Tidy up and auto-format [ci skip]

examples/keras_parikh_entailment/spacy_hook.py

@@ -16,9 +16,9 @@ class KerasSimilarityShim(object):
         if get_features is None:
             get_features = get_word_ids
 
-        with (path / 'config.json').open() as file_:
+        with (path / "config.json").open() as file_:
             model = model_from_json(file_.read())
-        with (path / 'model').open('rb') as file_:
+        with (path / "model").open("rb") as file_:
             weights = pickle.load(file_)
 
         embeddings = get_embeddings(nlp.vocab)
@@ -33,8 +33,8 @@ class KerasSimilarityShim(object):
         self.max_length = max_length
 
     def __call__(self, doc):
-        doc.user_hooks['similarity'] = self.predict
-        doc.user_span_hooks['similarity'] = self.predict
+        doc.user_hooks["similarity"] = self.predict
+        doc.user_span_hooks["similarity"] = self.predict
 
         return doc
 
@@ -54,7 +54,7 @@ def get_embeddings(vocab, nr_unk=100):
     oov = np.random.normal(size=(nr_unk, vocab.vectors_length))
     oov = oov / oov.sum(axis=1, keepdims=True)
 
-    vectors = np.zeros((num_vectors + nr_unk, vocab.vectors_length), dtype='float32')
+    vectors = np.zeros((num_vectors + nr_unk, vocab.vectors_length), dtype="float32")
     vectors[1 : (nr_unk + 1),] = oov
     for lex in vocab:
         if lex.has_vector and lex.vector_norm > 0:
@@ -64,7 +64,7 @@ def get_embeddings(vocab, nr_unk=100):
 
 
 def get_word_ids(docs, max_length=100, nr_unk=100):
-    Xs = np.zeros((len(docs), max_length), dtype='int32')
+    Xs = np.zeros((len(docs), max_length), dtype="int32")
 
     for i, doc in enumerate(docs):
         for j, token in enumerate(doc):

examples/training/rehearsal.py

@@ -80,7 +80,7 @@ def main(model_name, unlabelled_loc):
                 nlp.rehearse(raw_batch, sgd=optimizer, losses=r_losses)
             print("Losses", losses)
             print("R. Losses", r_losses)
-    print(nlp.get_pipe('ner').model.unseen_classes)
+    print(nlp.get_pipe("ner").model.unseen_classes)
     test_text = "Do you like horses?"
     doc = nlp(test_text)
     print("Entities in '%s'" % test_text)
@@ -88,7 +88,5 @@ def main(model_name, unlabelled_loc):
         print(ent.label_, ent.text)
 
 
-
-
 if __name__ == "__main__":
     plac.call(main)

examples/training/train_textcat.py

@@ -24,7 +24,7 @@ from spacy.util import minibatch, compounding
     output_dir=("Optional output directory", "option", "o", Path),
     n_texts=("Number of texts to train from", "option", "t", int),
     n_iter=("Number of training iterations", "option", "n", int),
-    init_tok2vec=("Pretrained tok2vec weights", "option", "t2v", Path)
+    init_tok2vec=("Pretrained tok2vec weights", "option", "t2v", Path),
 )
 def main(model=None, output_dir=None, n_iter=20, n_texts=2000, init_tok2vec=None):
     if output_dir is not None:
@@ -43,11 +43,7 @@ def main(model=None, output_dir=None, n_iter=20, n_texts=2000, init_tok2vec=None
     # nlp.create_pipe works for built-ins that are registered with spaCy
     if "textcat" not in nlp.pipe_names:
         textcat = nlp.create_pipe(
-            "textcat",
-            config={
-                "exclusive_classes": True,
-                "architecture": "simple_cnn",
-            }
+            "textcat", config={"exclusive_classes": True, "architecture": "simple_cnn"}
         )
         nlp.add_pipe(textcat, last=True)
     # otherwise, get it, so we can add labels to it