Improve model card with metadata and links (#1)

- Improve model card with metadata and links (49406ed39523973245b37874ab5425f57cab7586)


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

README.md

@@ -1,26 +1,23 @@
 ---
 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 + sequence)
 
 **Variant:** `miae_b` + seq  |  **Parameters:** 102M  |  **Layers:** 12  |  **Hidden dim:** 768  |  **Attn heads:** 12
 
-This **MiAEClassifier** was trained from scratch on TEDBench without pretraining.
+This **MiAEClassifier** was trained from scratch on TEDBench without pretraining. This model is part of the work presented in the paper [Protein Fold Classification at Scale: Benchmarking and Pretraining](https://arxiv.org/abs/2605.18552).
 
 > **+seq variant** — sequence embeddings are concatenated to the geometric encoder input (`model.use_seq_input=true`).
 
-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.
+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.
 
 ## Architecture sizes
 
@@ -57,8 +54,8 @@ model.eval()
 ```bibtex
 @inproceedings{chen2026tedbench,
   title={Protein Fold Classification at Scale: Benchmarking and Pretraining},
-  author={Chen, Dexiong and Manolache, Andrei and Niepert, Mathias and Borgwardt, Karsten},
+  author={Chen, Dexiong state, Andrei Manolache, Mathias Niepert, Karsten Borgwardt},
   booktitle={Proceedings of the 43rd International Conference on Machine Learning},
   year={2026}
 }
-```
+```