keep only relevant params

app.py

@@ -85,8 +85,8 @@ def update_table(
     show_deleted: bool,
     query: str,
 ):
-    filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted)
-    filtered_df = filter_queries(query, filtered_df)
+    # filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted)
+    filtered_df = filter_queries(query, hidden_df)
     df = select_columns(filtered_df, columns)
     return df
 
@@ -97,7 +97,7 @@ def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:
 
 def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
     always_here_cols = [
-        AutoEvalColumn.model_type_symbol.name,
+    #     AutoEvalColumn.model_type_symbol.name,
         AutoEvalColumn.model.name,
     ]
     # We use COLS to maintain sorting
@@ -138,8 +138,8 @@ def filter_models(
     filtered_df = df
 
     type_emoji = [t[0] for t in type_query]
-    filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
-    filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])]
+    # filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
+    # filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])]
 
     numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
     params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce")

src/display/utils.py

@@ -25,7 +25,7 @@ class ColumnContent:
 ## Leaderboard columns
 auto_eval_column_dict = []
 # Init
-auto_eval_column_dict.append(["model_type_symbol", ColumnContent, ColumnContent("T", "str", True, never_hidden=True)])
+auto_eval_column_dict.append(["model_type_symbol", ColumnContent, ColumnContent("T", "str", False, hidden=True)])
 auto_eval_column_dict.append(["model", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)])
 #Scores
 # auto_eval_column_dict.append(["average", ColumnContent, ColumnContent("Average ⬆️", "number", True)])
@@ -35,9 +35,9 @@ for task in Tasks:
 auto_eval_column_dict.append(["model_type", ColumnContent, ColumnContent("Type", "str", False, True)])
 auto_eval_column_dict.append(["architecture", ColumnContent, ColumnContent("Architecture", "str", False, True)])
 auto_eval_column_dict.append(["weight_type", ColumnContent, ColumnContent("Weight type", "str", False, True)])
-auto_eval_column_dict.append(["precision", ColumnContent, ColumnContent("Precision", "str", True)])
+auto_eval_column_dict.append(["precision", ColumnContent, ColumnContent("Precision", "str", False, True)])
 auto_eval_column_dict.append(["license", ColumnContent, ColumnContent("Hub License", "str", False, True)])
-auto_eval_column_dict.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", True)])
+auto_eval_column_dict.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", False, True)])
 auto_eval_column_dict.append(["likes", ColumnContent, ColumnContent("Hub ❤️", "number", False, True)])
 auto_eval_column_dict.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False, True)])
 auto_eval_column_dict.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, True)])