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import streamlit as st | |
import pandas as pd | |
from filter_dataframe import filter_dataframe | |
def get_language_stats_df(): | |
return pd.read_parquet("data/language_stats.parquet") | |
st.set_page_config(page_title="Language Statistics", page_icon="📈") | |
st.markdown("# Language Statistics") | |
st.write("""\ | |
The table below shows the per-language statistics of the MMS corpus. | |
You can use the **'Add filters'** checkbox to filter the table by any of the columns. | |
Column descriptions: | |
- **Language**: Language name, | |
- **Datasets**: Number of datasets in the MMS corpus for the given language, | |
- **News**: Number of datasets from news domain, | |
- **Reviews**: Number of datasets from reviews domain, | |
- **Social media**: Number of datasets from social media domain, | |
- **Other**: Number of datasets from other domains, | |
- **Negative**: Number of examples with negative sentiment, | |
- **Neutral**: Number of examples with neutral sentiment, | |
- **Positive**: Number of examples with positive sentiment, | |
- **Words**: The average number of words in a single example, | |
- **Characters**: The average number of characters in a single example,""") | |
df = get_language_stats_df() | |
st.dataframe(filter_dataframe(df, numeric_as_categorical=False)) |