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library_name: tedbench
license: bsd-3-clause
pipeline_tag: graph-ml
tags:
- protein
- structure
- fold-classification
- tedbench
---
# TEDBench — Supervised from scratch (structure only)
**Variant:** `miae_s` | **Parameters:** 29M | **Layers:** 6 | **Hidden dim:** 512 | **Attn heads:** 8
This **MiAEClassifier** was trained from scratch on TEDBench without pretraining.
It is part of the work presented in 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.
- **Paper:** [Protein Fold Classification at Scale: Benchmarking and Pretraining](https://huggingface.co/papers/2605.18552)
- **Repository:** [https://github.com/BorgwardtLab/TEDBench](https://github.com/BorgwardtLab/TEDBench)
## Architecture sizes
| Variant | Params | Layers | Hidden dim | Attn heads |
|---------|-------:|-------:|-----------:|-----------:|
| `miae_s` | 29 M | 6 | 512 | 8 |
| `miae_b` | 102 M | 12 | 768 | 12 |
| `miae_l` | 339 M | 24 | 1 024 | 16 |
Append `+model.use_seq_input=true` to `miae_b` for the **+seq** variant.
## Usage
### Load from the HuggingFace Hub
```python
from tedbench.utils.io import load_from_hf
model = load_from_hf("TEDBench/miae-s-sc")
model.eval()
```
### From a Lightning checkpoint
```python
from tedbench.model import MiAEClassifier
model = MiAEClassifier.load_from_checkpoint("model.ckpt", weights_only=False)
model.eval()
```
## Citation
```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},
year={2026}
}
``` |