explosion/spaCy
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Merge branch 'develop' of https://github.com/explosion/spaCy into develop

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ines 2017-11-01 21:11:21 +01:00
commit e5eb5e5bf6
1 changed files with 12 additions and 23 deletions
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35
spacy/pipeline.pyx
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@ -307,7 +307,6 @@ class Tensorizer(Pipe):
return tokvecs, bp_tokvecs
def get_loss(self, docs, golds, scores):
# TODO: implement
raise NotImplementedError
def begin_training(self, gold_tuples=tuple(), pipeline=None):
@ -336,11 +335,7 @@ class Tagger(Pipe):
@property
def labels(self):
return self.cfg.setdefault('tag_names', [])
@labels.setter
def labels(self, value):
self.cfg['tag_names'] = value
return self.vocab.morphology.tag_names
def __call__(self, doc):
tags = self.predict([doc])
@ -369,7 +364,6 @@ class Tagger(Pipe):
cdef Doc doc
cdef int idx = 0
cdef Vocab vocab = self.vocab
tags = list(self.labels)
for i, doc in enumerate(docs):
doc_tag_ids = batch_tag_ids[i]
if hasattr(doc_tag_ids, 'get'):
@ -377,7 +371,7 @@ class Tagger(Pipe):
for j, tag_id in enumerate(doc_tag_ids):
# Don't clobber preset POS tags
if doc.c[j].tag == 0 and doc.c[j].pos == 0:
vocab.morphology.assign_tag(&doc.c[j], tags[tag_id])
vocab.morphology.assign_tag_id(&doc.c[j], tag_id)
idx += 1
doc.is_tagged = True
@ -425,12 +419,9 @@ class Tagger(Pipe):
new_tag_map[tag] = {POS: X}
cdef Vocab vocab = self.vocab
if new_tag_map:
new_tag_map.update(orig_tag_map)
vocab.morphology = Morphology(vocab.strings, new_tag_map,
vocab.morphology.lemmatizer,
exc=vocab.morphology.exc)
for tag in vocab.morphology.tag_names:
self.add_label(tag)
if self.model is True:
self.cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1]
self.model = self.Model(self.vocab.morphology.n_tags, **self.cfg)
@ -443,14 +434,15 @@ class Tagger(Pipe):
def add_label(self, label):
if label in self.labels:
return 0
if self.model not in (True, False, None):
smaller = self.model._layers[-1]
larger = Softmax(len(self.labels)+1, smaller.nI)
copy_array(larger.W[:smaller.nO], smaller.W)
copy_array(larger.b[:smaller.nO], smaller.b)
self.model._layers[-1] = larger
self.labels.append(label)
return 1
raise NotImplementedError
#if self.model not in (True, False, None):
# smaller = self.model._layers[-1]
# larger = Softmax(len(self.labels)+1, smaller.nI)
# copy_array(larger.W[:smaller.nO], smaller.W)
# copy_array(larger.b[:smaller.nO], smaller.b)
# self.model._layers[-1] = larger
#self.labels.append(label)
#return 1
def use_params(self, params):
with self.model.use_params(params):
@ -484,12 +476,11 @@ class Tagger(Pipe):
self.vocab.strings, tag_map=tag_map,
lemmatizer=self.vocab.morphology.lemmatizer,
exc=self.vocab.morphology.exc)
for tag in self.vocab.morphology.tag_names:
self.add_label(tag)
deserialize = OrderedDict((
('vocab', lambda b: self.vocab.from_bytes(b)),
('tag_map', load_tag_map),
('cfg', lambda b: self.cfg.update(ujson.loads(b))),
('model', lambda b: load_model(b)),
))
util.from_bytes(bytes_data, deserialize, exclude)
@ -521,8 +512,6 @@ class Tagger(Pipe):
self.vocab.strings, tag_map=tag_map,
lemmatizer=self.vocab.morphology.lemmatizer,
exc=self.vocab.morphology.exc)
for tag in self.vocab.morphology.tag_names:
self.add_label(tag)
deserialize = OrderedDict((
('cfg', lambda p: self.cfg.update(_load_cfg(p))),