Add model card for ShapPFN

README.md

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-Checkpoint for the ShapPFN model
+---
+pipeline_tag: other
+---
+
+# ShapPFN
+
+ShapPFN is a foundation model for tabular data that integrates Shapley value regression directly into its architecture, producing both predictions and explanations in a single forward pass. On standard benchmarks, ShapPFN achieves competitive performance while producing high-fidelity explanations ($R^2 \approx 0.96$, cosine $\approx 0.99$) over 1000x faster than KernelSHAP.
+
+- **Paper:** [Real-Time Explanations for Tabular Foundation Models](https://huggingface.co/papers/2603.29946)
+- **GitHub Repository:** [kunumi/ShapPFN](https://github.com/kunumi/ShapPFN)
+
+## Architecture
+
+ShapPFN combines several key components:
+- **PFN-style tabular transformers:** Uses alternating attention over rows and features (similar to TabPFN / nanoTabPFN).
+- **Additive decomposition:** Separate decoder heads for `base` and per-feature contributions.
+- **Shapley value regression:** Training explanations as part of the model output (ViaSHAP-style) rather than as a post-hoc procedure.
+
+## Setup
+
+To use the code and the model from the official repository, you can install it via:
+
+```bash
+pip install -e .
+```
+
+## Quickstart
+
+The official repository provides scripts for various tasks:
+- **Data Generation:** `bash scripts/generate_data.sh`
+- **Training:** `bash scripts/train_shappfn.sh`
+- **Evaluation:** `bash scripts/eval_openml.sh` (OpenML suite) and `bash scripts/eval_shap.sh` (KernelExplainer comparison)