metadata
language: en
license: mit
datasets:
- ronig/protein_binding_sequences
Peptriever BiEncoder for Protein-Peptide Binding
The model and training process is outlined in this application note. Training code can be found here.
For more details see the application page
Usage
import torch
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("ronig/protein_biencoder")
model = AutoModel.from_pretrained("ronig/protein_biencoder", trust_remote_code=True)
model.eval()
peptide_sequence = "AAA"
protein_sequence = "MMM"
encoded_peptide = tokenizer.encode_plus(peptide_sequence, return_tensors='pt')
encoded_protein = tokenizer.encode_plus(protein_sequence, return_tensors='pt')
with torch.no_grad():
peptide_output = model.forward1(encoded_peptide)
protein_output = model.forward2(encoded_protein)
print("distance: ", torch.norm(peptide_output - protein_output, p=2))
Version
Model checkpint: peptriever_2023-06-23T16:07:24.508460