spaCy/spacy/pipeline.pyx

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Update draft of parser neural network model Model is good, but code is messy. Currently requires Chainer, which may cause the build to fail on machines without a GPU. Outline of the model: We first predict context-sensitive vectors for each word in the input: (embed_lower | embed_prefix | embed_suffix | embed_shape) >> Maxout(token_width) >> convolution ** 4 This convolutional layer is shared between the tagger and the parser. This prevents the parser from needing tag features. To boost the representation, we make a "super tag" with POS, morphology and dependency label. The tagger predicts this by adding a softmax layer onto the convolutional layer --- so, we're teaching the convolutional layer to give us a representation that's one affine transform from this informative lexical information. This is obviously good for the parser (which backprops to the convolutions too). The parser model makes a state vector by concatenating the vector representations for its context tokens. Current results suggest few context tokens works well. Maybe this is a bug. The current context tokens: * S0, S1, S2: Top three words on the stack * B0, B1: First two words of the buffer * S0L1, S0L2: Leftmost and second leftmost children of S0 * S0R1, S0R2: Rightmost and second rightmost children of S0 * S1L1, S1L2, S1R2, S1R, B0L1, B0L2: Likewise for S1 and B0 This makes the state vector quite long: 13*T, where T is the token vector width (128 is working well). Fortunately, there's a way to structure the computation to save some expense (and make it more GPU friendly). The parser typically visits 2*N states for a sentence of length N (although it may visit more, if it back-tracks with a non-monotonic transition). A naive implementation would require 2*N (B, 13*T) @ (13*T, H) matrix multiplications for a batch of size B. We can instead perform one (B*N, T) @ (T, 13*H) multiplication, to pre-compute the hidden weights for each positional feature wrt the words in the batch. (Note that our token vectors come from the CNN -- so we can't play this trick over the vocabulary. That's how Stanford's NN parser works --- and why its model is so big.) This pre-computation strategy allows a nice compromise between GPU-friendliness and implementation simplicity. The CNN and the wide lower layer are computed on the GPU, and then the precomputed hidden weights are moved to the CPU, before we start the transition-based parsing process. This makes a lot of things much easier. We don't have to worry about variable-length batch sizes, and we don't have to implement the dynamic oracle in CUDA to train. Currently the parser's loss function is multilabel log loss, as the dynamic oracle allows multiple states to be 0 cost. This is defined as: (exp(score) / Z) - (exp(score) / gZ) Where gZ is the sum of the scores assigned to gold classes. I'm very interested in regressing on the cost directly, but so far this isn't working well. Machinery is in place for beam-search, which has been working well for the linear model. Beam search should benefit greatly from the pre-computation trick.
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# cython: infer_types=True
# cython: profile=True
# coding: utf8
from __future__ import unicode_literals
from thinc.api import chain, layerize, with_getitem
from thinc.neural import Model, Softmax
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import numpy
Update draft of parser neural network model Model is good, but code is messy. Currently requires Chainer, which may cause the build to fail on machines without a GPU. Outline of the model: We first predict context-sensitive vectors for each word in the input: (embed_lower | embed_prefix | embed_suffix | embed_shape) >> Maxout(token_width) >> convolution ** 4 This convolutional layer is shared between the tagger and the parser. This prevents the parser from needing tag features. To boost the representation, we make a "super tag" with POS, morphology and dependency label. The tagger predicts this by adding a softmax layer onto the convolutional layer --- so, we're teaching the convolutional layer to give us a representation that's one affine transform from this informative lexical information. This is obviously good for the parser (which backprops to the convolutions too). The parser model makes a state vector by concatenating the vector representations for its context tokens. Current results suggest few context tokens works well. Maybe this is a bug. The current context tokens: * S0, S1, S2: Top three words on the stack * B0, B1: First two words of the buffer * S0L1, S0L2: Leftmost and second leftmost children of S0 * S0R1, S0R2: Rightmost and second rightmost children of S0 * S1L1, S1L2, S1R2, S1R, B0L1, B0L2: Likewise for S1 and B0 This makes the state vector quite long: 13*T, where T is the token vector width (128 is working well). Fortunately, there's a way to structure the computation to save some expense (and make it more GPU friendly). The parser typically visits 2*N states for a sentence of length N (although it may visit more, if it back-tracks with a non-monotonic transition). A naive implementation would require 2*N (B, 13*T) @ (13*T, H) matrix multiplications for a batch of size B. We can instead perform one (B*N, T) @ (T, 13*H) multiplication, to pre-compute the hidden weights for each positional feature wrt the words in the batch. (Note that our token vectors come from the CNN -- so we can't play this trick over the vocabulary. That's how Stanford's NN parser works --- and why its model is so big.) This pre-computation strategy allows a nice compromise between GPU-friendliness and implementation simplicity. The CNN and the wide lower layer are computed on the GPU, and then the precomputed hidden weights are moved to the CPU, before we start the transition-based parsing process. This makes a lot of things much easier. We don't have to worry about variable-length batch sizes, and we don't have to implement the dynamic oracle in CUDA to train. Currently the parser's loss function is multilabel log loss, as the dynamic oracle allows multiple states to be 0 cost. This is defined as: (exp(score) / Z) - (exp(score) / gZ) Where gZ is the sum of the scores assigned to gold classes. I'm very interested in regressing on the cost directly, but so far this isn't working well. Machinery is in place for beam-search, which has been working well for the linear model. Beam search should benefit greatly from the pre-computation trick.
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cimport numpy as np
import cytoolz
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import util
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from collections import OrderedDict
import ujson
import msgpack
from thinc.api import add, layerize, chain, clone, concatenate, with_flatten
from thinc.neural import Model, Maxout, Softmax, Affine
from thinc.neural._classes.hash_embed import HashEmbed
from thinc.neural.util import to_categorical
from thinc.neural.pooling import Pooling, max_pool, mean_pool
from thinc.neural._classes.difference import Siamese, CauchySimilarity
from thinc.neural._classes.convolution import ExtractWindow
from thinc.neural._classes.resnet import Residual
from thinc.neural._classes.batchnorm import BatchNorm as BN
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from .tokens.doc cimport Doc
from .syntax.parser cimport Parser as LinearParser
from .syntax.nn_parser cimport Parser as NeuralParser
from .syntax.parser import get_templates as get_feature_templates
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from .syntax.beam_parser cimport BeamParser
from .syntax.ner cimport BiluoPushDown
from .syntax.arc_eager cimport ArcEager
from .tagger import Tagger
from .syntax.stateclass cimport StateClass
Update draft of parser neural network model Model is good, but code is messy. Currently requires Chainer, which may cause the build to fail on machines without a GPU. Outline of the model: We first predict context-sensitive vectors for each word in the input: (embed_lower | embed_prefix | embed_suffix | embed_shape) >> Maxout(token_width) >> convolution ** 4 This convolutional layer is shared between the tagger and the parser. This prevents the parser from needing tag features. To boost the representation, we make a "super tag" with POS, morphology and dependency label. The tagger predicts this by adding a softmax layer onto the convolutional layer --- so, we're teaching the convolutional layer to give us a representation that's one affine transform from this informative lexical information. This is obviously good for the parser (which backprops to the convolutions too). The parser model makes a state vector by concatenating the vector representations for its context tokens. Current results suggest few context tokens works well. Maybe this is a bug. The current context tokens: * S0, S1, S2: Top three words on the stack * B0, B1: First two words of the buffer * S0L1, S0L2: Leftmost and second leftmost children of S0 * S0R1, S0R2: Rightmost and second rightmost children of S0 * S1L1, S1L2, S1R2, S1R, B0L1, B0L2: Likewise for S1 and B0 This makes the state vector quite long: 13*T, where T is the token vector width (128 is working well). Fortunately, there's a way to structure the computation to save some expense (and make it more GPU friendly). The parser typically visits 2*N states for a sentence of length N (although it may visit more, if it back-tracks with a non-monotonic transition). A naive implementation would require 2*N (B, 13*T) @ (13*T, H) matrix multiplications for a batch of size B. We can instead perform one (B*N, T) @ (T, 13*H) multiplication, to pre-compute the hidden weights for each positional feature wrt the words in the batch. (Note that our token vectors come from the CNN -- so we can't play this trick over the vocabulary. That's how Stanford's NN parser works --- and why its model is so big.) This pre-computation strategy allows a nice compromise between GPU-friendliness and implementation simplicity. The CNN and the wide lower layer are computed on the GPU, and then the precomputed hidden weights are moved to the CPU, before we start the transition-based parsing process. This makes a lot of things much easier. We don't have to worry about variable-length batch sizes, and we don't have to implement the dynamic oracle in CUDA to train. Currently the parser's loss function is multilabel log loss, as the dynamic oracle allows multiple states to be 0 cost. This is defined as: (exp(score) / Z) - (exp(score) / gZ) Where gZ is the sum of the scores assigned to gold classes. I'm very interested in regressing on the cost directly, but so far this isn't working well. Machinery is in place for beam-search, which has been working well for the linear model. Beam search should benefit greatly from the pre-computation trick.
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from .gold cimport GoldParse
from .morphology cimport Morphology
from .vocab cimport Vocab
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from .syntax import nonproj
from .compat import json_dumps
Update draft of parser neural network model Model is good, but code is messy. Currently requires Chainer, which may cause the build to fail on machines without a GPU. Outline of the model: We first predict context-sensitive vectors for each word in the input: (embed_lower | embed_prefix | embed_suffix | embed_shape) >> Maxout(token_width) >> convolution ** 4 This convolutional layer is shared between the tagger and the parser. This prevents the parser from needing tag features. To boost the representation, we make a "super tag" with POS, morphology and dependency label. The tagger predicts this by adding a softmax layer onto the convolutional layer --- so, we're teaching the convolutional layer to give us a representation that's one affine transform from this informative lexical information. This is obviously good for the parser (which backprops to the convolutions too). The parser model makes a state vector by concatenating the vector representations for its context tokens. Current results suggest few context tokens works well. Maybe this is a bug. The current context tokens: * S0, S1, S2: Top three words on the stack * B0, B1: First two words of the buffer * S0L1, S0L2: Leftmost and second leftmost children of S0 * S0R1, S0R2: Rightmost and second rightmost children of S0 * S1L1, S1L2, S1R2, S1R, B0L1, B0L2: Likewise for S1 and B0 This makes the state vector quite long: 13*T, where T is the token vector width (128 is working well). Fortunately, there's a way to structure the computation to save some expense (and make it more GPU friendly). The parser typically visits 2*N states for a sentence of length N (although it may visit more, if it back-tracks with a non-monotonic transition). A naive implementation would require 2*N (B, 13*T) @ (13*T, H) matrix multiplications for a batch of size B. We can instead perform one (B*N, T) @ (T, 13*H) multiplication, to pre-compute the hidden weights for each positional feature wrt the words in the batch. (Note that our token vectors come from the CNN -- so we can't play this trick over the vocabulary. That's how Stanford's NN parser works --- and why its model is so big.) This pre-computation strategy allows a nice compromise between GPU-friendliness and implementation simplicity. The CNN and the wide lower layer are computed on the GPU, and then the precomputed hidden weights are moved to the CPU, before we start the transition-based parsing process. This makes a lot of things much easier. We don't have to worry about variable-length batch sizes, and we don't have to implement the dynamic oracle in CUDA to train. Currently the parser's loss function is multilabel log loss, as the dynamic oracle allows multiple states to be 0 cost. This is defined as: (exp(score) / Z) - (exp(score) / gZ) Where gZ is the sum of the scores assigned to gold classes. I'm very interested in regressing on the cost directly, but so far this isn't working well. Machinery is in place for beam-search, which has been working well for the linear model. Beam search should benefit greatly from the pre-computation trick.
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from .attrs import ID, LOWER, PREFIX, SUFFIX, SHAPE, TAG, DEP, POS
from ._ml import rebatch, Tok2Vec, flatten, get_col, doc2feats
from ._ml import build_text_classifier
from .parts_of_speech import X
class BaseThincComponent(object):
name = None
@classmethod
def Model(cls, *shape, **kwargs):
raise NotImplementedError
def __init__(self, vocab, model=True, **cfg):
raise NotImplementedError
def __call__(self, doc):
scores = self.predict([doc])
self.set_annotations([doc], scores)
return doc
def pipe(self, stream, batch_size=128, n_threads=-1):
for docs in cytoolz.partition_all(batch_size, stream):
docs = list(docs)
scores = self.predict(docs)
self.set_annotations(docs, scores)
yield from docs
def predict(self, docs):
raise NotImplementedError
def set_annotations(self, docs, scores):
raise NotImplementedError
def update(self, docs_tensors, golds, state=None, drop=0., sgd=None, losses=None):
raise NotImplementedError
def get_loss(self, docs, golds, scores):
raise NotImplementedError
def begin_training(self, gold_tuples=tuple(), pipeline=None):
token_vector_width = pipeline[0].model.nO
if self.model is True:
self.model = self.Model(1, token_vector_width)
def use_params(self, params):
with self.model.use_params(params):
yield
def to_bytes(self, **exclude):
serialize = OrderedDict((
('model', lambda: self.model.to_bytes()),
('vocab', lambda: self.vocab.to_bytes())
))
return util.to_bytes(serialize, exclude)
def from_bytes(self, bytes_data, **exclude):
if self.model is True:
self.model = self.Model()
deserialize = OrderedDict((
('model', lambda b: self.model.from_bytes(b)),
('vocab', lambda b: self.vocab.from_bytes(b))
))
util.from_bytes(bytes_data, deserialize, exclude)
return self
def to_disk(self, path, **exclude):
serialize = OrderedDict((
('model', lambda p: p.open('wb').write(self.model.to_bytes())),
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('vocab', lambda p: self.vocab.to_disk(p)),
('cfg', lambda p: p.open('w').write(json_dumps(self.cfg)))
))
util.to_disk(path, serialize, exclude)
def from_disk(self, path, **exclude):
if self.model is True:
self.model = self.Model()
deserialize = OrderedDict((
('model', lambda p: self.model.from_bytes(p.open('rb').read())),
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('vocab', lambda p: self.vocab.from_disk(p)),
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('cfg', lambda p: self.cfg.update(_load_cfg(p)))
))
util.from_disk(path, deserialize, exclude)
return self
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def _load_cfg(path):
if path.exists():
return ujson.load(path.open())
else:
return {}
class TokenVectorEncoder(BaseThincComponent):
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"""Assign position-sensitive vectors to tokens, using a CNN or RNN."""
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name = 'tensorizer'
Update draft of parser neural network model Model is good, but code is messy. Currently requires Chainer, which may cause the build to fail on machines without a GPU. Outline of the model: We first predict context-sensitive vectors for each word in the input: (embed_lower | embed_prefix | embed_suffix | embed_shape) >> Maxout(token_width) >> convolution ** 4 This convolutional layer is shared between the tagger and the parser. This prevents the parser from needing tag features. To boost the representation, we make a "super tag" with POS, morphology and dependency label. The tagger predicts this by adding a softmax layer onto the convolutional layer --- so, we're teaching the convolutional layer to give us a representation that's one affine transform from this informative lexical information. This is obviously good for the parser (which backprops to the convolutions too). The parser model makes a state vector by concatenating the vector representations for its context tokens. Current results suggest few context tokens works well. Maybe this is a bug. The current context tokens: * S0, S1, S2: Top three words on the stack * B0, B1: First two words of the buffer * S0L1, S0L2: Leftmost and second leftmost children of S0 * S0R1, S0R2: Rightmost and second rightmost children of S0 * S1L1, S1L2, S1R2, S1R, B0L1, B0L2: Likewise for S1 and B0 This makes the state vector quite long: 13*T, where T is the token vector width (128 is working well). Fortunately, there's a way to structure the computation to save some expense (and make it more GPU friendly). The parser typically visits 2*N states for a sentence of length N (although it may visit more, if it back-tracks with a non-monotonic transition). A naive implementation would require 2*N (B, 13*T) @ (13*T, H) matrix multiplications for a batch of size B. We can instead perform one (B*N, T) @ (T, 13*H) multiplication, to pre-compute the hidden weights for each positional feature wrt the words in the batch. (Note that our token vectors come from the CNN -- so we can't play this trick over the vocabulary. That's how Stanford's NN parser works --- and why its model is so big.) This pre-computation strategy allows a nice compromise between GPU-friendliness and implementation simplicity. The CNN and the wide lower layer are computed on the GPU, and then the precomputed hidden weights are moved to the CPU, before we start the transition-based parsing process. This makes a lot of things much easier. We don't have to worry about variable-length batch sizes, and we don't have to implement the dynamic oracle in CUDA to train. Currently the parser's loss function is multilabel log loss, as the dynamic oracle allows multiple states to be 0 cost. This is defined as: (exp(score) / Z) - (exp(score) / gZ) Where gZ is the sum of the scores assigned to gold classes. I'm very interested in regressing on the cost directly, but so far this isn't working well. Machinery is in place for beam-search, which has been working well for the linear model. Beam search should benefit greatly from the pre-computation trick.
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@classmethod
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def Model(cls, width=128, embed_size=7500, **cfg):
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"""Create a new statistical model for the class.
width (int): Output size of the model.
embed_size (int): Number of vectors in the embedding table.
**cfg: Config parameters.
RETURNS (Model): A `thinc.neural.Model` or similar instance.
"""
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width = util.env_opt('token_vector_width', width)
embed_size = util.env_opt('embed_size', embed_size)
return Tok2Vec(width, embed_size, preprocess=None)
Update draft of parser neural network model Model is good, but code is messy. Currently requires Chainer, which may cause the build to fail on machines without a GPU. Outline of the model: We first predict context-sensitive vectors for each word in the input: (embed_lower | embed_prefix | embed_suffix | embed_shape) >> Maxout(token_width) >> convolution ** 4 This convolutional layer is shared between the tagger and the parser. This prevents the parser from needing tag features. To boost the representation, we make a "super tag" with POS, morphology and dependency label. The tagger predicts this by adding a softmax layer onto the convolutional layer --- so, we're teaching the convolutional layer to give us a representation that's one affine transform from this informative lexical information. This is obviously good for the parser (which backprops to the convolutions too). The parser model makes a state vector by concatenating the vector representations for its context tokens. Current results suggest few context tokens works well. Maybe this is a bug. The current context tokens: * S0, S1, S2: Top three words on the stack * B0, B1: First two words of the buffer * S0L1, S0L2: Leftmost and second leftmost children of S0 * S0R1, S0R2: Rightmost and second rightmost children of S0 * S1L1, S1L2, S1R2, S1R, B0L1, B0L2: Likewise for S1 and B0 This makes the state vector quite long: 13*T, where T is the token vector width (128 is working well). Fortunately, there's a way to structure the computation to save some expense (and make it more GPU friendly). The parser typically visits 2*N states for a sentence of length N (although it may visit more, if it back-tracks with a non-monotonic transition). A naive implementation would require 2*N (B, 13*T) @ (13*T, H) matrix multiplications for a batch of size B. We can instead perform one (B*N, T) @ (T, 13*H) multiplication, to pre-compute the hidden weights for each positional feature wrt the words in the batch. (Note that our token vectors come from the CNN -- so we can't play this trick over the vocabulary. That's how Stanford's NN parser works --- and why its model is so big.) This pre-computation strategy allows a nice compromise between GPU-friendliness and implementation simplicity. The CNN and the wide lower layer are computed on the GPU, and then the precomputed hidden weights are moved to the CPU, before we start the transition-based parsing process. This makes a lot of things much easier. We don't have to worry about variable-length batch sizes, and we don't have to implement the dynamic oracle in CUDA to train. Currently the parser's loss function is multilabel log loss, as the dynamic oracle allows multiple states to be 0 cost. This is defined as: (exp(score) / Z) - (exp(score) / gZ) Where gZ is the sum of the scores assigned to gold classes. I'm very interested in regressing on the cost directly, but so far this isn't working well. Machinery is in place for beam-search, which has been working well for the linear model. Beam search should benefit greatly from the pre-computation trick.
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def __init__(self, vocab, model=True, **cfg):
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"""Construct a new statistical model. Weights are not allocated on
initialisation.
vocab (Vocab): A `Vocab` instance. The model must share the same `Vocab`
instance with the `Doc` objects it will process.
model (Model): A `Model` instance or `True` allocate one later.
**cfg: Config parameters.
EXAMPLE:
>>> from spacy.pipeline import TokenVectorEncoder
>>> tok2vec = TokenVectorEncoder(nlp.vocab)
>>> tok2vec.model = tok2vec.Model(128, 5000)
"""
self.vocab = vocab
self.doc2feats = doc2feats()
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self.model = model
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self.cfg = dict(cfg)
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def __call__(self, doc):
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"""Add context-sensitive vectors to a `Doc`, e.g. from a CNN or LSTM
model. Vectors are set to the `Doc.tensor` attribute.
docs (Doc or iterable): One or more documents to add vectors to.
RETURNS (dict or None): Intermediate computations.
"""
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tokvecses = self.predict([doc])
self.set_annotations([doc], tokvecses)
return doc
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def pipe(self, stream, batch_size=128, n_threads=-1):
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"""Process `Doc` objects as a stream.
stream (iterator): A sequence of `Doc` objects to process.
batch_size (int): Number of `Doc` objects to group.
n_threads (int): Number of threads.
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YIELDS (iterator): A sequence of `Doc` objects, in order of input.
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"""
for docs in cytoolz.partition_all(batch_size, stream):
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docs = list(docs)
tokvecses = self.predict(docs)
self.set_annotations(docs, tokvecses)
yield from docs
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def predict(self, docs):
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"""Return a single tensor for a batch of documents.
docs (iterable): A sequence of `Doc` objects.
RETURNS (object): Vector representations for each token in the documents.
"""
feats = self.doc2feats(docs)
tokvecs = self.model(feats)
return tokvecs
def set_annotations(self, docs, tokvecses):
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"""Set the tensor attribute for a batch of documents.
docs (iterable): A sequence of `Doc` objects.
tokvecs (object): Vector representation for each token in the documents.
"""
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for doc, tokvecs in zip(docs, tokvecses):
assert tokvecs.shape[0] == len(doc)
doc.tensor = tokvecs
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def update(self, docs, golds, state=None, drop=0., sgd=None, losses=None):
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"""Update the model.
docs (iterable): A batch of `Doc` objects.
golds (iterable): A batch of `GoldParse` objects.
drop (float): The droput rate.
sgd (callable): An optimizer.
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RETURNS (dict): Results from the update.
"""
if isinstance(docs, Doc):
docs = [docs]
feats = self.doc2feats(docs)
tokvecs, bp_tokvecs = self.model.begin_update(feats, drop=drop)
return tokvecs, bp_tokvecs
def get_loss(self, docs, golds, scores):
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# TODO: implement
raise NotImplementedError
def begin_training(self, gold_tuples=tuple(), pipeline=None):
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"""Allocate models, pre-process training data and acquire a trainer and
optimizer.
gold_tuples (iterable): Gold-standard training data.
pipeline (list): The pipeline the model is part of.
"""
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self.doc2feats = doc2feats()
if self.model is True:
self.model = self.Model()
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class NeuralTagger(BaseThincComponent):
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name = 'tagger'
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def __init__(self, vocab, model=True, **cfg):
self.vocab = vocab
self.model = model
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self.cfg = dict(cfg)
def __call__(self, doc):
tags = self.predict([doc.tensor])
self.set_annotations([doc], tags)
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return doc
def pipe(self, stream, batch_size=128, n_threads=-1):
for docs in cytoolz.partition_all(batch_size, stream):
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tokvecs = [d.tensor for d in docs]
tag_ids = self.predict(tokvecs)
self.set_annotations(docs, tag_ids)
yield from docs
def predict(self, tokvecs):
scores = self.model(tokvecs)
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scores = self.model.ops.flatten(scores)
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guesses = scores.argmax(axis=1)
if not isinstance(guesses, numpy.ndarray):
guesses = guesses.get()
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guesses = self.model.ops.unflatten(guesses,
[tv.shape[0] for tv in tokvecs])
return guesses
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def set_annotations(self, docs, batch_tag_ids):
if isinstance(docs, Doc):
docs = [docs]
cdef Doc doc
cdef int idx = 0
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cdef Vocab vocab = self.vocab
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for i, doc in enumerate(docs):
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doc_tag_ids = batch_tag_ids[i]
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for j, tag_id in enumerate(doc_tag_ids):
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# Don't clobber preset POS tags
if doc.c[j].tag == 0 and doc.c[j].pos == 0:
vocab.morphology.assign_tag_id(&doc.c[j], tag_id)
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idx += 1
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doc.is_tagged = True
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def update(self, docs_tokvecs, golds, drop=0., sgd=None, losses=None):
docs, tokvecs = docs_tokvecs
if self.model.nI is None:
self.model.nI = tokvecs[0].shape[1]
tag_scores, bp_tag_scores = self.model.begin_update(tokvecs, drop=drop)
loss, d_tag_scores = self.get_loss(docs, golds, tag_scores)
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d_tokvecs = bp_tag_scores(d_tag_scores, sgd=sgd)
return d_tokvecs
def get_loss(self, docs, golds, scores):
scores = self.model.ops.flatten(scores)
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tag_index = {tag: i for i, tag in enumerate(self.vocab.morphology.tag_names)}
Update draft of parser neural network model Model is good, but code is messy. Currently requires Chainer, which may cause the build to fail on machines without a GPU. Outline of the model: We first predict context-sensitive vectors for each word in the input: (embed_lower | embed_prefix | embed_suffix | embed_shape) >> Maxout(token_width) >> convolution ** 4 This convolutional layer is shared between the tagger and the parser. This prevents the parser from needing tag features. To boost the representation, we make a "super tag" with POS, morphology and dependency label. The tagger predicts this by adding a softmax layer onto the convolutional layer --- so, we're teaching the convolutional layer to give us a representation that's one affine transform from this informative lexical information. This is obviously good for the parser (which backprops to the convolutions too). The parser model makes a state vector by concatenating the vector representations for its context tokens. Current results suggest few context tokens works well. Maybe this is a bug. The current context tokens: * S0, S1, S2: Top three words on the stack * B0, B1: First two words of the buffer * S0L1, S0L2: Leftmost and second leftmost children of S0 * S0R1, S0R2: Rightmost and second rightmost children of S0 * S1L1, S1L2, S1R2, S1R, B0L1, B0L2: Likewise for S1 and B0 This makes the state vector quite long: 13*T, where T is the token vector width (128 is working well). Fortunately, there's a way to structure the computation to save some expense (and make it more GPU friendly). The parser typically visits 2*N states for a sentence of length N (although it may visit more, if it back-tracks with a non-monotonic transition). A naive implementation would require 2*N (B, 13*T) @ (13*T, H) matrix multiplications for a batch of size B. We can instead perform one (B*N, T) @ (T, 13*H) multiplication, to pre-compute the hidden weights for each positional feature wrt the words in the batch. (Note that our token vectors come from the CNN -- so we can't play this trick over the vocabulary. That's how Stanford's NN parser works --- and why its model is so big.) This pre-computation strategy allows a nice compromise between GPU-friendliness and implementation simplicity. The CNN and the wide lower layer are computed on the GPU, and then the precomputed hidden weights are moved to the CPU, before we start the transition-based parsing process. This makes a lot of things much easier. We don't have to worry about variable-length batch sizes, and we don't have to implement the dynamic oracle in CUDA to train. Currently the parser's loss function is multilabel log loss, as the dynamic oracle allows multiple states to be 0 cost. This is defined as: (exp(score) / Z) - (exp(score) / gZ) Where gZ is the sum of the scores assigned to gold classes. I'm very interested in regressing on the cost directly, but so far this isn't working well. Machinery is in place for beam-search, which has been working well for the linear model. Beam search should benefit greatly from the pre-computation trick.
2017-05-13 00:09:15 +03:00
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cdef int idx = 0
Update draft of parser neural network model Model is good, but code is messy. Currently requires Chainer, which may cause the build to fail on machines without a GPU. Outline of the model: We first predict context-sensitive vectors for each word in the input: (embed_lower | embed_prefix | embed_suffix | embed_shape) >> Maxout(token_width) >> convolution ** 4 This convolutional layer is shared between the tagger and the parser. This prevents the parser from needing tag features. To boost the representation, we make a "super tag" with POS, morphology and dependency label. The tagger predicts this by adding a softmax layer onto the convolutional layer --- so, we're teaching the convolutional layer to give us a representation that's one affine transform from this informative lexical information. This is obviously good for the parser (which backprops to the convolutions too). The parser model makes a state vector by concatenating the vector representations for its context tokens. Current results suggest few context tokens works well. Maybe this is a bug. The current context tokens: * S0, S1, S2: Top three words on the stack * B0, B1: First two words of the buffer * S0L1, S0L2: Leftmost and second leftmost children of S0 * S0R1, S0R2: Rightmost and second rightmost children of S0 * S1L1, S1L2, S1R2, S1R, B0L1, B0L2: Likewise for S1 and B0 This makes the state vector quite long: 13*T, where T is the token vector width (128 is working well). Fortunately, there's a way to structure the computation to save some expense (and make it more GPU friendly). The parser typically visits 2*N states for a sentence of length N (although it may visit more, if it back-tracks with a non-monotonic transition). A naive implementation would require 2*N (B, 13*T) @ (13*T, H) matrix multiplications for a batch of size B. We can instead perform one (B*N, T) @ (T, 13*H) multiplication, to pre-compute the hidden weights for each positional feature wrt the words in the batch. (Note that our token vectors come from the CNN -- so we can't play this trick over the vocabulary. That's how Stanford's NN parser works --- and why its model is so big.) This pre-computation strategy allows a nice compromise between GPU-friendliness and implementation simplicity. The CNN and the wide lower layer are computed on the GPU, and then the precomputed hidden weights are moved to the CPU, before we start the transition-based parsing process. This makes a lot of things much easier. We don't have to worry about variable-length batch sizes, and we don't have to implement the dynamic oracle in CUDA to train. Currently the parser's loss function is multilabel log loss, as the dynamic oracle allows multiple states to be 0 cost. This is defined as: (exp(score) / Z) - (exp(score) / gZ) Where gZ is the sum of the scores assigned to gold classes. I'm very interested in regressing on the cost directly, but so far this isn't working well. Machinery is in place for beam-search, which has been working well for the linear model. Beam search should benefit greatly from the pre-computation trick.
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correct = numpy.zeros((scores.shape[0],), dtype='i')
guesses = scores.argmax(axis=1)
Update draft of parser neural network model Model is good, but code is messy. Currently requires Chainer, which may cause the build to fail on machines without a GPU. Outline of the model: We first predict context-sensitive vectors for each word in the input: (embed_lower | embed_prefix | embed_suffix | embed_shape) >> Maxout(token_width) >> convolution ** 4 This convolutional layer is shared between the tagger and the parser. This prevents the parser from needing tag features. To boost the representation, we make a "super tag" with POS, morphology and dependency label. The tagger predicts this by adding a softmax layer onto the convolutional layer --- so, we're teaching the convolutional layer to give us a representation that's one affine transform from this informative lexical information. This is obviously good for the parser (which backprops to the convolutions too). The parser model makes a state vector by concatenating the vector representations for its context tokens. Current results suggest few context tokens works well. Maybe this is a bug. The current context tokens: * S0, S1, S2: Top three words on the stack * B0, B1: First two words of the buffer * S0L1, S0L2: Leftmost and second leftmost children of S0 * S0R1, S0R2: Rightmost and second rightmost children of S0 * S1L1, S1L2, S1R2, S1R, B0L1, B0L2: Likewise for S1 and B0 This makes the state vector quite long: 13*T, where T is the token vector width (128 is working well). Fortunately, there's a way to structure the computation to save some expense (and make it more GPU friendly). The parser typically visits 2*N states for a sentence of length N (although it may visit more, if it back-tracks with a non-monotonic transition). A naive implementation would require 2*N (B, 13*T) @ (13*T, H) matrix multiplications for a batch of size B. We can instead perform one (B*N, T) @ (T, 13*H) multiplication, to pre-compute the hidden weights for each positional feature wrt the words in the batch. (Note that our token vectors come from the CNN -- so we can't play this trick over the vocabulary. That's how Stanford's NN parser works --- and why its model is so big.) This pre-computation strategy allows a nice compromise between GPU-friendliness and implementation simplicity. The CNN and the wide lower layer are computed on the GPU, and then the precomputed hidden weights are moved to the CPU, before we start the transition-based parsing process. This makes a lot of things much easier. We don't have to worry about variable-length batch sizes, and we don't have to implement the dynamic oracle in CUDA to train. Currently the parser's loss function is multilabel log loss, as the dynamic oracle allows multiple states to be 0 cost. This is defined as: (exp(score) / Z) - (exp(score) / gZ) Where gZ is the sum of the scores assigned to gold classes. I'm very interested in regressing on the cost directly, but so far this isn't working well. Machinery is in place for beam-search, which has been working well for the linear model. Beam search should benefit greatly from the pre-computation trick.
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for gold in golds:
for tag in gold.tags:
if tag is None:
correct[idx] = guesses[idx]
else:
correct[idx] = tag_index[tag]
Update draft of parser neural network model Model is good, but code is messy. Currently requires Chainer, which may cause the build to fail on machines without a GPU. Outline of the model: We first predict context-sensitive vectors for each word in the input: (embed_lower | embed_prefix | embed_suffix | embed_shape) >> Maxout(token_width) >> convolution ** 4 This convolutional layer is shared between the tagger and the parser. This prevents the parser from needing tag features. To boost the representation, we make a "super tag" with POS, morphology and dependency label. The tagger predicts this by adding a softmax layer onto the convolutional layer --- so, we're teaching the convolutional layer to give us a representation that's one affine transform from this informative lexical information. This is obviously good for the parser (which backprops to the convolutions too). The parser model makes a state vector by concatenating the vector representations for its context tokens. Current results suggest few context tokens works well. Maybe this is a bug. The current context tokens: * S0, S1, S2: Top three words on the stack * B0, B1: First two words of the buffer * S0L1, S0L2: Leftmost and second leftmost children of S0 * S0R1, S0R2: Rightmost and second rightmost children of S0 * S1L1, S1L2, S1R2, S1R, B0L1, B0L2: Likewise for S1 and B0 This makes the state vector quite long: 13*T, where T is the token vector width (128 is working well). Fortunately, there's a way to structure the computation to save some expense (and make it more GPU friendly). The parser typically visits 2*N states for a sentence of length N (although it may visit more, if it back-tracks with a non-monotonic transition). A naive implementation would require 2*N (B, 13*T) @ (13*T, H) matrix multiplications for a batch of size B. We can instead perform one (B*N, T) @ (T, 13*H) multiplication, to pre-compute the hidden weights for each positional feature wrt the words in the batch. (Note that our token vectors come from the CNN -- so we can't play this trick over the vocabulary. That's how Stanford's NN parser works --- and why its model is so big.) This pre-computation strategy allows a nice compromise between GPU-friendliness and implementation simplicity. The CNN and the wide lower layer are computed on the GPU, and then the precomputed hidden weights are moved to the CPU, before we start the transition-based parsing process. This makes a lot of things much easier. We don't have to worry about variable-length batch sizes, and we don't have to implement the dynamic oracle in CUDA to train. Currently the parser's loss function is multilabel log loss, as the dynamic oracle allows multiple states to be 0 cost. This is defined as: (exp(score) / Z) - (exp(score) / gZ) Where gZ is the sum of the scores assigned to gold classes. I'm very interested in regressing on the cost directly, but so far this isn't working well. Machinery is in place for beam-search, which has been working well for the linear model. Beam search should benefit greatly from the pre-computation trick.
2017-05-13 00:09:15 +03:00
idx += 1
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correct = self.model.ops.xp.array(correct, dtype='i')
Update draft of parser neural network model Model is good, but code is messy. Currently requires Chainer, which may cause the build to fail on machines without a GPU. Outline of the model: We first predict context-sensitive vectors for each word in the input: (embed_lower | embed_prefix | embed_suffix | embed_shape) >> Maxout(token_width) >> convolution ** 4 This convolutional layer is shared between the tagger and the parser. This prevents the parser from needing tag features. To boost the representation, we make a "super tag" with POS, morphology and dependency label. The tagger predicts this by adding a softmax layer onto the convolutional layer --- so, we're teaching the convolutional layer to give us a representation that's one affine transform from this informative lexical information. This is obviously good for the parser (which backprops to the convolutions too). The parser model makes a state vector by concatenating the vector representations for its context tokens. Current results suggest few context tokens works well. Maybe this is a bug. The current context tokens: * S0, S1, S2: Top three words on the stack * B0, B1: First two words of the buffer * S0L1, S0L2: Leftmost and second leftmost children of S0 * S0R1, S0R2: Rightmost and second rightmost children of S0 * S1L1, S1L2, S1R2, S1R, B0L1, B0L2: Likewise for S1 and B0 This makes the state vector quite long: 13*T, where T is the token vector width (128 is working well). Fortunately, there's a way to structure the computation to save some expense (and make it more GPU friendly). The parser typically visits 2*N states for a sentence of length N (although it may visit more, if it back-tracks with a non-monotonic transition). A naive implementation would require 2*N (B, 13*T) @ (13*T, H) matrix multiplications for a batch of size B. We can instead perform one (B*N, T) @ (T, 13*H) multiplication, to pre-compute the hidden weights for each positional feature wrt the words in the batch. (Note that our token vectors come from the CNN -- so we can't play this trick over the vocabulary. That's how Stanford's NN parser works --- and why its model is so big.) This pre-computation strategy allows a nice compromise between GPU-friendliness and implementation simplicity. The CNN and the wide lower layer are computed on the GPU, and then the precomputed hidden weights are moved to the CPU, before we start the transition-based parsing process. This makes a lot of things much easier. We don't have to worry about variable-length batch sizes, and we don't have to implement the dynamic oracle in CUDA to train. Currently the parser's loss function is multilabel log loss, as the dynamic oracle allows multiple states to be 0 cost. This is defined as: (exp(score) / Z) - (exp(score) / gZ) Where gZ is the sum of the scores assigned to gold classes. I'm very interested in regressing on the cost directly, but so far this isn't working well. Machinery is in place for beam-search, which has been working well for the linear model. Beam search should benefit greatly from the pre-computation trick.
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d_scores = scores - to_categorical(correct, nb_classes=scores.shape[1])
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d_scores /= d_scores.shape[0]
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loss = (d_scores**2).sum()
d_scores = self.model.ops.unflatten(d_scores, [len(d) for d in docs])
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return float(loss), d_scores
def begin_training(self, gold_tuples=tuple(), pipeline=None):
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orig_tag_map = dict(self.vocab.morphology.tag_map)
new_tag_map = {}
for raw_text, annots_brackets in gold_tuples:
for annots, brackets in annots_brackets:
ids, words, tags, heads, deps, ents = annots
for tag in tags:
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if tag in orig_tag_map:
new_tag_map[tag] = orig_tag_map[tag]
else:
new_tag_map[tag] = {POS: X}
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if 'SP' not in new_tag_map:
new_tag_map['SP'] = orig_tag_map.get('SP', {POS: X})
cdef Vocab vocab = self.vocab
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if new_tag_map:
vocab.morphology = Morphology(vocab.strings, new_tag_map,
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vocab.morphology.lemmatizer,
exc=vocab.morphology.exc)
token_vector_width = pipeline[0].model.nO
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if self.model is True:
self.model = self.Model(self.vocab.morphology.n_tags, token_vector_width)
@classmethod
def Model(cls, n_tags, token_vector_width):
return with_flatten(
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chain(Maxout(token_vector_width, token_vector_width),
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Softmax(n_tags, token_vector_width)))
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def use_params(self, params):
with self.model.use_params(params):
yield
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def to_bytes(self, **exclude):
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serialize = OrderedDict((
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('model', lambda: self.model.to_bytes()),
('vocab', lambda: self.vocab.to_bytes()),
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('tag_map', lambda: msgpack.dumps(self.vocab.morphology.tag_map,
use_bin_type=True,
encoding='utf8'))
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))
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return util.to_bytes(serialize, exclude)
def from_bytes(self, bytes_data, **exclude):
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def load_model(b):
if self.model is True:
token_vector_width = util.env_opt('token_vector_width', 128)
self.model = self.Model(self.vocab.morphology.n_tags, token_vector_width)
self.model.from_bytes(b)
def load_tag_map(b):
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tag_map = msgpack.loads(b, encoding='utf8')
self.vocab.morphology = Morphology(
self.vocab.strings, tag_map=tag_map,
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lemmatizer=self.vocab.morphology.lemmatizer,
exc=self.vocab.morphology.exc)
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deserialize = OrderedDict((
('vocab', lambda b: self.vocab.from_bytes(b)),
('tag_map', load_tag_map),
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('model', lambda b: load_model(b)),
))
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util.from_bytes(bytes_data, deserialize, exclude)
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return self
def to_disk(self, path, **exclude):
serialize = OrderedDict((
('vocab', lambda p: self.vocab.to_disk(p)),
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('tag_map', lambda p: p.open('wb').write(msgpack.dumps(
self.vocab.morphology.tag_map,
use_bin_type=True,
encoding='utf8'))),
('model', lambda p: p.open('wb').write(self.model.to_bytes())),
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('cfg', lambda p: p.open('w').write(json_dumps(self.cfg)))
))
util.to_disk(path, serialize, exclude)
def from_disk(self, path, **exclude):
def load_model(p):
if self.model is True:
token_vector_width = util.env_opt('token_vector_width', 128)
self.model = self.Model(self.vocab.morphology.n_tags, token_vector_width)
self.model.from_bytes(p.open('rb').read())
def load_tag_map(p):
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with p.open('rb') as file_:
tag_map = msgpack.loads(file_.read(), encoding='utf8')
self.vocab.morphology = Morphology(
self.vocab.strings, tag_map=tag_map,
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lemmatizer=self.vocab.morphology.lemmatizer,
exc=self.vocab.morphology.exc)
deserialize = OrderedDict((
('vocab', lambda p: self.vocab.from_disk(p)),
('tag_map', load_tag_map),
('model', load_model),
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('cfg', lambda p: self.cfg.update(_load_cfg(p)))
))
util.from_disk(path, deserialize, exclude)
return self
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class NeuralLabeller(NeuralTagger):
name = 'nn_labeller'
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def __init__(self, vocab, model=True, **cfg):
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self.vocab = vocab
self.model = model
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self.cfg = dict(cfg)
@property
def labels(self):
return self.cfg.get('labels', {})
@labels.setter
def labels(self, value):
self.cfg['labels'] = value
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def set_annotations(self, docs, dep_ids):
pass
def begin_training(self, gold_tuples=tuple(), pipeline=None):
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gold_tuples = nonproj.preprocess_training_data(gold_tuples)
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for raw_text, annots_brackets in gold_tuples:
for annots, brackets in annots_brackets:
ids, words, tags, heads, deps, ents = annots
for dep in deps:
if dep not in self.labels:
self.labels[dep] = len(self.labels)
token_vector_width = pipeline[0].model.nO
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if self.model is True:
self.model = self.Model(len(self.labels), token_vector_width)
@classmethod
def Model(cls, n_tags, token_vector_width):
return with_flatten(
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chain(Maxout(token_vector_width, token_vector_width),
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Softmax(n_tags, token_vector_width)))
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def get_loss(self, docs, golds, scores):
scores = self.model.ops.flatten(scores)
cdef int idx = 0
correct = numpy.zeros((scores.shape[0],), dtype='i')
guesses = scores.argmax(axis=1)
for gold in golds:
for tag in gold.labels:
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if tag is None or tag not in self.labels:
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correct[idx] = guesses[idx]
else:
correct[idx] = self.labels[tag]
idx += 1
correct = self.model.ops.xp.array(correct, dtype='i')
d_scores = scores - to_categorical(correct, nb_classes=scores.shape[1])
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d_scores /= d_scores.shape[0]
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loss = (d_scores**2).sum()
d_scores = self.model.ops.unflatten(d_scores, [len(d) for d in docs])
return float(loss), d_scores
class SimilarityHook(BaseThincComponent):
"""
Experimental
A pipeline component to install a hook for supervised similarity into
Doc objects. Requires a Tensorizer to pre-process documents. The similarity
model can be any object obeying the Thinc Model interface. By default,
the model concatenates the elementwise mean and elementwise max of the two
tensors, and compares them using the Cauchy-like similarity function
from Chen (2013):
similarity = 1. / (1. + (W * (vec1-vec2)**2).sum())
Where W is a vector of dimension weights, initialized to 1.
"""
name = 'similarity'
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def __init__(self, vocab, model=True, **cfg):
self.vocab = vocab
self.model = model
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self.cfg = dict(cfg)
@classmethod
def Model(cls, length):
return Siamese(Pooling(max_pool, mean_pool), CauchySimilarity(length))
def __call__(self, doc):
'''Install similarity hook'''
doc.user_hooks['similarity'] = self.predict
return doc
def pipe(self, docs, **kwargs):
for doc in docs:
yield self(doc)
def predict(self, doc1, doc2):
return self.model.predict([(doc1.tensor, doc2.tensor)])
def update(self, doc1_tensor1_doc2_tensor2, golds, sgd=None, drop=0.):
doc1s, tensor1s, doc2s, tensor2s = doc1_tensor1_doc2_tensor2
sims, bp_sims = self.model.begin_update(zip(tensor1s, tensor2s),
drop=drop)
d_tensor1s, d_tensor2s = bp_sims(golds, sgd=sgd)
return d_tensor1s, d_tensor2s
def begin_training(self, _=tuple(), pipeline=None):
"""
Allocate model, using width from tensorizer in pipeline.
gold_tuples (iterable): Gold-standard training data.
pipeline (list): The pipeline the model is part of.
"""
if self.model is True:
self.model = self.Model(pipeline[0].model.nO)
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class TextCategorizer(BaseThincComponent):
name = 'textcat'
@classmethod
def Model(cls, nr_class=1, width=64, **cfg):
return build_text_classifier(nr_class, width, **cfg)
def __init__(self, vocab, model=True, **cfg):
self.vocab = vocab
self.model = model
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self.cfg = dict(cfg)
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@property
def labels(self):
return self.cfg.get('labels', ['LABEL'])
@labels.setter
def labels(self, value):
self.cfg['labels'] = value
def __call__(self, doc):
scores = self.predict([doc])
self.set_annotations([doc], scores)
return doc
def pipe(self, stream, batch_size=128, n_threads=-1):
for docs in cytoolz.partition_all(batch_size, stream):
docs = list(docs)
scores = self.predict(docs)
self.set_annotations(docs, scores)
yield from docs
def predict(self, docs):
scores = self.model(docs)
scores = self.model.ops.asarray(scores)
return scores
def set_annotations(self, docs, scores):
for i, doc in enumerate(docs):
for j, label in enumerate(self.labels):
doc.cats[label] = float(scores[i, j])
def update(self, docs_tensors, golds, state=None, drop=0., sgd=None, losses=None):
docs, tensors = docs_tensors
scores, bp_scores = self.model.begin_update(docs, drop=drop)
loss, d_scores = self.get_loss(docs, golds, scores)
d_tensors = bp_scores(d_scores, sgd=sgd)
if losses is not None:
losses.setdefault(self.name, 0.0)
losses[self.name] += loss
return d_tensors
def get_loss(self, docs, golds, scores):
truths = numpy.zeros((len(golds), len(self.labels)), dtype='f')
for i, gold in enumerate(golds):
for j, label in enumerate(self.labels):
truths[i, j] = label in gold.cats
truths = self.model.ops.asarray(truths)
d_scores = (scores-truths) / scores.shape[0]
mean_square_error = ((scores-truths)**2).sum(axis=1).mean()
return mean_square_error, d_scores
def begin_training(self, gold_tuples=tuple(), pipeline=None):
if pipeline:
token_vector_width = pipeline[0].model.nO
else:
token_vector_width = 64
if self.model is True:
self.model = self.Model(len(self.labels), token_vector_width)
cdef class EntityRecognizer(LinearParser):
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"""Annotate named entities on Doc objects."""
TransitionSystem = BiluoPushDown
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feature_templates = get_feature_templates('ner')
def add_label(self, label):
LinearParser.add_label(self, label)
if isinstance(label, basestring):
label = self.vocab.strings[label]
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cdef class BeamEntityRecognizer(BeamParser):
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"""Annotate named entities on Doc objects."""
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TransitionSystem = BiluoPushDown
feature_templates = get_feature_templates('ner')
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def add_label(self, label):
LinearParser.add_label(self, label)
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if isinstance(label, basestring):
label = self.vocab.strings[label]
cdef class DependencyParser(LinearParser):
TransitionSystem = ArcEager
feature_templates = get_feature_templates('basic')
def add_label(self, label):
LinearParser.add_label(self, label)
if isinstance(label, basestring):
label = self.vocab.strings[label]
cdef class NeuralDependencyParser(NeuralParser):
name = 'parser'
TransitionSystem = ArcEager
def __reduce__(self):
return (NeuralDependencyParser, (self.vocab, self.moves, self.model), None, None)
cdef class NeuralEntityRecognizer(NeuralParser):
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name = 'ner'
TransitionSystem = BiluoPushDown
nr_feature = 6
def predict_confidences(self, docs):
tensors = [d.tensor for d in docs]
samples = []
for i in range(10):
states = self.parse_batch(docs, tensors, drop=0.3)
for state in states:
samples.append(self._get_entities(state))
def __reduce__(self):
return (NeuralEntityRecognizer, (self.vocab, self.moves, self.model), None, None)
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cdef class BeamDependencyParser(BeamParser):
TransitionSystem = ArcEager
feature_templates = get_feature_templates('basic')
def add_label(self, label):
Parser.add_label(self, label)
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if isinstance(label, basestring):
label = self.vocab.strings[label]
__all__ = ['Tagger', 'DependencyParser', 'EntityRecognizer', 'BeamDependencyParser',
'BeamEntityRecognizer', 'TokenVectorEnoder']