Update pipeline tag and add paper/code links (#1)

- Update pipeline tag and add paper/code links (8abb73f99cd2a0cd797f7cd07c5bd5df02a2b79d)


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

README.md

@@ -1,25 +1,27 @@
 ---
 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 — Fine-tuned from pretrained MiAE (structure only)
 
 **Variant:** `miae_b`  |  **Parameters:** 102M  |  **Layers:** 12  |  **Hidden dim:** 768  |  **Attn heads:** 12
 
-This **MiAEClassifier** was initialised from a pretrained MiAE and fine-tuned on TEDBench for fold classification.
+This **MiAEClassifier** was initialised from a pretrained MiAE and fine-tuned on TEDBench for fold classification. 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.
 
+Official Code: [https://github.com/BorgwardtLab/TEDBench](https://github.com/BorgwardtLab/TEDBench)
+
 ## Architecture sizes
 
 | Variant | Params | Layers | Hidden dim | Attn heads |
@@ -55,8 +57,8 @@ 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 house, Andrei Manolache, Mathias Niepert, Karsten Borgwardt},
   booktitle={Proceedings of the 43rd International Conference on Machine Learning},
   year={2026}
 }
-```
+```