metadata
library_name: tedbench
license: bsd-3-clause
pipeline_tag: graph-ml
tags:
- protein
- structure
- fold-classification
- tedbench
TEDBench — Pretrained autoencoder (structure only)
Variant: miae_s | Parameters: 29M | Layers: 6 | Hidden dim: 512 | Attn heads: 8
This repository contains a pretrained MiAE checkpoint. Use it as a feature extractor or as the starting point for fine-tuning on protein structure tasks.
MiAE (Masked Invariant Autoencoder) is an $\mathrm{SE(3)}$-invariant masked autoencoder that masks up to 90% of backbone frames and reconstructs the full structure with a lightweight decoder. It was introduced in the paper Protein Fold Classification at Scale: Benchmarking and Pretraining.
- Paper: Protein Fold Classification at Scale: Benchmarking and Pretraining
- Repository: 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
You can load the model using the tedbench library:
from tedbench.utils.io import load_from_hf
model = load_from_hf("TEDBench/miae-s")
model.eval()
Alternatively, using the library's high-level API:
import tedbench
model = tedbench.load_model("miae-s")
model.eval()
From a Lightning checkpoint
from tedbench.model import MiAE
model = MiAE.load_from_checkpoint("model.ckpt", weights_only=False)
model.eval()
Citation
@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 (ICML)},
year={2026}
}