Improve model card metadata and links (#1)

- Improve model card metadata and links (3b480265ba868eb7bf01d978d392d837bb1d197f)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,12 +1,12 @@
 ---
 library_name: tedbench
-tags:
-  - protein
-  - structure
-  - fold-classification
-  - tedbench
-pipeline_tag: feature-extraction
 license: bsd-3-clause
+pipeline_tag: graph-ml
+tags:
+- protein
+- structure
+- fold-classification
+- tedbench
 ---
 
 # TEDBench — Pretrained autoencoder (structure only)
@@ -15,10 +15,12 @@ license: bsd-3-clause
 
 This is a **pretrained MiAE** checkpoint. Use it as a feature extractor or as the starting point for fine-tuning.
 
-Part of the [TEDBench](https://github.com/BorgwardtLab/TEDBench) benchmark for
-protein fold classification (ICML 2026). MiAE is an SE(3)-invariant masked
-autoencoder that masks up to 90% of backbone frames and reconstructs the full
-structure with a lightweight decoder.
+This model was presented in the paper [Protein Fold Classification at Scale: Benchmarking and Pretraining](https://huggingface.co/papers/2605.18552).
+
+Part of the [TEDBench](https://github.com/BorgwardtLab/TEDBench) benchmark for protein fold classification (ICML 2026). MiAE is an SE(3)-invariant masked autoencoder that masks up to 90% of backbone frames and reconstructs the full structure with a lightweight decoder.
+
+- **Code:** [GitHub Repository](https://github.com/BorgwardtLab/TEDBench)
+- **Paper:** [Hugging Face Paper Page](https://huggingface.co/papers/2605.18552)
 
 ## Architecture sizes
 
@@ -59,4 +61,4 @@ model.eval()
   booktitle={Proceedings of the 43rd International Conference on Machine Learning},
   year={2026}
 }
-```
+```