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README.md
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By accessing TabPFN, you agree to:
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1. Not use the model in ways that could harm individuals or communities
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2. Comply with all applicable laws and regulations
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3. Properly cite the model and its creators in any resulting publications
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4. Report any discovered vulnerabilities or safety concerns to Prior Labs
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Organization:
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description: Country where you or your organization is based
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options:
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- Academic Research
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- Personal Learning
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- label: Other
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value: other
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description: Primary intended use of TabPFN
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value: other
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description: Your industry sector
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type: select
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options:
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- 1000-10000 rows
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- 10000-100000 rows
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- '>100000 rows'
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description: Typical size of datasets you plan to use
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label: >-
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I agree to the terms of the non-commercial license for research and
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evaluation
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Contact Permission:
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type: checkbox
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required: false
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label: Prior Labs may contact me about my use case and provide support (optional)
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pipeline_tag: tabular-classification
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---
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#
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TabPFN is a transformer-based foundation model for tabular data that leverages prior-data based learning to achieve strong performance on small tabular
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##
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TabPFN is a novel approach to tabular data modeling that uses transformer architectures combined with prior knowledge injection to create a foundation model specifically designed for tabular data tasks.
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- **Developed by:** Prior Labs
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- **Model type:** Transformer-based foundation model for tabular data
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- **
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### Model Sources
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- **Repository:** https://github.com/priorlabs/tabpfn
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- **Paper:** [More Information Needed]
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- **Demo:** Available via API access
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## Uses
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### Direct Use
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TabPFN can be directly used for:
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- Classification tasks on small to medium-sized tabular datasets
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- Automated machine learning workflows
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- Quick prototyping and baseline model creation
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- Transfer learning applications for tabular data
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### Downstream Use
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- A foundation for transfer learning on domain-specific tabular data
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- A component in automated ML pipelines
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- A baseline model for benchmarking
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### Out-of-Scope Use
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- The model is not designed for:
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- Very large datasets (currently optimized for smaller datasets)
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- Non-tabular data formats
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- Time series forecasting
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- Direct regression tasks
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## Bias, Risks, and Limitations
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- Performance may vary based on dataset size and characteristics
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- Model behavior heavily depends on the quality and representativeness of training data
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- May not perform optimally on highly imbalanced datasets
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- Resource intensive for very large datasets
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### Recommendations
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- Use on datasets with clear structure and well-defined features
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- Validate model outputs especially for sensitive applications
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- Consider dataset size limitations when applying the model
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- Monitor performance across different subgroups in the data
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## How to Get Started with the Model
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```python
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from tabpfn import
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# Initialize model
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classifier.fit(X_train, y_train)
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predictions = classifier.predict(X_test)
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```
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[More Information Needed]
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## Environmental Impact
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications
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### Model Architecture and Objective
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TabPFN uses a transformer-based architecture specifically designed for tabular data processing, with modifications to handle varying input sizes and feature types.
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### Compute Infrastructure
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#### Hardware
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Recommended minimum specifications:
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- CPU: Modern multi-core processor
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- RAM: 16GB+
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- GPU: Optional, CPU inference supported
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#### Software
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- Python 3.7+
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- Key dependencies: PyTorch, NumPy, Pandas
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## Model Card Contact
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For more information, contact Prior Labs.
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pipeline_tag: tabular-classification
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---
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# TabPFN v2: A Tabular Foundation Model
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TabPFN is a transformer-based foundation model for tabular data that leverages prior-data based learning to achieve strong performance on small tabular regression tasks without requiring task-specific training.
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## Installation
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```bash
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pip install tabpfn
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```
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## Model Details
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- **Developed by:** Prior Labs
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- **Model type:** Transformer-based foundation model for tabular data
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- **License:** TBD
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- **Paper:** Published in Nature (January 2024)
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- **Repository:** [GitHub - priorlabs/tabpfn](https://github.com/priorlabs/tabpfn)
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### Citation
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TBD
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## Quick Start
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```python
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from tabpfn import TabPFNRegressor
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# Initialize model
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regressor = TabPFNRegressor()
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regressor.fit(X_train, y_train)
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predictions = regressor.predict(X_test)
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```
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## Technical Requirements
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- Python ≥ 3.9
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- PyTorch ≥ 2.1
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- scikit-learn ≥ 1.0
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- Hardware: 16GB+ RAM, CPU (GPU optional)
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## Limitations
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- Not designed for very large datasets
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- Not suitable for non-tabular data formats
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## Resources
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- **Documentation:** https://priorlabs.ai/docs
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- **Source:** https://github.com/priorlabs/tabpfn
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- **Paper:** https://doi.org/10.1038/s41586-024-08328-6
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### Team
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- Noah Hollmann
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- Samuel Müller
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- Lennart Purucker
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- Arjun Krishnakumar
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- Max Körfer
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- Shi Bin Hoo
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- Robin Tibor Schirrmeister
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- Frank Hutter
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- Eddie Bergman
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