v1.34
Browse files
app.py
CHANGED
@@ -130,36 +130,43 @@ def create_leaderboard():
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# Create Gradio interface with a nice theme
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with gr.Blocks(theme=gr.themes.Soft(), title="Financial Model Performance Leaderboard") as demo:
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gr.Markdown("# Financial Model Performance Leaderboard")
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with gr.Row():
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with gr.Column():
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with gr.Tab("Robustness Results"):
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gr.DataFrame(
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value=formatted_robustness_df,
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label="Robustness Results",
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wrap=True,
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elem_classes=["custom-table"]
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)
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with gr.Tab("Context Grounding Results"):
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gr.DataFrame(
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value=formatted_context_grounding_df,
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label="Context Grounding Results",
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wrap=True,
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elem_classes=["custom-table"]
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)
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with gr.Tab("Top 3 Winners"):
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gr.DataFrame(
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value=winners_df,
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label="Top 3 Models",
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wrap=True,
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elem_classes=["custom-table"]
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)
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with gr.Tab("About"):
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gr.HTML("""
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<div style="padding: 20px;">
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<h2>About This Leaderboard</h2>
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<p>This Financial Model Performance Leaderboard compares the performance of various AI models across robustness and context grounding metrics. The data is sourced from evaluations conducted on February 18, 2025, and reflects the models' ability to handle financial tasks under different conditions.</p>
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<p>For more information, contact us at <a href="mailto:support@
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</div>
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""")
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with gr.Row():
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# Create Gradio interface with a nice theme
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with gr.Blocks(theme=gr.themes.Soft(), title="Financial Model Performance Leaderboard") as demo:
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gr.Markdown("# Financial Model Performance Leaderboard")
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gr.HTML("""
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<div style="padding: 20px;">
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<h2>About This Leaderboard</h2>
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<p>This Financial Model Performance Leaderboard compares the performance of various AI models across robustness and context grounding metrics. The data is sourced from evaluations conducted on February 18, 2025, and reflects the models' ability to handle financial tasks under different conditions.</p>
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<p>For more information, contact us at <a href="mailto:[email protected]">[email protected]</a>.</p>
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</div>
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""")
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with gr.Row():
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with gr.Column():
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with gr.Tab("π― Robustness Results"):
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gr.DataFrame(
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value=formatted_robustness_df,
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label="Robustness Results",
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wrap=True,
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elem_classes=["custom-table"]
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)
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with gr.Tab("π§© Context Grounding Results"):
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gr.DataFrame(
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value=formatted_context_grounding_df,
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label="Context Grounding Results",
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wrap=True,
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elem_classes=["custom-table"]
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)
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with gr.Tab("π
Top 3 Winners"):
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gr.DataFrame(
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value=winners_df,
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label="Top 3 Models",
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wrap=True,
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elem_classes=["custom-table"]
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)
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with gr.Tab("π About FailSafe"):
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gr.HTML("""
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<div style="padding: 20px;">
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<h2>About This Leaderboard</h2>
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<p>This Financial Model Performance Leaderboard compares the performance of various AI models across robustness and context grounding metrics. The data is sourced from evaluations conducted on February 18, 2025, and reflects the models' ability to handle financial tasks under different conditions.</p>
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<p>For more information, contact us at <a href="mailto:support@writer.com">support@writer.com</a>.</p>
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</div>
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""")
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with gr.Row():
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