import gradio as gr import pandas as pd import numpy as np # Sample data based on your table (you'll need to update this with the full dataset) data_synthesized_full = { 'Method': ['BM25', 'DPR (roberta)', 'ANCE (roberta)', 'QAGNN (roberta)', 'ada-002', 'voyage-l2-instruct', 'LLM2Vec', 'GritLM-7b', 'multi-ada-002', 'ColBERTv2'], 'STARK-AMAZON_Hit@1': [44.94, 15.29, 30.96, 26.56, 39.16, 40.93, 21.74, 42.08, 40.07, 46.10], 'STARK-AMAZON_Hit@5': [67.42, 47.93, 51.06, 50.01, 62.73, 64.37, 41.65, 66.87, 64.98, 66.02], 'STARK-AMAZON_R@20': [53.77, 44.49, 41.95, 52.05, 53.29, 54.28, 33.22, 56.52, 55.12, 53.44], 'STARK-AMAZON_MRR': [55.30, 30.20, 40.66, 37.75, 50.35, 51.60, 31.47, 53.46, 51.55, 55.51], 'STARK-MAG_Hit@1': [25.85, 10.51, 21.96, 12.88, 29.08, 30.06, 18.01, 37.90, 25.92, 31.18], 'STARK-MAG_Hit@5': [45.25, 35.23, 36.50, 39.01, 49.61, 50.58, 34.85, 56.74, 50.43, 46.42], 'STARK-MAG_R@20': [45.69, 42.11, 35.32, 46.97, 48.36, 50.49, 35.46, 46.40, 50.80, 43.94], 'STARK-MAG_MRR': [34.91, 21.34, 29.14, 29.12, 38.62, 39.66, 26.10, 47.25, 36.94, 38.39], 'STARK-PRIME_Hit@1': [12.75, 4.46, 6.53, 8.85, 12.63, 10.85, 10.10, 15.57, 15.10, 11.75], 'STARK-PRIME_Hit@5': [27.92, 21.85, 15.67, 21.35, 31.49, 30.23, 22.49, 33.42, 33.56, 23.85], 'STARK-PRIME_R@20': [31.25, 30.13, 16.52, 29.63, 36.00, 37.83, 26.34, 39.09, 38.05, 25.04], 'STARK-PRIME_MRR': [19.84, 12.38, 11.05, 14.73, 21.41, 19.99, 16.12, 24.11, 23.49, 17.39] } data_synthesized_10 = { 'Method': ['BM25', 'DPR (roberta)', 'ANCE (roberta)', 'QAGNN (roberta)', 'ada-002', 'voyage-l2-instruct', 'LLM2Vec', 'GritLM-7b', 'multi-ada-002', 'ColBERTv2', 'Claude3 Reranker', 'GPT4 Reranker'], 'STARK-AMAZON_Hit@1': [42.68, 16.46, 30.09, 25.00, 39.02, 43.29, 18.90, 43.29, 40.85, 44.31, 45.49, 44.79], 'STARK-AMAZON_Hit@5': [67.07, 50.00, 49.27, 48.17, 64.02, 67.68, 37.80, 71.34, 62.80, 65.24, 71.13, 71.17], 'STARK-AMAZON_R@20': [54.48, 42.15, 41.91, 51.65, 49.30, 56.04, 34.73, 56.14, 52.47, 51.00, 53.77, 55.35], 'STARK-AMAZON_MRR': [54.02, 30.20, 39.30, 36.87, 50.32, 54.20, 28.76, 55.07, 51.54, 55.07, 55.91, 55.69], 'STARK-MAG_Hit@1': [27.81, 11.65, 22.89, 12.03, 28.20, 34.59, 19.17, 38.35, 25.56, 31.58, 36.54, 40.90], 'STARK-MAG_Hit@5': [45.48, 36.84, 37.26, 37.97, 52.63, 50.75, 33.46, 58.64, 50.37, 47.36, 53.17, 58.18], 'STARK-MAG_R@20': [44.59, 42.30, 44.16, 47.98, 49.25, 50.75, 29.85, 46.38, 53.03, 45.72, 48.36, 48.60], 'STARK-MAG_MRR': [35.97, 21.82, 30.00, 28.70, 38.55, 42.90, 26.06, 48.25, 36.82, 38.98, 44.15, 49.00], 'STARK-PRIME_Hit@1': [13.93, 5.00, 6.78, 7.14, 15.36, 12.14, 9.29, 16.79, 15.36, 15.00, 17.79, 18.28], 'STARK-PRIME_Hit@5': [31.07, 23.57, 16.15, 17.14, 31.07, 31.42, 20.7, 34.29, 32.86, 26.07, 36.90, 37.28], 'STARK-PRIME_R@20': [32.84, 30.50, 17.07, 32.95, 37.88, 37.34, 25.54, 41.11, 40.99, 27.78, 35.57, 34.05], 'STARK-PRIME_MRR': [21.68, 13.50, 11.42, 16.27, 23.50, 21.23, 15.00, 24.99, 23.70, 19.98, 26.27, 26.55] } data_human_generated = { 'Method': ['BM25', 'DPR (roberta)', 'ANCE (roberta)', 'QAGNN (roberta)', 'ada-002', 'voyage-l2-instruct', 'LLM2Vec', 'GritLM-7b', 'multi-ada-002', 'ColBERTv2', 'Claude3 Reranker', 'GPT4 Reranker'], 'STARK-AMAZON_Hit@1': [27.16, 16.05, 25.93, 22.22, 39.50, 35.80, 29.63, 40.74, 46.91, 33.33, 53.09, 50.62], 'STARK-AMAZON_Hit@5': [51.85, 39.51, 54.32, 49.38, 64.19, 62.96, 46.91, 71.60, 72.84, 55.56, 74.07, 75.31], 'STARK-AMAZON_R@20': [29.23, 15.23, 23.69, 21.54, 35.46, 33.01, 21.21, 36.30, 40.22, 29.03, 35.46, 35.46], 'STARK-AMAZON_MRR': [18.79, 27.21, 37.12, 31.33, 52.65, 47.84, 38.61, 53.21, 58.74, 43.77, 62.11, 61.06], 'STARK-MAG_Hit@1': [32.14, 4.72, 25.00, 20.24, 28.57, 22.62, 16.67, 34.52, 23.81, 33.33, 38.10, 36.90], 'STARK-MAG_Hit@5': [41.67, 9.52, 30.95, 26.19, 41.67, 36.90, 28.57, 44.04, 41.67, 36.90, 45.24, 46.43], 'STARK-MAG_R@20': [32.46, 25.00, 27.24, 28.76, 35.95, 32.44, 21.74, 34.57, 39.85, 30.50, 35.95, 35.95], 'STARK-MAG_MRR': [37.42, 7.90, 27.98, 25.53, 35.81, 29.68, 21.59, 38.72, 31.43, 35.97, 42.00, 40.65], 'STARK-PRIME_Hit@1': [22.45, 2.04, 7.14, 6.12, 17.35, 16.33, 9.18, 25.51, 24.49, 15.31, 28.57, 28.57], 'STARK-PRIME_Hit@5': [41.84, 9.18, 13.27, 13.27, 34.69, 32.65, 21.43, 41.84, 39.80, 26.53, 46.94, 44.90], 'STARK-PRIME_R@20': [42.32, 10.69, 11.72, 17.62, 41.09, 39.01, 26.77, 48.10, 47.21, 25.56, 41.61, 41.61], 'STARK-PRIME_MRR': [30.37, 7.05, 10.07, 9.39, 26.35, 24.33, 15.24, 34.28, 32.98, 19.67, 36.32, 34.82] } df_synthesized_full = pd.DataFrame(data_synthesized_full) df_synthesized_10 = pd.DataFrame(data_synthesized_10) df_human_generated = pd.DataFrame(data_human_generated) def format_dataframe(df, dataset): # Filter the dataframe for the selected dataset columns = ['Method'] + [col for col in df.columns if dataset in col] filtered_df = df[columns].copy() # Rename columns filtered_df.columns = [col.split('_')[-1] if '_' in col else col for col in filtered_df.columns] # Sort by MRR filtered_df = filtered_df.sort_values('MRR', ascending=False) return filtered_df model_types = { 'Sparse Retriever': ['BM25'], 'Small Dense Retrievers': ['DPR (roberta)', 'ANCE (roberta)', 'QAGNN (roberta)'], 'LLM-based Dense Retrievers': ['ada-002', 'voyage-l2-instruct', 'LLM2Vec', 'GritLM-7b'], 'Multivector Retrievers': ['multi-ada-002', 'ColBERTv2'], 'LLM Rerankers': ['Claude3 Reranker', 'GPT4 Reranker'] } def filter_by_model_type(df, selected_types): if not selected_types: # If no types are selected, return an empty DataFrame return df.head(0) selected_models = [model for type in selected_types for model in model_types[type]] return df[df['Method'].isin(selected_models)] def format_dataframe(df, dataset): columns = ['Method'] + [col for col in df.columns if dataset in col] filtered_df = df[columns].copy() filtered_df.columns = [col.split('_')[-1] if '_' in col else col for col in filtered_df.columns] filtered_df = filtered_df.sort_values('MRR', ascending=False) return filtered_df def update_tables(selected_types): filtered_df_full = filter_by_model_type(df_synthesized_full, selected_types) filtered_df_10 = filter_by_model_type(df_synthesized_10, selected_types) filtered_df_human = filter_by_model_type(df_human_generated, selected_types) outputs = [] for df in [filtered_df_full, filtered_df_10, filtered_df_human]: for dataset in ['AMAZON', 'MAG', 'PRIME']: outputs.append(format_dataframe(df, f"STARK-{dataset}")) return outputs with gr.Blocks(css=css) as demo: gr.Markdown("# Semi-structured Retrieval Benchmark (STaRK) Leaderboard") gr.Markdown("Refer to the [STaRK paper](https://arxiv.org/pdf/2404.13207) for details on metrics, tasks and models.") with gr.Row(): model_type_filter = gr.CheckboxGroup( choices=list(model_types.keys()), value=list(model_types.keys()), label="Model types", interactive=True ) with gr.Tabs() as outer_tabs: with gr.TabItem("Synthesized (full)"): with gr.Tabs() as inner_tabs_synthesized_full: syn_full_dfs = [gr.DataFrame(interactive=False) for _ in range(3)] for df, dataset in zip(syn_full_dfs, ['AMAZON', 'MAG', 'PRIME']): with gr.TabItem(dataset): df.render() with gr.TabItem("Synthesized (10%)"): with gr.Tabs() as inner_tabs_synthesized_10: syn_10_dfs = [gr.DataFrame(interactive=False) for _ in range(3)] for df, dataset in zip(syn_10_dfs, ['AMAZON', 'MAG', 'PRIME']): with gr.TabItem(dataset): df.render() with gr.TabItem("Human-Generated"): with gr.Tabs() as inner_tabs_human: human_dfs = [gr.DataFrame(interactive=False) for _ in range(3)] for df, dataset in zip(human_dfs, ['AMAZON', 'MAG', 'PRIME']): with gr.TabItem(dataset): df.render() all_dfs = syn_full_dfs + syn_10_dfs + human_dfs model_type_filter.change( update_tables, inputs=[model_type_filter], outputs=all_dfs ) demo.load( update_tables, inputs=[model_type_filter], outputs=all_dfs ) demo.launch()