mirror of https://github.com/explosion/spaCy.git
214 lines
8.1 KiB
Cython
214 lines
8.1 KiB
Cython
# cython: infer_types=True, profile=True, binding=True
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from collections import defaultdict
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from typing import Optional, Iterable
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from thinc.api import Model, Config
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from .transition_parser cimport Parser
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from ._parser_internals.ner cimport BiluoPushDown
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from ..language import Language
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from ..scorer import get_ner_prf, PRFScore
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from ..training import validate_examples
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default_model_config = """
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[model]
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@architectures = "spacy.TransitionBasedParser.v2"
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state_type = "ner"
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extra_state_tokens = false
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hidden_width = 64
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maxout_pieces = 2
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use_upper = true
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[model.tok2vec]
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@architectures = "spacy.HashEmbedCNN.v2"
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pretrained_vectors = null
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width = 96
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depth = 4
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embed_size = 2000
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window_size = 1
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maxout_pieces = 3
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subword_features = true
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"""
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DEFAULT_NER_MODEL = Config().from_str(default_model_config)["model"]
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@Language.factory(
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"ner",
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assigns=["doc.ents", "token.ent_iob", "token.ent_type"],
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default_config={
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"moves": None,
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"update_with_oracle_cut_size": 100,
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"model": DEFAULT_NER_MODEL,
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},
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default_score_weights={"ents_f": 1.0, "ents_p": 0.0, "ents_r": 0.0, "ents_per_type": None},
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)
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def make_ner(
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nlp: Language,
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name: str,
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model: Model,
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moves: Optional[list],
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update_with_oracle_cut_size: int,
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):
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"""Create a transition-based EntityRecognizer component. The entity recognizer
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identifies non-overlapping labelled spans of tokens.
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The transition-based algorithm used encodes certain assumptions that are
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effective for "traditional" named entity recognition tasks, but may not be
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a good fit for every span identification problem. Specifically, the loss
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function optimizes for whole entity accuracy, so if your inter-annotator
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agreement on boundary tokens is low, the component will likely perform poorly
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on your problem. The transition-based algorithm also assumes that the most
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decisive information about your entities will be close to their initial tokens.
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If your entities are long and characterised by tokens in their middle, the
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component will likely do poorly on your task.
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model (Model): The model for the transition-based parser. The model needs
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to have a specific substructure of named components --- see the
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spacy.ml.tb_framework.TransitionModel for details.
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moves (list[str]): A list of transition names. Inferred from the data if not
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provided.
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update_with_oracle_cut_size (int):
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During training, cut long sequences into shorter segments by creating
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intermediate states based on the gold-standard history. The model is
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not very sensitive to this parameter, so you usually won't need to change
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it. 100 is a good default.
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"""
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return EntityRecognizer(
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nlp.vocab,
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model,
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name,
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moves=moves,
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update_with_oracle_cut_size=update_with_oracle_cut_size,
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multitasks=[],
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min_action_freq=1,
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learn_tokens=False,
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beam_width=1,
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beam_density=0.0,
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beam_update_prob=0.0,
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)
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@Language.factory(
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"beam_ner",
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assigns=["doc.ents", "token.ent_iob", "token.ent_type"],
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default_config={
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"moves": None,
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"update_with_oracle_cut_size": 100,
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"model": DEFAULT_NER_MODEL,
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"beam_density": 0.01,
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"beam_update_prob": 0.5,
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"beam_width": 32
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},
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default_score_weights={"ents_f": 1.0, "ents_p": 0.0, "ents_r": 0.0, "ents_per_type": None},
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)
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def make_beam_ner(
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nlp: Language,
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name: str,
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model: Model,
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moves: Optional[list],
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update_with_oracle_cut_size: int,
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beam_width: int,
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beam_density: float,
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beam_update_prob: float,
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):
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"""Create a transition-based EntityRecognizer component that uses beam-search.
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The entity recognizer identifies non-overlapping labelled spans of tokens.
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The transition-based algorithm used encodes certain assumptions that are
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effective for "traditional" named entity recognition tasks, but may not be
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a good fit for every span identification problem. Specifically, the loss
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function optimizes for whole entity accuracy, so if your inter-annotator
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agreement on boundary tokens is low, the component will likely perform poorly
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on your problem. The transition-based algorithm also assumes that the most
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decisive information about your entities will be close to their initial tokens.
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If your entities are long and characterised by tokens in their middle, the
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component will likely do poorly on your task.
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|
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model (Model): The model for the transition-based parser. The model needs
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to have a specific substructure of named components --- see the
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spacy.ml.tb_framework.TransitionModel for details.
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moves (list[str]): A list of transition names. Inferred from the data if not
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provided.
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update_with_oracle_cut_size (int):
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During training, cut long sequences into shorter segments by creating
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intermediate states based on the gold-standard history. The model is
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not very sensitive to this parameter, so you usually won't need to change
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it. 100 is a good default.
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beam_width (int): The number of candidate analyses to maintain.
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beam_density (float): The minimum ratio between the scores of the first and
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last candidates in the beam. This allows the parser to avoid exploring
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candidates that are too far behind. This is mostly intended to improve
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efficiency, but it can also improve accuracy as deeper search is not
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always better.
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beam_update_prob (float): The chance of making a beam update, instead of a
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greedy update. Greedy updates are an approximation for the beam updates,
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and are faster to compute.
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"""
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return EntityRecognizer(
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nlp.vocab,
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model,
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name,
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moves=moves,
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update_with_oracle_cut_size=update_with_oracle_cut_size,
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multitasks=[],
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min_action_freq=1,
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learn_tokens=False,
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beam_width=beam_width,
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beam_density=beam_density,
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beam_update_prob=beam_update_prob,
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)
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cdef class EntityRecognizer(Parser):
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"""Pipeline component for named entity recognition.
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DOCS: https://spacy.io/api/entityrecognizer
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"""
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TransitionSystem = BiluoPushDown
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def add_multitask_objective(self, mt_component):
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"""Register another component as a multi-task objective. Experimental."""
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self._multitasks.append(mt_component)
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def init_multitask_objectives(self, get_examples, nlp=None, **cfg):
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"""Setup multi-task objective components. Experimental and internal."""
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# TODO: transfer self.model.get_ref("tok2vec") to the multitask's model ?
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for labeller in self._multitasks:
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labeller.model.set_dim("nO", len(self.labels))
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if labeller.model.has_ref("output_layer"):
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labeller.model.get_ref("output_layer").set_dim("nO", len(self.labels))
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labeller.initialize(get_examples, nlp=nlp)
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@property
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def labels(self):
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# Get the labels from the model by looking at the available moves, e.g.
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# B-PERSON, I-PERSON, L-PERSON, U-PERSON
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labels = set(move.split("-")[1] for move in self.move_names
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if move[0] in ("B", "I", "L", "U"))
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return tuple(sorted(labels))
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def score(self, examples, **kwargs):
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"""Score a batch of examples.
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examples (Iterable[Example]): The examples to score.
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RETURNS (Dict[str, Any]): The NER precision, recall and f-scores.
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DOCS: https://spacy.io/api/entityrecognizer#score
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"""
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validate_examples(examples, "EntityRecognizer.score")
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return get_ner_prf(examples)
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def scored_ents(self, beams):
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"""Return a dictionary of (start, end, label) tuples with corresponding scores
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for each beam/doc that was processed.
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"""
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entity_scores = []
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for beam in beams:
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score_dict = defaultdict(float)
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for score, ents in self.moves.get_beam_parses(beam):
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for start, end, label in ents:
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score_dict[(start, end, label)] += score
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entity_scores.append(score_dict)
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return entity_scores
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