Update app.py

app.py

@@ -7,7 +7,7 @@ import string
 
 from transformers import BertTokenizer, BertForMaskedLM
 
-st.set_page_config(page_title='Compare BERT models qualitatively', page_icon=None, layout='centered', initial_sidebar_state='auto')
+st.set_page_config(page_title='Compare pretrained BERT models qualitatively', page_icon=None, layout='centered', initial_sidebar_state='auto')
 
 @st.cache()
 def load_bert_model(model_name):
@@ -135,7 +135,7 @@ def main():
   
 
   
-  st.markdown("<h3 style='text-align: center;'>Compare BERT models qualitatively</h3>", unsafe_allow_html=True)
+  st.markdown("<h3 style='text-align: center;'>Compare pretrained BERT models qualitatively</h3>", unsafe_allow_html=True)
   st.markdown("""
         <small style="font-size:20px; color: #2f2f2f"><br/>Why compare pretrained models <b>before fine-tuning</b>?</small><br/><small style="font-size:18px; color: #7f7f7f">Pretrained BERT models can be used as is, <a href="https://ajitrajasekharan.github.io/2021/01/02/my-first-post.html"><b>with no fine tuning to perform tasks like NER.</b><br/></a>This can be done ideally by using both fill-mask and CLS predictions, or minimally using fill-mask predictions alone if they are adequate</small>
         """, unsafe_allow_html=True)