mirror of https://github.com/explosion/spaCy.git
127 lines
3.8 KiB
Cython
127 lines
3.8 KiB
Cython
# cython: infer_types=True, profile=True, binding=True
|
|
from typing import Optional, Iterable
|
|
from thinc.api import Model, Config
|
|
|
|
from .transition_parser cimport Parser
|
|
from ._parser_internals.arc_eager cimport ArcEager
|
|
|
|
from .functions import merge_subtokens
|
|
from ..language import Language
|
|
from ._parser_internals import nonproj
|
|
from ..scorer import Scorer
|
|
|
|
|
|
default_model_config = """
|
|
[model]
|
|
@architectures = "spacy.TransitionBasedParser.v1"
|
|
nr_feature_tokens = 8
|
|
hidden_width = 64
|
|
maxout_pieces = 2
|
|
|
|
[model.tok2vec]
|
|
@architectures = "spacy.HashEmbedCNN.v1"
|
|
pretrained_vectors = null
|
|
width = 96
|
|
depth = 4
|
|
embed_size = 2000
|
|
window_size = 1
|
|
maxout_pieces = 3
|
|
subword_features = true
|
|
dropout = null
|
|
"""
|
|
DEFAULT_PARSER_MODEL = Config().from_str(default_model_config)["model"]
|
|
|
|
|
|
@Language.factory(
|
|
"parser",
|
|
assigns=["token.dep", "token.is_sent_start", "doc.sents"],
|
|
default_config={
|
|
"moves": None,
|
|
"update_with_oracle_cut_size": 100,
|
|
"multitasks": [],
|
|
"learn_tokens": False,
|
|
"min_action_freq": 30,
|
|
"model": DEFAULT_PARSER_MODEL,
|
|
},
|
|
scores=["dep_uas", "dep_las", "dep_las_per_type", "sents_p", "sents_r", "sents_f"],
|
|
default_score_weights={"dep_uas": 0.5, "dep_las": 0.5, "sents_f": 0.0},
|
|
)
|
|
def make_parser(
|
|
nlp: Language,
|
|
name: str,
|
|
model: Model,
|
|
moves: Optional[list],
|
|
update_with_oracle_cut_size: int,
|
|
multitasks: Iterable,
|
|
learn_tokens: bool,
|
|
min_action_freq: int
|
|
):
|
|
return DependencyParser(
|
|
nlp.vocab,
|
|
model,
|
|
name,
|
|
moves=moves,
|
|
update_with_oracle_cut_size=update_with_oracle_cut_size,
|
|
multitasks=multitasks,
|
|
learn_tokens=learn_tokens,
|
|
min_action_freq=min_action_freq
|
|
)
|
|
|
|
|
|
cdef class DependencyParser(Parser):
|
|
"""Pipeline component for dependency parsing.
|
|
|
|
DOCS: https://spacy.io/api/dependencyparser
|
|
"""
|
|
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, mt_component):
|
|
self._multitasks.append(mt_component)
|
|
|
|
def init_multitask_objectives(self, get_examples, pipeline, sgd=None, **cfg):
|
|
# TODO: transfer self.model.get_ref("tok2vec") to the multitask's model ?
|
|
for labeller in self._multitasks:
|
|
labeller.model.set_dim("nO", len(self.labels))
|
|
if labeller.model.has_ref("output_layer"):
|
|
labeller.model.get_ref("output_layer").set_dim("nO", len(self.labels))
|
|
labeller.begin_training(get_examples, pipeline=pipeline, sgd=sgd)
|
|
|
|
@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))
|
|
|
|
def score(self, examples, **kwargs):
|
|
"""Score a batch of examples.
|
|
|
|
examples (Iterable[Example]): The examples to score.
|
|
RETURNS (Dict[str, Any]): The scores, produced by Scorer.score_spans
|
|
and Scorer.score_deps.
|
|
|
|
DOCS: https://spacy.io/api/dependencyparser#score
|
|
"""
|
|
def dep_getter(token, attr):
|
|
dep = getattr(token, attr)
|
|
dep = token.vocab.strings.as_string(dep).lower()
|
|
return dep
|
|
results = {}
|
|
results.update(Scorer.score_spans(examples, "sents", **kwargs))
|
|
results.update(Scorer.score_deps(examples, "dep", getter=dep_getter,
|
|
ignore_labels=("p", "punct"), **kwargs))
|
|
del results["sents_per_type"]
|
|
return results
|