---
license: mit
metrics:
- accuracy
- roc_auc
- demographic_parity
- equalized_odds
- equal_opportunity
pipeline_tag: tabular-classification
tags:
- tabular
- fairness
- TFM
base_model:
- FairTFM-0.7-epoch_10000.pt
---
# Model Card: FairTFM

## Model Description
FairTFM is a **tabular foundation model** for fairness-aware, in-context prediction on tabular data. It is a transformer-based architecture (built on components from [nanoTabPFN](https://github.com/automl/TFM-Playground)) that jointly embeds features, targets, and a **sensitive attribute** via a dedicated `SensitiveAttributeEncoder`, then predicts labels for unseen (test) rows conditioned on a small set of in-context training rows — no per-dataset fine-tuning required.

- **Architecture:** Transformer encoder stack with separate feature/target/sensitive-attribute embeddings, decoded to per-row logits. Memory-chunked attention/MLP for efficient inference.
- **Task type:** Binary classification with a fairness objective over a specified sensitive attribute.
- **Release type:** Inference-only.

### Model Sources
 

- **Repository:** https://github.com/patrikken/FairTFM-inference
- **Paper:** https://openreview.net/forum?id=ajIvCEbadL

## Checkpoints
Four checkpoints are provided, corresponding to different fairness-regularization strengths (λ) used to trace a fairness/accuracy Pareto front: **λ = 0.7, 1.0, 10, 25**. Higher λ trades predictive performance for lower fairness-metric disparity.

## Intended Use
- Fair classification on tabular datasets where a sensitive attribute (e.g., sex, race, age) is known at both train and inference time.
- Includes benchmark support for the folktables **ACS PUMS** tasks (income, employment, mobility, public coverage, travel time), among other tabular fairness datasets.
- Sensitive attributes evaluated: `SEX`, `RAC1P` (race, White/Black), `AGEP` (age, median-binarized).


## Limitations
- Restricted to binary sensitive attributes and binary classification targets in current released checkpoints.
- Fairness metrics are computed with respect to the single sensitive attribute provided at inference; intersectional fairness is not directly modeled. 

## How to Use

```python
from fairtfm import FairTFMClassifier, compute_fairness_metrics

# Load checkpoint
classifier = FairTFMClassifier(model="path/to/checkpoint")

# Fit on training data
classifier.fit(X_train, y_train, s_train)

# Predict
predictions = classifier.predict(X_test, s_test)
probabilities = classifier.predict_proba(X_test, s_test)
 
# Fairness metrics (returns dict with performance metrics)
compute_fairness_metrics(X_test, y_test, s_test)
``` 

## Citation
```bibtex
@inproceedings{
kenfack2026training,
title={Training Fair Tabular Foundation Models},
author={Patrik Kenfack and Jesse C. Cresswell and Anthony L. Caterini and Samira Ebrahimi Kahou and Ulrich A{\"\i}vodji},
booktitle={2nd ICML Workshop on Foundation Models for Structured Data},
year={2026},
url={https://openreview.net/forum?id=ajIvCEbadL}
}
```