Update README.md

README.md

@@ -43,17 +43,17 @@ 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:
+### Example for PTM mode:
 ```python
 import torch
 from transformers import AutoModel, AutoTokenizer
 
-# Load PDeepPP model
+# Load `PDeepPP` model
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 print(f"Using {device} device")
-model = AutoModel.from_pretrained("YourModelName/PDeepPP", trust_remote_code=True)
+model = AutoModel.from_pretrained("YourModelName/`PDeepPP`", trust_remote_code=True)
 model.to(device)
 
 # Example protein sequences
@@ -69,15 +69,15 @@ model.eval()
 outputs = model(**inputs)
 print(outputs["logits"])
 ```
-Example for BPS mode:
+### Example for BPS mode:
 ```python
 import torch
 from transformers import AutoModel
 
-# Load PDeepPP model
+# Load `PDeepPP` model
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 print(f"Using {device} device")
-model = AutoModel.from_pretrained("YourModelName/PDeepPP", trust_remote_code=True)
+model = AutoModel.from_pretrained("YourModelName/`PDeepPP`", trust_remote_code=True)
 model.to(device)
 
 # Example protein sequences
@@ -94,21 +94,23 @@ outputs = model(**inputs)
 print(outputs["logits"])
 ```
 
-Training and customization
-PDeepPP supports fine-tuning on custom datasets. The model uses a configuration class (PDeepPPConfig) to specify hyperparameters such as:
+## Training and customization
 
-Number of transformer layers
-Hidden layer size
-Dropout rate
-PTM type and other task-specific parameters
-Refer to PDeepPPConfig for details.
+`PDeepPP` supports fine-tuning on custom datasets. The model uses a configuration class (`PDeepPPConfig`) to specify hyperparameters such as:
 
-Citation
-If you use PDeepPP in your research, please cite the associated paper or repository:
+- **Number of transformer layers**
+- **Hidden layer size**
+- **Dropout rate**
+- **PTM type** and other task-specific parameters
+
+Refer to `PDeepPPConfig` for details.
+
+## Citation
+If you use `PDeepPP` in your research, please cite the associated paper or repository:
 
 ```
 @article{your_reference,
-  title={PDeepPP: A Hybrid Model for Protein Sequence Analysis},
+  title={`PDeepPP`: A Hybrid Model for Protein Sequence Analysis},
   author={Author Name},
   journal={Journal Name},
   year={2025}