Add dataset card

README.md

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+---
+license: cc-by-4.0
+tags:
+  - sparse-matrices
+  - linear-systems
+  - preconditioners
+  - numerical-linear-algebra
+  - graph-neural-networks
+  - scientific-computing
+---
+
+# MatrixPFN Notebooks
+
+Executable Colab notebooks for [MatrixPFN](https://pypi.org/project/matrixpfn/) — Graph Neural Networks as learned preconditioners for sparse linear systems.
+
+## Notebooks
+
+| Notebook | Description |
+|---|---|
+| `07_MatrixPFN_EndToEnd.ipynb` | Full pipeline: train ContextResGCN, benchmark against Jacobi, solve with FGMRES |
+| `colab_benchmark.ipynb` | SuiteSparse benchmark: 838/867 matrices × 6 classical preconditioners (ILU, AMG, Jacobi, Block Jacobi, GMRES-Inner, None) |
+| `ablation_edge_features_v3.ipynb` | Definitive GCN vs MPNN ablation: 5 seeds, 1000 epochs, 3 domains |
+
+## Usage
+
+Open any notebook in Google Colab:
+
+```
+https://colab.research.google.com/github/...
+```
+
+Or download via the HuggingFace Hub:
+
+```python
+from huggingface_hub import hf_hub_download
+
+path = hf_hub_download(
+    repo_id="Csed-dev/matrixpfn-notebooks",
+    repo_type="dataset",
+    filename="colab_benchmark.ipynb",
+)
+```
+
+## Related
+
+- **Package**: [matrixpfn on PyPI](https://pypi.org/project/matrixpfn/)
+- **Evaluation dataset**: [Csed-dev/matrixpfn-suitesparse](https://huggingface.co/datasets/Csed-dev/matrixpfn-suitesparse) (867 SuiteSparse matrices)
+- **Paper reference**: GNP ([arXiv 2406.00809v3](https://arxiv.org/abs/2406.00809v3))