Update app.py

app.py

@@ -135,8 +135,9 @@ elif menu == "Training":
     
     st.markdown('''
         To train a QA-NLU model on the data we created, we use the `run_squad.py` script from [huggingface](https://github.com/huggingface/transformers/blob/master/examples/legacy/question-answering/run_squad.py) and a SQuAD-trained QA model as our base. As an example, we can use `deepset/roberta-base-squad2` model from [here](https://huggingface.co/deepset/roberta-base-squad2) (assuming 8 GPUs are present):
-        
-        ````
+    ''')
+    
+    st.code('''
         mkdir models
         
         python -m torch.distributed.launch --nproc_per_node=8 run_squad.py \\
@@ -158,7 +159,6 @@ elif menu == "Training":
             --save_steps 100000 \\
             --gradient_accumulation_steps 8 \\
             --seed $RANDOM
-        ````
     ''')
 
 elif menu == "Evaluation":