Update README.md

README.md

@@ -10,4 +10,32 @@ library_name: adapter-transformers
 tags:
 - biology
 pipeline_tag: text-classification
----
+---
+## Example
+```
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load the fine-tuned model and tokenizer
+model_name = "sihuapeng/ESM2-finetuned-PPSL"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Simulate a protein sequence
+protein_sequence = "MSKKVLITGGAGYIGSVLTPILLEKGYEVCVIDNLMFDQISLLSCFHNKNFTFINGDAMDENLIRQEVAKADIIIPLAALVGAPLCKRNPKLAKMINYEAVKMISDFASPSQIFIYPNTNSGYGIGEKDAMCTEESPLRPISEYGIDKVHAEQYLLDKGNCVTFRLATVFGISPRMRLDLLVNDFTYRAYRDKFIVLFEEHFRRNYIHVRDVVKGFIHGIENYDKMKGQAYNMGLSSANLTKRQLAETIKKYIPDFYIHSANIGEDPDKRDYLVSNTKLEATGWKPDNTLEDGIKELLRAFKMMKVNRFANFN"
+
+# Encode the sequence as model input
+inputs = tokenizer(protein_sequence, return_tensors="pt")
+
+# Perform inference using the model
+with torch.no_grad():
+    outputs = model(**inputs)
+
+# Get the prediction results
+logits = outputs.logits
+predicted_class_id = torch.argmax(logits, dim=-1).item()
+
+# Output the predicted class
+print(f"Predicted class ID: {predicted_class_id}")
+
+```