Update README.md

README.md

@@ -43,36 +43,12 @@ pip install torch torchvision torchaudio --index-url https://download.pytorch.or
 pip install transformers
 ```
 
-Here is an example of how to use `PDeepPP` to process protein sequences and obtain predictions:
+Here is an example of how to use PDeepPP to process protein sequences and obtain predictions:
 
-### Example for PTM mode:
-```python
-import torch
-from transformers import AutoModel, AutoTokenizer
-
-# Load `PDeepPP` model
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-print(f"Using {device} device")
-model = AutoModel.from_pretrained("fondress/PDeepPP_ACE", trust_remote_code=True)
-model.to(device)
-
-# Example protein sequences
-protein_sequences = ["MKVSTYSTQ", "MSRSTYV"]
-
-# Preprocess sequences (PTM mode)
-from processing_pdeeppp import PDeepPPProcessor
-processor = PDeepPPProcessor(pad_char="X", target_length=33)
-inputs = processor(sequences=protein_sequences, ptm_mode=True, return_tensors="pt")
-
-# Make predictions
-model.eval()
-outputs = model(**inputs)
-print(outputs["logits"])
-```
-### Example for BPS mode:
 ```python
 import torch
 from transformers import AutoModel
+from processing_pdeeppp import PDeepPPProcessor
 
 # Load `PDeepPP` model
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -83,10 +59,9 @@ model.to(device)
 # Example protein sequences
 protein_sequences = ["MKVSTYSTQ", "MSRSTYV"]
 
-# Preprocess sequences (BPS mode)
-from processing_pdeeppp import PDeepPPProcessor
+# Preprocess sequences
 processor = PDeepPPProcessor(pad_char="X", target_length=33)
-inputs = processor(sequences=protein_sequences, ptm_mode=False, overlapping=True, step_size=5, return_tensors="pt")
+inputs = processor(sequences=protein_sequences, ptm_mode=True, return_tensors="pt")  # Set ptm_mode=True for PTM processing
 
 # Make predictions
 model.eval()