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Tidy up and auto-format [ci skip]

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This commit is contained in:
Ines Montani 2019-08-31 13:39:31 +02:00
parent cd90752193
commit dad5621166
3 changed files with 18 additions and 24 deletions
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14
examples/keras_parikh_entailment/spacy_hook.py
View File

@ -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
@ -54,8 +54,8 @@ 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[1:(nr_unk + 1), ] = oov
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:
vectors[nr_unk + lex.rank + 1] = lex.vector / lex.vector_norm
@ -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):

4
examples/training/rehearsal.py
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@ -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)

8
examples/training/train_textcat.py
View File

@ -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