diff --git a/spacy/cli/__init__.py b/spacy/cli/__init__.py
index 8aea6ef45..4c3adc5d3 100644
--- a/spacy/cli/__init__.py
+++ b/spacy/cli/__init__.py
@@ -21,8 +21,8 @@ from .project.clone import project_clone  # noqa: F401
 from .project.assets import project_assets  # noqa: F401
 from .project.run import project_run  # noqa: F401
 from .project.dvc import project_update_dvc  # noqa: F401
-from .project.push import project_push # noqa: F401
-from .project.pull import project_pull # noqa: F401
+from .project.push import project_push  # noqa: F401
+from .project.pull import project_pull  # noqa: F401
 
 
 @app.command("link", no_args_is_help=True, deprecated=True, hidden=True)
diff --git a/spacy/cli/train.py b/spacy/cli/train.py
index 1366e0b38..15188cd4e 100644
--- a/spacy/cli/train.py
+++ b/spacy/cli/train.py
@@ -75,7 +75,9 @@ def train(
         msg.info("Using CPU")
     msg.info(f"Loading config and nlp from: {config_path}")
     with show_validation_error(config_path):
-        config = util.load_config(config_path, overrides=config_overrides, interpolate=True)
+        config = util.load_config(
+            config_path, overrides=config_overrides, interpolate=True
+        )
     if config.get("training", {}).get("seed") is not None:
         fix_random_seed(config["training"]["seed"])
     # Use original config here before it's resolved to functions
@@ -208,7 +210,9 @@ def create_evaluation_callback(
         scores = nlp.evaluate(dev_examples)
         # Calculate a weighted sum based on score_weights for the main score
         try:
-            weighted_score = sum(scores.get(s, 0.0) * weights.get(s, 0.0) for s in weights)
+            weighted_score = sum(
+                scores.get(s, 0.0) * weights.get(s, 0.0) for s in weights
+            )
         except KeyError as e:
             keys = list(scores.keys())
             err = Errors.E983.format(dict="score_weights", key=str(e), keys=keys)
@@ -378,7 +382,8 @@ def setup_printer(
 
         try:
             scores = [
-                "{0:.2f}".format(float(info["other_scores"].get(col, 0.0))) for col in score_cols
+                "{0:.2f}".format(float(info["other_scores"].get(col, 0.0)))
+                for col in score_cols
             ]
         except KeyError as e:
             raise KeyError(
diff --git a/spacy/language.py b/spacy/language.py
index 6fc780f3e..57abcca0e 100644
--- a/spacy/language.py
+++ b/spacy/language.py
@@ -774,7 +774,13 @@ class Language:
             # we have no components to insert before/after, or we're replacing the last component
             self.add_pipe(factory_name, name=name, config=config, validate=validate)
         else:
-            self.add_pipe(factory_name, name=name, before=pipe_index, config=config, validate=validate)
+            self.add_pipe(
+                factory_name,
+                name=name,
+                before=pipe_index,
+                config=config,
+                validate=validate,
+            )
 
     def rename_pipe(self, old_name: str, new_name: str) -> None:
         """Rename a pipeline component.
diff --git a/spacy/ml/models/entity_linker.py b/spacy/ml/models/entity_linker.py
index 55d8614e1..6792f3e59 100644
--- a/spacy/ml/models/entity_linker.py
+++ b/spacy/ml/models/entity_linker.py
@@ -30,6 +30,7 @@ def load_kb(kb_path: str) -> Callable[[Vocab], KnowledgeBase]:
         kb = KnowledgeBase(vocab, entity_vector_length=1)
         kb.from_disk(kb_path)
         return kb
+
     return kb_from_file
 
 
@@ -37,6 +38,7 @@ def load_kb(kb_path: str) -> Callable[[Vocab], KnowledgeBase]:
 def empty_kb(entity_vector_length: int) -> Callable[[Vocab], KnowledgeBase]:
     def empty_kb_factory(vocab):
         return KnowledgeBase(vocab=vocab, entity_vector_length=entity_vector_length)
+
     return empty_kb_factory
 
 
diff --git a/spacy/tests/regression/test_issue5230.py b/spacy/tests/regression/test_issue5230.py
index 58d03ca8b..2ac886625 100644
--- a/spacy/tests/regression/test_issue5230.py
+++ b/spacy/tests/regression/test_issue5230.py
@@ -76,6 +76,7 @@ def entity_linker():
             kb = KnowledgeBase(vocab, entity_vector_length=1)
             kb.add_entity("test", 0.0, zeros((1, 1), dtype="f"))
             return kb
+
         return create_kb
 
     config = {"kb_loader": {"@assets": "TestIssue5230KB.v1"}}