Update README.md

README.md

@@ -28,3 +28,51 @@ trainable params: 71046 || all params: 17246053 || trainable%: 0.411955129675178
 'eval_auc': 0.8167222019799886,
 'eval_mcc': 0.3730152153022164
 ```
+
+To use this model, run:
+
+```python
+from transformers import AutoModelForTokenClassification, AutoTokenizer
+from peft import PeftModel
+import torch
+
+# Path to the saved LoRA model
+model_path = "AmelieSchreiber/esm2_t12_35M_qlora_binding_2600K_cp1"
+# ESM2 base model
+base_model_path = "facebook/esm2_t12_35M_UR50D"
+
+# Load the model
+base_model = AutoModelForTokenClassification.from_pretrained(base_model_path)
+loaded_model = PeftModel.from_pretrained(base_model, model_path)
+
+# Ensure the model is in evaluation mode
+loaded_model.eval()
+
+# Load the tokenizer
+loaded_tokenizer = AutoTokenizer.from_pretrained(base_model_path)
+
+# Protein sequence for inference
+protein_sequence = "MAVPETRPNHTIYINNLNEKIKKDELKKSLHAIFSRFGQILDILVSRSLKMRGQAFVIFKEVSSATNALRSMQGFPFYDKPMRIQYAKTDSDIIAKMKGT"  # Replace with your actual sequence
+
+# Tokenize the sequence
+inputs = loaded_tokenizer(protein_sequence, return_tensors="pt", truncation=True, max_length=1024, padding='max_length')
+
+# Run the model
+with torch.no_grad():
+    logits = loaded_model(**inputs).logits
+
+# Get predictions
+tokens = loaded_tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])  # Convert input ids back to tokens
+predictions = torch.argmax(logits, dim=2)
+
+# Define labels
+id2label = {
+    0: "No binding site",
+    1: "Binding site"
+}
+
+# Print the predicted labels for each token
+for token, prediction in zip(tokens, predictions[0].numpy()):
+    if token not in ['<pad>', '<cls>', '<eos>']:
+        print((token, id2label[prediction]))
+```