fix comment

mmeval/metrics/word_accuracy.py

@@ -23,7 +23,7 @@ class WordAccuracy(BaseMetric):
             not cared characters. Defaults to '[^A-Za-z0-9\u4e00-\u9fa5]'
         **kwargs: Keyword parameters passed to :class:`BaseMetric`.
 
-    Example:
+    Examples:
         >>> from mmeval import WordAccuracy
         >>> metric = WordAccuracy()
         >>> metric(['hello', 'hello', 'hello'], ['hello', 'HELLO', '$HELLO$'])
@@ -46,19 +46,19 @@ def __init__(self,
         assert isinstance(mode, (str, list))
         if isinstance(mode, str):
             mode = [mode]
-        assert all([isinstance(item, str) for item in mode])
-        assert set(mode).issubset(
-            {'exact', 'ignore_case', 'ignore_case_symbol'})
+        assert all(isinstance(item, str) for item in mode)
         self.mode = set(mode)  # type: ignore
+        assert set(self.mode).issubset(
+            {'exact', 'ignore_case', 'ignore_case_symbol'})
 
-    def add(self, predictions: Sequence[str], labels: Sequence[str]) -> None:  # type: ignore # yapf: disable # noqa: E501
+    def add(self, predictions: Sequence[str], groundtruths: Sequence[str]) -> None:  # type: ignore # yapf: disable # noqa: E501
         """Process one batch of data and predictions.
 
         Args:
             predictions (list[str]): The prediction texts.
-            labels (list[str]): The ground truth texts.
+            groundtruths (list[str]): The ground truth texts.
         """
-        for pred, label in zip(predictions, labels):
+        for pred, label in zip(predictions, groundtruths):
             num, ignore_case_num, ignore_case_symbol_num = 0, 0, 0
             if 'exact' in self.mode:
                 num = pred == label
@@ -85,22 +85,23 @@ def compute_metric(self, results: List[Tuple[int, int, int]]) -> Dict:
 
             - accuracy (float): Accuracy at word level.
             - ignore_case_accuracy (float): Accuracy at word level, ignoring
-                letter case.
+              letter case.
             - ignore_case_symbol_accuracy (float): Accuracy at word level,
-                ignoring letter case and symbol.
+              ignoring letter case and symbol.
         """
-        eval_res = {}
+        metric_results = {}
         gt_word_num = max(len(results), 1.0)
         exact_sum, ignore_case_sum, ignore_case_symbol_sum = 0.0, 0.0, 0.0
         for exact, ignore_case, ignore_case_symbol in results:
             exact_sum += exact
             ignore_case_sum += ignore_case
             ignore_case_symbol_sum += ignore_case_symbol
         if 'exact' in self.mode:
-            eval_res['accuracy'] = exact_sum / gt_word_num
+            metric_results['accuracy'] = exact_sum / gt_word_num
         if 'ignore_case' in self.mode:
-            eval_res['ignore_case_accuracy'] = ignore_case_sum / gt_word_num
+            metric_results[
+                'ignore_case_accuracy'] = ignore_case_sum / gt_word_num
         if 'ignore_case_symbol' in self.mode:
-            eval_res['ignore_case_symbol_accuracy'] =\
+            metric_results['ignore_case_symbol_accuracy'] =\
                 ignore_case_symbol_sum / gt_word_num
-        return eval_res
+        return metric_results