Update app.py

app.py

@@ -167,7 +167,9 @@ elif menu == "Evaluation":
     st.markdown('''
         To assess the performance of the trained model, we can use the `calculate_pr.py` script from the [QA-NLU Amazon Research repository](https://github.com/amazon-research/question-answering-nlu).
         
-        Feel free to query the pre-trained QA-NLU model using the buttons below. *Please note that this model has been trained on ATIS and may be need to be further fine-tuned to support intents and slots that are not covered in ATIS*.
+        Feel free to query the pre-trained QA-NLU model using the buttons below. 
+        
+        *Please note that this model has been trained on ATIS and may be need to be further fine-tuned to support intents and slots that are not covered in ATIS*.
     ''')
     
     tokenizer = AutoTokenizer.from_pretrained("AmazonScience/qanlu")