ajitrajasekharan commited on
Commit
3856ec6
·
1 Parent(s): 9866878

Update app.py

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Files changed (1) hide show
  1. app.py +1 -1
app.py CHANGED
@@ -137,7 +137,7 @@ def main():
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  st.markdown("<h3 style='text-align: center;'>Compare BERT models qualitatively</h3>", unsafe_allow_html=True)
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  st.markdown("""
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- <small style="font-size:20px; color: #2f2f2f">Why compare models?</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>
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  """, unsafe_allow_html=True)
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  st.write("This app can be used to examine both model prediction for a masked position as well as the neighborhood of CLS vector")
 
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  st.markdown("<h3 style='text-align: center;'>Compare BERT models qualitatively</h3>", unsafe_allow_html=True)
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  st.markdown("""
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+ <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>
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  """, unsafe_allow_html=True)
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  st.write("This app can be used to examine both model prediction for a masked position as well as the neighborhood of CLS vector")