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c08f926
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1 Parent(s): bd9bf85

Update app.py

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  1. app.py +10 -9
app.py CHANGED
@@ -8,15 +8,15 @@ df = dataset["train"].to_pandas()
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  unique_src = df[["item_id", "src_text"]].drop_duplicates(subset="item_id")
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  langs = list(df["lang_id"].unique())
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- st.title("DivEMT Explorer")
 
 
 
 
 
 
 
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- cc1, _ = st.columns([2, 1])
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- with cc1:
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- st.write("""
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- The DivEMT Explorer is a tool to explore translations and edits contained in the DivEMT corpus.
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- Use the expandable section "Explore examples" below to visualize some of the original source sentences. When you found a sentence that you might be interested in, insert its numeric id (between 0 and 429) in the box below, and select all the languages for which you want to visualize the results.
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- Inside every generated section you will find the translations for all the available settings, alongside aligned edits and a collection of collected metadata. You can filter the showed settings to better see the aligned edits annotations.
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- """)
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  with st.expander("Explore examples"):
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  col1, col2, _ = st.columns([3,2,5])
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  with col1:
@@ -46,7 +46,8 @@ with col2_main:
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  'Select languages',
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  options=langs
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  )
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- st.markdown("<b>Source text:</b> <span style='color: #ff4b4b'> " + unique_src.iloc[int(item_id)]["src_text"] + "</span>", unsafe_allow_html=True)
 
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  task_names = ["From Scratch (HT)", "Google PE (PE1)", "mBART PE (PE2)"]
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  for lang in langs:
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  with st.expander(f"View {lang.upper()} data"):
 
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  unique_src = df[["item_id", "src_text"]].drop_duplicates(subset="item_id")
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  langs = list(df["lang_id"].unique())
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+ st.title("DivEMT Explorer πŸ” 🌍")
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+ st.markdown("""
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+ ##### The DivEMT Explorer is a tool to explore translations and edits in the DivEMT corpus.
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+
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+ ##### Use the expandable section "Explore examples" below to visualize some of the original source sentences. When you find an interesting sentence, insert its numeric id (between 0 and 429) in the box below, and select all the available languages you want to use for visualizing the results.
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+
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+ ##### Inside every generated language section, you will find the translations for all the available settings, alongside aligned edits and a collection of collected metadata. You can filter the shown settings to see the aligned edits annotations.
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+ """)
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  with st.expander("Explore examples"):
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  col1, col2, _ = st.columns([3,2,5])
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  with col1:
 
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  'Select languages',
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  options=langs
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  )
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+ st.markdown("##### Source text")
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+ st.markdown("##### <span style='color: #ff4b4b'> " + unique_src.iloc[int(item_id)]["src_text"] + "</span>", unsafe_allow_html=True)
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  task_names = ["From Scratch (HT)", "Google PE (PE1)", "mBART PE (PE2)"]
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  for lang in langs:
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  with st.expander(f"View {lang.upper()} data"):