mirror of
https://github.com/explosion/spaCy.git
synced 2025-11-04 09:57:26 +03:00
135 lines
4.8 KiB
Cython
135 lines
4.8 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.ner cimport BiluoPushDown
|
|
|
|
from ..language import Language
|
|
from ..scorer import get_ner_prf, PRFScore
|
|
from ..training import validate_examples
|
|
|
|
|
|
default_model_config = """
|
|
[model]
|
|
@architectures = "spacy.TransitionBasedParser.v1"
|
|
state_type = "ner"
|
|
extra_state_tokens = false
|
|
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
|
|
"""
|
|
DEFAULT_NER_MODEL = Config().from_str(default_model_config)["model"]
|
|
|
|
|
|
@Language.factory(
|
|
"ner",
|
|
assigns=["doc.ents", "token.ent_iob", "token.ent_type"],
|
|
default_config={
|
|
"moves": None,
|
|
"update_with_oracle_cut_size": 100,
|
|
"model": DEFAULT_NER_MODEL,
|
|
},
|
|
default_score_weights={"ents_f": 1.0, "ents_p": 0.0, "ents_r": 0.0, "ents_per_type": None},
|
|
|
|
)
|
|
def make_ner(
|
|
nlp: Language,
|
|
name: str,
|
|
model: Model,
|
|
moves: Optional[list],
|
|
update_with_oracle_cut_size: int,
|
|
):
|
|
"""Create a transition-based EntityRecognizer component. The entity recognizer
|
|
identifies non-overlapping labelled spans of tokens.
|
|
|
|
The transition-based algorithm used encodes certain assumptions that are
|
|
effective for "traditional" named entity recognition tasks, but may not be
|
|
a good fit for every span identification problem. Specifically, the loss
|
|
function optimizes for whole entity accuracy, so if your inter-annotator
|
|
agreement on boundary tokens is low, the component will likely perform poorly
|
|
on your problem. The transition-based algorithm also assumes that the most
|
|
decisive information about your entities will be close to their initial tokens.
|
|
If your entities are long and characterised by tokens in their middle, the
|
|
component will likely do poorly on your task.
|
|
|
|
model (Model): The model for the transition-based parser. The model needs
|
|
to have a specific substructure of named components --- see the
|
|
spacy.ml.tb_framework.TransitionModel for details.
|
|
moves (list[str]): A list of transition names. Inferred from the data if not
|
|
provided.
|
|
update_with_oracle_cut_size (int):
|
|
During training, cut long sequences into shorter segments by creating
|
|
intermediate states based on the gold-standard history. The model is
|
|
not very sensitive to this parameter, so you usually won't need to change
|
|
it. 100 is a good default.
|
|
"""
|
|
return EntityRecognizer(
|
|
nlp.vocab,
|
|
model,
|
|
name,
|
|
moves=moves,
|
|
update_with_oracle_cut_size=update_with_oracle_cut_size,
|
|
multitasks=[],
|
|
min_action_freq=1,
|
|
learn_tokens=False,
|
|
)
|
|
|
|
|
|
cdef class EntityRecognizer(Parser):
|
|
"""Pipeline component for named entity recognition.
|
|
|
|
DOCS: https://nightly.spacy.io/api/entityrecognizer
|
|
"""
|
|
TransitionSystem = BiluoPushDown
|
|
|
|
def add_multitask_objective(self, mt_component):
|
|
"""Register another component as a multi-task objective. Experimental."""
|
|
self._multitasks.append(mt_component)
|
|
|
|
def init_multitask_objectives(self, get_examples, nlp=None, **cfg):
|
|
"""Setup multi-task objective components. Experimental and internal."""
|
|
# 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.initialize(get_examples, nlp=nlp)
|
|
|
|
@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))
|
|
|
|
def score(self, examples, **kwargs):
|
|
"""Score a batch of examples.
|
|
|
|
examples (Iterable[Example]): The examples to score.
|
|
RETURNS (Dict[str, Any]): The NER precision, recall and f-scores.
|
|
|
|
DOCS: https://nightly.spacy.io/api/entityrecognizer#score
|
|
"""
|
|
validate_examples(examples, "EntityRecognizer.score")
|
|
score_per_type = get_ner_prf(examples)
|
|
totals = PRFScore()
|
|
for prf in score_per_type.values():
|
|
totals += prf
|
|
return {
|
|
"ents_p": totals.precision,
|
|
"ents_r": totals.recall,
|
|
"ents_f": totals.fscore,
|
|
"ents_per_type": {k: v.to_dict() for k, v in score_per_type.items()},
|
|
}
|