File size: 1,561 Bytes
1c21e62 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | ---
library_name: tedbench
tags:
- protein
- structure-sequence
- fold-classification
- tedbench
- saprot
pipeline_tag: other
license: bsd-3-clause
---
# TEDBench — SaProt-650M fine-tuned on TEDBench
Backbone: SaProt-650M (33 layers, hidden dim 1280). Requires [Foldseek](https://github.com/steineggerlab/foldseek) for structure-aware tokens.
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.saprot_classifier import SaProtClassifier
from omegaconf import OmegaConf
from huggingface_hub import snapshot_download
local_dir = Path(snapshot_download("TEDBench/saprot-650M-ft"))
with open(local_dir / "config.json") as f:
import json
cfg = OmegaConf.create(json.load(f))
model = SaProtClassifier(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/saprot_test_ted.py train.ckpt_path=TEDBench/saprot-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}
}
```
|