Apply formatters to src/populate.py

src/populate.py

@@ -13,29 +13,32 @@ def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchm
     raw_data = get_raw_eval_results(results_path, requests_path)
     all_data_json = [v.to_dict() for v in raw_data]
 
-
-
     df = pd.DataFrame.from_records(all_data_json)
-    
+
     score_cols = [
-        'ALT E to J BLEU', 'ALT J to E BLEU', 'WikiCorpus E to J BLEU', 'WikiCorpus J to E BLEU', 
-        'XL-Sum JA BLEU', 'XL-Sum ROUGE1', 'XL-Sum ROUGE2', 'XL-Sum ROUGE-Lsum'
+        "ALT E to J BLEU",
+        "ALT J to E BLEU",
+        "WikiCorpus E to J BLEU",
+        "WikiCorpus J to E BLEU",
+        "XL-Sum JA BLEU",
+        "XL-Sum ROUGE1",
+        "XL-Sum ROUGE2",
+        "XL-Sum ROUGE-Lsum",
     ]
-    
+
     existing_score_cols = [col for col in score_cols if col in df.columns]
-    
+
     # スコア列を100で割り、.4f形式でフォーマット
-    df[existing_score_cols] = (df[existing_score_cols] / 100).applymap(lambda x: f'{x:.4f}')
+    df[existing_score_cols] = (df[existing_score_cols] / 100).applymap(lambda x: f"{x:.4f}")
     df = df.sort_values(by=[AutoEvalColumn.AVG.name], ascending=False)
     df = df[cols].round(decimals=2)
-    
+
     # filter out if any of the benchmarks have not been produced
     df = df[has_no_nan_values(df, benchmark_cols)]
 
-    df['Model'] = df['Model'].apply(lambda x: f'[{x.split("/")[-1]}]({x})' if isinstance(x, str) else x)
-    
-    return df
+    df["Model"] = df["Model"].apply(lambda x: f'[{x.split("/")[-1]}]({x})' if isinstance(x, str) else x)
 
+    return df
 
 
 def get_evaluation_queue_df(save_path: str, cols: list) -> list[pd.DataFrame]: