

Examples

Runnable scripts and notebooks for extensions and general use cases live in the examples/ directory of this repository:

• embedding/ — access TabPFN's internal dense sample embeddings
• interpretability/ — SHAP values, partial dependence plots, feature selection
• many_class/ — classification with more classes than your checkpoint supports
• pval_crt/ — statistical feature relevance testing
• survival/ — survival analysis
• tabebm/ — data augmentation via TabEBM
• unsupervised/ — data generation, imputation, and outlier detection
• hpo/ (deprecated) — TunedTabPFN* automatic hyperparameter tuning
• phe/ (deprecated) — AutoTabPFN* post-hoc ensembles

TabPFN Docs pages

In-depth guides for selected extensions are available on docs.priorlabs.ai:

• Many-class

Per-extension READMEs

Some extensions ship a dedicated README alongside their source code:

• interpretability/
• pval_crt/
• tabebm/
• hpo/ (deprecated)
• post_hoc_ensembles/ (deprecated)

Interactive notebook

The main TabPFN demo notebook also covers several extensions — in particular the unsupervised and interpretability extensions:

License

This project is licensed under the Apache License 2.0 — see the LICENSE file for details.

Telemetry

For details on telemetry, please see our Telemetry Reference and our Privacy Policy.

For Contributors

Interested in adding your own extension? We welcome contributions!

We use uv to manage the project's environment, so install that first.

# Clone and set up for development
git clone https://github.com/PriorLabs/tabpfn-extensions.git
cd tabpfn-extensions
uv sync
source .venv/bin/activate

# If you add optional dependencies for your extension in pyproject.toml, install them
# like this
uv sync --extra [your extension name]

# Test your extension with fast mode
FAST_TEST_MODE=1 pytest tests/test_your_extension.py -v

See our Contribution Guide for more details.

Built with ❤️ by the TabPFN community