https://github.com/priorlabs/tabpfn-extensions
Community extensions for TabPFN - the foundation model for tabular data. Built with TabPFN! 🤗
https://github.com/priorlabs/tabpfn-extensions
data-science machine-learning tabpfn tabular-data
Last synced: 6 days ago
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Community extensions for TabPFN - the foundation model for tabular data. Built with TabPFN! 🤗
- Host: GitHub
- URL: https://github.com/priorlabs/tabpfn-extensions
- Owner: PriorLabs
- License: apache-2.0
- Created: 2024-12-05T10:43:40.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2026-05-25T16:10:08.000Z (14 days ago)
- Last Synced: 2026-05-25T18:11:18.561Z (13 days ago)
- Topics: data-science, machine-learning, tabpfn, tabular-data
- Language: Python
- Homepage: https://www.priorlabs.ai
- Size: 2.7 MB
- Stars: 299
- Watchers: 5
- Forks: 58
- Open Issues: 18
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Codeowners: .github/CODEOWNERS
- Security: SECURITY.md
Awesome Lists containing this project
README
# TabPFN Extensions
[](https://badge.fury.io/py/tabpfn-extensions)
[](https://pepy.tech/project/tabpfn-extensions)
[](https://opensource.org/licenses/Apache-2.0)
[](https://discord.gg/BHnX2Ptf4j)
[](https://twitter.com/Prior_Labs)
[](https://colab.research.google.com/github/PriorLabs/TabPFN/blob/main/examples/notebooks/TabPFN_Demo_Local.ipynb)
> [!WARNING]
>
> #### Experimental Code Notice
> Please note that the extensions in this repository are experimental.
> - They are less rigorously tested than the core `tabpfn` library.
> - APIs are subject to change without notice in future releases.
> We welcome your feedback and contributions to help improve and stabilize them!
## Interactive Notebook Tutorial
> [!TIP]
>
> Dive right in with our interactive Colab notebook! It's the best way to get a hands-on feel for TabPFN, walking you through installation, classification, and regression examples.
>
> [](https://colab.research.google.com/github/PriorLabs/TabPFN/blob/main/examples/notebooks/TabPFN_Demo_Local.ipynb)
## Installation
```bash
# 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
- **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](#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:
```bash
pip install tabpfn
```
2. **TabPFN Client** — Lightweight API client for cloud-based inference:
```bash
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](#examples) are usually the fastest way to get started; for deeper conceptual guides, see the [TabPFN Docs pages](#tabpfn-docs-pages).
### Examples
Runnable scripts and notebooks for extensions and general use cases live in the [`examples/`](https://github.com/PriorLabs/tabpfn-extensions/tree/main/examples) directory of this repository:
- [`embedding/`](https://github.com/PriorLabs/tabpfn-extensions/tree/main/examples/embedding) — access TabPFN's internal dense sample embeddings
- [`interpretability/`](https://github.com/PriorLabs/tabpfn-extensions/tree/main/examples/interpretability) — SHAP values, partial dependence plots, feature selection
- [`many_class/`](https://github.com/PriorLabs/tabpfn-extensions/tree/main/examples/many_class) — classification with more classes than your checkpoint supports
- [`predictive_distribution/`](https://github.com/PriorLabs/tabpfn-extensions/tree/main/examples/predictive_distribution) — visualize the full predictive distribution from `TabPFNRegressor` and derive point estimates / credible intervals
- [`pval_crt/`](https://github.com/PriorLabs/tabpfn-extensions/tree/main/examples/pval_crt) — statistical feature relevance testing
- [`survival/`](https://github.com/PriorLabs/tabpfn-extensions/tree/main/examples/survival) — survival analysis
- [`tabebm/`](https://github.com/PriorLabs/tabpfn-extensions/tree/main/examples/tabebm) — data augmentation via TabEBM
- [`unsupervised/`](https://github.com/PriorLabs/tabpfn-extensions/tree/main/examples/unsupervised) — data generation, imputation, and outlier detection
- [`hpo/`](https://github.com/PriorLabs/tabpfn-extensions/tree/main/examples/hpo) *(deprecated)* — `TunedTabPFN*` automatic hyperparameter tuning
- [`phe/`](https://github.com/PriorLabs/tabpfn-extensions/tree/main/examples/phe) *(deprecated)* — `AutoTabPFN*` post-hoc ensembles
### TabPFN Docs pages
In-depth guides for selected extensions are available on [docs.priorlabs.ai](https://docs.priorlabs.ai):
- [Many-class](https://docs.priorlabs.ai/extensions/many-class)
### Per-extension READMEs
Some extensions ship a dedicated README alongside their source code:
- [`interpretability/`](https://github.com/PriorLabs/tabpfn-extensions/blob/main/src/tabpfn_extensions/interpretability/README.md)
- [`pval_crt/`](https://github.com/PriorLabs/tabpfn-extensions/blob/main/src/tabpfn_extensions/pval_crt/README.md)
- [`tabebm/`](https://github.com/PriorLabs/tabpfn-extensions/blob/main/src/tabpfn_extensions/tabebm/README.md)
- [`hpo/`](https://github.com/PriorLabs/tabpfn-extensions/blob/main/src/tabpfn_extensions/hpo/README.md) *(deprecated)*
- [`post_hoc_ensembles/`](https://github.com/PriorLabs/tabpfn-extensions/blob/main/src/tabpfn_extensions/post_hoc_ensembles/README.md) *(deprecated)*
### Interactive notebook
The main TabPFN demo notebook also covers several extensions — in particular the [unsupervised](https://github.com/PriorLabs/tabpfn-extensions/tree/main/src/tabpfn_extensions/unsupervised) and [interpretability](https://github.com/PriorLabs/tabpfn-extensions/tree/main/src/tabpfn_extensions/interpretability) extensions:
[](https://colab.research.google.com/github/PriorLabs/TabPFN/blob/main/examples/notebooks/TabPFN_Demo_Local.ipynb)
## License
This project is licensed under the Apache License 2.0 — see the [LICENSE](LICENSE) file for details.
## Telemetry
For details on telemetry, please see our [Telemetry Reference](https://github.com/PriorLabs/TabPFN/blob/main/TELEMETRY.md) and our [Privacy Policy](https://priorlabs.ai/privacy_policy/).
## For Contributors
Interested in adding your own extension? We welcome contributions!
We use [uv](https://docs.astral.sh/uv/getting-started/installation/) to manage the project's environment, so install that first.
```bash
# 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](CONTRIBUTING.md) for more details.
[](https://github.com/priorlabs/tabpfn-extensions/graphs/contributors)
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