Improve model card: update pipeline tag and add paper/code links

This PR improves the model card by:
- Updating the `pipeline_tag` to `graph-ml` for better discoverability.
- Adding a link to the paper [Protein Fold Classification at Scale: Benchmarking and Pretraining](https://huggingface.co/papers/2605.18552).
- Adding a link to the official GitHub repository.
- Ensuring the metadata is correctly formatted and the usage examples are preserved.

README.md

@@ -1,12 +1,12 @@
 ---
 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)
@@ -15,10 +15,9 @@ license: bsd-3-clause
 
 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.
+This model is part of 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.
+
+**Code:** [https://github.com/BorgwardtLab/TEDBench](https://github.com/BorgwardtLab/TEDBench)
 
 ## Architecture sizes
 
@@ -59,4 +58,4 @@ model.eval()
   booktitle={Proceedings of the 43rd International Conference on Machine Learning},
   year={2026}
 }
-```
+```