Update src/display/utils.py

src/display/utils.py

@@ -19,17 +19,17 @@ class ColumnContent:
 ## Leaderboard columns
 auto_eval_column_dict = []
 # Init
-auto_eval_column_dict.append(["rank", ColumnContent, ColumnContent("Rank", "str", True, False)])
-auto_eval_column_dict.append(["model_source", ColumnContent, ColumnContent("Source", "str", True, False)])
-auto_eval_column_dict.append(["model_category", ColumnContent, ColumnContent("Size", "str", True, False)])
-auto_eval_column_dict.append(["model", ColumnContent, ColumnContent("Model Name", "markdown", True, never_hidden=True)])
+auto_eval_column_dict.append(["rank", ColumnContent, field(default_factory=lambda: ColumnContent("Rank", "str", True, False))])
+auto_eval_column_dict.append(["model_source", ColumnContent, field(default_factory=lambda: ColumnContent("Source", "str", True, False))])
+auto_eval_column_dict.append(["model_category", ColumnContent, field(default_factory=lambda: ColumnContent("Size", "str", True, False))])
+auto_eval_column_dict.append(["model", ColumnContent, field(default_factory=lambda: ColumnContent("Model Name", "markdown", True, never_hidden=True))])
 #Scores
-auto_eval_column_dict.append(["average_score", ColumnContent, ColumnContent("Benchmark Score (0-10)", "number", True)])
+auto_eval_column_dict.append(["average_score", ColumnContent, field(default_factory=lambda: ColumnContent("Benchmark Score (0-10)", "number", True))])
 for eval_dim in EvalDimensions:
     if eval_dim.value.metric in ["speed", "contamination_score"]:
-         auto_eval_column_dict.append([eval_dim.name, ColumnContent, ColumnContent(eval_dim.value.col_name, "number", True)])
+         auto_eval_column_dict.append([eval_dim.name, ColumnContent, field(default_factory=lambda: ColumnContent(eval_dim.value.col_name, "number", True))])
     else:
-        auto_eval_column_dict.append([eval_dim.name, ColumnContent, ColumnContent(eval_dim.value.col_name, "number", False)])
+        auto_eval_column_dict.append([eval_dim.name, ColumnContent, field(default_factory=lambda: ColumnContent(eval_dim.value.col_name, "number", False))])
 
 
 # We use make dataclass to dynamically fill the scores from Tasks