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

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This commit is contained in:
Matthew Honnibal 2019-07-10 22:54:17 +02:00
commit a388888074
3 changed files with 9 additions and 1 deletions
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2
requirements.txt
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@ -1,7 +1,7 @@
# Our libraries # Our libraries
cymem>=2.0.2,<2.1.0 cymem>=2.0.2,<2.1.0
preshed>=2.0.1,<2.1.0 preshed>=2.0.1,<2.1.0
thinc>=7.0.2,<7.1.0 thinc>=7.0.5,<7.1.0
blis>=0.2.2,<0.3.0 blis>=0.2.2,<0.3.0
murmurhash>=0.28.0,<1.1.0 murmurhash>=0.28.0,<1.1.0
wasabi>=0.2.0,<1.1.0 wasabi>=0.2.0,<1.1.0

1
spacy/errors.py
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@ -403,6 +403,7 @@ class Errors(object):
E140 = ("The list of entities, prior probabilities and entity vectors should be of equal length.") E140 = ("The list of entities, prior probabilities and entity vectors should be of equal length.")
E141 = ("Entity vectors should be of length {required} instead of the provided {found}.") E141 = ("Entity vectors should be of length {required} instead of the provided {found}.")
E142 = ("Unsupported loss_function '{loss_func}'. Use either 'L2' or 'cosine'") E142 = ("Unsupported loss_function '{loss_func}'. Use either 'L2' or 'cosine'")
E143 = ("Labels for component '{name}' not initialized. Did you forget to call add_label()?")
@add_codes @add_codes

7
spacy/pipeline/pipes.pyx
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@ -902,6 +902,11 @@ class TextCategorizer(Pipe):
def labels(self): def labels(self):
return tuple(self.cfg.setdefault("labels", [])) return tuple(self.cfg.setdefault("labels", []))
def require_labels(self):
"""Raise an error if the component's model has no labels defined."""
if not self.labels:
raise ValueError(Errors.E143.format(name=self.name))
@labels.setter @labels.setter
def labels(self, value): def labels(self, value):
self.cfg["labels"] = tuple(value) self.cfg["labels"] = tuple(value)
@ -931,6 +936,7 @@ class TextCategorizer(Pipe):
doc.cats[label] = float(scores[i, j]) doc.cats[label] = float(scores[i, j])
def update(self, docs, golds, state=None, drop=0., sgd=None, losses=None): def update(self, docs, golds, state=None, drop=0., sgd=None, losses=None):
self.require_model()
scores, bp_scores = self.model.begin_update(docs, drop=drop) scores, bp_scores = self.model.begin_update(docs, drop=drop)
loss, d_scores = self.get_loss(docs, golds, scores) loss, d_scores = self.get_loss(docs, golds, scores)
bp_scores(d_scores, sgd=sgd) bp_scores(d_scores, sgd=sgd)
@ -985,6 +991,7 @@ class TextCategorizer(Pipe):
def begin_training(self, get_gold_tuples=lambda: [], pipeline=None, sgd=None, **kwargs): def begin_training(self, get_gold_tuples=lambda: [], pipeline=None, sgd=None, **kwargs):
if self.model is True: if self.model is True:
self.cfg["pretrained_vectors"] = kwargs.get("pretrained_vectors") self.cfg["pretrained_vectors"] = kwargs.get("pretrained_vectors")
self.require_labels()
self.model = self.Model(len(self.labels), **self.cfg) self.model = self.Model(len(self.labels), **self.cfg)
link_vectors_to_models(self.vocab) link_vectors_to_models(self.vocab)
if sgd is None: if sgd is None: