{"id":26063774,"url":"https://github.com/priorlabs/tabpfn-extensions","last_synced_at":"2026-06-02T11:00:31.435Z","repository":{"id":271250567,"uuid":"898949330","full_name":"PriorLabs/tabpfn-extensions","owner":"PriorLabs","description":"Community extensions for TabPFN - the foundation model for tabular data. 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It's the best way to get a hands-on feel for TabPFN, walking you through installation, classification, and regression examples.\n\u003e\n\u003e [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/PriorLabs/TabPFN/blob/main/examples/notebooks/TabPFN_Demo_Local.ipynb)\n\n## Installation\n\n```bash\n# Clone and install the repository\npip install \"tabpfn-extensions[all] @ git+https://github.com/PriorLabs/tabpfn-extensions.git\"\n```\n\n## Available Extensions\n\n- **interpretability**: Explain TabPFN predictions with SHAP values and feature selection\n- **many_class**: Handle classification problems with more classes than your TabPFN checkpoint supports\n- **unsupervised**: Data generation and outlier detection\n- **embedding**: Get TabPFN's internal dense sample embeddings\n- **tabebm**: Data augmentation using TabPFN-based Energy-Based Models\n- **pval_crt**: Statistical feature relevance testing (p-values)\n- **post_hoc_ensembles** *(deprecated)*: `AutoTabPFN*` — improve performance with model combination via AutoGluon. Scheduled for removal in a future release.\n- **hpo** *(deprecated)*: `TunedTabPFN*` — automatic hyperparameter tuning for TabPFN via Hyperopt. Scheduled for removal in a future release.\n\nSee the [Documentation](#documentation) section below for guides, examples, and per-extension READMEs.\n\n### Backend Options\n\nMany TabPFN Extensions works with two TabPFN implementations:\n\n1. **TabPFN Package** — Full PyTorch implementation for local inference:\n   ```bash\n   pip install tabpfn\n   ```\n\n2. **TabPFN Client** — Lightweight API client for cloud-based inference:\n   ```bash\n   pip install tabpfn-client\n   ```\n\nChoose the backend that fits your needs - most extensions work with either option!\n\nExceptions to this are **post_hoc_ensembles** *(deprecated)* and **embedding**, which only work with the local `tabpfn` package.\n\n## Documentation\n\nDocumentation 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).\n\n### Examples\n\nRunnable 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:\n\n- [`embedding/`](https://github.com/PriorLabs/tabpfn-extensions/tree/main/examples/embedding) — access TabPFN's internal dense sample embeddings\n- [`interpretability/`](https://github.com/PriorLabs/tabpfn-extensions/tree/main/examples/interpretability) — SHAP values, partial dependence plots, feature selection\n- [`many_class/`](https://github.com/PriorLabs/tabpfn-extensions/tree/main/examples/many_class) — classification with more classes than your checkpoint supports\n- [`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\n- [`pval_crt/`](https://github.com/PriorLabs/tabpfn-extensions/tree/main/examples/pval_crt) — statistical feature relevance testing\n- [`survival/`](https://github.com/PriorLabs/tabpfn-extensions/tree/main/examples/survival) — survival analysis\n- [`tabebm/`](https://github.com/PriorLabs/tabpfn-extensions/tree/main/examples/tabebm) — data augmentation via TabEBM\n- [`unsupervised/`](https://github.com/PriorLabs/tabpfn-extensions/tree/main/examples/unsupervised) — data generation, imputation, and outlier detection\n- [`hpo/`](https://github.com/PriorLabs/tabpfn-extensions/tree/main/examples/hpo) *(deprecated)* — `TunedTabPFN*` automatic hyperparameter tuning\n- [`phe/`](https://github.com/PriorLabs/tabpfn-extensions/tree/main/examples/phe) *(deprecated)* — `AutoTabPFN*` post-hoc ensembles\n\n### TabPFN Docs pages\n\nIn-depth guides for selected extensions are available on [docs.priorlabs.ai](https://docs.priorlabs.ai):\n\n- [Many-class](https://docs.priorlabs.ai/extensions/many-class)\n\n### Per-extension READMEs\n\nSome extensions ship a dedicated README alongside their source code:\n\n- [`interpretability/`](https://github.com/PriorLabs/tabpfn-extensions/blob/main/src/tabpfn_extensions/interpretability/README.md)\n- [`pval_crt/`](https://github.com/PriorLabs/tabpfn-extensions/blob/main/src/tabpfn_extensions/pval_crt/README.md)\n- [`tabebm/`](https://github.com/PriorLabs/tabpfn-extensions/blob/main/src/tabpfn_extensions/tabebm/README.md)\n- [`hpo/`](https://github.com/PriorLabs/tabpfn-extensions/blob/main/src/tabpfn_extensions/hpo/README.md) *(deprecated)*\n- [`post_hoc_ensembles/`](https://github.com/PriorLabs/tabpfn-extensions/blob/main/src/tabpfn_extensions/post_hoc_ensembles/README.md) *(deprecated)*\n\n### Interactive notebook\n\nThe 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:\n\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/PriorLabs/TabPFN/blob/main/examples/notebooks/TabPFN_Demo_Local.ipynb)\n\n## License\n\nThis project is licensed under the Apache License 2.0 — see the [LICENSE](LICENSE) file for details.\n\n\n## Telemetry\n\nFor 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/).\n\n## For Contributors\n\nInterested in adding your own extension? We welcome contributions!\n\nWe use [uv](https://docs.astral.sh/uv/getting-started/installation/) to manage the project's environment, so install that first.\n\n```bash\n# Clone and set up for development\ngit clone https://github.com/PriorLabs/tabpfn-extensions.git\ncd tabpfn-extensions\nuv sync\nsource .venv/bin/activate\n\n# If you add optional dependencies for your extension in pyproject.toml, install them\n# like this\nuv sync --extra [your extension name]\n\n# Test your extension with fast mode\nFAST_TEST_MODE=1 pytest tests/test_your_extension.py -v\n```\n\nSee our [Contribution Guide](CONTRIBUTING.md) for more details.\n\n[![Contributors](https://contrib.rocks/image?repo=priorlabs/tabpfn-extensions)](https://github.com/priorlabs/tabpfn-extensions/graphs/contributors)\n\n---\nBuilt with ❤️ by the TabPFN community\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpriorlabs%2Ftabpfn-extensions","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpriorlabs%2Ftabpfn-extensions","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpriorlabs%2Ftabpfn-extensions/lists"}