diff --git a/bert_score/score.py b/bert_score/score.py
index 018e73e..2c2abd7 100644
--- a/bert_score/score.py
+++ b/bert_score/score.py
@@ -168,15 +168,22 @@ def score(
         print(f"done in {time_diff:.2f} seconds, {len(refs) / time_diff:.2f} sentences/sec")
 
     if return_hash:
-        return tuple([out, get_hash(model_type, num_layers, idf, rescale_with_baseline,
-                                    use_custom_baseline=use_custom_baseline)])
+        return tuple(
+            [out, get_hash(model_type, num_layers, idf, rescale_with_baseline, use_custom_baseline=use_custom_baseline)]
+        )
 
     return out
 
 
 def plot_example(
-    candidate, reference, model_type=None, num_layers=None, lang=None, rescale_with_baseline=False,
-    baseline_path=None, fname="",
+    candidate,
+    reference,
+    model_type=None,
+    num_layers=None,
+    lang=None,
+    rescale_with_baseline=False,
+    baseline_path=None,
+    fname="",
 ):
     """
     BERTScore metric.
diff --git a/bert_score/scorer.py b/bert_score/scorer.py
index 057976e..18e6c7f 100644
--- a/bert_score/scorer.py
+++ b/bert_score/scorer.py
@@ -112,7 +112,9 @@ def __init__(
         self.baseline_path = baseline_path
         self.use_custom_baseline = self.baseline_path is not None
         if self.baseline_path is None:
-            self.baseline_path = os.path.join(os.path.dirname(__file__), f"rescale_baseline/{self.lang}/{self.model_type}.tsv")
+            self.baseline_path = os.path.join(
+                os.path.dirname(__file__), f"rescale_baseline/{self.lang}/{self.model_type}.tsv"
+            )
 
     @property
     def lang(self):
@@ -143,18 +145,19 @@ def baseline_vals(self):
                         pd.read_csv(self.baseline_path).iloc[self.num_layers].to_numpy()
                     )[1:].float()
                 else:
-                    self._baseline_vals = torch.from_numpy(
-                        pd.read_csv(self.baseline_path).to_numpy()
-                    )[:, 1:].unsqueeze(1).float()
+                    self._baseline_vals = (
+                        torch.from_numpy(pd.read_csv(self.baseline_path).to_numpy())[:, 1:].unsqueeze(1).float()
+                    )
             else:
-                raise ValueError(
-                    f"Baseline not Found for {self.model_type} on {self.lang} at {self.baseline_path}")
+                raise ValueError(f"Baseline not Found for {self.model_type} on {self.lang} at {self.baseline_path}")
 
         return self._baseline_vals
 
     @property
     def hash(self):
-        return get_hash(self.model_type, self.num_layers, self.idf, self.rescale_with_baseline, self.use_custom_baseline)
+        return get_hash(
+            self.model_type, self.num_layers, self.idf, self.rescale_with_baseline, self.use_custom_baseline
+        )
 
     def compute_idf(self, sents):
         """
diff --git a/bert_score/utils.py b/bert_score/utils.py
index 632ec5a..649ebeb 100644
--- a/bert_score/utils.py
+++ b/bert_score/utils.py
@@ -106,12 +106,16 @@ def sent_encode(tokenizer, sent):
     elif isinstance(tokenizer, GPT2Tokenizer):
         # for RoBERTa and GPT-2
         import transformers
+
         if LooseVersion(transformers.__version__) >= LooseVersion("3.0.0"):
-            return tokenizer.encode(sent, add_special_tokens=True, add_prefix_space=True, max_length=tokenizer.max_len, truncation=True)
+            return tokenizer.encode(
+                sent, add_special_tokens=True, add_prefix_space=True, max_length=tokenizer.max_len, truncation=True
+            )
         else:
             return tokenizer.encode(sent, add_special_tokens=True, add_prefix_space=True, max_length=tokenizer.max_len)
     else:
         import transformers
+
         if LooseVersion(transformers.__version__) >= LooseVersion("3.0.0"):
             return tokenizer.encode(sent, add_special_tokens=True, max_length=tokenizer.max_len, truncation=True)
         else: