Adjust text

app.py

@@ -130,7 +130,7 @@ visualize_ner(
     colors={"CONDITION": "#FF4B76", "BENEFIT": "#629B68"},
 )
 
-st.info("""The first processing step is to identify Conditions or Benefits with Named Entity Recognition. Conditions are diseases, symptoms and general health problems (e.g. joint pain), while Benefits are positive desired health aspects (e.g. energy)""")
+st.info("""The NER identifies two labels: 'Condition' and 'Benefit'. 'Condition' entities are generally diseases, symptoms, or general health problems (e.g. joint pain), while 'Benefit' entities are positive desired health aspects (e.g. energy)""")
 
 st.markdown("""---""")
 
@@ -148,10 +148,10 @@ for doc_clause, clause in zip(clauses, doc._.clauses):
     )
     st.markdown("\n")
 
-st.info("""The review is segmented into sub-clauses and then classified by a Text Classification model. We additionally blind the found entities to improve generalization and also to inform the model about our current target entity of which we want to get the prediction of.
-The Text Classification predicts four exclusive classes: 'Positive', 'Negative', 'Neutral', 'Anamnesis', they represent the health effect.""")
+st.info("""The text is segmented into clauses and classified by a Text Classification model. We additionally blind found entities to improve generalization and to inform the model about our current target entity.
+The Text Classification predicts four exclusive classes that represent the health effect: 'Positive', 'Negative', 'Neutral', 'Anamnesis'.""")
 
-st.info("""The 'Anamnesis' class is defined as the current state of health of a reviewer (e.g. 'I am diagnosed with joint pain'). It is used to link stated health effects that are mentioned in later sentences.""")
+st.info("""The 'Anamnesis' class is defined as the current state of health of a reviewer (e.g. 'I am diagnosed with joint pain'). It is used to link health aspects to health effects that are mentioned later in a review.""")
 
 st.markdown("""---""")