avichr commited on
Commit
0ef2ca5
1 Parent(s): 00e4af7

Update app.py

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Files changed (1) hide show
  1. app.py +1 -2
app.py CHANGED
@@ -14,7 +14,6 @@ st.write(x, "squared is", x * x)
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  st.title("Find sentiment")
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  st.write("HebEMO is a tool to detect polarity and extract emotions from Hebrew user-generated content (UGC), which was trained on a unique Covid-19 related dataset that we collected and annotated. HebEMO yielded a high performance of weighted average F1-score = 0.96 for polarity classification. Emotion detection reached an F1-score of 0.78-0.97, with the exception of *surprise*, which the model failed to capture (F1 = 0.41). These results are better than the best-reported performance, even when compared to the English language.")
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  sent = st.text_area("Text", "write here", height = 20)
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- interact(HebEMO_model.hebemo, text=sent, plot=fixed(True), input_path=fixed(False), save_results=fixed(False),)
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- interact(HebEMO_model.hebemo, text='讛讞讬讬诐 讬驻讬诐 讜诪讗讜砖专讬', plot=fixed(True), input_path=fixed(False), save_results=fixed(False),)
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  st.write (HebEMO_model.hebemo(sent, plot=True))
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  st.title("Find sentiment")
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  st.write("HebEMO is a tool to detect polarity and extract emotions from Hebrew user-generated content (UGC), which was trained on a unique Covid-19 related dataset that we collected and annotated. HebEMO yielded a high performance of weighted average F1-score = 0.96 for polarity classification. Emotion detection reached an F1-score of 0.78-0.97, with the exception of *surprise*, which the model failed to capture (F1 = 0.41). These results are better than the best-reported performance, even when compared to the English language.")
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  sent = st.text_area("Text", "write here", height = 20)
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+ # interact(HebEMO_model.hebemo, text='讛讞讬讬诐 讬驻讬诐 讜诪讗讜砖专讬', plot=fixed(True), input_path=fixed(False), save_results=fixed(False),)
 
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  st.write (HebEMO_model.hebemo(sent, plot=True))
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