fixing the layout for Ner classifications

app.py

@@ -11,26 +11,18 @@ from spacy import glossary
 
 entities = ["CARDINAL", "DATE", "EVENT", "FAC", "GPE", "LANGUAGE", "LAW", "LOC", "MONEY", "NORP", "ORDINAL", "ORG", "PERCENT", "PERSON", "PRODUCT", "QUANTITY", "TIME", "WORK_OF_ART"]
 ent_dict = glossary.GLOSSARY
-ent_sum = [f'{ent} = <i>{ent_dict[ent]}</i>' for ent in entities ]
+ent_sum = [f'<b>{ent}</b> = <i>{ent_dict[ent]}</i>' for ent in entities ]
 
 with gr.Blocks() as demo:
     gr.Markdown(
         f"""
-        <style>
-        .list {{
-            display: grid;
-            grid-template-columns: 1fr 1fr 1fr;
-            }}
-        </style>
         <div style="display: block; text-align: left; padding:0; margin:0;">
             <h1 style="text-align: center">Babel Machine Demo</h1>
             <p>This is a demo for text classification using language models finetuned on data labeled by <a href="https://www.comparativeagendas.net/">CAP</a>, <a href="https://manifesto-project.wzb.eu/">Manifesto Project</a>, sentiment, and emotion coding systems.<br>
             For the coding of complete datasets, please visit the official <a href="https://babel.poltextlab.com/">Babel Machine</a> site.<br>
             Please note that named entity inputs are case sensitive.<br>
-            For named entity recognition the following labels are used:</p>
-            <ul class="list">
-            <li class="list-item"> {'</li> <li class="list-item">'.join(ent_sum)} </li>
-            </ul>
+            For named entity recognition the following labels are used:<br>
+             {';\t'.join(ent_sum)}</p>
         </div>
         """)