Update app.py

app.py

@@ -68,12 +68,13 @@ def update_table(
     type_query: list,
     precision_query: str,
     size_query: list,
+    add_special_tokens_query: list,
     show_deleted: bool,
     show_merges: bool,
     show_flagged: bool,
     query: str,
 ):
-    filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted, show_merges, show_flagged)
+    filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, add_special_tokens_query, show_deleted, show_merges, show_flagged)
     filtered_df = filter_queries(query, filtered_df)
     df = select_columns(filtered_df, columns)
     return df
@@ -121,7 +122,7 @@ def filter_queries(query: str, filtered_df: pd.DataFrame):
 
 
 def filter_models(
-    df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, show_deleted: bool, show_merges: bool, show_flagged: bool
+    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
 ) -> pd.DataFrame:
     # Show all models
     if show_deleted:
@@ -138,6 +139,8 @@ def filter_models(
     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.add_special_tokens.name].isin(add_special_tokens_query)]
+    
 
     numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
     params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce")