fix

src/display/utils.py

@@ -32,15 +32,10 @@ for task in Tasks:
     auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)])
 # Model information
 auto_eval_column_dict.append(["model_type", ColumnContent, ColumnContent("Type", "str", False)])
-#auto_eval_column_dict.append(["architecture", ColumnContent, ColumnContent("Architecture", "str", False)])
 auto_eval_column_dict.append(["weight_type", ColumnContent, ColumnContent("Weight type", "str", False, True)])
 auto_eval_column_dict.append(["precision", ColumnContent, ColumnContent("Precision", "str", False)])
-#auto_eval_column_dict.append(["license", ColumnContent, ColumnContent("Hub License", "str", False)])
 auto_eval_column_dict.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", False)])
-#auto_eval_column_dict.append(["likes", ColumnContent, ColumnContent("Hub ❤️", "number", False)])
-#auto_eval_column_dict.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False)])
 auto_eval_column_dict.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, False)])
-#auto_eval_column_dict.append(["merged", ColumnContent, ColumnContent("Merged", "bool", False)])
 auto_eval_column_dict.append(["flagged", ColumnContent, ColumnContent("Flagged", "bool", False, hidden=True)])
 auto_eval_column_dict.append(["moe", ColumnContent, ColumnContent("MoE", "bool", False, hidden=True)])
 # Dummy column for the search bar (hidden by the custom CSS)

src/populate.py

@@ -4,7 +4,7 @@ import os
 import pandas as pd
 
 from src.display.formatting import has_no_nan_values, make_clickable_model
-from src.display.utils import AutoEvalColumn, EvalQueueColumn, baseline_row
+from src.display.utils import AutoEvalColumn, EvalQueueColumn  #, baseline_row
 from src.leaderboard.read_evals import get_raw_eval_results
 from src.leaderboard.filter_models import filter_models_flags
 
@@ -13,7 +13,7 @@ def get_leaderboard_df(results_path: str, requests_path: str, dynamic_path: str,
     """Creates a dataframe from all the individual experiment results"""
     raw_data = get_raw_eval_results(results_path, requests_path, dynamic_path)
     all_data_json = [v.to_dict() for v in raw_data]
-    all_data_json.append(baseline_row)
+    #all_data_json.append(baseline_row)
     filter_models_flags(all_data_json)
 
     df = pd.DataFrame.from_records(all_data_json)