diff --git a/spacy/lang/ja/__init__.py b/spacy/lang/ja/__init__.py
index 791b1ec33..056a6893b 100644
--- a/spacy/lang/ja/__init__.py
+++ b/spacy/lang/ja/__init__.py
@@ -39,8 +39,8 @@ def resolve_pos(token):
     """
 
     # this is only used for consecutive ascii spaces
-    if token.pos == '空白':
-        return '空白'
+    if token.pos == "空白":
+        return "空白"
 
     # TODO: This is a first take. The rules here are crude approximations.
     # For many of these, full dependencies are needed to properly resolve
@@ -78,7 +78,7 @@ def detailed_tokens(tokenizer, text):
         scount = node.next.rlength - node.next.length
         spaces.append(bool(scount))
         while scount > 1:
-            words.append(ShortUnitWord(' ', ' ', '空白'))
+            words.append(ShortUnitWord(" ", " ", "空白"))
             spaces.append(False)
             scount -= 1
 
diff --git a/spacy/tests/lang/ja/test_tokenizer.py b/spacy/tests/lang/ja/test_tokenizer.py
index 38ca37bc9..ad8bfaa00 100644
--- a/spacy/tests/lang/ja/test_tokenizer.py
+++ b/spacy/tests/lang/ja/test_tokenizer.py
@@ -48,8 +48,9 @@ def test_ja_tokenizer_pos(ja_tokenizer, text, expected_pos):
     pos = [token.pos_ for token in ja_tokenizer(text)]
     assert pos == expected_pos
 
+
 def test_extra_spaces(ja_tokenizer):
     # note: three spaces after "I"
     tokens = ja_tokenizer("I   like cheese.")
-    assert tokens[1].orth_ == ' '
-    assert tokens[2].orth_ == ' '
+    assert tokens[1].orth_ == " "
+    assert tokens[2].orth_ == " "
diff --git a/spacy/tests/lang/lt/test_lemmatizer.py b/spacy/tests/lang/lt/test_lemmatizer.py
index 5c3ed34f8..b98d63935 100644
--- a/spacy/tests/lang/lt/test_lemmatizer.py
+++ b/spacy/tests/lang/lt/test_lemmatizer.py
@@ -17,4 +17,6 @@ TEST_CASES = [
 
 @pytest.mark.parametrize("tokens,lemmas", TEST_CASES)
 def test_lt_lemmatizer(lt_lemmatizer, tokens, lemmas):
-    assert lemmas == [lt_lemmatizer.lookup_table.get_string(token, token) for token in tokens]
+    assert lemmas == [
+        lt_lemmatizer.lookup_table.get_string(token, token) for token in tokens
+    ]
diff --git a/spacy/tests/vocab_vectors/test_lookups.py b/spacy/tests/vocab_vectors/test_lookups.py
index 7cdf8ff68..daab5e585 100644
--- a/spacy/tests/vocab_vectors/test_lookups.py
+++ b/spacy/tests/vocab_vectors/test_lookups.py
@@ -78,7 +78,6 @@ def test_lookups_to_from_disk():
     assert table2.get_string("b") == 2
 
 
-
 # This fails on Python 3.5
 @pytest.mark.xfail
 def test_lookups_to_from_bytes_via_vocab():
@@ -97,6 +96,7 @@ def test_lookups_to_from_bytes_via_vocab():
     assert table.get_string("hello") == "world"
     assert new_vocab.to_bytes() == vocab_bytes
 
+
 # This fails on Python 3.5
 @pytest.mark.xfail
 def test_lookups_to_from_disk_via_vocab():
diff --git a/website/docs/api/annotation.md b/website/docs/api/annotation.md
index 2c52d197a..f44019752 100644
--- a/website/docs/api/annotation.md
+++ b/website/docs/api/annotation.md
@@ -80,8 +80,8 @@ training corpus and can be defined in the respective language data's
 
 <Accordion title="Universal Part-of-speech Tags" id="pos-universal">
 
-spaCy also maps all language-specific part-of-speech tags to a small, fixed set
-of word type tags following the
+spaCy maps all language-specific part-of-speech tags to a small, fixed set of
+word type tags following the
 [Universal Dependencies scheme](http://universaldependencies.org/u/pos/). The
 universal tags don't code for any morphological features and only cover the word
 type. They're available as the [`Token.pos`](/api/token#attributes) and