Update README.md

README.md

@@ -16,9 +16,11 @@ The model backbone is RoBERTa-base.
 ## Model description
 The model is tuned with unlabeled data using a learning objective called first sentence prediction (FSP). 
 The FSP task is designed by considering both the nature of the unlabeled corpus and the input/output format of classification tasks. 
-The training and validation sets are constructed from the unlabeled corpus using FSP. During tuning, BERT-like pre-trained masked language 
+The training and validation sets are constructed from the unlabeled corpus using FSP. 
+
+During tuning, BERT-like pre-trained masked language 
 models such as RoBERTa and ALBERT are employed as the backbone, and an output layer for classification is added. 
-The learning objective for FSP is to predict the index of the positive option. 
+The learning objective for FSP is to predict the index of the correct label. 
 A cross-entropy loss is used for tuning the model.
 
 ## Intended uses & limitations