Update README.md

README.md

@@ -1,8 +1,7 @@
 ---
 tags:
 - protein language model
-datasets:
-- IEDB
+pipeline_tag: text-classification
 ---
 
 # PDeepPP model
@@ -42,11 +41,12 @@ To use `PDeepPP`, you need to install the required dependencies, including `torc
 ```bash
 pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
 pip install transformers
+```
 
 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
 
@@ -68,8 +68,9 @@ inputs = processor(sequences=protein_sequences, ptm_mode=True, return_tensors="p
 model.eval()
 outputs = model(**inputs)
 print(outputs["logits"])
-
+```
 Example for BPS mode:
+```python
 import torch
 from transformers import AutoModel
 
@@ -91,6 +92,7 @@ inputs = processor(sequences=protein_sequences, ptm_mode=False, overlapping=True
 model.eval()
 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:
@@ -104,9 +106,11 @@ 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},
   author={Author Name},
   journal={Journal Name},
   year={2025}
-}
+}
+```