Improve model card: update pipeline tag and add paper link

README.md

@@ -1,24 +1,24 @@
 ---
 library_name: tedbench
-tags:
-  - protein
-  - structure
-  - fold-classification
-  - tedbench
-pipeline_tag: other
 license: bsd-3-clause
+pipeline_tag: graph-ml
+tags:
+- protein
+- structure
+- fold-classification
+- tedbench
 ---
 
 # TEDBench — Supervised from scratch (structure only)
 
 **Variant:** `miae_s`  |  **Parameters:** 29M  |  **Layers:** 6  |  **Hidden dim:** 512  |  **Attn heads:** 8
 
-This **MiAEClassifier** was trained from scratch on TEDBench without pretraining.
+This **MiAEClassifier** was trained from scratch on TEDBench without pretraining. 
 
-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.
+It is part of the work presented in the paper [Protein Fold Classification at Scale: Benchmarking and Pretraining](https://huggingface.co/papers/2605.18552). 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.
+
+- **Paper:** [Protein Fold Classification at Scale: Benchmarking and Pretraining](https://huggingface.co/papers/2605.18552)
+- **Repository:** [https://github.com/BorgwardtLab/TEDBench](https://github.com/BorgwardtLab/TEDBench)
 
 ## Architecture sizes
 
@@ -59,4 +59,4 @@ model.eval()
   booktitle={Proceedings of the 43rd International Conference on Machine Learning},
   year={2026}
 }
-```
+```