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spaCy
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Pass option for pretrained vectors in pipeline

This commit is contained in:
Matthew Honnibal 2017-09-16 12:46:02 -05:00
parent 2a93404da6
commit 84e637e2e6
1 changed files with 25 additions and 20 deletions
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45
spacy/pipeline.pyx
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@ -41,7 +41,7 @@ from .syntax import nonproj
from .compat import json_dumps from .compat import json_dumps
from .attrs import ID, LOWER, PREFIX, SUFFIX, SHAPE, TAG, DEP, POS from .attrs import ID, LOWER, PREFIX, SUFFIX, SHAPE, TAG, DEP, POS
from ._ml import rebatch, Tok2Vec, flatten, get_col, doc2feats from ._ml import rebatch, Tok2Vec, flatten
from ._ml import build_text_classifier, build_tagger_model from ._ml import build_text_classifier, build_tagger_model
from .parts_of_speech import X from .parts_of_speech import X
@ -137,6 +137,7 @@ class BaseThincComponent(object):
def from_bytes(self, bytes_data, **exclude): def from_bytes(self, bytes_data, **exclude):
def load_model(b): def load_model(b):
if self.model is True: if self.model is True:
self.cfg['pretrained_dims'] = self.vocab.vectors_length
self.model = self.Model(**self.cfg) self.model = self.Model(**self.cfg)
self.model.from_bytes(b) self.model.from_bytes(b)
@ -159,6 +160,7 @@ class BaseThincComponent(object):
def from_disk(self, path, **exclude): def from_disk(self, path, **exclude):
def load_model(p): def load_model(p):
if self.model is True: if self.model is True:
self.cfg['pretrained_dims'] = self.vocab.vectors_length
self.model = self.Model(**self.cfg) self.model = self.Model(**self.cfg)
self.model.from_bytes(p.open('rb').read()) self.model.from_bytes(p.open('rb').read())
@ -193,7 +195,7 @@ class TokenVectorEncoder(BaseThincComponent):
""" """
width = util.env_opt('token_vector_width', width) width = util.env_opt('token_vector_width', width)
embed_size = util.env_opt('embed_size', embed_size) embed_size = util.env_opt('embed_size', embed_size)
return Tok2Vec(width, embed_size, preprocess=None) return Tok2Vec(width, embed_size, **cfg)
def __init__(self, vocab, model=True, **cfg): def __init__(self, vocab, model=True, **cfg):
"""Construct a new statistical model. Weights are not allocated on """Construct a new statistical model. Weights are not allocated on
@ -210,7 +212,6 @@ class TokenVectorEncoder(BaseThincComponent):
>>> tok2vec.model = tok2vec.Model(128, 5000) >>> tok2vec.model = tok2vec.Model(128, 5000)
""" """
self.vocab = vocab self.vocab = vocab
self.doc2feats = doc2feats()
self.model = model self.model = model
self.cfg = dict(cfg) self.cfg = dict(cfg)
@ -245,8 +246,7 @@ class TokenVectorEncoder(BaseThincComponent):
docs (iterable): A sequence of `Doc` objects. docs (iterable): A sequence of `Doc` objects.
RETURNS (object): Vector representations for each token in the documents. RETURNS (object): Vector representations for each token in the documents.
""" """
feats = self.doc2feats(docs) tokvecs = self.model(docs)
tokvecs = self.model(feats)
return tokvecs return tokvecs
def set_annotations(self, docs, tokvecses): def set_annotations(self, docs, tokvecses):
@ -270,8 +270,7 @@ class TokenVectorEncoder(BaseThincComponent):
""" """
if isinstance(docs, Doc): if isinstance(docs, Doc):
docs = [docs] docs = [docs]
feats = self.doc2feats(docs) tokvecs, bp_tokvecs = self.model.begin_update(docs, drop=drop)
tokvecs, bp_tokvecs = self.model.begin_update(feats, drop=drop)
return tokvecs, bp_tokvecs return tokvecs, bp_tokvecs
def get_loss(self, docs, golds, scores): def get_loss(self, docs, golds, scores):
@ -285,9 +284,8 @@ class TokenVectorEncoder(BaseThincComponent):
gold_tuples (iterable): Gold-standard training data. gold_tuples (iterable): Gold-standard training data.
pipeline (list): The pipeline the model is part of. pipeline (list): The pipeline the model is part of.
""" """
self.doc2feats = doc2feats()
if self.model is True: if self.model is True:
self.model = self.Model() self.model = self.Model(**self.cfg)
class NeuralTagger(BaseThincComponent): class NeuralTagger(BaseThincComponent):
@ -394,12 +392,14 @@ class NeuralTagger(BaseThincComponent):
exc=vocab.morphology.exc) exc=vocab.morphology.exc)
token_vector_width = pipeline[0].model.nO token_vector_width = pipeline[0].model.nO
if self.model is True: if self.model is True:
self.model = self.Model(self.vocab.morphology.n_tags, token_vector_width) self.model = self.Model(self.vocab.morphology.n_tags, token_vector_width,
pretrained_dims=self.vocab.vectors_length)
@classmethod @classmethod
def Model(cls, n_tags, token_vector_width): def Model(cls, n_tags, token_vector_width, pretrained_dims=0):
return build_tagger_model(n_tags, token_vector_width) return build_tagger_model(n_tags, token_vector_width,
pretrained_dims)
def use_params(self, params): def use_params(self, params):
with self.model.use_params(params): with self.model.use_params(params):
yield yield
@ -419,7 +419,8 @@ class NeuralTagger(BaseThincComponent):
if self.model is True: if self.model is True:
token_vector_width = util.env_opt('token_vector_width', token_vector_width = util.env_opt('token_vector_width',
self.cfg.get('token_vector_width', 128)) self.cfg.get('token_vector_width', 128))
self.model = self.Model(self.vocab.morphology.n_tags, token_vector_width) self.model = self.Model(self.vocab.morphology.n_tags, token_vector_width,
pretrained_dims=self.vocab.vectors_length)
self.model.from_bytes(b) self.model.from_bytes(b)
def load_tag_map(b): def load_tag_map(b):
@ -428,7 +429,7 @@ class NeuralTagger(BaseThincComponent):
self.vocab.strings, tag_map=tag_map, self.vocab.strings, tag_map=tag_map,
lemmatizer=self.vocab.morphology.lemmatizer, lemmatizer=self.vocab.morphology.lemmatizer,
exc=self.vocab.morphology.exc) exc=self.vocab.morphology.exc)
deserialize = OrderedDict(( deserialize = OrderedDict((
('vocab', lambda b: self.vocab.from_bytes(b)), ('vocab', lambda b: self.vocab.from_bytes(b)),
('tag_map', load_tag_map), ('tag_map', load_tag_map),
@ -454,7 +455,8 @@ class NeuralTagger(BaseThincComponent):
if self.model is True: if self.model is True:
token_vector_width = util.env_opt('token_vector_width', token_vector_width = util.env_opt('token_vector_width',
self.cfg.get('token_vector_width', 128)) self.cfg.get('token_vector_width', 128))
self.model = self.Model(self.vocab.morphology.n_tags, token_vector_width) self.model = self.Model(self.vocab.morphology.n_tags, token_vector_width,
pretrained_dims=self.vocab.vectors_length)
self.model.from_bytes(p.open('rb').read()) self.model.from_bytes(p.open('rb').read())
def load_tag_map(p): def load_tag_map(p):
@ -503,12 +505,14 @@ class NeuralLabeller(NeuralTagger):
self.labels[dep] = len(self.labels) self.labels[dep] = len(self.labels)
token_vector_width = pipeline[0].model.nO token_vector_width = pipeline[0].model.nO
if self.model is True: if self.model is True:
self.model = self.Model(len(self.labels), token_vector_width) self.model = self.Model(len(self.labels), token_vector_width,
pretrained_dims=self.vocab.vectors_length)
@classmethod @classmethod
def Model(cls, n_tags, token_vector_width): def Model(cls, n_tags, token_vector_width, pretrained_dims=0):
return build_tagger_model(n_tags, token_vector_width) return build_tagger_model(n_tags, token_vector_width,
pretrained_dims)
def get_loss(self, docs, golds, scores): def get_loss(self, docs, golds, scores):
scores = self.model.ops.flatten(scores) scores = self.model.ops.flatten(scores)
cdef int idx = 0 cdef int idx = 0
@ -653,6 +657,7 @@ class TextCategorizer(BaseThincComponent):
else: else:
token_vector_width = 64 token_vector_width = 64
if self.model is True: if self.model is True:
self.cfg['pretrained_dims'] = self.vocab.vectors_length
self.model = self.Model(len(self.labels), token_vector_width, self.model = self.Model(len(self.labels), token_vector_width,
**self.cfg) **self.cfg)