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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 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.

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