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README.md

@@ -58,8 +58,12 @@ from transformers import AutoModel
 device = torch.device("cpu")
 pad_char = "X"  # Padding character
 target_length = 33  # Target length for sequence padding
-mode = "BPS"  # Mode setting (only configured in example.py)
-esm_ratio = 1  # Ratio for ESM embeddings
+mode = "PTMS"  # Mode setting (only configured in example.py)
+esm_ratio = 0.95  # Ratio for ESM embeddings
+
+# Load the PDeepPP model
+model_name = "fondress/PDeepPP_N-linked-glycosylation-N"
+model = AutoModel.from_pretrained(model_name, trust_remote_code=True)  # Directly load the model
 
 # Initialize the PDeepPPProcessor
 processor = PDeepPPProcessor(pad_char=pad_char, target_length=target_length)
@@ -96,10 +100,6 @@ pretrained_features = pretrainer.create_embeddings(
 # Ensure pretrained features are on the same device
 inputs["input_embeds"] = pretrained_features.to(device)
 
-# Load the PDeepPP model
-model_name = "fondress/PDeepPP_ACE"
-model = AutoModel.from_pretrained(model_name, trust_remote_code=True)  # Directly load the model
-
 # Perform prediction
 model.eval()
 outputs = model(input_embeds=inputs["input_embeds"])  # Use pretrained features as model input