diff --git a/src/transformers/file_utils.py b/src/transformers/file_utils.py
index 51408df74158c5..b0d82a92b8489f 100644
--- a/src/transformers/file_utils.py
+++ b/src/transformers/file_utils.py
@@ -1619,7 +1619,8 @@ def _get_module(self, module_name: str) -> ModuleType:
 
 class AggregationStrategy(ExplicitEnum):
     """
-    Possible values for the ``aggregation_strategy`` argument in :meth:`TokenClassificationPipeline.__init__`. Useful for tab-completion in an IDE.
+    Possible values for the ``aggregation_strategy`` argument in :meth:`TokenClassificationPipeline.__init__`. Useful
+    for tab-completion in an IDE.
     """
 
     FIRST = "first"
diff --git a/src/transformers/pipelines/token_classification.py b/src/transformers/pipelines/token_classification.py
index 23a67f13bb1f3e..f4254dc6a808a0 100644
--- a/src/transformers/pipelines/token_classification.py
+++ b/src/transformers/pipelines/token_classification.py
@@ -2,7 +2,7 @@
 
 import numpy as np
 
-from ..file_utils import add_end_docstrings, is_tf_available, is_torch_available, AggregationStrategy
+from ..file_utils import AggregationStrategy, add_end_docstrings, is_tf_available, is_torch_available
 from ..modelcard import ModelCard
 from ..models.bert.tokenization_bert import BasicTokenizer
 from ..tokenization_utils import PreTrainedTokenizer
@@ -22,7 +22,7 @@
 
     from ..models.auto.modeling_auto import MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
 
-    
+
 class TokenClassificationArgumentHandler(ArgumentHandler):
     """
     Handles arguments for token classification.
@@ -191,23 +191,23 @@ def __call__(self, inputs: Union[str, List[str]], **kwargs):
                 entity = {}
                 if offset_mapping is not None:
                     start_ind, end_ind = offset_mapping[idx]
-                    entity['start'], entity['end'] = (start_ind, end_ind)
+                    entity["start"], entity["end"] = (start_ind, end_ind)
                     word_ref = sentence[start_ind:end_ind]
                     word = self.tokenizer.convert_ids_to_tokens([int(input_ids[idx])])[0]
-                    entity['word'] = word
-                    entity['is_subword'] = len(word_ref) != len(word)
+                    entity["word"] = word
+                    entity["is_subword"] = len(word_ref) != len(word)
 
                     if int(input_ids[idx]) == self.tokenizer.unk_token_id:
-                        entity['word'] = word_ref
-                        entity['is_subword'] = False
+                        entity["word"] = word_ref
+                        entity["is_subword"] = False
                 else:
-                    entity['word'] = self.tokenizer.convert_ids_to_tokens(int(input_ids[idx]))
+                    entity["word"] = self.tokenizer.convert_ids_to_tokens(int(input_ids[idx]))
 
-                    entity['start'] = None
-                    entity['end'] = None
+                    entity["start"] = None
+                    entity["end"] = None
 
-                entity['score'] = score[idx]
-                entity['index'] = idx
+                entity["score"] = score[idx]
+                entity["index"] = idx
 
                 entities += [entity]
 
diff --git a/tests/test_pipelines_ner.py b/tests/test_pipelines_ner.py
index f9025b540ee739..67dc6117014350 100644
--- a/tests/test_pipelines_ner.py
+++ b/tests/test_pipelines_ner.py
@@ -12,10 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import unittest
-
 import json
 import os
+import unittest
+
 import numpy as np
 
 from transformers import AutoTokenizer, pipeline
@@ -27,6 +27,7 @@
 
 VALID_INPUTS = ["A simple string", ["list of strings", "A simple string that is quite a bit longer"]]
 
+
 class NerPipelineTests(CustomInputPipelineCommonMixin, unittest.TestCase):
     pipeline_task = "ner"
     small_models = [
@@ -562,16 +563,15 @@ def _test_pipeline(self, nlp: Pipeline):
             ],
         ]
 
-        
         expected_aligned_results_filepath = os.path.join(
-            os.path.dirname(os.path.abspath(__file__)),
-            "fixtures/ner_pipeline_aligned.json")
+            os.path.dirname(os.path.abspath(__file__)), "fixtures/ner_pipeline_aligned.json"
+        )
         with open(expected_aligned_results_filepath) as expected_aligned_results_file:
             expected_aligned_results = json.load(expected_aligned_results_file)
 
         expected_aligned_results_w_subword_filepath = os.path.join(
-            os.path.dirname(os.path.abspath(__file__)),
-            "fixtures/ner_pipeline_aligned_w_subwords.json")
+            os.path.dirname(os.path.abspath(__file__)), "fixtures/ner_pipeline_aligned_w_subwords.json"
+        )
         with open(expected_aligned_results_w_subword_filepath) as expected_aligned_results_w_subword_file:
             expected_aligned_results_w_subword = json.load(expected_aligned_results_w_subword_file)