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update
Browse files
app.py
CHANGED
@@ -12,17 +12,17 @@ from src.about import (
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data = {
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"Method": [
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-
"
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"Zero-shot Text2SQL (Llama-3.1-70B)",
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"Zero-shot Text2SQL + LM Generation (Llama-3.1-70B)",
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"RAG (E5 + Llama-3.1-70B)",
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"RAG (E5) + LM Rerank (Llama-3.1-70B)",
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"
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"Zero-shot Text2SQL (GPT-4o)",
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"Zero-shot Text2SQL + LM Generation (GPT-4o)",
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"RAG (E5 + GPT-4o)",
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"RAG (E5) + LM Rerank (GPT-4o)",
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"
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"Zero-shot Text2SQL (o3-mini)",
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"Zero-shot Text2SQL + LM Generation (o3-mini)",
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"RAG (E5 + o3-mini)",
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@@ -40,7 +40,7 @@ leaderboard_df = pd.DataFrame(data)
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leaderboard_df = leaderboard_df.sort_values(
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"Execution Accuracy", ascending=False
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).reset_index(drop=True)
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-
leaderboard_df.insert(0, "Rank", leaderboard_df.index -
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leaderboard_df.loc[0, "Rank"] = ""
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leaderboard_df.loc[1, "Rank"] = ""
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leaderboard_df.loc[2, "Rank"] = ""
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@@ -74,7 +74,7 @@ with gr.Blocks() as demo:
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"""
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<div style="text-align: center;">
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<h1 style="font-size: 2.5rem; margin-bottom: 0.5rem;">TAG Leaderboard</h1>
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<p style="font-size: 1.25rem; color: gray;">
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</div>
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"""
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)
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data = {
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"Method": [
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+
"Human Performance\nHandwritten LOTUS (Llama-3.1-70B)",
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"Zero-shot Text2SQL (Llama-3.1-70B)",
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"Zero-shot Text2SQL + LM Generation (Llama-3.1-70B)",
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"RAG (E5 + Llama-3.1-70B)",
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"RAG (E5) + LM Rerank (Llama-3.1-70B)",
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+
"Human Performance\nHandwritten LOTUS (GPT-4o)",
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"Zero-shot Text2SQL (GPT-4o)",
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"Zero-shot Text2SQL + LM Generation (GPT-4o)",
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"RAG (E5 + GPT-4o)",
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"RAG (E5) + LM Rerank (GPT-4o)",
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+
"Human Performance\nHandwritten LOTUS (o3-mini)",
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"Zero-shot Text2SQL (o3-mini)",
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"Zero-shot Text2SQL + LM Generation (o3-mini)",
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"RAG (E5 + o3-mini)",
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leaderboard_df = leaderboard_df.sort_values(
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"Execution Accuracy", ascending=False
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).reset_index(drop=True)
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leaderboard_df.insert(0, "Rank", leaderboard_df.index - 2)
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leaderboard_df.loc[0, "Rank"] = ""
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leaderboard_df.loc[1, "Rank"] = ""
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leaderboard_df.loc[2, "Rank"] = ""
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"""
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<div style="text-align: center;">
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<h1 style="font-size: 2.5rem; margin-bottom: 0.5rem;">TAG Leaderboard</h1>
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<p style="font-size: 1.25rem; color: gray;">A benchmark for natural language queries over data</p>
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</div>
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"""
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)
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