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ajitrajasekharan
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739fa69
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Parent(s):
4e4bcde
Update app.py
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app.py
CHANGED
@@ -248,10 +248,9 @@ def main():
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st.markdown("""
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<small style="font-size:16px; color: #7f7f7f; text-align: left"><br/><br/>Models used: <br/>(1) <a href='https://huggingface.co/ajitrajasekharan/biomedical' target='_blank'>Biomedical model</a> pretrained on Pubmed,Clinical trials and BookCorpus subset.<br/>(2) Bert-base-cased (for PHI entities - Person/location/organization etc.)<br/>(3) Flair POS tagger</small>
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st.markdown("""
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<small style="font-size:16px; color: #8f8f8f; text-align: left"><i><b>Note:</b> The example sentences in the pull-down above test biomedical & PHI entities. To see valid predictions specifically for biomedical entities <a href='https://huggingface.co/spaces/ajitrajasekharan/self-supervised-ner-biomedical' target='_blank'>use this app</a>. For PHI entities only (Person,location, Organization) <a href='https://huggingface.co/spaces/ajitrajasekharan/self-supervised-ner-PER-ORG-LOC' target='_blank'>use this app</a> </i></small>
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""", unsafe_allow_html=True)
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st.markdown("""
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<small style="font-size:16px; color: #7f7f7f; text-align: left"><br/><br/>Models used: <br/>(1) <a href='https://huggingface.co/ajitrajasekharan/biomedical' target='_blank'>Biomedical model</a> pretrained on Pubmed,Clinical trials and BookCorpus subset.<br/>(2) Bert-base-cased (for PHI entities - Person/location/organization etc.)<br/>(3) Flair POS tagger</small>
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