Update README.md

README.md

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 # Model description
 **MHC-II-EpiPred** (MHC-II-EpiPred, MHC II molecular epitope prediction) is a protein language model fine-tuned from [**ESM2**](https://github.com/facebookresearch/esm) pretrained model [(***facebook/esm2_t33_650M_UR50D***)](https://huggingface.co/facebook/esm2_t33_650M_UR50D) on a T cell MHC II epitope dataset.
 
-**MHC-II-EpiPred** is a classification model for predicting the class of MHC II epitope.
-# Results
-**MHC-II-EpiPred** achieved the following results:  
-Training Loss (mse): 0.1407  
-Training Accuracy: 0.9898  
-Evaluation Loss (mse): 0.0836  
-Evaluation Accuracy: 0.9703  
-Epochs: 324    
+**MHC-II-EpiPred** is a classification model for predicting the class of MHC II epitope. 
 
 # The dataset for training **MHC-II-EpiPred**
 The original data was downloaded from IEDB data base at https://www.iedb.org/home_v3.php.
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 This dataset comprises 543,717 T-cell epitope entries, spanning a variety of species and infections caused by diverse viruses. The epitope information included encompasses a broad range of potential sources, including data relevant to disease immunotherapy.
 
 Finally, the dataset we used to train the model contains 60,256 positive and negative samples, which is stored in https://github.com/pengsihua2023/MHC-II-EpiPred/tree/main/data.  
+
+# Results
+**MHC-II-EpiPred** achieved the following results:  
+Training Loss (mse): 0.1407  
+Training Accuracy: 0.9898  
+Evaluation Loss (mse): 0.0836  
+Evaluation Accuracy: 0.9703  
+Epochs: 324   
+
 # Model training code at GitHub
 https://github.com/pengsihua2023/MHC-II-EpiPred