Improve model card metadata and content (#1)

- Improve model card metadata and content (5d10d45b177c9de2405e1a85c8e950a17f856b15)


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

README.md

@@ -1,24 +1,25 @@
 ---
 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_s`  |  **Parameters:** 29M  |  **Layers:** 6  |  **Hidden dim:** 512  |  **Attn heads:** 8
 
-This **MiAEClassifier** was initialised from a pretrained MiAE and fine-tuned on TEDBench for fold classification.
+This **MiAEClassifier** was initialized from a pretrained MiAE and fine-tuned on TEDBench for fold classification. It is based on the research 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.
 
-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.
+## Resources
+- **Code:** [https://github.com/BorgwardtLab/TEDBench](https://github.com/BorgwardtLab/TEDBench)
+- **Paper:** [Protein Fold Classification at Scale: Benchmarking and Pretraining](https://huggingface.co/papers/2605.18552)
 
 ## Architecture sizes
 
@@ -34,6 +35,21 @@ Append `+model.use_seq_input=true` to `miae_b` for the **+seq** variant.
 
 ### Load from the HuggingFace Hub
 
+First, install the `tedbench` library:
+```bash
+pip install tedbench
+```
+
+Then you can load the model using the high-level API:
+```python
+import tedbench
+
+# Loads the fine-tuned MiAE-S fold classifier
+model = tedbench.load_model("miae-s-ft")
+model.eval()
+```
+
+Alternatively, use the low-level loader:
 ```python
 from tedbench.utils.io import load_from_hf
 
@@ -59,4 +75,4 @@ model.eval()
   booktitle={Proceedings of the 43rd International Conference on Machine Learning},
   year={2026}
 }
-```
+```