Improve model card and update metadata

Hi, I'm Niels from the Hugging Face community science team.

This PR improves your model card by:
- Updating the `pipeline_tag` to `graph-ml` to improve discoverability in the Graph ML section of the Hub.
- Adding a direct link to the associated paper: [Protein Fold Classification at Scale: Benchmarking and Pretraining](https://huggingface.co/papers/2605.18552).
- Adding a link to the official GitHub repository.
- Ensuring metadata like `library_name` is properly set.

Please review and merge if this looks good!

README.md

@@ -1,24 +1,24 @@
 ---
 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)
 
 **Variant:** `miae_b`  |  **Parameters:** 102M  |  **Layers:** 12  |  **Hidden dim:** 768  |  **Attn heads:** 12
 
-This is a **pretrained MiAE** checkpoint. Use it as a feature extractor or as the starting point for fine-tuning.
+This repository contains the **pretrained MiAE-B** (base) checkpoint, a self-supervised model for protein structure representation learning introduced in the paper [Protein Fold Classification at Scale: Benchmarking and Pretraining](https://huggingface.co/papers/2605.18552). 
+
+MiAE (Masked Invariant Autoencoders) is an $\mathrm{SE(3)}$-invariant autoencoder that uses an extremely high masking ratio (up to 90%) of backbone frames to reconstruct full protein structures. It can be used as a feature extractor or as the starting point for fine-tuning on protein fold classification tasks.
 
-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.
+- **Authors:** Dexiong Chen, Andrei Manolache, Mathias Niepert, Karsten Borgwardt
+- **Official Code:** [BorgwardtLab/TEDBench](https://github.com/BorgwardtLab/TEDBench)
 
 ## Architecture sizes
 
@@ -34,6 +34,8 @@ Append `+model.use_seq_input=true` to `miae_b` for the **+seq** variant.
 
 ### Load from the HuggingFace Hub
 
+Using the `tedbench` library:
+
 ```python
 from tedbench.utils.io import load_from_hf
 
@@ -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},
-  booktitle={Proceedings of the 43rd International Conference on Machine Learning},
+  author={Chen, Dexiong explorer and Manolache, Andrei and Niepert, Mathias and Borgwardt, Karsten},
+  booktitle={Proceedings of the 43rd International Conference on Machine Learning (ICML)},
   year={2026}
 }
-```
+```