Update app.py
Browse files
app.py
CHANGED
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@@ -27,7 +27,7 @@ def create_option(value, label):
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COLUMN_GROUPS = {
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"uncensored_ugi_cats": ["Hazardous", "Entertainment", "SocPol"],
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"w10_sub_scores": ["W/10-Direct", "W/10-Adherence"],
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"natint_sub_scores": ["
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"writing_repetition_group": [
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"lexical_stuckness", "originality_score", "internal_semantic_redundancy"
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],
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@@ -803,7 +803,7 @@ MASTER_COLUMN_ORDER = [
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"W/10 π", "W/10-Direct", "W/10-Adherence",
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# Intelligence
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"NatInt π‘",
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"
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'wm_recipe_percent_error', 'wm_geoguesser_mae', 'wm_weight_percent_error',
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'wm_music_mae',
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"Show Rec Score", # Main Score
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@@ -855,7 +855,7 @@ ALL_COLUMN_DEFS = {
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"W/10-Adherence": create_numeric_column("W/10-Adherence", width=120, filterParams={"defaultOption": "greaterThanOrEqual"}),
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# --- NatInt Categories ---
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"
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"Pop Culture": create_numeric_column("Pop Culture", width=120, filterParams={"defaultOption": "greaterThanOrEqual"}),
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"World Model": create_numeric_column("World Model", width=120, filterParams={"defaultOption": "greaterThanOrEqual"}),
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'wm_recipe_percent_error': create_numeric_column('wm_recipe_percent_error', headerName="Cooking (% Error)", width=120, cellClass="border-left", filterParams={"defaultOption": "lessThanOrEqual"}, sortingOrder=['asc', 'desc']),
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@@ -1189,7 +1189,7 @@ app.layout = html.Div([
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html.Details([
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html.Summary("Intelligence Metrics", style={'fontWeight': 'normal', 'fontSize': '1em', 'marginLeft': '20px', 'cursor': 'pointer'}),
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html.Ul([
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html.Li([html.Strong("
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html.Li([html.Strong("Pop Culture:"), " Knowledge of specific details from things like video games, movies, music, and internet culture."]),
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html.Li([html.Strong("World Model:"), " Tasks that test a model's understanding of real-world properties and patterns."]),
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html.Ul([
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COLUMN_GROUPS = {
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"uncensored_ugi_cats": ["Hazardous", "Entertainment", "SocPol"],
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"w10_sub_scores": ["W/10-Direct", "W/10-Adherence"],
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+
"natint_sub_scores": ["Textbook", "Pop Culture", "World Model"],
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"writing_repetition_group": [
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"lexical_stuckness", "originality_score", "internal_semantic_redundancy"
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],
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"W/10 π", "W/10-Direct", "W/10-Adherence",
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# Intelligence
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"NatInt π‘",
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"Textbook", "Pop Culture", "World Model",
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'wm_recipe_percent_error', 'wm_geoguesser_mae', 'wm_weight_percent_error',
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'wm_music_mae',
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"Show Rec Score", # Main Score
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"W/10-Adherence": create_numeric_column("W/10-Adherence", width=120, filterParams={"defaultOption": "greaterThanOrEqual"}),
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# --- NatInt Categories ---
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"Textbook": create_numeric_column("Textbook", width=120, cellClass="border-left", filterParams={"defaultOption": "greaterThanOrEqual"}),
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"Pop Culture": create_numeric_column("Pop Culture", width=120, filterParams={"defaultOption": "greaterThanOrEqual"}),
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"World Model": create_numeric_column("World Model", width=120, filterParams={"defaultOption": "greaterThanOrEqual"}),
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'wm_recipe_percent_error': create_numeric_column('wm_recipe_percent_error', headerName="Cooking (% Error)", width=120, cellClass="border-left", filterParams={"defaultOption": "lessThanOrEqual"}, sortingOrder=['asc', 'desc']),
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html.Details([
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html.Summary("Intelligence Metrics", style={'fontWeight': 'normal', 'fontSize': '1em', 'marginLeft': '20px', 'cursor': 'pointer'}),
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html.Ul([
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html.Li([html.Strong("Textbook:"), " Measures knowledge of standard, factual information like history, statistics, math, and logic."]),
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html.Li([html.Strong("Pop Culture:"), " Knowledge of specific details from things like video games, movies, music, and internet culture."]),
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html.Li([html.Strong("World Model:"), " Tasks that test a model's understanding of real-world properties and patterns."]),
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html.Ul([
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