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Running
on
CPU Upgrade
Update app.py
Browse files
app.py
CHANGED
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@@ -23,6 +23,7 @@ from src.display.utils import (
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WeightType,
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Precision,
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AddSpecialTokens,
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NUMERIC_INTERVALS,
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TYPES,
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)
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@@ -69,12 +70,13 @@ def update_table(
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precision_query: str,
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size_query: list,
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add_special_tokens_query: list,
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show_deleted: bool,
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show_merges: bool,
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show_flagged: bool,
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query: str,
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):
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filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, add_special_tokens_query, show_deleted, show_merges, show_flagged)
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filtered_df = filter_queries(query, filtered_df)
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df = select_columns(filtered_df, columns)
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return df
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@@ -122,7 +124,7 @@ def filter_queries(query: str, filtered_df: pd.DataFrame):
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def filter_models(
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df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, add_special_tokens_query: list, show_deleted: bool, show_merges: bool, show_flagged: bool
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) -> pd.DataFrame:
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# Show all models
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if show_deleted:
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@@ -140,6 +142,7 @@ def filter_models(
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filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
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filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])]
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filtered_df = filtered_df.loc[df[AutoEvalColumn.add_special_tokens.name].isin(add_special_tokens_query)]
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numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
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@@ -148,7 +151,7 @@ def filter_models(
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filtered_df = filtered_df.loc[mask]
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return filtered_df
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leaderboard_df = filter_models(leaderboard_df, [t.to_str(" : ") for t in ModelType], list(NUMERIC_INTERVALS.keys()), [i.value.name for i in Precision], [i.value.name for i in AddSpecialTokens], False, False, False)
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demo = gr.Blocks(css=custom_css)
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with demo:
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@@ -221,6 +224,13 @@ with demo:
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interactive=True,
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elem_id="filter-columns-add-special-tokens",
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)
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leaderboard_table = gr.components.Dataframe(
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value=leaderboard_df[
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@@ -252,6 +262,7 @@ with demo:
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filter_columns_precision,
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filter_columns_size,
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filter_columns_add_special_tokens,
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deleted_models_visibility,
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merged_models_visibility,
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flagged_models_visibility,
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@@ -271,6 +282,7 @@ with demo:
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filter_columns_precision,
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filter_columns_size,
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filter_columns_add_special_tokens,
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deleted_models_visibility,
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merged_models_visibility,
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flagged_models_visibility,
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@@ -281,7 +293,7 @@ with demo:
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# Check query parameter once at startup and update search bar + hidden component
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demo.load(load_query, inputs=[], outputs=[search_bar, hidden_search_bar])
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for selector in [shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size, filter_columns_add_special_tokens, deleted_models_visibility, merged_models_visibility, flagged_models_visibility]:
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selector.change(
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update_table,
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[
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@@ -291,6 +303,7 @@ with demo:
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filter_columns_precision,
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filter_columns_size,
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filter_columns_add_special_tokens,
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deleted_models_visibility,
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merged_models_visibility,
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flagged_models_visibility,
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WeightType,
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Precision,
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AddSpecialTokens,
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NumFewShots,
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NUMERIC_INTERVALS,
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TYPES,
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)
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precision_query: str,
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size_query: list,
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add_special_tokens_query: list,
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num_few_shots_query: list,
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show_deleted: bool,
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show_merges: bool,
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show_flagged: bool,
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query: str,
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):
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filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, add_special_tokens_query, num_few_shots_query, show_deleted, show_merges, show_flagged)
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filtered_df = filter_queries(query, filtered_df)
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df = select_columns(filtered_df, columns)
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return df
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def filter_models(
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df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, add_special_tokens_query: list, num_few_shots_query: list, show_deleted: bool, show_merges: bool, show_flagged: bool
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) -> pd.DataFrame:
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# Show all models
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if show_deleted:
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filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
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filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])]
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filtered_df = filtered_df.loc[df[AutoEvalColumn.add_special_tokens.name].isin(add_special_tokens_query)]
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filtered_df = filtered_df.loc[df[AutoEvalColumn.num_few_shots.name].isin(num_few_shots_query)]
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numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
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filtered_df = filtered_df.loc[mask]
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return filtered_df
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leaderboard_df = filter_models(leaderboard_df, [t.to_str(" : ") for t in ModelType], list(NUMERIC_INTERVALS.keys()), [i.value.name for i in Precision], [i.value.name for i in AddSpecialTokens], [i.value.name for i in NumFewShots], False, False, False)
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demo = gr.Blocks(css=custom_css)
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with demo:
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interactive=True,
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elem_id="filter-columns-add-special-tokens",
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)
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filter_columns_num_few_shots = gr.CheckboxGroup(
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label="Num Few Shots",
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choices=[i.value.name for i in NumFewShots],
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value=[i.value.name for i in NumFewShots],
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interactive=True,
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elem_id="filter-columns-num-few-shots",
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)
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leaderboard_table = gr.components.Dataframe(
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value=leaderboard_df[
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filter_columns_precision,
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filter_columns_size,
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filter_columns_add_special_tokens,
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filter_columns_num_few_shots,
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deleted_models_visibility,
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merged_models_visibility,
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flagged_models_visibility,
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filter_columns_precision,
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filter_columns_size,
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filter_columns_add_special_tokens,
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filter_columns_num_few_shots,
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deleted_models_visibility,
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merged_models_visibility,
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flagged_models_visibility,
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# Check query parameter once at startup and update search bar + hidden component
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demo.load(load_query, inputs=[], outputs=[search_bar, hidden_search_bar])
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for selector in [shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size, filter_columns_add_special_tokens, filter_columns_num_few_shots, deleted_models_visibility, merged_models_visibility, flagged_models_visibility]:
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selector.change(
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update_table,
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[
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filter_columns_precision,
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filter_columns_size,
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filter_columns_add_special_tokens,
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filter_columns_num_few_shots,
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deleted_models_visibility,
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merged_models_visibility,
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flagged_models_visibility,
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