Update README.md

README.md

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 The model was developed by Ahmed Elnaggar et al. and more information can be found on the [GitHub repository](https://github.com/agemagician/ProtTrans) and in the [accompanying paper](https://ieeexplore.ieee.org/document/9477085). This repository is a fork of their [HuggingFace repository](https://huggingface.co/Rostlab/prot_xlnet/tree/main).
 
+# Inference example
+
+```python
+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](https://choosealicense.com/licenses/afl-3.0/), Copyright (c) 2025 Ahmed Elnaggar. The other code is licensed under the MIT license, Copyright (c) 2025 Maksim Pavlov.