| Name | tabpfn JSON |
| Version |
2.1.0
JSON |
| download |
| home_page | None |
| Summary | TabPFN: Foundation model for tabular data |
| upload_time | 2025-07-09 16:29:15 |
| maintainer | None |
| docs_url | None |
| author | Noah Hollmann, Samuel Müller, Lennart Purucker, Arjun Krishnakumar, Max Körfer, Shi Bin Hoo, Robin Tibor Schirrmeister, Frank Hutter, Eddie Bergman, Leo Grinsztajn |
| requires_python | >=3.9 |
| license |
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Version 1.1, May 2025
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This license is a derivative of the Apache 2.0 license
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# TabPFN
[](https://badge.fury.io/py/tabpfn)
[](https://pepy.tech/project/tabpfn)
[](https://discord.com/channels/1285598202732482621/)
[](https://priorlabs.ai/docs)
[](https://colab.research.google.com/github/PriorLabs/TabPFN/blob/main/examples/notebooks/TabPFN_Demo_Local.ipynb)
[](https://pypi.org/project/tabpfn/)
<img src="https://github.com/PriorLabs/tabpfn-extensions/blob/main/tabpfn_summary.webp" width="80%" alt="TabPFN Summary">
⚠️ **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==0.1.11`.
📚 For detailed usage examples and best practices, check out [Interactive Colab Tutorial](https://colab.research.google.com/github/PriorLabs/TabPFN/blob/main/examples/notebooks/TabPFN_Demo_Local.ipynb)
## 🏁 Quick Start
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.
> ⚡ **GPU Recommended**:
> For optimal performance, use a GPU (even older ones with ~8GB VRAM work well; 16GB needed for some large datasets).
> On CPU, only small datasets (≲1000 samples) are feasible.
> No GPU? Use our free hosted inference via [TabPFN Client](https://github.com/PriorLabs/tabpfn-client).
### Installation
Official installation (pip)
```bash
pip install tabpfn
```
OR installation from source
```bash
pip install "tabpfn @ git+https://github.com/PriorLabs/TabPFN.git"
```
OR local development installation
```bash
git clone https://github.com/PriorLabs/TabPFN.git
pip install -e "TabPFN[dev]"
```
### Basic Usage
#### Classification
```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))
```
#### Regression
```python
from sklearn.datasets import fetch_openml
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import train_test_split
from tabpfn import TabPFNRegressor
# Load Boston Housing data
df = fetch_openml(data_id=531, as_frame=True) # Boston Housing dataset
X = df.data
y = df.target.astype(float) # Ensure target is float for regression
# Train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)
# Initialize the regressor
regressor = TabPFNRegressor()
regressor.fit(X_train, y_train)
# Predict on the test set
predictions = regressor.predict(X_test)
# Evaluate the model
mse = mean_squared_error(y_test, predictions)
r2 = r2_score(y_test, predictions)
print("Mean Squared Error (MSE):", mse)
print("R² Score:", r2)
```
### 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, device="cuda") # 120 seconds tuning time
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)
```
## 🌐 TabPFN Ecosystem
Choose the right TabPFN implementation for your needs:
- **[TabPFN Client](https://github.com/priorlabs/tabpfn-client)**
Simple API client for using TabPFN via cloud-based inference.
- **[TabPFN Extensions](https://github.com/priorlabs/tabpfn-extensions)**
A powerful companion repository packed with advanced utilities, integrations, and features - great place to contribute:
- 🔍 **`interpretability`**: Gain insights with SHAP-based explanations, feature importance, and selection tools.
- 🕵️♂️ **`unsupervised`**: Tools for outlier detection and synthetic tabular data generation.
- 🧬 **`embeddings`**: Extract and use TabPFN’s internal learned embeddings for downstream tasks or analysis.
- 🧠 **`many_class`**: Handle multi-class classification problems that exceed TabPFN's built-in class limit.
- 🌲 **`rf_pfn`**: Combine TabPFN with traditional models like Random Forests for hybrid approaches.
- ⚙️ **`hpo`**: Automated hyperparameter optimization tailored to TabPFN.
- 🔁 **`post_hoc_ensembles`**: Boost performance by ensembling multiple TabPFN models post-training.
✨ To install:
```bash
git clone https://github.com/priorlabs/tabpfn-extensions.git
pip install -e tabpfn-extensions
```
- **[TabPFN (this repo)](https://github.com/priorlabs/tabpfn)**
Core implementation for fast and local inference with PyTorch and CUDA support.
- **[TabPFN UX](https://ux.priorlabs.ai)**
No-code graphical interface to explore TabPFN capabilities—ideal for business users and prototyping.
## 📜 License
Prior Labs License (Apache 2.0 with additional attribution requirement): [here](https://priorlabs.ai/tabpfn-license/)
## 🤝 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
## 📚 Citation
You can read our paper explaining TabPFN [here](https://doi.org/10.1038/s41586-024-08328-6).
```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},
}
@inproceedings{hollmann2023tabpfn,
title={TabPFN: A transformer that solves small tabular classification problems in a second},
author={Hollmann, Noah and M{\"u}ller, Samuel and Eggensperger, Katharina and Hutter, Frank},
booktitle={International Conference on Learning Representations 2023},
year={2023}
}
```
## ❓ FAQ
### **Usage & Compatibility**
**Q: What dataset sizes work best with TabPFN?**
A: TabPFN is optimized for **datasets up to 10,000 rows**. For larger datasets, consider using **Random Forest preprocessing** or other extensions. See our [Colab notebook](https://colab.research.google.com/drive/154SoIzNW1LHBWyrxNwmBqtFAr1uZRZ6a#scrollTo=OwaXfEIWlhC8) for strategies.
**Q: Why can't I use TabPFN with Python 3.8?**
A: TabPFN v2 requires **Python 3.9+** due to newer language features. Compatible versions: **3.9, 3.10, 3.11, 3.12, 3.13**.
### **Installation & Setup**
**Q: How do I use TabPFN without an internet connection?**
TabPFN automatically downloads model weights when first used. For offline usage:
**Using the Provided Download Script**
If you have the TabPFN repository, you can use the included script to download all models (including ensemble variants):
```bash
# After installing TabPFN
python scripts/download_all_models.py
```
This script will download the main classifier and regressor models, as well as all ensemble variant models to your system's default cache directory.
**Manual Download**
1. Download the model files manually from HuggingFace:
- Classifier: [tabpfn-v2-classifier.ckpt](https://huggingface.co/Prior-Labs/TabPFN-v2-clf/resolve/main/tabpfn-v2-classifier.ckpt)
- Regressor: [tabpfn-v2-regressor.ckpt](https://huggingface.co/Prior-Labs/TabPFN-v2-reg/resolve/main/tabpfn-v2-regressor.ckpt)
2. Place the file in one of these locations:
- Specify directly: `TabPFNClassifier(model_path="/path/to/model.ckpt")`
- Set environment variable: `os.environ["TABPFN_MODEL_CACHE_DIR"] = "/path/to/dir"`
- Default OS cache directory:
- Windows: `%APPDATA%\tabpfn\`
- macOS: `~/Library/Caches/tabpfn/`
- Linux: `~/.cache/tabpfn/`
**Q: I'm getting a `pickle` error when loading the model. What should I do?**
A: Try the following:
- Download the newest version of tabpfn `pip install tabpfn --upgrade`
- Ensure model files downloaded correctly (re-download if needed)
**Q: How do I save and load a trained TabPFN model?**
A: Use :func:`save_fitted_tabpfn_model` to persist a fitted estimator and reload
it later with :func:`load_fitted_tabpfn_model` (or the corresponding
``load_from_fit_state`` class methods).
```python
from tabpfn import TabPFNRegressor
from tabpfn.model.loading import (
load_fitted_tabpfn_model,
save_fitted_tabpfn_model,
)
# Train the regressor on GPU
reg = TabPFNRegressor(device="cuda")
reg.fit(X_train, y_train)
save_fitted_tabpfn_model(reg, "my_reg.tabpfn_fit")
# Later or on a CPU-only machine
reg_cpu = load_fitted_tabpfn_model("my_reg.tabpfn_fit", device="cpu")
```
To store just the foundation model weights (without a fitted estimator) use
``save_tabpfn_model(reg.model_, "my_tabpfn.ckpt")``. This merely saves a
checkpoint of the pre-trained weights so you can later create and fit a fresh
estimator. Reload the checkpoint with ``load_model_criterion_config``.
### **Performance & Limitations**
**Q: Can TabPFN handle missing values?**
A: **Yes!**
**Q: How can I improve TabPFN’s performance?**
A: Best practices:
- Use **AutoTabPFNClassifier** from [TabPFN Extensions](https://github.com/priorlabs/tabpfn-extensions) for post-hoc ensembling
- Feature engineering: Add domain-specific features to improve model performance
Not effective:
- Adapt feature scaling
- Convert categorical features to numerical values (e.g., one-hot encoding)
## 🛠️ 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
Raw data
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"author": "Noah Hollmann, Samuel M\u00fcller, Lennart Purucker, Arjun Krishnakumar, Max K\u00f6rfer, Shi Bin Hoo, Robin Tibor Schirrmeister, Frank Hutter, Eddie Bergman, Leo Grinsztajn",
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"description": "# TabPFN\n\n[](https://badge.fury.io/py/tabpfn)\n[](https://pepy.tech/project/tabpfn)\n[](https://discord.com/channels/1285598202732482621/)\n[](https://priorlabs.ai/docs)\n[](https://colab.research.google.com/github/PriorLabs/TabPFN/blob/main/examples/notebooks/TabPFN_Demo_Local.ipynb)\n[](https://pypi.org/project/tabpfn/)\n\n<img src=\"https://github.com/PriorLabs/tabpfn-extensions/blob/main/tabpfn_summary.webp\" width=\"80%\" alt=\"TabPFN Summary\">\n\n\u26a0\ufe0f **Major Update: Version 2.0:** Complete codebase overhaul with new architecture and\nfeatures. Previous version available at [v1.0.0](../../tree/v1.0.0) and\n`pip install tabpfn==0.1.11`.\n\n\ud83d\udcda For detailed usage examples and best practices, check out [Interactive Colab Tutorial](https://colab.research.google.com/github/PriorLabs/TabPFN/blob/main/examples/notebooks/TabPFN_Demo_Local.ipynb)\n\n## \ud83c\udfc1 Quick Start\n\nTabPFN is a foundation model for tabular data that outperforms traditional methods while\nbeing dramatically faster. This repository contains the core PyTorch implementation with\nCUDA optimization.\n\n> \u26a1 **GPU Recommended**:\n> For optimal performance, use a GPU (even older ones with ~8GB VRAM work well; 16GB needed for some large datasets).\n> On CPU, only small datasets (\u22721000 samples) are feasible.\n> No GPU? Use our free hosted inference via [TabPFN Client](https://github.com/PriorLabs/tabpfn-client).\n\n### Installation\nOfficial installation (pip)\n```bash\npip install tabpfn\n```\nOR installation from source\n```bash\npip install \"tabpfn @ git+https://github.com/PriorLabs/TabPFN.git\"\n```\nOR local development installation\n```bash\n\ngit clone https://github.com/PriorLabs/TabPFN.git\npip install -e \"TabPFN[dev]\"\n```\n\n### Basic Usage\n\n#### Classification\n```python\nfrom sklearn.datasets import load_breast_cancer\nfrom sklearn.metrics import accuracy_score, roc_auc_score\nfrom sklearn.model_selection import train_test_split\n\nfrom tabpfn import TabPFNClassifier\n\n# Load data\nX, y = load_breast_cancer(return_X_y=True)\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)\n\n# Initialize a classifier\nclf = TabPFNClassifier()\nclf.fit(X_train, y_train)\n\n# Predict probabilities\nprediction_probabilities = clf.predict_proba(X_test)\nprint(\"ROC AUC:\", roc_auc_score(y_test, prediction_probabilities[:, 1]))\n\n# Predict labels\npredictions = clf.predict(X_test)\nprint(\"Accuracy\", accuracy_score(y_test, predictions))\n```\n\n#### Regression\n```python\nfrom sklearn.datasets import fetch_openml\nfrom sklearn.metrics import mean_squared_error, r2_score\nfrom sklearn.model_selection import train_test_split\n\nfrom tabpfn import TabPFNRegressor\n\n# Load Boston Housing data\ndf = fetch_openml(data_id=531, as_frame=True) # Boston Housing dataset\nX = df.data\ny = df.target.astype(float) # Ensure target is float for regression\n\n# Train-test split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)\n\n# Initialize the regressor\nregressor = TabPFNRegressor()\nregressor.fit(X_train, y_train)\n\n# Predict on the test set\npredictions = regressor.predict(X_test)\n\n# Evaluate the model\nmse = mean_squared_error(y_test, predictions)\nr2 = r2_score(y_test, predictions)\n\nprint(\"Mean Squared Error (MSE):\", mse)\nprint(\"R\u00b2 Score:\", r2)\n```\n\n### Best Results\n\nFor 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.\n\n**Steps for Best Results:**\n1. Install the extensions:\n ```bash\n git clone https://github.com/priorlabs/tabpfn-extensions.git\n pip install -e tabpfn-extensions\n ```\n\n2.\n ```python\n from tabpfn_extensions.post_hoc_ensembles.sklearn_interface import AutoTabPFNClassifier\n\n clf = AutoTabPFNClassifier(max_time=120, device=\"cuda\") # 120 seconds tuning time\n clf.fit(X_train, y_train)\n predictions = clf.predict(X_test)\n ```\n\n## \ud83c\udf10 TabPFN Ecosystem\n\nChoose the right TabPFN implementation for your needs:\n\n- **[TabPFN Client](https://github.com/priorlabs/tabpfn-client)**\n Simple API client for using TabPFN via cloud-based inference.\n\n- **[TabPFN Extensions](https://github.com/priorlabs/tabpfn-extensions)**\n A powerful companion repository packed with advanced utilities, integrations, and features - great place to contribute:\n\n - \ud83d\udd0d **`interpretability`**: Gain insights with SHAP-based explanations, feature importance, and selection tools.\n - \ud83d\udd75\ufe0f\u200d\u2642\ufe0f **`unsupervised`**: Tools for outlier detection and synthetic tabular data generation.\n - \ud83e\uddec **`embeddings`**: Extract and use TabPFN\u2019s internal learned embeddings for downstream tasks or analysis.\n - \ud83e\udde0 **`many_class`**: Handle multi-class classification problems that exceed TabPFN's built-in class limit.\n - \ud83c\udf32 **`rf_pfn`**: Combine TabPFN with traditional models like Random Forests for hybrid approaches.\n - \u2699\ufe0f **`hpo`**: Automated hyperparameter optimization tailored to TabPFN.\n - \ud83d\udd01 **`post_hoc_ensembles`**: Boost performance by ensembling multiple TabPFN models post-training.\n\n \u2728 To install:\n ```bash\n git clone https://github.com/priorlabs/tabpfn-extensions.git\n pip install -e tabpfn-extensions\n ```\n\n- **[TabPFN (this repo)](https://github.com/priorlabs/tabpfn)**\n Core implementation for fast and local inference with PyTorch and CUDA support.\n\n- **[TabPFN UX](https://ux.priorlabs.ai)**\n No-code graphical interface to explore TabPFN capabilities\u2014ideal for business users and prototyping.\n\n## \ud83d\udcdc License\n\nPrior Labs License (Apache 2.0 with additional attribution requirement): [here](https://priorlabs.ai/tabpfn-license/)\n\n## \ud83e\udd1d Join Our Community\n\nWe're building the future of tabular machine learning and would love your involvement:\n\n1. **Connect & Learn**:\n - Join our [Discord Community](https://discord.gg/VJRuU3bSxt)\n - Read our [Documentation](https://priorlabs.ai/docs)\n - Check out [GitHub Issues](https://github.com/priorlabs/tabpfn/issues)\n\n2. **Contribute**:\n - Report bugs or request features\n - Submit pull requests\n - Share your research and use cases\n\n3. **Stay Updated**: Star the repo and join Discord for the latest updates\n\n## \ud83d\udcda Citation\n\nYou can read our paper explaining TabPFN [here](https://doi.org/10.1038/s41586-024-08328-6).\n\n```bibtex\n@article{hollmann2025tabpfn,\n title={Accurate predictions on small data with a tabular foundation model},\n author={Hollmann, Noah and M{\\\"u}ller, Samuel and Purucker, Lennart and\n Krishnakumar, Arjun and K{\\\"o}rfer, Max and Hoo, Shi Bin and\n Schirrmeister, Robin Tibor and Hutter, Frank},\n journal={Nature},\n year={2025},\n month={01},\n day={09},\n doi={10.1038/s41586-024-08328-6},\n publisher={Springer Nature},\n url={https://www.nature.com/articles/s41586-024-08328-6},\n}\n\n@inproceedings{hollmann2023tabpfn,\n title={TabPFN: A transformer that solves small tabular classification problems in a second},\n author={Hollmann, Noah and M{\\\"u}ller, Samuel and Eggensperger, Katharina and Hutter, Frank},\n booktitle={International Conference on Learning Representations 2023},\n year={2023}\n}\n```\n\n\n\n## \u2753 FAQ\n\n### **Usage & Compatibility**\n\n**Q: What dataset sizes work best with TabPFN?**\nA: TabPFN is optimized for **datasets up to 10,000 rows**. For larger datasets, consider using **Random Forest preprocessing** or other extensions. See our [Colab notebook](https://colab.research.google.com/drive/154SoIzNW1LHBWyrxNwmBqtFAr1uZRZ6a#scrollTo=OwaXfEIWlhC8) for strategies.\n\n**Q: Why can't I use TabPFN with Python 3.8?**\nA: TabPFN v2 requires **Python 3.9+** due to newer language features. Compatible versions: **3.9, 3.10, 3.11, 3.12, 3.13**.\n\n### **Installation & Setup**\n\n**Q: How do I use TabPFN without an internet connection?**\n\nTabPFN automatically downloads model weights when first used. For offline usage:\n\n**Using the Provided Download Script**\n\nIf you have the TabPFN repository, you can use the included script to download all models (including ensemble variants):\n\n```bash\n# After installing TabPFN\npython scripts/download_all_models.py\n```\n\nThis script will download the main classifier and regressor models, as well as all ensemble variant models to your system's default cache directory.\n\n**Manual Download**\n\n1. Download the model files manually from HuggingFace:\n - Classifier: [tabpfn-v2-classifier.ckpt](https://huggingface.co/Prior-Labs/TabPFN-v2-clf/resolve/main/tabpfn-v2-classifier.ckpt)\n - Regressor: [tabpfn-v2-regressor.ckpt](https://huggingface.co/Prior-Labs/TabPFN-v2-reg/resolve/main/tabpfn-v2-regressor.ckpt)\n\n2. Place the file in one of these locations:\n - Specify directly: `TabPFNClassifier(model_path=\"/path/to/model.ckpt\")`\n - Set environment variable: `os.environ[\"TABPFN_MODEL_CACHE_DIR\"] = \"/path/to/dir\"`\n - Default OS cache directory:\n - Windows: `%APPDATA%\\tabpfn\\`\n - macOS: `~/Library/Caches/tabpfn/`\n - Linux: `~/.cache/tabpfn/`\n\n**Q: I'm getting a `pickle` error when loading the model. What should I do?**\nA: Try the following:\n- Download the newest version of tabpfn `pip install tabpfn --upgrade`\n- Ensure model files downloaded correctly (re-download if needed)\n\n**Q: How do I save and load a trained TabPFN model?**\nA: Use :func:`save_fitted_tabpfn_model` to persist a fitted estimator and reload\nit later with :func:`load_fitted_tabpfn_model` (or the corresponding\n``load_from_fit_state`` class methods).\n\n```python\nfrom tabpfn import TabPFNRegressor\nfrom tabpfn.model.loading import (\n load_fitted_tabpfn_model,\n save_fitted_tabpfn_model,\n)\n\n# Train the regressor on GPU\nreg = TabPFNRegressor(device=\"cuda\")\nreg.fit(X_train, y_train)\nsave_fitted_tabpfn_model(reg, \"my_reg.tabpfn_fit\")\n\n# Later or on a CPU-only machine\nreg_cpu = load_fitted_tabpfn_model(\"my_reg.tabpfn_fit\", device=\"cpu\")\n```\n\nTo store just the foundation model weights (without a fitted estimator) use\n``save_tabpfn_model(reg.model_, \"my_tabpfn.ckpt\")``. This merely saves a\ncheckpoint of the pre-trained weights so you can later create and fit a fresh\nestimator. Reload the checkpoint with ``load_model_criterion_config``.\n\n### **Performance & Limitations**\n\n**Q: Can TabPFN handle missing values?**\nA: **Yes!**\n\n**Q: How can I improve TabPFN\u2019s performance?**\nA: Best practices:\n- Use **AutoTabPFNClassifier** from [TabPFN Extensions](https://github.com/priorlabs/tabpfn-extensions) for post-hoc ensembling\n- Feature engineering: Add domain-specific features to improve model performance\nNot effective:\n - Adapt feature scaling\n - Convert categorical features to numerical values (e.g., one-hot encoding)\n\n## \ud83d\udee0\ufe0f Development\n\n1. Setup environment:\n```bash\npython -m venv venv\nsource venv/bin/activate # On Windows: venv\\Scripts\\activate\ngit clone https://github.com/PriorLabs/TabPFN.git\ncd TabPFN\npip install -e \".[dev]\"\npre-commit install\n```\n\n2. Before committing:\n```bash\npre-commit run --all-files\n```\n\n3. Run tests:\n```bash\npytest tests/\n```\n\n---\n\nBuilt with \u2764\ufe0f by [Prior Labs](https://priorlabs.ai) - Copyright (c) 2025 Prior Labs GmbH\n",
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"license": "\n Prior Labs License\n Version 1.1, May 2025\n http://priorlabs.ai/tabpfn-license\n \n This license is a derivative of the Apache 2.0 license\n (http://www.apache.org/licenses/) with a single modification:\n The added Paragraph 10 introduces an enhanced attribution requirement\n inspired by the Llama 3 license.\n \n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n \n 1. Definitions.\n \n \"License\" shall mean the terms and conditions for use, reproduction,\n and distribution as defined by Sections 1 through 9 of this document.\n \n \"Licensor\" shall mean the copyright owner or entity authorized by\n the copyright owner that is granting the License.\n \n \"Legal Entity\" shall mean the union of the acting entity and all\n other entities that control, are controlled by, or are under common\n control with that entity. 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