---
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      1. Not use the model in ways that could harm individuals or communities
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      3. Properly cite the model and its creators in any resulting publications
      4. Report any discovered vulnerabilities or safety concerns to Prior Labs
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pipeline_tag: tabular-classification
---

# Model Card for TabPFN-v2

TabPFN is a transformer-based foundation model for tabular data that leverages prior-data based learning to achieve strong performance on small tabular datasets without requiring task-specific training.

## Model Details

### Model Description

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.

- **Developed by:** Prior Labs
- **Model type:** Transformer-based foundation model for tabular data
- **Language(s):** Python
- **License:** Dual licensing - Open source for research/non-commercial use
- **Finetuned from model:** Custom architecture, trained from scratch

### Model Sources

- **Repository:** https://github.com/priorlabs/tabpfn
- **Paper:** [More Information Needed]
- **Demo:** Available via API access

## Uses

### Direct Use

TabPFN can be directly used for:
- Classification tasks on small to medium-sized tabular datasets
- Automated machine learning workflows
- Quick prototyping and baseline model creation
- Transfer learning applications for tabular data

### Downstream Use

The model can be used as:
- A feature extractor for downstream tasks
- A foundation for transfer learning on domain-specific tabular data
- A component in automated ML pipelines
- A baseline model for benchmarking

### Out-of-Scope Use

- The model is not designed for:
  - Very large datasets (currently optimized for smaller datasets)
  - Non-tabular data formats
  - Time series forecasting
  - Direct regression tasks

## Bias, Risks, and Limitations

- Performance may vary based on dataset size and characteristics
- Model behavior heavily depends on the quality and representativeness of training data
- May not perform optimally on highly imbalanced datasets
- Resource intensive for very large datasets

### Recommendations

- Use on datasets with clear structure and well-defined features
- Validate model outputs especially for sensitive applications
- Consider dataset size limitations when applying the model
- Monitor performance across different subgroups in the data

## How to Get Started with the Model

```python
from tabpfn import TabPFNClassifier

# Initialize model
classifier = TabPFNClassifier()

# Fit and predict
classifier.fit(X_train, y_train)
predictions = classifier.predict(X_test)
```

## Training Details

### Training Data

[More Information Needed]

### Training Procedure

#### Training Hyperparameters

- **Training regime:** Mixed precision training

## Evaluation

### Testing Data, Factors & Metrics

#### Metrics

- Classification accuracy
- F1 score
- ROC-AUC
- Precision-Recall curves

### Results

[More Information Needed]

## Environmental Impact

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications

### Model Architecture and Objective

TabPFN uses a transformer-based architecture specifically designed for tabular data processing, with modifications to handle varying input sizes and feature types.

### Compute Infrastructure

#### Hardware

Recommended minimum specifications:
- CPU: Modern multi-core processor
- RAM: 16GB+
- GPU: Optional, CPU inference supported

#### Software

- Python 3.7+
- Key dependencies: PyTorch, NumPy, Pandas

## Model Card Contact

For more information, contact Prior Labs.