Apply ruff formatting to 8 files

spacy/cli/find_threshold.py

@@ -207,7 +207,9 @@ def find_threshold(
             ),
         )
         if hasattr(pipe, "cfg"):
-            nlp.get_pipe(pipe_name).cfg = set_nested_item(pipe.cfg, config_keys, threshold)
+            nlp.get_pipe(pipe_name).cfg = set_nested_item(
+                pipe.cfg, config_keys, threshold
+            )
 
         eval_scores = nlp.evaluate(dev_dataset)
         if scores_key not in eval_scores:

spacy/ml/_precomputable_affine.py

@@ -1,7 +1,6 @@
 from thinc.api import Model, normal_init
 
 
-
 def PrecomputableAffine(nO, nI, nF, nP, dropout=0.1):
     model = Model(
         "precomputable_affine",

spacy/ml/extract_spans.py

@@ -4,7 +4,6 @@ from thinc.api import Model, to_numpy
 from thinc.types import Ints1d, Ragged
 
 
-
 def extract_spans() -> Model[Tuple[Ragged, Ragged], Ragged]:
     """Extract spans from a sequence of source arrays, as specified by an array
     of (start, end) indices. The output is a ragged array of the

spacy/tests/lang/bg/test_tokenizer.py

@@ -1,5 +1,3 @@
-
-
 def test_bg_tokenizer_handles_final_diacritics(bg_tokenizer):
     text = "Ня̀маше яйца̀. Ня̀маше яйца̀."
     tokens = bg_tokenizer(text)

spacy/tests/lang/la/test_exception.py

@@ -1,5 +1,3 @@
-
-
 def test_la_tokenizer_handles_exc_in_text(la_tokenizer):
     text = "scio te omnia facturum, ut nobiscum quam primum sis"
     tokens = la_tokenizer(text)

spacy/tests/lang/sl/test_text.py

@@ -1,5 +1,3 @@
-
-
 def test_long_text(sl_tokenizer):
     # Excerpt: European Convention on Human Rights
     text = """

spacy/tests/lang/sq/test_text.py

@@ -1,5 +1,3 @@
-
-
 def test_long_text(sq_tokenizer):
     # Excerpt: European Convention on Human Rights
     text = """

spacy/tests/lang/xx/test_text.py

@@ -1,5 +1,3 @@
-
-
 def test_long_text(xx_tokenizer):
     # Excerpt: Text in Skolt Sami taken from https://www.samediggi.fi
     text = """