Mark7549 commited on
Commit
593c285
·
1 Parent(s): ed8240c

Updated description for nearest neigh, cosine sim and 3d graph tabs

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Files changed (1) hide show
  1. app.py +13 -2
app.py CHANGED
@@ -132,7 +132,11 @@ if selected == "App":
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  with st.container():
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  st.markdown("## Nearest Neighbours")
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- 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.')
 
 
 
 
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  target_word = st.multiselect("Enter a word", options=all_models_words, max_selections=1)
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  if len(target_word) > 0:
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  target_word = target_word[0]
@@ -199,7 +203,12 @@ if selected == "App":
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  eligible_models_1 = []
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  eligible_models_2 = []
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  st.markdown("## Cosine similarity")
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- 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.')
 
 
 
 
 
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  col1, col2 = st.columns(2)
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  col3, col4 = st.columns(2)
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  with col1:
@@ -235,6 +244,8 @@ if selected == "App":
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  st.markdown("## 3D graph")
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  st.markdown('''
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  Here you can generate a 3D representation of the semantic space surrounding a target lemma. Please choose the lemma and the time slice.\
 
 
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  **NB**: the 3D representations are reductions of the multi-dimensional representations created by the models. \
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  This is necessary for visualization, but while reducing the dimnesions some informations gets lost. \
 
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  with st.container():
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  st.markdown("## Nearest Neighbours")
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+ st.markdown(
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+ 'Here you can extract the nearest neighbours to a chosen lemma. \
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+ Please select one or more time slices and the preferred number of nearest neighbours. \
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+ Only type in Greek, with correct spirits and accents.'
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+ )
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  target_word = st.multiselect("Enter a word", options=all_models_words, max_selections=1)
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  if len(target_word) > 0:
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  target_word = target_word[0]
 
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  eligible_models_1 = []
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  eligible_models_2 = []
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  st.markdown("## Cosine similarity")
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+ st.markdown(
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+ 'Here you can extract the cosine similarity between two lemmas. \
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+ Please select a time slice for each lemma. \
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+ You can also calculate the cosine similarity between two vectors of the same lemma in different time slices. \
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+ Only type in Greek, with correct spirits and accents.'
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+ )
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  col1, col2 = st.columns(2)
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  col3, col4 = st.columns(2)
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  with col1:
 
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  st.markdown("## 3D graph")
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  st.markdown('''
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  Here you can generate a 3D representation of the semantic space surrounding a target lemma. Please choose the lemma and the time slice.\
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+ Only type in Greek, with correct spirits and accents. \
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+
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  **NB**: the 3D representations are reductions of the multi-dimensional representations created by the models. \
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  This is necessary for visualization, but while reducing the dimnesions some informations gets lost. \