Update README.md

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}")

```

README.md

@@ -1,3 +1,12 @@
 ---
 license: mit
+datasets:
+- proj-persona/PersonaHub
+language:
+- ab
+metrics:
+- accuracy
+library_name: adapter-transformers
+tags:
+- biology
 ---