base on ⚡ TabPFN: Foundation Model for Tabular Data ⚡ # TabPFN [](https://badge.fury.io/py/tabpfn) [](https://pepy.tech/project/tabpfn) [](https://discord.com/channels/1285598202732482621/) [](https://priorlabs.ai/docs) [](https://tinyurl.com/tabpfn-colab-local) TabPFN is a foundation model for tabular data that outperforms traditional methods while being dramatically faster. This repository contains the core PyTorch implementation with CUDA optimization. ⚠️ **Major Update: Version 2.0:** Complete codebase overhaul with new architecture and features. Previous version available at [v1.0.0](../../tree/v1.0.0) and `pip install tabpfn<2`. 📚 For detailed usage examples and best practices, check out [Interactive Colab Tutorial](https://tinyurl.com/tabpfn-colab-local) ## 🚀 We're Hiring! Prior Labs is building breakthrough foundation models for tabular data. [Join our team in Freiburg and Berlin ](https://jobs.ashbyhq.com/prior-labs) ## 🌐 TabPFN Ecosystem Choose the right TabPFN implementation for your needs: - **[TabPFN Client](https://github.com/automl/tabpfn-client)**: Easy-to-use API client for cloud-based inference - **[TabPFN Extensions](https://github.com/priorlabs/tabpfn-extensions)**: Community extensions and integrations - **TabPFN (this repo)**: Core implementation for local deployment and research - **[TabPFN UX](https://ux.priorlabs.ai)**: No-code TabPFN usage Try our [Interactive Colab Tutorial](https://colab.research.google.com/drive/1SHa43VuHASLjevzO7y3-wPCxHY18-2H6?usp=sharing) to get started quickly. ## 🏁 Quick Start ### Installation ```bash # Simple installation pip install tabpfn # Local development installation git clone https://github.com/PriorLabs/TabPFN.git pip install -e "TabPFN[dev]" ``` ### Basic Usage ```python from sklearn.datasets import load_breast_cancer from sklearn.metrics import accuracy_score, roc_auc_score from sklearn.model_selection import train_test_split from tabpfn import TabPFNClassifier # Load data X, y = load_breast_cancer(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42) # Initialize a classifier clf = TabPFNClassifier() clf.fit(X_train, y_train) # Predict probabilities prediction_probabilities = clf.predict_proba(X_test) print("ROC AUC:", roc_auc_score(y_test, prediction_probabilities[:, 1])) # Predict labels predictions = clf.predict(X_test) print("Accuracy", accuracy_score(y_test, predictions)) ``` ### Best Results For optimal performance, use the `AutoTabPFNClassifier` or `AutoTabPFNRegressor` for post-hoc ensembling. These can be found in the [TabPFN Extensions](https://github.com/PriorLabs/tabpfn-extensions) repository. Post-hoc ensembling combines multiple TabPFN models into an ensemble. **Steps for Best Results:** 1. Install the extensions: ```bash git clone https://github.com/priorlabs/tabpfn-extensions.git pip install -e tabpfn-extensions ``` 2. ```python from tabpfn_extensions.post_hoc_ensembles.sklearn_interface import AutoTabPFNClassifier clf = AutoTabPFNClassifier(max_time=120) # 120 seconds tuning time clf.fit(X_train, y_train) predictions = clf.predict(X_test) ``` See our [Colab](https://colab.research.google.com/drive/1SHa43VuHASLjevzO7y3-wPCxHY18-2H6#scrollTo=49sMXWT5DYzj&line=1&uniqifier=1) ## 🤝 Join Our Community We're building the future of tabular machine learning and would love your involvement: 1. **Connect & Learn**: - Join our [Discord Community](https://discord.gg/VJRuU3bSxt) - Read our [Documentation](https://priorlabs.ai/docs) - Check out [GitHub Issues](https://github.com/priorlabs/tabpfn/issues) 2. **Contribute**: - Report bugs or request features - Submit pull requests - Share your research and use cases 3. **Stay Updated**: Star the repo and join Discord for the latest updates ## 📜 License Prior Labs License (Apache 2.0 with additional attribution requirement): [here](https://priorlabs.ai/tabpfn-license/) ``` Attribution clause (cropped): If You distribute or make available the Work or any Derivative Work thereof relating to any part of the source or model weights, or a product or service (including another AI model) that contains any source or model weights, You shall (A) provide a copy of this License with any such materials; and (B) prominently display “Built with TabPFN” on each related website, user interface, blogpost, about page, or product documentation. If You use the source or model weights or model outputs to create, train, fine tune, distil, or otherwise improve an AI model, which is distributed or made available, you shall also include “TabPFN” at the beginning of any such AI model name. To clarify, internal benchmarking and testing without external communication shall not qualify as distribution or making available pursuant to this Section 10 and no attribution under this Section 10 shall be required. ``` ## 📚 Citation ```bibtex @article{hollmann2025tabpfn, title={Accurate predictions on small data with a tabular foundation model}, author={Hollmann, Noah and M{\"u}ller, Samuel and Purucker, Lennart and Krishnakumar, Arjun and K{\"o}rfer, Max and Hoo, Shi Bin and Schirrmeister, Robin Tibor and Hutter, Frank}, journal={Nature}, year={2025}, month={01}, day={09}, doi={10.1038/s41586-024-08328-6}, publisher={Springer Nature}, url={https://www.nature.com/articles/s41586-024-08328-6}, } ``` ## 🛠️ Development 1. Setup environment: ```bash python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate git clone https://github.com/PriorLabs/TabPFN.git cd tabpfn pip install -e ".[dev]" pre-commit install ``` 2. Before committing: ```bash pre-commit run --all-files ``` 3. Run tests: ```bash pytest tests/ ``` --- Built with ❤️ by [Prior Labs](https://priorlabs.ai) - Copyright (c) 2025 Prior Labs GmbH ", Assign "at most 3 tags" to the expected json: {"id":"12693","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"