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Update app.py
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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:
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@@ -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("
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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 π π")
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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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##### 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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##### 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"):
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