fix comment

mmeval/metrics/one_minus_norm_edit_distance.py

@@ -21,17 +21,17 @@ class OneMinusNormEditDistance(BaseMetric):
 
             - unchanged: Do not change prediction texts and labels.
             - upper: Convert prediction texts and labels into uppercase
-                     characters.
+              characters.
             - lower: Convert prediction texts and labels into lowercase
-                     characters.
+              characters.
 
             Usually, it only works for English characters. Defaults to
             'unchanged'.
         invalid_symbol (str): A regular expression to filter out invalid or
-            not cared characters. Defaults to '[^A-Z^a-z^0-9^\u4e00-\u9fa5]'.
+            not cared characters. Defaults to '[^A-Za-z0-9\u4e00-\u9fa5]'.
         **kwargs: Keyword parameters passed to :class:`BaseMetric`.
 
-    Example:
+    Examples:
         >>> from mmeval import OneMinusNormEditDistance
         >>> metric = OneMinusNormEditDistance()
         >>> metric(['helL', 'HEL'], ['hello', 'HELLO'])
@@ -43,22 +43,22 @@ class OneMinusNormEditDistance(BaseMetric):
 
     def __init__(self,
                  letter_case: str = 'unchanged',
-                 invalid_symbol: str = '[^A-Z^a-z^0-9^\u4e00-\u9fa5]',
+                 invalid_symbol: str = '[^A-Za-z0-9\u4e00-\u9fa5]',
                  **kwargs):
         super().__init__(**kwargs)
 
         assert letter_case in ['unchanged', 'upper', 'lower']
         self.letter_case = letter_case
         self.invalid_symbol = re.compile(invalid_symbol)
 
-    def add(self, predictions: Sequence[str], labels: Sequence[str]):  # type: ignore # yapf: disable # noqa: E501
+    def add(self, predictions: Sequence[str], groundtruths: Sequence[str]):  # 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):
             if self.letter_case in ['upper', 'lower']:
                 pred = getattr(pred, self.letter_case)()
                 label = getattr(label, self.letter_case)()
@@ -75,11 +75,12 @@ def compute_metric(self, results: List[float]) -> Dict:
 
         Returns:
             dict[str, float]: Nested dicts as results.
-              - 1-N.E.D (float): One minus the normalized edit distance.
+
+            - 1-N.E.D (float): One minus the normalized edit distance.
         """
         gt_word_num = len(results)
         norm_ed_sum = sum(results)
         normalized_edit_distance = norm_ed_sum / max(1.0, gt_word_num)
-        eval_res = {}
-        eval_res['1-N.E.D'] = 1.0 - normalized_edit_distance
-        return eval_res
+        metric_results = {}
+        metric_results['1-N.E.D'] = 1.0 - normalized_edit_distance
+        return metric_results