Updated description for nearest neigh, cosine sim and 3d graph tabs
Browse files
app.py
CHANGED
|
@@ -132,7 +132,11 @@ if selected == "App":
|
|
| 132 |
|
| 133 |
with st.container():
|
| 134 |
st.markdown("## Nearest Neighbours")
|
| 135 |
-
st.markdown(
|
|
|
|
|
|
|
|
|
|
|
|
|
| 136 |
target_word = st.multiselect("Enter a word", options=all_models_words, max_selections=1)
|
| 137 |
if len(target_word) > 0:
|
| 138 |
target_word = target_word[0]
|
|
@@ -199,7 +203,12 @@ if selected == "App":
|
|
| 199 |
eligible_models_1 = []
|
| 200 |
eligible_models_2 = []
|
| 201 |
st.markdown("## Cosine similarity")
|
| 202 |
-
st.markdown(
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 203 |
col1, col2 = st.columns(2)
|
| 204 |
col3, col4 = st.columns(2)
|
| 205 |
with col1:
|
|
@@ -235,6 +244,8 @@ if selected == "App":
|
|
| 235 |
st.markdown("## 3D graph")
|
| 236 |
st.markdown('''
|
| 237 |
Here you can generate a 3D representation of the semantic space surrounding a target lemma. Please choose the lemma and the time slice.\
|
|
|
|
|
|
|
| 238 |
|
| 239 |
**NB**: the 3D representations are reductions of the multi-dimensional representations created by the models. \
|
| 240 |
This is necessary for visualization, but while reducing the dimnesions some informations gets lost. \
|
|
|
|
| 132 |
|
| 133 |
with st.container():
|
| 134 |
st.markdown("## Nearest Neighbours")
|
| 135 |
+
st.markdown(
|
| 136 |
+
'Here you can extract the nearest neighbours to a chosen lemma. \
|
| 137 |
+
Please select one or more time slices and the preferred number of nearest neighbours. \
|
| 138 |
+
Only type in Greek, with correct spirits and accents.'
|
| 139 |
+
)
|
| 140 |
target_word = st.multiselect("Enter a word", options=all_models_words, max_selections=1)
|
| 141 |
if len(target_word) > 0:
|
| 142 |
target_word = target_word[0]
|
|
|
|
| 203 |
eligible_models_1 = []
|
| 204 |
eligible_models_2 = []
|
| 205 |
st.markdown("## Cosine similarity")
|
| 206 |
+
st.markdown(
|
| 207 |
+
'Here you can extract the cosine similarity between two lemmas. \
|
| 208 |
+
Please select a time slice for each lemma. \
|
| 209 |
+
You can also calculate the cosine similarity between two vectors of the same lemma in different time slices. \
|
| 210 |
+
Only type in Greek, with correct spirits and accents.'
|
| 211 |
+
)
|
| 212 |
col1, col2 = st.columns(2)
|
| 213 |
col3, col4 = st.columns(2)
|
| 214 |
with col1:
|
|
|
|
| 244 |
st.markdown("## 3D graph")
|
| 245 |
st.markdown('''
|
| 246 |
Here you can generate a 3D representation of the semantic space surrounding a target lemma. Please choose the lemma and the time slice.\
|
| 247 |
+
Only type in Greek, with correct spirits and accents. \
|
| 248 |
+
|
| 249 |
|
| 250 |
**NB**: the 3D representations are reductions of the multi-dimensional representations created by the models. \
|
| 251 |
This is necessary for visualization, but while reducing the dimnesions some informations gets lost. \
|