metadata
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 for structure-aware tokens.
Fine-tuned on TEDBench for protein fold classification into 965 CATH topology (T-level) classes (ICML 2026).
Usage
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:
python baselines/saprot_test_ted.py train.ckpt_path=TEDBench/saprot-650M-ft
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},
year={2026}
}