Update README.md

README.md

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 ---
 license: mit
+language:
+- en
+library_name: transformers
+tags:
+- esm
+- esm-2
+- protein
+- binding-site
+- biology
 ---
+# ESM-2 for RNA Binding Site Prediction
+
+A small RNA binding site predictor trained on dataset "S1" from [Data of protein-RNA binding sites](https://www.sciencedirect.com/science/article/pii/S2352340916308022#s0035) 
+using [facebook/esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D). 
+
+It only has a validation loss of `0.12738210861297214`.  
+
+To use, try running:
+
+```python3
+import torch
+from transformers import AutoTokenizer, EsmForTokenClassification
+
+# Define the class mapping
+class_mapping = {
+    0: 'Not Binding Site',
+    1: 'Binding Site',
+}
+
+# Load the trained model and tokenizer
+model = EsmForTokenClassification.from_pretrained("AmelieSchreiber/esm2_t12_35M_UR50D_rna_binding_site_predictor")
+tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t12_35M_UR50D")
+
+# Define the new sequences
+new_sequences = [
+    'VLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTK',
+    'SQETFSDLWKLLPENNVLSPLPSQAMDDLMLSPDDIEQWF',
+    # ... add more sequences here ...
+]
+
+# Iterate over the new sequences
+for seq in new_sequences:
+    # Convert sequence to input IDs
+    inputs = tokenizer(seq, truncation=True, padding='max_length', max_length=1290, return_tensors="pt")["input_ids"]
+
+    # Apply the model to get the logits
+    with torch.no_grad():
+        outputs = model(inputs)
+
+    # Get the predictions by picking the label (class) with the highest logit
+    predictions = torch.argmax(outputs.logits, dim=-1)
+
+    # Convert the tensor to a list of integers
+    prediction_list = predictions.tolist()[0]
+
+    # Convert the predicted class indices to class names
+    predicted_labels = [class_mapping[pred] for pred in prediction_list]
+
+    # Create a list that matches each amino acid in the sequence to its predicted class label
+    residue_to_label = list(zip(list(seq), predicted_labels))
+
+    # Print out the list
+    for i, (residue, predicted_label) in enumerate(residue_to_label):
+        print(f"Position {i+1} - {residue}: {predicted_label}")
+```
+