Added some text underneath the headers of the tabs

app.py

@@ -98,6 +98,7 @@ if active_tab == "Nearest neighbours":
     
     with st.container():
         st.markdown("## Nearest Neighbours")
+        st.markdown('###### Here you can extract the nearest neighbours to a chosen lemma. Please select one or more time slices and the preferred number of nearest neighbours.')
         target_word = st.multiselect("Enter a word", options=all_models_words, max_selections=1)
         if len(target_word) > 0:
             target_word = target_word[0]
@@ -159,6 +160,7 @@ elif active_tab == "Cosine similarity":
         eligible_models_1 = []
         eligible_models_2 = []
         st.markdown("## Cosine similarity")
+        st.markdown('###### Here you can extract the cosine similarity between two lemmas. Please select a time slice for each lemma. You can also calculate the cosine similarity between two vectors of the same lemma in different time slices.')
         col1, col2 = st.columns(2)
         col3, col4 = st.columns(2)
         with col1:
@@ -191,6 +193,9 @@ elif active_tab == "Cosine similarity":
 
 # 3D graph tab
 elif active_tab == "3D graph":
+    st.markdown("## 3D graph")
+    st.markdown('###### Here you can generate a 3D representation of the semantic space surrounding a target lemma. Please choose the lemma and the time slice.')
+    
     col1, col2 = st.columns(2)
     
     # Load compressed word list
@@ -231,11 +236,15 @@ elif active_tab == "3D graph":
 elif active_tab == "Dictionary":
     
     with st.container():
+        st.markdown('## Dictionary')
+        st.markdown('###### Search a word in the Liddell-Scott-Jones dictionary (only Greek, no whitespaces).')
+
+
         all_lemmas = load_all_lemmas()
         
         # query_word = st.multiselect("Search a word in the LSJ dictionary", all_lemmas, max_selections=1)
         
-        query_tag = st_tags(label = 'Search a word in the LSJ dictionary',
+        query_tag = st_tags(label='',
                             text = '',
                             value = [],
                             suggestions = all_lemmas,
@@ -331,6 +340,11 @@ elif active_tab == "FAQ":
                 (in this interface, we focus on the extraction of semantic information) or to perform specific linguistic tasks. \
                 The models on which this interface is based are Word Embedding models."
                 )
+        
+    with st.expander("Which corpus was used to train the models?"):
+        st.write(
+            "The five models on which this interface is based were trained on five slices of the Diorisis Ancient Greek Corpus (Vatri & McGillivray 2018)."
+        )