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library_name: tedbench
tags:
- protein
- sequence
- fold-classification
- tedbench
- esm2
pipeline_tag: other
license: bsd-3-clause
---
# TEDBench — ESM2-650M fine-tuned on TEDBench
Backbone: [ESM2-650M](https://huggingface.co/facebook/esm2_t33_650M_UR50D) (33 layers, hidden dim 1280).
Fine-tuned on [TEDBench](https://github.com/BorgwardtLab/TEDBench) for protein
fold classification into 965 CATH topology (T-level) classes (ICML 2026).
## Usage
```python
import sys
sys.path.insert(0, "baselines") # from repo root
from pathlib import Path
import torch
from models.esm2_classifier import ESM2Classifier
from omegaconf import OmegaConf
from huggingface_hub import snapshot_download
local_dir = Path(snapshot_download("TEDBench/esm2-650M-ft"))
with open(local_dir / "config.json") as f:
import json
cfg = OmegaConf.create(json.load(f))
model = ESM2Classifier(cfg)
sd = torch.load(local_dir / "pytorch_model.bin", map_location="cpu", weights_only=False)
model.load_state_dict(sd)
model.eval()
```
Or pass the repo ID directly to the test script:
```bash
python baselines/esm2_test_ted.py train.ckpt_path=TEDBench/esm2-650M-ft
```
## 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}
}
```
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