README

README.md

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----
-extra_gated_prompt: |-
-  By accessing TabPFN, you agree to:
-      1. Not use the model in ways that could harm individuals or communities
-      2. Comply with all applicable laws and regulations
-      3. Properly cite the model and its creators in any resulting publications
-      4. Report any discovered vulnerabilities or safety concerns to Prior Labs
-extra_gated_fields:
-  Organization:
-    type: text
-    required: true
-    description: Company or institution you represent
-  Role:
-    type: text
-    required: true
-    description: Your role in the organization
-  Country:
-    type: country
-    required: true
-    description: Country where you or your organization is based
-  Intended Use:
-    type: select
-    required: true
-    options:
-    - Academic Research
-    - Education/Teaching
-    - Commercial Evaluation
-    - Non-profit Use
-    - Personal Learning
-    - label: Other
-      value: other
-    description: Primary intended use of TabPFN
-  Industry:
-    type: select
-    required: true
-    options:
-    - Healthcare/Life Sciences
-    - Financial Services
-    - Technology
-    - Education
-    - Manufacturing
-    - Research Institution
-    - label: Other
-      value: other
-    description: Your industry sector
-  Dataset Size:
-    type: select
-    required: true
-    options:
-    - <1000 rows
-    - 1000-10000 rows
-    - 10000-100000 rows
-    - '>100000 rows'
-    description: Typical size of datasets you plan to use
-  License Agreement:
-    type: checkbox
-    required: true
-    label: >-
-      I agree to the terms of the non-commercial license for research and
-      evaluation
-  Contact Permission:
-    type: checkbox
-    required: false
-    label: Prior Labs may contact me about my use case and provide support (optional)
-pipeline_tag: tabular-classification
----
-
-# Model Card for TabPFN-v2
+# TabPFN v2: A Tabular Foundation Model
 
 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.
+## Installation
+```bash
+pip install tabpfn
+```
 
+## Model Details
 - **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
+- **License:** TBD
+- **Paper:** Published in Nature (January 2024)
+- **Repository:** [GitHub - priorlabs/tabpfn](https://github.com/priorlabs/tabpfn)
 
-### 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
+### Citation
+TBD
 
+## Quick Start
 ```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.
+## Technical Requirements
+- Python ≥ 3.9
+- PyTorch ≥ 2.1
+- scikit-learn ≥ 1.0
+- Hardware: 16GB+ RAM, CPU (GPU optional)
+
+## Limitations
+- Not designed for very large datasets
+- Not suitable for non-tabular data formats
+
+## Resources
+- **Documentation:** https://priorlabs.ai/docs
+- **Source:** https://github.com/priorlabs/tabpfn
+- **Paper:** https://doi.org/10.1038/s41586-024-08328-6
+
+### Team
+- Noah Hollmann
+- Samuel Müller
+- Lennart Purucker
+- Arjun Krishnakumar
+- Max Körfer
+- Shi Bin Hoo
+- Robin Tibor Schirrmeister
+- Frank Hutter
+- Eddie Bergman