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Update app.py
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app.py
CHANGED
@@ -87,8 +87,13 @@ elif selected_model== "ner_conll_elmo":
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st.markdown("<h2>"+app_description+"</h2>" , unsafe_allow_html=True)
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st.markdown("**`PER`** **,** **`LOC`** **,** **`ORG`** **,** **`MISC` **", unsafe_allow_html=True)
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elif selected_model=="ner_conll_albert_large_uncased":
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st.markdown("<h2>"+app_description+"</h2>" , unsafe_allow_html=True)
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st.markdown("**`PER`** **,** **`LOC`** **,** **`ORG`** **,** **`MISC` **", unsafe_allow_html=True)
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elif selected_model== "ner_mit_movie_complex_distilbert_base_cased":
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app_title= "Detect up to 12 entity types in movie domain texts"
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app_description= "Named Entity Recognition model aimed to detect up to 12 entity types from movie domain texts. This model was trained on the MIT Movie Corpus complex queries dataset to detect movie trivia using Spark NLP, and it is available in Spark NLP Models hub (https://nlp.johnsnowlabs.com/models)"
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st.title(app_title)
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st.markdown("<h2>"+app_description+"</h2>" , unsafe_allow_html=True)
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st.markdown("""**`ACTOR`** **,** **`AWARD`** **,** **`CHARACTER_NAME`** **,** **`DIRECTOR`** **,** **`GENRE`** **,** **`OPINION`** **,** **`ORIGIN`** **,** **`PLOT`**,
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**`QUOTE`** **,** **`RELATIONSHIP`** **,** **`SOUNDTRACK`** **,** **`YEAR` **""", unsafe_allow_html=True)
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elif selected_model=="ner_conll_albert_large_uncased":
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