mirror of
https://github.com/explosion/spaCy.git
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e2b70df012
* Use isort with Black profile * isort all the things * Fix import cycles as a result of import sorting * Add DOCBIN_ALL_ATTRS type definition * Add isort to requirements * Remove isort from build dependencies check * Typo
263 lines
9.8 KiB
Cython
263 lines
9.8 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 Callable, Iterable, Optional
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from thinc.api import Config, Model
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from ._parser_internals.transition_system import TransitionSystem
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from ._parser_internals.ner cimport BiluoPushDown
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from .transition_parser cimport Parser
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from ..language import Language
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from ..scorer import PRFScore, get_ner_prf
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from ..training import remove_bilu_prefix
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from ..util import registry
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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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"incorrect_spans_key": None,
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"scorer": {"@scorers": "spacy.ner_scorer.v1"},
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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[TransitionSystem],
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update_with_oracle_cut_size: int,
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incorrect_spans_key: Optional[str],
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scorer: Optional[Callable],
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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 (Optional[TransitionSystem]): This defines how the parse-state is created,
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updated and evaluated. If 'moves' is None, a new instance is
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created with `self.TransitionSystem()`. Defaults to `None`.
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update_with_oracle_cut_size (int): During training, cut long sequences into
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shorter segments by creating intermediate states based on the gold-standard
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history. The model is not very sensitive to this parameter, so you usually
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won't need to change it. 100 is a good default.
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incorrect_spans_key (Optional[str]): Identifies spans that are known
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to be incorrect entity annotations. The incorrect entity annotations
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can be stored in the span group, under this key.
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scorer (Optional[Callable]): The scoring method.
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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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incorrect_spans_key=incorrect_spans_key,
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multitasks=[],
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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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scorer=scorer,
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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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"incorrect_spans_key": None,
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"scorer": None,
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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[TransitionSystem],
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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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incorrect_spans_key: Optional[str],
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scorer: Optional[Callable],
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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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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 (Optional[TransitionSystem]): This defines how the parse-state is created,
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updated and evaluated. If 'moves' is None, a new instance is
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created with `self.TransitionSystem()`. Defaults to `None`.
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update_with_oracle_cut_size (int): During training, cut long sequences into
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shorter segments by creating intermediate states based on the gold-standard
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history. The model is not very sensitive to this parameter, so you usually
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won't need to change 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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incorrect_spans_key (Optional[str]): Optional key into span groups of
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entities known to be non-entities.
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scorer (Optional[Callable]): The scoring method.
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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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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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incorrect_spans_key=incorrect_spans_key,
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scorer=scorer,
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)
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def ner_score(examples, **kwargs):
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return get_ner_prf(examples, **kwargs)
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@registry.scorers("spacy.ner_scorer.v1")
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def make_ner_scorer():
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return ner_score
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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 __init__(
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self,
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vocab,
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model,
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name="ner",
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moves=None,
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*,
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update_with_oracle_cut_size=100,
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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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multitasks=tuple(),
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incorrect_spans_key=None,
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scorer=ner_score,
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):
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"""Create an EntityRecognizer.
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"""
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super().__init__(
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vocab,
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model,
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name,
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moves,
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update_with_oracle_cut_size=update_with_oracle_cut_size,
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min_action_freq=1, # not relevant for NER
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learn_tokens=False, # not relevant for NER
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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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multitasks=multitasks,
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incorrect_spans_key=incorrect_spans_key,
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scorer=scorer,
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)
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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(remove_bilu_prefix(move) 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 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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