virtual-human-chc/prot_xlnet

ProtXLNet

The model was developed by Ahmed Elnaggar et al. and more information can be found on the GitHub repository and in the accompanying paper. This repository is a fork of their HuggingFace repository.

Inference example

from transformers import AutoTokenizer, AutoModel
import torch
import re

# Load tokenizer and model

tokenizer = AutoTokenizer.from_pretrained("virtual-human-chc/prot_xlnet", use_fast=False)
model = AutoModel.from_pretrained("virtual-human-chc/prot_xlnet").eval()

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)

# Example protein sequences
sequences = ["A E T C Z A O", "S K T Z P"]
sequences = [re.sub(r"[UZOB]", "X", sequence) for sequence in sequences]

# Tokenize and extract embeddings
inputs = tokenizer(sequences, padding=True, return_tensors="pt")
# In case of GPU
inputs = {k: v.to(device) for k, v in inputs.items()}

with torch.no_grad():
    outputs = model(**inputs)

print(outputs.last_hidden_state)

Copyright

Code derived from https://github.com/agemagician/ProtTrans is licensed under the MIT License, Copyright (c) 2025 Ahmed Elnaggar. The ProtTrans pretrained models are released under the under terms of the Academic Free License v3.0 License, Copyright (c) 2025 Ahmed Elnaggar. The other code is licensed under the MIT license, Copyright (c) 2025 Maksim Pavlov.