Hotfix

src/display/utils.py

@@ -50,7 +50,7 @@ auto_eval_column_dict.append(["dummy", ColumnContent, ColumnContent("model_name_
 
 auto_eval_column_dict.append(["eval_name", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)])
 auto_eval_column_dict.append(["precision", ColumnContent, ColumnContent("Precision", "str", True)])
-auto_eval_column_dict.append(["hf_model_id", ColumnContent, ColumnContent("Model link (temporary)", "str", True)])
+auto_eval_column_dict.append(["hf_model_id", ColumnContent, ColumnContent("Model link (temporary)", "str", False)])
 auto_eval_column_dict.append(["agree_cs", ColumnContent, ColumnContent("AGREE", "number", True)])
 auto_eval_column_dict.append(["anli_cs", ColumnContent, ColumnContent("ANLI", "number", True)])
 auto_eval_column_dict.append(["arc_challenge_cs", ColumnContent, ColumnContent("ARC-Challenge", "number", True)])

src/populate.py

@@ -19,7 +19,7 @@ def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchm
     df.replace(r'\s+', np.nan, regex=True)
     # filter out if any of the benchmarks have not been produced
     df = df[has_no_nan_values(df, benchmark_cols)]
-    df['Model'] = df.apply(lambda row: model_hyperlink(row['hf_model_id'], row['Model']), axis=1)
+    df['Model'] = df.apply(lambda row: model_hyperlink(row['Model link (temporary)'], row['Model']), axis=1)
     
     return raw_data, df