diff --git a/spacy/ml/models/tok2vec.py b/spacy/ml/models/tok2vec.py
index 65d2bffbb..61edb86c4 100644
--- a/spacy/ml/models/tok2vec.py
+++ b/spacy/ml/models/tok2vec.py
@@ -1,4 +1,4 @@
-from typing import Optional, List, Union, Dict
+from typing import Optional, List, Union
 from thinc.types import Floats2d
 from thinc.api import chain, clone, concatenate, with_array, with_padded
 from thinc.api import Model, noop, list2ragged, ragged2list, HashEmbed
@@ -11,7 +11,7 @@ from ...ml import _character_embed
 from ..staticvectors import StaticVectors
 from ..featureextractor import FeatureExtractor
 from ...pipeline.tok2vec import Tok2VecListener
-from ...attrs import ORTH, NORM, LOWER, PREFIX, SUFFIX, SHAPE, intify_attr
+from ...attrs import intify_attr
 
 
 @registry.architectures.register("spacy.Tok2VecListener.v1")
@@ -29,7 +29,7 @@ def build_hash_embed_cnn_tok2vec(
     window_size: int,
     maxout_pieces: int,
     subword_features: bool,
-    pretrained_vectors: Optional[bool]
+    pretrained_vectors: Optional[bool],
 ) -> Model[List[Doc], List[Floats2d]]:
     """Build spaCy's 'standard' tok2vec layer, which uses hash embedding
     with subword features and a CNN with layer-normalized maxout.
@@ -56,7 +56,7 @@ def build_hash_embed_cnn_tok2vec(
     """
     if subword_features:
         attrs = ["NORM", "PREFIX", "SUFFIX", "SHAPE"]
-        row_sizes = [embed_size, embed_size//2, embed_size//2, embed_size//2]
+        row_sizes = [embed_size, embed_size // 2, embed_size // 2, embed_size // 2]
     else:
         attrs = ["NORM"]
         row_sizes = [embed_size]
@@ -120,7 +120,7 @@ def MultiHashEmbed(
     layer is used to map the vectors to the specified width before concatenating
     it with the other embedding outputs. A single Maxout layer is then used to
     reduce the concatenated vectors to the final width.
-    
+
     The `rows` parameter controls the number of rows used by the `HashEmbed`
     tables. The HashEmbed layer needs surprisingly few rows, due to its use of
     the hashing trick. Generally between 2000 and 10000 rows is sufficient,
@@ -143,13 +143,7 @@ def MultiHashEmbed(
     def make_hash_embed(index):
         nonlocal seed
         seed += 1
-        return HashEmbed(
-            width,
-            rows[index],
-            column=index,
-            seed=seed,
-            dropout=0.0,
-        )
+        return HashEmbed(width, rows[index], column=index, seed=seed, dropout=0.0)
 
     embeddings = [make_hash_embed(i) for i in range(len(attrs))]
     concat_size = width * (len(embeddings) + include_static_vectors)