Fiddling with util display

src/display/utils.py

@@ -20,28 +20,29 @@ class ColumnContent:
     hidden: bool = False
     never_hidden: bool = False
     dummy: bool = False
+    advanced: bool = False
 
 ## 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", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)])
+auto_eval_column_dict.append(["model_type_symbol", ColumnContent, ColumnContent(name="T", type="str", displayed_by_default=True, never_hidden=True)])
+auto_eval_column_dict.append(["model", ColumnContent, ColumnContent(name="Model", type="markdown", displayed_by_default=True, never_hidden=True)])
 #Scores
-auto_eval_column_dict.append(["average", ColumnContent, ColumnContent("Average ⬆️", "number", True)])
+auto_eval_column_dict.append(["average", ColumnContent, ColumnContent(name="Average ⬆️", type="number", displayed_by_default=True)])
 for task in Tasks:
-    auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)])
+    auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(name=task.value.col_name, type="number", displayed_by_default=True)])
 # Model information
-auto_eval_column_dict.append(["model_type", ColumnContent, ColumnContent("Type", "str", False, hidden=True)])
-auto_eval_column_dict.append(["architecture", ColumnContent, ColumnContent("Architecture", "str", False, hidden=True)])
-auto_eval_column_dict.append(["weight_type", ColumnContent, ColumnContent("Weight type", "str", False, True, hidden=True)])
-auto_eval_column_dict.append(["precision", ColumnContent, ColumnContent("Precision", "str", False, hidden=True)])
-auto_eval_column_dict.append(["license", ColumnContent, ColumnContent("Hub License", "str", False, hidden=True)])
-auto_eval_column_dict.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", False, hidden=True)])
-auto_eval_column_dict.append(["likes", ColumnContent, ColumnContent("Hub ❤️", "number", False, hidden=True)])
-auto_eval_column_dict.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False, hidden=True)])
-auto_eval_column_dict.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, False, hidden=True)])
+auto_eval_column_dict.append(["model_type", ColumnContent, ColumnContent(name="Type", type="str", displayed_by_default=False, advanced=True)])
+auto_eval_column_dict.append(["architecture", ColumnContent, ColumnContent("Architecture", "str", displayed_by_default=False, advanced=True)])
+auto_eval_column_dict.append(["weight_type", ColumnContent, ColumnContent("Weight type", "str", displayed_by_default=False, advanced=True)])
+auto_eval_column_dict.append(["precision", ColumnContent, ColumnContent("Precision", "str", displayed_by_default=False, advanced=True)])
+auto_eval_column_dict.append(["license", ColumnContent, ColumnContent("Hub License", "str", displayed_by_default=False, advanced=True)])
+auto_eval_column_dict.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", displayed_by_default=False, advanced=True)])
+auto_eval_column_dict.append(["likes", ColumnContent, ColumnContent("Hub ❤️", "number", displayed_by_default=False, advanced=True)])
+auto_eval_column_dict.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", displayed_by_default=False, advanced=True)])
+auto_eval_column_dict.append(["revision", ColumnContent, ColumnContent("Model sha", "str", displayed_by_default=False, advanced=True)])
 # Dummy column for the search bar (hidden by the custom CSS)
-auto_eval_column_dict.append(["dummy", ColumnContent, ColumnContent("model_name_for_query", "str", False, dummy=True)])
+auto_eval_column_dict.append(["dummy", ColumnContent, ColumnContent("model_name_for_query", "str", displayed_by_default=False, dummy=True)])
 
 # We use make dataclass to dynamically fill the scores from Tasks
 AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True)