Spaces:
Sleeping
Sleeping
File size: 2,364 Bytes
0b41ab5 f6590f0 0b41ab5 6f65006 0b41ab5 6f65006 0b41ab5 6f65006 0b41ab5 6f65006 0b41ab5 f6590f0 c52847e 6f65006 0b41ab5 6f65006 0b41ab5 6f65006 0b41ab5 6f65006 87bb867 6f65006 0b41ab5 6f65006 0b41ab5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 |
import gradio as gr
import pandas as pd
from pages.summarization_playground import custom_css
# Sample data for the leaderboard
data = {
'Rank': [1, 2, 3, 4, 5],
'Methods': ['METHOD1_PLACEHOLDER', 'METHOD2_PLACEHOLDER', 'METHOD3_PLACEHOLDER', 'METHOD4_PLACEHOLDER', 'METHOD5_PLACEHOLDER'],
'METRIC1_PLACEHOLDER Score': [1287, 1272, 1267, 1262, 1258],
'METRIC2_PLACEHOLDER Score': [56905, 24913, 42981, 49828, 55567],
'METRIC3_PLACEHOLDER Score': [3423, 3423, 2152, 4353, 2342],
'Authors': ['AUTHOR1_PLACEHOLDER', 'AUTHOR2_PLACEHOLDER', 'AUTHOR3_PLACEHOLDER', 'AUTHOR4_PLACEHOLDER', 'AUTHOR5_PLACEHOLDER'],
}
df = pd.DataFrame(data)
def update_leaderboard(sort_by):
# In a real implementation, this would filter the data based on the category
sorted_df = df.sort_values(by=sort_by, ascending=False)
# Update ranks based on new sorting
sorted_df['Rank'] = range(1, len(sorted_df) + 1)
# Convert DataFrame to HTML with clickable headers for sorting
html = sorted_df.to_html(index=False, escape=False)
# Add sorting links to column headers
for column in sorted_df.columns:
html = html.replace(f'<th>{column}</th>',
f'<th><a href="#" onclick="sortBy(\'{column}\'); return false;">{column}</a></th>')
return html
def create_leaderboard():
with gr.Blocks(theme=gr.themes.Soft(spacing_size="sm",text_size="sm"), css=custom_css) as demo:
gr.Markdown("# 🏆 Summarization Arena Leaderboard")
with gr.Row():
gr.Markdown("[Blog](placeholder) | [GitHub](placeholder) | [Paper](placeholder) | [Dataset](placeholder) | [Twitter](placeholder) | [Discord](placeholder)")
gr.Markdown("Welcome to our open platform for evaluating LLM summarization capabilities. We use the DATASET_NAME_PLACEHOLDER dataset to generate summaries with MODEL_NAME_PLACEHOLDER. These summaries are then evaluated by STRONGER_MODEL_NAME_PLACEHOLDER using the METRIC1_PLACEHOLDER and METRIC2_PLACEHOLDER metrics")
sort_by = gr.Dropdown(list(df.columns), label="Sort by", value="Rank")
gr.Markdown("**Performance**\n\n**methods**: 4, **questions**: 150")
leaderboard = gr.HTML(update_leaderboard("Rank"), elem_id="leaderboard")
sort_by.change(update_leaderboard, inputs=[sort_by], outputs=[leaderboard])
return demo |