spaCy/spacy/pipeline/pipes.pyx

1683 lines
64 KiB
Cython
Raw Blame History

This file contains ambiguous Unicode characters

This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

# cython: infer_types=True, profile=True
import numpy
import srsly
import random
from thinc.api import CosineDistance, to_categorical, get_array_module
from thinc.api import set_dropout_rate
import warnings
from ..tokens.doc cimport Doc
from ..syntax.nn_parser cimport Parser
from ..syntax.ner cimport BiluoPushDown
from ..syntax.arc_eager cimport ArcEager
from ..morphology cimport Morphology
from ..vocab cimport Vocab
from .functions import merge_subtokens
from ..language import Language, component
from ..syntax import nonproj
from ..gold import Example
from ..attrs import POS, ID
from ..util import link_vectors_to_models, create_default_optimizer
from ..parts_of_speech import X
from ..kb import KnowledgeBase
from ..errors import Errors, TempErrors, Warnings
from .. import util
def _load_cfg(path):
if path.exists():
return srsly.read_json(path)
else:
return {}
class Pipe(object):
"""This class is not instantiated directly. Components inherit from it, and
it defines the interface that components should follow to function as
components in a spaCy analysis pipeline.
"""
name = None
@classmethod
def from_nlp(cls, nlp, model, **cfg):
return cls(nlp.vocab, model, **cfg)
def _get_doc(self, example):
""" Use this method if the `example` can be both a Doc or an Example """
if isinstance(example, Doc):
return example
return example.doc
def __init__(self, vocab, model, **cfg):
"""Create a new pipe instance."""
raise NotImplementedError
def __call__(self, example):
"""Apply the pipe to one document. The document is
modified in-place, and returned.
Both __call__ and pipe should delegate to the `predict()`
and `set_annotations()` methods.
"""
doc = self._get_doc(example)
predictions = self.predict([doc])
if isinstance(predictions, tuple) and len(predictions) == 2:
scores, tensors = predictions
self.set_annotations([doc], scores, tensors=tensors)
else:
self.set_annotations([doc], predictions)
if isinstance(example, Example):
example.doc = doc
return example
return doc
def pipe(self, stream, batch_size=128, n_threads=-1, as_example=False):
"""Apply the pipe to a stream of documents.
Both __call__ and pipe should delegate to the `predict()`
and `set_annotations()` methods.
"""
for examples in util.minibatch(stream, size=batch_size):
docs = [self._get_doc(ex) for ex in examples]
predictions = self.predict(docs)
if isinstance(predictions, tuple) and len(tuple) == 2:
scores, tensors = predictions
self.set_annotations(docs, scores, tensors=tensors)
else:
self.set_annotations(docs, predictions)
if as_example:
for ex, doc in zip(examples, docs):
ex.doc = doc
yield ex
else:
yield from docs
def predict(self, docs):
"""Apply the pipeline's model to a batch of docs, without
modifying them.
"""
raise NotImplementedError
def set_annotations(self, docs, scores, tensors=None):
"""Modify a batch of documents, using pre-computed scores."""
raise NotImplementedError
def update(self, examples, set_annotations=False, drop=0.0, sgd=None, losses=None):
"""Learn from a batch of documents and gold-standard information,
updating the pipe's model.
Delegates to predict() and get_loss().
"""
if set_annotations:
docs = (self._get_doc(ex) for ex in examples)
docs = list(self.pipe(docs))
def rehearse(self, examples, sgd=None, losses=None, **config):
pass
def get_loss(self, examples, scores):
"""Find the loss and gradient of loss for the batch of
examples (with embedded docs) and their predicted scores."""
raise NotImplementedError
def add_label(self, label):
"""Add an output label, to be predicted by the model.
It's possible to extend pretrained models with new labels,
but care should be taken to avoid the "catastrophic forgetting"
problem.
"""
raise NotImplementedError
def create_optimizer(self):
return create_default_optimizer()
def begin_training(
self, get_examples=lambda: [], pipeline=None, sgd=None, **kwargs
):
"""Initialize the pipe for training, using data exampes if available.
If no model has been initialized yet, the model is added."""
self.model.initialize()
if hasattr(self, "vocab"):
link_vectors_to_models(self.vocab)
if sgd is None:
sgd = self.create_optimizer()
return sgd
def set_output(self, nO):
self.model.set_dim("nO", nO)
if self.model.has_ref("output_layer"):
self.model.get_ref("output_layer").set_dim("nO", nO)
def get_gradients(self):
"""Get non-zero gradients of the model's parameters, as a dictionary
keyed by the parameter ID. The values are (weights, gradients) tuples.
"""
gradients = {}
queue = [self.model]
seen = set()
for node in queue:
if node.id in seen:
continue
seen.add(node.id)
if hasattr(node, "_mem") and node._mem.gradient.any():
gradients[node.id] = [node._mem.weights, node._mem.gradient]
if hasattr(node, "_layers"):
queue.extend(node._layers)
return gradients
def use_params(self, params):
"""Modify the pipe's model, to use the given parameter values."""
with self.model.use_params(params):
yield
def to_bytes(self, exclude=tuple(), **kwargs):
"""Serialize the pipe to a bytestring.
exclude (list): String names of serialization fields to exclude.
RETURNS (bytes): The serialized object.
"""
serialize = {}
serialize["cfg"] = lambda: srsly.json_dumps(self.cfg)
serialize["model"] = self.model.to_bytes
if hasattr(self, "vocab"):
serialize["vocab"] = self.vocab.to_bytes
exclude = util.get_serialization_exclude(serialize, exclude, kwargs)
return util.to_bytes(serialize, exclude)
def from_bytes(self, bytes_data, exclude=tuple(), **kwargs):
"""Load the pipe from a bytestring."""
def load_model(b):
try:
self.model.from_bytes(b)
except AttributeError:
raise ValueError(Errors.E149)
deserialize = {}
if hasattr(self, "vocab"):
deserialize["vocab"] = lambda b: self.vocab.from_bytes(b)
deserialize["cfg"] = lambda b: self.cfg.update(srsly.json_loads(b))
deserialize["model"] = load_model
exclude = util.get_serialization_exclude(deserialize, exclude, kwargs)
util.from_bytes(bytes_data, deserialize, exclude)
return self
def to_disk(self, path, exclude=tuple(), **kwargs):
"""Serialize the pipe to disk."""
serialize = {}
serialize["cfg"] = lambda p: srsly.write_json(p, self.cfg)
serialize["vocab"] = lambda p: self.vocab.to_disk(p)
serialize["model"] = lambda p: p.open("wb").write(self.model.to_bytes())
exclude = util.get_serialization_exclude(serialize, exclude, kwargs)
util.to_disk(path, serialize, exclude)
def from_disk(self, path, exclude=tuple(), **kwargs):
"""Load the pipe from disk."""
def load_model(p):
try:
self.model.from_bytes(p.open("rb").read())
except AttributeError:
raise ValueError(Errors.E149)
deserialize = {}
deserialize["vocab"] = lambda p: self.vocab.from_disk(p)
deserialize["cfg"] = lambda p: self.cfg.update(_load_cfg(p))
deserialize["model"] = load_model
exclude = util.get_serialization_exclude(deserialize, exclude, kwargs)
util.from_disk(path, deserialize, exclude)
return self
@component("tensorizer", assigns=["doc.tensor"])
class Tensorizer(Pipe):
"""Pre-train position-sensitive vectors for tokens."""
def __init__(self, vocab, model, **cfg):
"""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.
**cfg: Config parameters.
"""
self.vocab = vocab
self.model = model
self.input_models = []
self.cfg = dict(cfg)
def __call__(self, example):
"""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.
"""
doc = self._get_doc(example)
tokvecses = self.predict([doc])
self.set_annotations([doc], tokvecses)
if isinstance(example, Example):
example.doc = doc
return example
return doc
def pipe(self, stream, batch_size=128, n_threads=-1, as_example=False):
"""Process `Doc` objects as a stream.
stream (iterator): A sequence of `Doc` or `Example` objects to process.
batch_size (int): Number of `Doc` or `Example` objects to group.
YIELDS (iterator): A sequence of `Doc` or `Example` objects, in order of input.
"""
for examples in util.minibatch(stream, size=batch_size):
docs = [self._get_doc(ex) for ex in examples]
tensors = self.predict(docs)
self.set_annotations(docs, tensors)
if as_example:
for ex, doc in zip(examples, docs):
ex.doc = doc
yield ex
else:
yield from docs
def predict(self, docs):
"""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 docs.
"""
inputs = self.model.ops.flatten([doc.tensor for doc in docs])
outputs = self.model(inputs)
return self.model.ops.unflatten(outputs, [len(d) for d in docs])
def set_annotations(self, docs, tensors):
"""Set the tensor attribute for a batch of documents.
docs (iterable): A sequence of `Doc` objects.
tensors (object): Vector representation for each token in the docs.
"""
for doc, tensor in zip(docs, tensors):
if tensor.shape[0] != len(doc):
raise ValueError(Errors.E076.format(rows=tensor.shape[0], words=len(doc)))
doc.tensor = tensor
def update(self, examples, state=None, drop=0.0, set_annotations=False, sgd=None, losses=None):
"""Update the model.
docs (iterable): A batch of `Doc` objects.
golds (iterable): A batch of `GoldParse` objects.
drop (float): The dropout rate.
sgd (callable): An optimizer.
RETURNS (dict): Results from the update.
"""
examples = Example.to_example_objects(examples)
inputs = []
bp_inputs = []
set_dropout_rate(self.model, drop)
for tok2vec in self.input_models:
set_dropout_rate(tok2vec, drop)
tensor, bp_tensor = tok2vec.begin_update([ex.doc for ex in examples])
inputs.append(tensor)
bp_inputs.append(bp_tensor)
inputs = self.model.ops.xp.hstack(inputs)
scores, bp_scores = self.model.begin_update(inputs)
loss, d_scores = self.get_loss(examples, scores)
d_inputs = bp_scores(d_scores, sgd=sgd)
d_inputs = self.model.ops.xp.split(d_inputs, len(self.input_models), axis=1)
for d_input, bp_input in zip(d_inputs, bp_inputs):
bp_input(d_input)
if sgd is not None:
for tok2vec in self.input_models:
tok2vec.finish_update(sgd)
self.model.finish_update(sgd)
if losses is not None:
losses.setdefault(self.name, 0.0)
losses[self.name] += loss
return loss
def get_loss(self, examples, prediction):
examples = Example.to_example_objects(examples)
ids = self.model.ops.flatten([ex.doc.to_array(ID).ravel() for ex in examples])
target = self.vocab.vectors.data[ids]
d_scores = (prediction - target) / prediction.shape[0]
loss = (d_scores ** 2).sum()
return loss, d_scores
def begin_training(self, get_examples=lambda: [], pipeline=None, sgd=None, **kwargs):
"""Allocate models, pre-process training data and acquire an
optimizer.
get_examples (iterable): Gold-standard training data.
pipeline (list): The pipeline the model is part of.
"""
if pipeline is not None:
for name, model in pipeline:
if model.has_ref("tok2vec"):
self.input_models.append(model.get_ref("tok2vec"))
self.model.initialize()
link_vectors_to_models(self.vocab)
if sgd is None:
sgd = self.create_optimizer()
return sgd
@component("tagger", assigns=["token.tag", "token.pos", "token.lemma"])
class Tagger(Pipe):
"""Pipeline component for part-of-speech tagging.
DOCS: https://spacy.io/api/tagger
"""
def __init__(self, vocab, model, **cfg):
self.vocab = vocab
self.model = model
self._rehearsal_model = None
self.cfg = dict(sorted(cfg.items()))
@property
def labels(self):
return tuple(self.vocab.morphology.tag_names)
def __call__(self, example):
doc = self._get_doc(example)
tags = self.predict([doc])
self.set_annotations([doc], tags)
if isinstance(example, Example):
example.doc = doc
return example
return doc
def pipe(self, stream, batch_size=128, n_threads=-1, as_example=False):
for examples in util.minibatch(stream, size=batch_size):
docs = [self._get_doc(ex) for ex in examples]
tag_ids = self.predict(docs)
assert len(docs) == len(examples)
assert len(tag_ids) == len(examples)
self.set_annotations(docs, tag_ids)
if as_example:
for ex, doc in zip(examples, docs):
ex.doc = doc
yield ex
else:
yield from docs
def predict(self, docs):
if not any(len(doc) for doc in docs):
# Handle cases where there are no tokens in any docs.
n_labels = len(self.labels)
guesses = [self.model.ops.alloc((0, n_labels)) for doc in docs]
assert len(guesses) == len(docs)
return guesses
scores = self.model.predict(docs)
assert len(scores) == len(docs), (len(scores), len(docs))
guesses = self._scores2guesses(scores)
assert len(guesses) == len(docs)
return guesses
def _scores2guesses(self, scores):
guesses = []
for doc_scores in scores:
doc_guesses = doc_scores.argmax(axis=1)
if not isinstance(doc_guesses, numpy.ndarray):
doc_guesses = doc_guesses.get()
guesses.append(doc_guesses)
return guesses
def set_annotations(self, docs, batch_tag_ids):
if isinstance(docs, Doc):
docs = [docs]
cdef Doc doc
cdef int idx = 0
cdef Vocab vocab = self.vocab
assign_morphology = self.cfg.get("set_morphology", True)
for i, doc in enumerate(docs):
doc_tag_ids = batch_tag_ids[i]
if hasattr(doc_tag_ids, "get"):
doc_tag_ids = doc_tag_ids.get()
for j, tag_id in enumerate(doc_tag_ids):
# Don't clobber preset POS tags
if doc.c[j].tag == 0:
if doc.c[j].pos == 0 and assign_morphology:
# Don't clobber preset lemmas
lemma = doc.c[j].lemma
vocab.morphology.assign_tag_id(&doc.c[j], tag_id)
if lemma != 0 and lemma != doc.c[j].lex.orth:
doc.c[j].lemma = lemma
else:
doc.c[j].tag = self.vocab.strings[self.labels[tag_id]]
idx += 1
doc.is_tagged = True
def update(self, examples, drop=0., sgd=None, losses=None, set_annotations=False):
examples = Example.to_example_objects(examples)
if losses is not None and self.name not in losses:
losses[self.name] = 0.
if not any(len(ex.doc) if ex.doc else 0 for ex in examples):
# Handle cases where there are no tokens in any docs.
return
set_dropout_rate(self.model, drop)
tag_scores, bp_tag_scores = self.model.begin_update([ex.doc for ex in examples])
loss, d_tag_scores = self.get_loss(examples, tag_scores)
bp_tag_scores(d_tag_scores)
if sgd not in (None, False):
self.model.finish_update(sgd)
if losses is not None:
losses[self.name] += loss
if set_annotations:
docs = [ex.doc for ex in examples]
self.set_annotations(docs, self._scores2guesses(tag_scores))
def rehearse(self, examples, drop=0., sgd=None, losses=None):
"""Perform a 'rehearsal' update, where we try to match the output of
an initial model.
"""
if self._rehearsal_model is None:
return
examples = Example.to_example_objects(examples)
docs = [ex.doc for ex in examples]
if not any(len(doc) for doc in docs):
# Handle cases where there are no tokens in any docs.
return
set_dropout_rate(self.model, drop)
guesses, backprop = self.model.begin_update(docs)
target = self._rehearsal_model(examples)
gradient = guesses - target
backprop(gradient)
self.model.finish_update(sgd)
if losses is not None:
losses.setdefault(self.name, 0.0)
losses[self.name] += (gradient**2).sum()
def get_loss(self, examples, scores):
scores = self.model.ops.flatten(scores)
tag_index = {tag: i for i, tag in enumerate(self.labels)}
cdef int idx = 0
correct = numpy.zeros((scores.shape[0],), dtype="i")
guesses = scores.argmax(axis=1)
known_labels = numpy.ones((scores.shape[0], 1), dtype="f")
for ex in examples:
gold = ex.gold
for tag in gold.tags:
if tag is None:
correct[idx] = guesses[idx]
elif tag in tag_index:
correct[idx] = tag_index[tag]
else:
correct[idx] = 0
known_labels[idx] = 0.
idx += 1
correct = self.model.ops.xp.array(correct, dtype="i")
d_scores = scores - to_categorical(correct, n_classes=scores.shape[1])
d_scores *= self.model.ops.asarray(known_labels)
loss = (d_scores**2).sum()
docs = [ex.doc for ex in examples]
d_scores = self.model.ops.unflatten(d_scores, [len(d) for d in docs])
return float(loss), d_scores
def begin_training(self, get_examples=lambda: [], pipeline=None, sgd=None,
**kwargs):
lemma_tables = ["lemma_rules", "lemma_index", "lemma_exc", "lemma_lookup"]
if not any(table in self.vocab.lookups for table in lemma_tables):
warnings.warn(Warnings.W022)
orig_tag_map = dict(self.vocab.morphology.tag_map)
new_tag_map = {}
for example in get_examples():
for tag in example.token_annotation.tags:
if tag in orig_tag_map:
new_tag_map[tag] = orig_tag_map[tag]
else:
new_tag_map[tag] = {POS: X}
cdef Vocab vocab = self.vocab
if new_tag_map:
vocab.morphology = Morphology(vocab.strings, new_tag_map,
vocab.morphology.lemmatizer,
exc=vocab.morphology.exc)
self.set_output(len(self.labels))
self.model.initialize()
# Get batch of example docs, example outputs to call begin_training().
# This lets the model infer shapes.
link_vectors_to_models(self.vocab)
if sgd is None:
sgd = self.create_optimizer()
return sgd
def add_label(self, label, values=None):
if not isinstance(label, str):
raise ValueError(Errors.E187)
if label in self.labels:
return 0
if self.model.has_dim("nO"):
# Here's how the model resizing will work, once the
# neuron-to-tag mapping is no longer controlled by
# the Morphology class, which sorts the tag names.
# The sorting makes adding labels difficult.
# 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
raise ValueError(TempErrors.T003)
tag_map = dict(self.vocab.morphology.tag_map)
if values is None:
values = {POS: "X"}
tag_map[label] = values
self.vocab.morphology = Morphology(
self.vocab.strings, tag_map=tag_map,
lemmatizer=self.vocab.morphology.lemmatizer,
exc=self.vocab.morphology.exc)
return 1
def use_params(self, params):
with self.model.use_params(params):
yield
def to_bytes(self, exclude=tuple(), **kwargs):
serialize = {}
serialize["model"] = self.model.to_bytes
serialize["vocab"] = self.vocab.to_bytes
serialize["cfg"] = lambda: srsly.json_dumps(self.cfg)
tag_map = dict(sorted(self.vocab.morphology.tag_map.items()))
serialize["tag_map"] = lambda: srsly.msgpack_dumps(tag_map)
exclude = util.get_serialization_exclude(serialize, exclude, kwargs)
return util.to_bytes(serialize, exclude)
def from_bytes(self, bytes_data, exclude=tuple(), **kwargs):
def load_model(b):
try:
self.model.from_bytes(b)
except AttributeError:
raise ValueError(Errors.E149)
def load_tag_map(b):
tag_map = srsly.msgpack_loads(b)
self.vocab.morphology = Morphology(
self.vocab.strings, tag_map=tag_map,
lemmatizer=self.vocab.morphology.lemmatizer,
exc=self.vocab.morphology.exc)
deserialize = {
"vocab": lambda b: self.vocab.from_bytes(b),
"tag_map": load_tag_map,
"cfg": lambda b: self.cfg.update(srsly.json_loads(b)),
"model": lambda b: load_model(b),
}
exclude = util.get_serialization_exclude(deserialize, exclude, kwargs)
util.from_bytes(bytes_data, deserialize, exclude)
return self
def to_disk(self, path, exclude=tuple(), **kwargs):
tag_map = dict(sorted(self.vocab.morphology.tag_map.items()))
serialize = {
"vocab": lambda p: self.vocab.to_disk(p),
"tag_map": lambda p: srsly.write_msgpack(p, tag_map),
"model": lambda p: p.open("wb").write(self.model.to_bytes()),
"cfg": lambda p: srsly.write_json(p, self.cfg),
}
exclude = util.get_serialization_exclude(serialize, exclude, kwargs)
util.to_disk(path, serialize, exclude)
def from_disk(self, path, exclude=tuple(), **kwargs):
def load_model(p):
with p.open("rb") as file_:
try:
self.model.from_bytes(file_.read())
except AttributeError:
raise ValueError(Errors.E149)
def load_tag_map(p):
tag_map = srsly.read_msgpack(p)
self.vocab.morphology = Morphology(
self.vocab.strings, tag_map=tag_map,
lemmatizer=self.vocab.morphology.lemmatizer,
exc=self.vocab.morphology.exc)
deserialize = {
"vocab": lambda p: self.vocab.from_disk(p),
"cfg": lambda p: self.cfg.update(_load_cfg(p)),
"tag_map": load_tag_map,
"model": load_model,
}
exclude = util.get_serialization_exclude(deserialize, exclude, kwargs)
util.from_disk(path, deserialize, exclude)
return self
@component("senter", assigns=["token.is_sent_start"])
class SentenceRecognizer(Tagger):
"""Pipeline component for sentence segmentation.
DOCS: https://spacy.io/api/sentencerecognizer
"""
def __init__(self, vocab, model, **cfg):
self.vocab = vocab
self.model = model
self._rehearsal_model = None
self.cfg = dict(sorted(cfg.items()))
@property
def labels(self):
# labels are numbered by index internally, so this matches GoldParse
# and Example where the sentence-initial tag is 1 and other positions
# are 0
return tuple(["I", "S"])
def set_annotations(self, docs, batch_tag_ids):
if isinstance(docs, Doc):
docs = [docs]
cdef Doc doc
for i, doc in enumerate(docs):
doc_tag_ids = batch_tag_ids[i]
if hasattr(doc_tag_ids, "get"):
doc_tag_ids = doc_tag_ids.get()
for j, tag_id in enumerate(doc_tag_ids):
# Don't clobber existing sentence boundaries
if doc.c[j].sent_start == 0:
if tag_id == 1:
doc.c[j].sent_start = 1
else:
doc.c[j].sent_start = -1
def get_loss(self, examples, scores):
scores = self.model.ops.flatten(scores)
tag_index = range(len(self.labels))
cdef int idx = 0
correct = numpy.zeros((scores.shape[0],), dtype="i")
guesses = scores.argmax(axis=1)
known_labels = numpy.ones((scores.shape[0], 1), dtype="f")
for ex in examples:
gold = ex.gold
for sent_start in gold.sent_starts:
if sent_start is None:
correct[idx] = guesses[idx]
elif sent_start in tag_index:
correct[idx] = sent_start
else:
correct[idx] = 0
known_labels[idx] = 0.
idx += 1
correct = self.model.ops.xp.array(correct, dtype="i")
d_scores = scores - to_categorical(correct, n_classes=scores.shape[1])
d_scores *= self.model.ops.asarray(known_labels)
loss = (d_scores**2).sum()
docs = [ex.doc for ex in examples]
d_scores = self.model.ops.unflatten(d_scores, [len(d) for d in docs])
return float(loss), d_scores
def begin_training(self, get_examples=lambda: [], pipeline=None, sgd=None,
**kwargs):
self.set_output(len(self.labels))
self.model.initialize()
link_vectors_to_models(self.vocab)
if sgd is None:
sgd = self.create_optimizer()
return sgd
def add_label(self, label, values=None):
raise NotImplementedError
def to_bytes(self, exclude=tuple(), **kwargs):
serialize = {}
serialize["model"] = self.model.to_bytes
serialize["vocab"] = self.vocab.to_bytes
serialize["cfg"] = lambda: srsly.json_dumps(self.cfg)
exclude = util.get_serialization_exclude(serialize, exclude, kwargs)
return util.to_bytes(serialize, exclude)
def from_bytes(self, bytes_data, exclude=tuple(), **kwargs):
def load_model(b):
try:
self.model.from_bytes(b)
except AttributeError:
raise ValueError(Errors.E149)
deserialize = {
"vocab": lambda b: self.vocab.from_bytes(b),
"cfg": lambda b: self.cfg.update(srsly.json_loads(b)),
"model": lambda b: load_model(b),
}
exclude = util.get_serialization_exclude(deserialize, exclude, kwargs)
util.from_bytes(bytes_data, deserialize, exclude)
return self
def to_disk(self, path, exclude=tuple(), **kwargs):
serialize = {
"vocab": lambda p: self.vocab.to_disk(p),
"model": lambda p: p.open("wb").write(self.model.to_bytes()),
"cfg": lambda p: srsly.write_json(p, self.cfg),
}
exclude = util.get_serialization_exclude(serialize, exclude, kwargs)
util.to_disk(path, serialize, exclude)
def from_disk(self, path, exclude=tuple(), **kwargs):
def load_model(p):
with p.open("rb") as file_:
try:
self.model.from_bytes(file_.read())
except AttributeError:
raise ValueError(Errors.E149)
deserialize = {
"vocab": lambda p: self.vocab.from_disk(p),
"cfg": lambda p: self.cfg.update(_load_cfg(p)),
"model": load_model,
}
exclude = util.get_serialization_exclude(deserialize, exclude, kwargs)
util.from_disk(path, deserialize, exclude)
return self
@component("nn_labeller")
class MultitaskObjective(Tagger):
"""Experimental: Assist training of a parser or tagger, by training a
side-objective.
"""
def __init__(self, vocab, model, target='dep_tag_offset', **cfg):
self.vocab = vocab
self.model = model
if target == "dep":
self.make_label = self.make_dep
elif target == "tag":
self.make_label = self.make_tag
elif target == "ent":
self.make_label = self.make_ent
elif target == "dep_tag_offset":
self.make_label = self.make_dep_tag_offset
elif target == "ent_tag":
self.make_label = self.make_ent_tag
elif target == "sent_start":
self.make_label = self.make_sent_start
elif hasattr(target, "__call__"):
self.make_label = target
else:
raise ValueError(Errors.E016)
self.cfg = dict(cfg)
# TODO: remove - put in config
self.cfg.setdefault("maxout_pieces", 2)
@property
def labels(self):
return self.cfg.setdefault("labels", {})
@labels.setter
def labels(self, value):
self.cfg["labels"] = value
def set_annotations(self, docs, dep_ids, tensors=None):
pass
def begin_training(self, get_examples=lambda: [], pipeline=None, tok2vec=None,
sgd=None, **kwargs):
gold_examples = nonproj.preprocess_training_data(get_examples())
# for raw_text, doc_annot in gold_tuples:
for example in gold_examples:
for i in range(len(example.token_annotation.ids)):
label = self.make_label(i, example.token_annotation)
if label is not None and label not in self.labels:
self.labels[label] = len(self.labels)
self.model.initialize()
link_vectors_to_models(self.vocab)
if sgd is None:
sgd = self.create_optimizer()
return sgd
def predict(self, docs):
tokvecs = self.model.get_ref("tok2vec")(docs)
scores = self.model.get_ref("softmax")(tokvecs)
return tokvecs, scores
def get_loss(self, examples, scores):
cdef int idx = 0
correct = numpy.zeros((scores.shape[0],), dtype="i")
guesses = scores.argmax(axis=1)
golds = [ex.gold for ex in examples]
docs = [ex.doc for ex in examples]
for i, gold in enumerate(golds):
for j in range(len(docs[i])):
# Handels alignment for tokenization differences
token_annotation = gold.get_token_annotation()
label = self.make_label(j, token_annotation)
if label is None or label not in self.labels:
correct[idx] = guesses[idx]
else:
correct[idx] = self.labels[label]
idx += 1
correct = self.model.ops.xp.array(correct, dtype="i")
d_scores = scores - to_categorical(correct, n_classes=scores.shape[1])
loss = (d_scores**2).sum()
return float(loss), d_scores
@staticmethod
def make_dep(i, token_annotation):
if token_annotation.deps[i] is None or token_annotation.heads[i] is None:
return None
return token_annotation.deps[i]
@staticmethod
def make_tag(i, token_annotation):
return token_annotation.tags[i]
@staticmethod
def make_ent(i, token_annotation):
if token_annotation.entities is None:
return None
return token_annotation.entities[i]
@staticmethod
def make_dep_tag_offset(i, token_annotation):
if token_annotation.deps[i] is None or token_annotation.heads[i] is None:
return None
offset = token_annotation.heads[i] - i
offset = min(offset, 2)
offset = max(offset, -2)
return f"{token_annotation.deps[i]}-{token_annotation.tags[i]}:{offset}"
@staticmethod
def make_ent_tag(i, token_annotation):
if token_annotation.entities is None or token_annotation.entities[i] is None:
return None
else:
return f"{token_annotation.tags[i]}-{token_annotation.entities[i]}"
@staticmethod
def make_sent_start(target, token_annotation, cache=True, _cache={}):
"""A multi-task objective for representing sentence boundaries,
using BILU scheme. (O is impossible)
The implementation of this method uses an internal cache that relies
on the identity of the heads array, to avoid requiring a new piece
of gold data. You can pass cache=False if you know the cache will
do the wrong thing.
"""
words = token_annotation.words
heads = token_annotation.heads
assert len(words) == len(heads)
assert target < len(words), (target, len(words))
if cache:
if id(heads) in _cache:
return _cache[id(heads)][target]
else:
for key in list(_cache.keys()):
_cache.pop(key)
sent_tags = ["I-SENT"] * len(words)
_cache[id(heads)] = sent_tags
else:
sent_tags = ["I-SENT"] * len(words)
def _find_root(child):
seen = set([child])
while child is not None and heads[child] != child:
seen.add(child)
child = heads[child]
return child
sentences = {}
for i in range(len(words)):
root = _find_root(i)
if root is None:
sent_tags[i] = None
else:
sentences.setdefault(root, []).append(i)
for root, span in sorted(sentences.items()):
if len(span) == 1:
sent_tags[span[0]] = "U-SENT"
else:
sent_tags[span[0]] = "B-SENT"
sent_tags[span[-1]] = "L-SENT"
return sent_tags[target]
class ClozeMultitask(Pipe):
def __init__(self, vocab, model, **cfg):
self.vocab = vocab
self.model = model
self.cfg = cfg
self.distance = CosineDistance(ignore_zeros=True, normalize=False)
def set_annotations(self, docs, dep_ids, tensors=None):
pass
def begin_training(self, get_examples=lambda: [], pipeline=None,
tok2vec=None, sgd=None, **kwargs):
link_vectors_to_models(self.vocab)
self.model.initialize()
X = self.model.ops.alloc((5, self.model.get_ref("tok2vec").get_dim("nO")))
self.model.output_layer.begin_training(X)
if sgd is None:
sgd = self.create_optimizer()
return sgd
def predict(self, docs):
tokvecs = self.model.get_ref("tok2vec")(docs)
vectors = self.model.get_ref("output_layer")(tokvecs)
return tokvecs, vectors
def get_loss(self, examples, vectors, prediction):
# The simplest way to implement this would be to vstack the
# token.vector values, but that's a bit inefficient, especially on GPU.
# Instead we fetch the index into the vectors table for each of our tokens,
# and look them up all at once. This prevents data copying.
ids = self.model.ops.flatten([ex.doc.to_array(ID).ravel() for ex in examples])
target = vectors[ids]
gradient = self.distance.get_grad(prediction, target)
loss = self.distance.get_loss(prediction, target)
return loss, gradient
def update(self, examples, drop=0., set_annotations=False, sgd=None, losses=None):
pass
def rehearse(self, examples, drop=0., sgd=None, losses=None):
examples = Example.to_example_objects(examples)
if losses is not None and self.name not in losses:
losses[self.name] = 0.
set_dropout_rate(self.model, drop)
predictions, bp_predictions = self.model.begin_update([ex.doc for ex in examples])
loss, d_predictions = self.get_loss(examples, self.vocab.vectors.data, predictions)
bp_predictions(d_predictions)
if sgd is not None:
self.model.finish_update(sgd)
if losses is not None:
losses[self.name] += loss
@component("textcat", assigns=["doc.cats"])
class TextCategorizer(Pipe):
"""Pipeline component for text classification.
DOCS: https://spacy.io/api/textcategorizer
"""
def __init__(self, vocab, model, **cfg):
self.vocab = vocab
self.model = model
self._rehearsal_model = None
self.cfg = dict(cfg)
@property
def labels(self):
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
def labels(self, value):
self.cfg["labels"] = tuple(value)
def pipe(self, stream, batch_size=128, n_threads=-1, as_example=False):
for examples in util.minibatch(stream, size=batch_size):
docs = [self._get_doc(ex) for ex in examples]
scores, tensors = self.predict(docs)
self.set_annotations(docs, scores, tensors=tensors)
if as_example:
for ex, doc in zip(examples, docs):
ex.doc = doc
yield ex
else:
yield from docs
def predict(self, docs):
tensors = [doc.tensor for doc in docs]
if not any(len(doc) for doc in docs):
# Handle cases where there are no tokens in any docs.
xp = get_array_module(tensors)
scores = xp.zeros((len(docs), len(self.labels)))
return scores, tensors
scores = self.model.predict(docs)
scores = self.model.ops.asarray(scores)
return scores, tensors
def set_annotations(self, docs, scores, tensors=None):
for i, doc in enumerate(docs):
for j, label in enumerate(self.labels):
doc.cats[label] = float(scores[i, j])
def update(self, examples, state=None, drop=0., set_annotations=False, sgd=None, losses=None):
examples = Example.to_example_objects(examples)
if not any(len(ex.doc) if ex.doc else 0 for ex in examples):
# Handle cases where there are no tokens in any docs.
return
set_dropout_rate(self.model, drop)
scores, bp_scores = self.model.begin_update([ex.doc for ex in examples])
loss, d_scores = self.get_loss(examples, scores)
bp_scores(d_scores)
if sgd is not None:
self.model.finish_update(sgd)
if losses is not None:
losses.setdefault(self.name, 0.0)
losses[self.name] += loss
if set_annotations:
docs = [ex.doc for ex in examples]
self.set_annotations(docs, scores=scores)
def rehearse(self, examples, drop=0., sgd=None, losses=None):
if self._rehearsal_model is None:
return
examples = Example.to_example_objects(examples)
docs=[ex.doc for ex in examples]
if not any(len(doc) for doc in docs):
# Handle cases where there are no tokens in any docs.
return
set_dropout_rate(self.model, drop)
scores, bp_scores = self.model.begin_update(docs)
target = self._rehearsal_model(examples)
gradient = scores - target
bp_scores(gradient)
if sgd is not None:
self.model.finish_update(sgd)
if losses is not None:
losses.setdefault(self.name, 0.0)
losses[self.name] += (gradient**2).sum()
def _examples_to_truth(self, examples):
golds = [ex.gold for ex in examples]
truths = numpy.zeros((len(golds), len(self.labels)), dtype="f")
not_missing = numpy.ones((len(golds), len(self.labels)), dtype="f")
for i, gold in enumerate(golds):
for j, label in enumerate(self.labels):
if label in gold.cats:
truths[i, j] = gold.cats[label]
else:
not_missing[i, j] = 0.
truths = self.model.ops.asarray(truths)
return truths, not_missing
def get_loss(self, examples, scores):
truths, not_missing = self._examples_to_truth(examples)
not_missing = self.model.ops.asarray(not_missing)
d_scores = (scores-truths) / scores.shape[0]
d_scores *= not_missing
mean_square_error = (d_scores**2).sum(axis=1).mean()
return float(mean_square_error), d_scores
def add_label(self, label):
if not isinstance(label, str):
raise ValueError(Errors.E187)
if label in self.labels:
return 0
if self.model.has_dim("nO"):
# This functionality was available previously, but was broken.
# The problem is that we resize the last layer, but the last layer
# is actually just an ensemble. We're not resizing the child layers
# - a huge problem.
raise ValueError(Errors.E116)
# smaller = self.model._layers[-1]
# larger = Linear(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 = tuple(list(self.labels) + [label])
return 1
def begin_training(self, get_examples=lambda: [], pipeline=None, sgd=None, **kwargs):
# TODO: begin_training is not guaranteed to see all data / labels ?
examples = list(get_examples())
for example in examples:
for cat in example.doc_annotation.cats:
self.add_label(cat)
self.require_labels()
docs = [Doc(Vocab(), words=["hello"])]
truths, _ = self._examples_to_truth(examples)
self.set_output(len(self.labels))
self.model.initialize(X=docs, Y=truths)
if sgd is None:
sgd = self.create_optimizer()
return sgd
cdef class DependencyParser(Parser):
"""Pipeline component for dependency parsing.
DOCS: https://spacy.io/api/dependencyparser
"""
# cdef classes can't have decorators, so we're defining this here
name = "parser"
factory = "parser"
assigns = ["token.dep", "token.is_sent_start", "doc.sents"]
requires = []
TransitionSystem = ArcEager
@property
def postprocesses(self):
output = [nonproj.deprojectivize]
if self.cfg.get("learn_tokens") is True:
output.append(merge_subtokens)
return tuple(output)
def add_multitask_objective(self, target):
if target == "cloze":
cloze = ClozeMultitask(self.vocab)
self._multitasks.append(cloze)
else:
labeller = MultitaskObjective(self.vocab, target=target)
self._multitasks.append(labeller)
def init_multitask_objectives(self, get_examples, pipeline, sgd=None, **cfg):
for labeller in self._multitasks:
tok2vec = self.model.get_ref("tok2vec")
labeller.begin_training(get_examples, pipeline=pipeline,
tok2vec=tok2vec, sgd=sgd)
def __reduce__(self):
return (DependencyParser, (self.vocab, self.moves, self.model), None, None)
@property
def labels(self):
labels = set()
# Get the labels from the model by looking at the available moves
for move in self.move_names:
if "-" in move:
label = move.split("-")[1]
if "||" in label:
label = label.split("||")[1]
labels.add(label)
return tuple(sorted(labels))
cdef class EntityRecognizer(Parser):
"""Pipeline component for named entity recognition.
DOCS: https://spacy.io/api/entityrecognizer
"""
name = "ner"
factory = "ner"
assigns = ["doc.ents", "token.ent_iob", "token.ent_type"]
requires = []
TransitionSystem = BiluoPushDown
def add_multitask_objective(self, target):
if target == "cloze":
cloze = ClozeMultitask(self.vocab)
self._multitasks.append(cloze)
else:
labeller = MultitaskObjective(self.vocab, target=target)
self._multitasks.append(labeller)
def init_multitask_objectives(self, get_examples, pipeline, sgd=None, **cfg):
for labeller in self._multitasks:
tok2vec = self.model.get_ref("tok2vec")
labeller.begin_training(get_examples, pipeline=pipeline,
tok2vec=tok2vec)
def __reduce__(self):
return (EntityRecognizer, (self.vocab, self.moves, self.model),
None, None)
@property
def labels(self):
# Get the labels from the model by looking at the available moves, e.g.
# B-PERSON, I-PERSON, L-PERSON, U-PERSON
labels = set(move.split("-")[1] for move in self.move_names
if move[0] in ("B", "I", "L", "U"))
return tuple(sorted(labels))
@component(
"entity_linker",
requires=["doc.ents", "doc.sents", "token.ent_iob", "token.ent_type"],
assigns=["token.ent_kb_id"]
)
class EntityLinker(Pipe):
"""Pipeline component for named entity linking.
DOCS: https://spacy.io/api/entitylinker
"""
NIL = "NIL" # string used to refer to a non-existing link
def __init__(self, vocab, model, **cfg):
self.vocab = vocab
self.model = model
self.kb = None
self.cfg = dict(cfg)
self.distance = CosineDistance(normalize=False)
def set_kb(self, kb):
self.kb = kb
def require_kb(self):
# Raise an error if the knowledge base is not initialized.
if getattr(self, "kb", None) in (None, True, False):
raise ValueError(Errors.E139.format(name=self.name))
def begin_training(self, get_examples=lambda: [], pipeline=None, sgd=None, **kwargs):
self.require_kb()
nO = self.kb.entity_vector_length
self.set_output(nO)
self.model.initialize()
if sgd is None:
sgd = self.create_optimizer()
return sgd
def update(self, examples, state=None, set_annotations=False, drop=0.0, sgd=None, losses=None):
self.require_kb()
if losses is not None:
losses.setdefault(self.name, 0.0)
if not examples:
return 0
examples = Example.to_example_objects(examples)
sentence_docs = []
docs = [ex.doc for ex in examples]
if set_annotations:
# This seems simpler than other ways to get that exact output -- but
# it does run the model twice :(
predictions = self.model.predict(docs)
golds = [ex.gold for ex in examples]
for doc, gold in zip(docs, golds):
ents_by_offset = dict()
for ent in doc.ents:
ents_by_offset[(ent.start_char, ent.end_char)] = ent
for entity, kb_dict in gold.links.items():
start, end = entity
mention = doc.text[start:end]
# the gold annotations should link to proper entities - if this fails, the dataset is likely corrupt
if not (start, end) in ents_by_offset:
raise RuntimeError(Errors.E188)
ent = ents_by_offset[(start, end)]
for kb_id, value in kb_dict.items():
# Currently only training on the positive instances - we assume there is at least 1 per doc/gold
if value:
try:
sentence_docs.append(ent.sent.as_doc())
except AttributeError:
# Catch the exception when ent.sent is None and provide a user-friendly warning
raise RuntimeError(Errors.E030)
set_dropout_rate(self.model, drop)
sentence_encodings, bp_context = self.model.begin_update(sentence_docs)
loss, d_scores = self.get_similarity_loss(scores=sentence_encodings, golds=golds)
bp_context(d_scores)
if sgd is not None:
self.model.finish_update(sgd)
if losses is not None:
losses[self.name] += loss
if set_annotations:
self.set_annotations(docs, predictions)
return loss
def get_similarity_loss(self, golds, scores):
entity_encodings = []
for gold in golds:
for entity, kb_dict in gold.links.items():
for kb_id, value in kb_dict.items():
# this loss function assumes we're only using positive examples
if value:
entity_encoding = self.kb.get_vector(kb_id)
entity_encodings.append(entity_encoding)
entity_encodings = self.model.ops.asarray(entity_encodings, dtype="float32")
if scores.shape != entity_encodings.shape:
raise RuntimeError(Errors.E147.format(method="get_similarity_loss", msg="gold entities do not match up"))
gradients = self.distance.get_grad(scores, entity_encodings)
loss = self.distance.get_loss(scores, entity_encodings)
loss = loss / len(entity_encodings)
return loss, gradients
def get_loss(self, examples, scores):
cats = []
for ex in examples:
for entity, kb_dict in ex.gold.links.items():
for kb_id, value in kb_dict.items():
cats.append([value])
cats = self.model.ops.asarray(cats, dtype="float32")
if len(scores) != len(cats):
raise RuntimeError(Errors.E147.format(method="get_loss", msg="gold entities do not match up"))
d_scores = (scores - cats)
loss = (d_scores ** 2).sum()
loss = loss / len(cats)
return loss, d_scores
def __call__(self, example):
doc = self._get_doc(example)
kb_ids, tensors = self.predict([doc])
self.set_annotations([doc], kb_ids, tensors=tensors)
if isinstance(example, Example):
example.doc = doc
return example
return doc
def pipe(self, stream, batch_size=128, n_threads=-1, as_example=False):
for examples in util.minibatch(stream, size=batch_size):
docs = [self._get_doc(ex) for ex in examples]
kb_ids, tensors = self.predict(docs)
self.set_annotations(docs, kb_ids, tensors=tensors)
if as_example:
for ex, doc in zip(examples, docs):
ex.doc = doc
yield ex
else:
yield from docs
def predict(self, docs):
""" Return the KB IDs for each entity in each doc, including NIL if there is no prediction """
self.require_kb()
entity_count = 0
final_kb_ids = []
final_tensors = []
if not docs:
return final_kb_ids, final_tensors
if isinstance(docs, Doc):
docs = [docs]
for i, doc in enumerate(docs):
if len(doc) > 0:
# Looping through each sentence and each entity
# This may go wrong if there are entities across sentences - which shouldn't happen normally.
for sent in doc.sents:
sent_doc = sent.as_doc()
# currently, the context is the same for each entity in a sentence (should be refined)
sentence_encoding = self.model.predict([sent_doc])[0]
xp = get_array_module(sentence_encoding)
sentence_encoding_t = sentence_encoding.T
sentence_norm = xp.linalg.norm(sentence_encoding_t)
for ent in sent_doc.ents:
entity_count += 1
to_discard = self.cfg.get("labels_discard", [])
if to_discard and ent.label_ in to_discard:
# ignoring this entity - setting to NIL
final_kb_ids.append(self.NIL)
final_tensors.append(sentence_encoding)
else:
candidates = self.kb.get_candidates(ent.text)
if not candidates:
# no prediction possible for this entity - setting to NIL
final_kb_ids.append(self.NIL)
final_tensors.append(sentence_encoding)
elif len(candidates) == 1:
# shortcut for efficiency reasons: take the 1 candidate
# TODO: thresholding
final_kb_ids.append(candidates[0].entity_)
final_tensors.append(sentence_encoding)
else:
random.shuffle(candidates)
# this will set all prior probabilities to 0 if they should be excluded from the model
prior_probs = xp.asarray([c.prior_prob for c in candidates])
if not self.cfg.get("incl_prior", True):
prior_probs = xp.asarray([0.0 for c in candidates])
scores = prior_probs
# add in similarity from the context
if self.cfg.get("incl_context", True):
entity_encodings = xp.asarray([c.entity_vector for c in candidates])
entity_norm = xp.linalg.norm(entity_encodings, axis=1)
if len(entity_encodings) != len(prior_probs):
raise RuntimeError(Errors.E147.format(method="predict", msg="vectors not of equal length"))
# cosine similarity
sims = xp.dot(entity_encodings, sentence_encoding_t) / (sentence_norm * entity_norm)
if sims.shape != prior_probs.shape:
raise ValueError(Errors.E161)
scores = prior_probs + sims - (prior_probs*sims)
# TODO: thresholding
best_index = scores.argmax()
best_candidate = candidates[best_index]
final_kb_ids.append(best_candidate.entity_)
final_tensors.append(sentence_encoding)
if not (len(final_tensors) == len(final_kb_ids) == entity_count):
raise RuntimeError(Errors.E147.format(method="predict", msg="result variables not of equal length"))
return final_kb_ids, final_tensors
def set_annotations(self, docs, kb_ids, tensors=None):
count_ents = len([ent for doc in docs for ent in doc.ents])
if count_ents != len(kb_ids):
raise ValueError(Errors.E148.format(ents=count_ents, ids=len(kb_ids)))
i=0
for doc in docs:
for ent in doc.ents:
kb_id = kb_ids[i]
i += 1
for token in ent:
token.ent_kb_id_ = kb_id
def to_disk(self, path, exclude=tuple(), **kwargs):
serialize = {}
self.cfg["entity_width"] = self.kb.entity_vector_length
serialize["cfg"] = lambda p: srsly.write_json(p, self.cfg)
serialize["vocab"] = lambda p: self.vocab.to_disk(p)
serialize["kb"] = lambda p: self.kb.dump(p)
serialize["model"] = lambda p: p.open("wb").write(self.model.to_bytes())
exclude = util.get_serialization_exclude(serialize, exclude, kwargs)
util.to_disk(path, serialize, exclude)
def from_disk(self, path, exclude=tuple(), **kwargs):
def load_model(p):
try:
self.model.from_bytes(p.open("rb").read())
except AttributeError:
raise ValueError(Errors.E149)
def load_kb(p):
kb = KnowledgeBase(vocab=self.vocab, entity_vector_length=self.cfg["entity_width"])
kb.load_bulk(p)
self.set_kb(kb)
deserialize = {}
deserialize["vocab"] = lambda p: self.vocab.from_disk(p)
deserialize["cfg"] = lambda p: self.cfg.update(_load_cfg(p))
deserialize["kb"] = load_kb
deserialize["model"] = load_model
exclude = util.get_serialization_exclude(deserialize, exclude, kwargs)
util.from_disk(path, deserialize, exclude)
return self
def rehearse(self, examples, sgd=None, losses=None, **config):
raise NotImplementedError
def add_label(self, label):
raise NotImplementedError
@component("sentencizer", assigns=["token.is_sent_start", "doc.sents"])
class Sentencizer(Pipe):
"""Segment the Doc into sentences using a rule-based strategy.
DOCS: https://spacy.io/api/sentencizer
"""
default_punct_chars = ['!', '.', '?', '։', '؟', '۔', '܀', '܁', '܂', '߹',
'', '', '', '', '', '', '', '', '', '', '', '', '',
'', '', '', '', '', '', '', '', '', '', '', '', '᱿',
'', '', '', '', '', '', '', '', '', '', '', '', '',
'', '', '', '', '', '', '', '', '', '', '', '', '',
'', '', '', '', '', '𐩖', '𐩗', '𑁇', '𑁈', '𑂾', '𑂿', '𑃀',
'𑃁', '𑅁', '𑅂', '𑅃', '𑇅', '𑇆', '𑇍', '𑇞', '𑇟', '𑈸', '𑈹', '𑈻', '𑈼',
'𑊩', '𑑋', '𑑌', '𑗂', '𑗃', '𑗉', '𑗊', '𑗋', '𑗌', '𑗍', '𑗎', '𑗏', '𑗐',
'𑗑', '𑗒', '𑗓', '𑗔', '𑗕', '𑗖', '𑗗', '𑙁', '𑙂', '𑜼', '𑜽', '𑜾', '𑩂',
'𑩃', '𑪛', '𑪜', '𑱁', '𑱂', '𖩮', '𖩯', '𖫵', '𖬷', '𖬸', '𖭄', '𛲟', '𝪈']
def __init__(self, punct_chars=None, **kwargs):
"""Initialize the sentencizer.
punct_chars (list): Punctuation characters to split on. Will be
serialized with the nlp object.
RETURNS (Sentencizer): The sentencizer component.
DOCS: https://spacy.io/api/sentencizer#init
"""
if punct_chars:
self.punct_chars = set(punct_chars)
else:
self.punct_chars = set(self.default_punct_chars)
@classmethod
def from_nlp(cls, nlp, model=None, **cfg):
return cls(**cfg)
def begin_training(
self, get_examples=lambda: [], pipeline=None, sgd=None, **kwargs
):
pass
def __call__(self, example):
"""Apply the sentencizer to a Doc and set Token.is_sent_start.
example (Doc or Example): The document to process.
RETURNS (Doc or Example): The processed Doc or Example.
DOCS: https://spacy.io/api/sentencizer#call
"""
doc = self._get_doc(example)
start = 0
seen_period = False
for i, token in enumerate(doc):
is_in_punct_chars = token.text in self.punct_chars
token.is_sent_start = i == 0
if seen_period and not token.is_punct and not is_in_punct_chars:
doc[start].is_sent_start = True
start = token.i
seen_period = False
elif is_in_punct_chars:
seen_period = True
if start < len(doc):
doc[start].is_sent_start = True
if isinstance(example, Example):
example.doc = doc
return example
return doc
def pipe(self, stream, batch_size=128, n_threads=-1, as_example=False):
for examples in util.minibatch(stream, size=batch_size):
docs = [self._get_doc(ex) for ex in examples]
predictions = self.predict(docs)
if isinstance(predictions, tuple) and len(tuple) == 2:
scores, tensors = predictions
self.set_annotations(docs, scores, tensors=tensors)
else:
self.set_annotations(docs, predictions)
if as_example:
for ex, doc in zip(examples, docs):
ex.doc = doc
yield ex
else:
yield from docs
def predict(self, docs):
"""Apply the pipeline's model to a batch of docs, without
modifying them.
"""
if not any(len(doc) for doc in docs):
# Handle cases where there are no tokens in any docs.
guesses = [[] for doc in docs]
return guesses
guesses = []
for doc in docs:
doc_guesses = [False] * len(doc)
if len(doc) > 0:
start = 0
seen_period = False
doc_guesses[0] = True
for i, token in enumerate(doc):
is_in_punct_chars = token.text in self.punct_chars
if seen_period and not token.is_punct and not is_in_punct_chars:
doc_guesses[start] = True
start = token.i
seen_period = False
elif is_in_punct_chars:
seen_period = True
if start < len(doc):
doc_guesses[start] = True
guesses.append(doc_guesses)
return guesses
def set_annotations(self, docs, batch_tag_ids, tensors=None):
if isinstance(docs, Doc):
docs = [docs]
cdef Doc doc
cdef int idx = 0
for i, doc in enumerate(docs):
doc_tag_ids = batch_tag_ids[i]
for j, tag_id in enumerate(doc_tag_ids):
# Don't clobber existing sentence boundaries
if doc.c[j].sent_start == 0:
if tag_id:
doc.c[j].sent_start = 1
else:
doc.c[j].sent_start = -1
def to_bytes(self, **kwargs):
"""Serialize the sentencizer to a bytestring.
RETURNS (bytes): The serialized object.
DOCS: https://spacy.io/api/sentencizer#to_bytes
"""
return srsly.msgpack_dumps({"punct_chars": list(self.punct_chars)})
def from_bytes(self, bytes_data, **kwargs):
"""Load the sentencizer from a bytestring.
bytes_data (bytes): The data to load.
returns (Sentencizer): The loaded object.
DOCS: https://spacy.io/api/sentencizer#from_bytes
"""
cfg = srsly.msgpack_loads(bytes_data)
self.punct_chars = set(cfg.get("punct_chars", self.default_punct_chars))
return self
def to_disk(self, path, exclude=tuple(), **kwargs):
"""Serialize the sentencizer to disk.
DOCS: https://spacy.io/api/sentencizer#to_disk
"""
path = util.ensure_path(path)
path = path.with_suffix(".json")
srsly.write_json(path, {"punct_chars": list(self.punct_chars)})
def from_disk(self, path, exclude=tuple(), **kwargs):
"""Load the sentencizer from disk.
DOCS: https://spacy.io/api/sentencizer#from_disk
"""
path = util.ensure_path(path)
path = path.with_suffix(".json")
cfg = srsly.read_json(path)
self.punct_chars = set(cfg.get("punct_chars", self.default_punct_chars))
return self
# Cython classes can't be decorated, so we need to add the factories here
Language.factories["parser"] = lambda nlp, model, **cfg: DependencyParser.from_nlp(nlp, model, **cfg)
Language.factories["ner"] = lambda nlp, model, **cfg: EntityRecognizer.from_nlp(nlp, model, **cfg)
__all__ = ["Tagger", "DependencyParser", "EntityRecognizer", "Tensorizer", "TextCategorizer", "EntityLinker", "Sentencizer", "SentenceRecognizer"]