diff --git a/textattack/metrics/quality_metrics/bert_score.py b/textattack/metrics/quality_metrics/bert_score.py
index d8dd5b740..e4f9e7947 100644
--- a/textattack/metrics/quality_metrics/bert_score.py
+++ b/textattack/metrics/quality_metrics/bert_score.py
@@ -13,7 +13,9 @@
 
 class BERTScoreMetric(Metric):
     def __init__(self, **kwargs):
-        self.use_obj = BERTScore(min_bert_score=0.5, model_name="microsoft/deberta-large-mnli",  num_layers=18)
+        self.use_obj = BERTScore(
+            min_bert_score=0.5, model_name="microsoft/deberta-large-mnli", num_layers=18
+        )
         self.original_candidates = []
         self.successful_candidates = []
         self.all_metrics = {}
diff --git a/textattack/metrics/quality_metrics/meteor_score.py b/textattack/metrics/quality_metrics/meteor_score.py
index fea0153c8..ffb92f0c8 100644
--- a/textattack/metrics/quality_metrics/meteor_score.py
+++ b/textattack/metrics/quality_metrics/meteor_score.py
@@ -6,8 +6,9 @@
 
 """
 
-from textattack.attack_results import FailedAttackResult, SkippedAttackResult
 import nltk
+
+from textattack.attack_results import FailedAttackResult, SkippedAttackResult
 from textattack.metrics import Metric
 
 
@@ -54,13 +55,20 @@ def calculate(self, results):
             elif isinstance(result, SkippedAttackResult):
                 continue
             else:
-                self.original_candidates.append(result.original_result.attacked_text.text)
-                self.successful_candidates.append(result.perturbed_result.attacked_text.text)
+                self.original_candidates.append(
+                    result.original_result.attacked_text.text
+                )
+                self.successful_candidates.append(
+                    result.perturbed_result.attacked_text.text
+                )
 
         meteor_scores = []
         for c in range(len(self.original_candidates)):
             meteor_scores.append(
-                nltk.translate.meteor([nltk.word_tokenize(self.original_candidates[c])], nltk.word_tokenize(self.successful_candidates[c]))
+                nltk.translate.meteor(
+                    [nltk.word_tokenize(self.original_candidates[c])],
+                    nltk.word_tokenize(self.successful_candidates[c]),
+                )
             )
 
         self.all_metrics["avg_attack_meteor_score"] = round(
diff --git a/textattack/metrics/quality_metrics/sentence_bert.py b/textattack/metrics/quality_metrics/sentence_bert.py
index 7bb157e26..f96660af6 100644
--- a/textattack/metrics/quality_metrics/sentence_bert.py
+++ b/textattack/metrics/quality_metrics/sentence_bert.py
@@ -13,13 +13,14 @@
 
 class SBERTMetric(Metric):
     def __init__(self, **kwargs):
-        self.use_obj = BERT(model_name="all-MiniLM-L6-v2",  metric="cosine")
+        self.use_obj = BERT(model_name="all-MiniLM-L6-v2", metric="cosine")
         self.original_candidates = []
         self.successful_candidates = []
         self.all_metrics = {}
 
     def calculate(self, results):
-        """Calculates average Sentence BERT similarity on all successfull attacks.
+        """Calculates average Sentence BERT similarity on all successfull
+        attacks.
 
         Args:
             results (``AttackResult`` objects):