mirror of
https://github.com/explosion/spaCy.git
synced 2025-11-04 01:48:04 +03:00
740 lines
30 KiB
Python
740 lines
30 KiB
Python
import warnings
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from typing import Optional, Iterable, Callable, Dict, Sequence, Union, List, Any, cast
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from numpy import dtype
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from thinc.types import Floats1d, Floats2d, Ints1d, Ragged
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from pathlib import Path
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from itertools import islice
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import srsly
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import random
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from thinc.api import CosineDistance, Model, Optimizer, Config
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from thinc.api import set_dropout_rate
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from ..kb import KnowledgeBase, Candidate
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from ..tokens import Doc, Span
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from .pipe import deserialize_config
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from .trainable_pipe import TrainablePipe
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from ..language import Language
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from ..vocab import Vocab
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from ..training import Example, validate_examples, validate_get_examples
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from ..errors import Errors, Warnings
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from ..util import SimpleFrozenList, registry
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from .. import util
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from ..scorer import Scorer
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ActivationsT = Dict[str, Union[List[Ragged], List[str]]]
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KNOWLEDGE_BASE_IDS = "kb_ids"
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default_model_config = """
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[model]
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@architectures = "spacy.EntityLinker.v2"
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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 = 2
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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_NEL_MODEL = Config().from_str(default_model_config)["model"]
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@Language.factory(
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"entity_linker",
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requires=["doc.ents", "doc.sents", "token.ent_iob", "token.ent_type"],
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assigns=["token.ent_kb_id"],
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default_config={
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"model": DEFAULT_NEL_MODEL,
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"labels_discard": [],
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"n_sents": 0,
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"incl_prior": True,
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"incl_context": True,
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"entity_vector_length": 64,
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"get_candidates": {"@misc": "spacy.CandidateGenerator.v1"},
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"get_candidates_batch": {"@misc": "spacy.CandidateBatchGenerator.v1"},
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"overwrite": False,
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"generate_empty_kb": {"@misc": "spacy.EmptyKB.v2"},
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"scorer": {"@scorers": "spacy.entity_linker_scorer.v1"},
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"use_gold_ents": True,
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"candidates_batch_size": 1,
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"threshold": None,
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"save_activations": False,
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},
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default_score_weights={
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"nel_micro_f": 1.0,
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"nel_micro_r": None,
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"nel_micro_p": None,
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},
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)
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def make_entity_linker(
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nlp: Language,
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name: str,
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model: Model,
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*,
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labels_discard: Iterable[str],
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n_sents: int,
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incl_prior: bool,
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incl_context: bool,
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entity_vector_length: int,
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get_candidates: Callable[[KnowledgeBase, Span], Iterable[Candidate]],
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get_candidates_batch: Callable[
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[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]
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],
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generate_empty_kb: Callable[[Vocab, int], KnowledgeBase],
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overwrite: bool,
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scorer: Optional[Callable],
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use_gold_ents: bool,
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candidates_batch_size: int,
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threshold: Optional[float] = None,
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save_activations: bool,
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):
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"""Construct an EntityLinker component.
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model (Model[List[Doc], Floats2d]): A model that learns document vector
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representations. Given a batch of Doc objects, it should return a single
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array, with one row per item in the batch.
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labels_discard (Iterable[str]): NER labels that will automatically get a "NIL" prediction.
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n_sents (int): The number of neighbouring sentences to take into account.
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incl_prior (bool): Whether or not to include prior probabilities from the KB in the model.
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incl_context (bool): Whether or not to include the local context in the model.
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entity_vector_length (int): Size of encoding vectors in the KB.
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get_candidates (Callable[[KnowledgeBase, Span], Iterable[Candidate]]): Function that
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produces a list of candidates, given a certain knowledge base and a textual mention.
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get_candidates_batch (
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Callable[[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]], Iterable[Candidate]]
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): Function that produces a list of candidates, given a certain knowledge base and several textual mentions.
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generate_empty_kb (Callable[[Vocab, int], KnowledgeBase]): Callable returning empty KnowledgeBase.
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scorer (Optional[Callable]): The scoring method.
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use_gold_ents (bool): Whether to copy entities from gold docs or not. If false, another
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component must provide entity annotations.
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candidates_batch_size (int): Size of batches for entity candidate generation.
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threshold (Optional[float]): Confidence threshold for entity predictions. If confidence is below the threshold,
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prediction is discarded. If None, predictions are not filtered by any threshold.
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save_activations (bool): save model activations in Doc when annotating.
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"""
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if not model.attrs.get("include_span_maker", False):
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raise ValueError(Errors.E4005)
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return EntityLinker(
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nlp.vocab,
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model,
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name,
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labels_discard=labels_discard,
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n_sents=n_sents,
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incl_prior=incl_prior,
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incl_context=incl_context,
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entity_vector_length=entity_vector_length,
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get_candidates=get_candidates,
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get_candidates_batch=get_candidates_batch,
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generate_empty_kb=generate_empty_kb,
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overwrite=overwrite,
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scorer=scorer,
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use_gold_ents=use_gold_ents,
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candidates_batch_size=candidates_batch_size,
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threshold=threshold,
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save_activations=save_activations,
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)
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def entity_linker_score(examples, **kwargs):
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return Scorer.score_links(examples, negative_labels=[EntityLinker.NIL], **kwargs)
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@registry.scorers("spacy.entity_linker_scorer.v1")
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def make_entity_linker_scorer():
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return entity_linker_score
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class EntityLinker(TrainablePipe):
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"""Pipeline component for named entity linking.
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DOCS: https://spacy.io/api/entitylinker
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"""
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NIL = "NIL" # string used to refer to a non-existing link
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def __init__(
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self,
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vocab: Vocab,
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model: Model,
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name: str = "entity_linker",
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*,
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labels_discard: Iterable[str],
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n_sents: int,
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incl_prior: bool,
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incl_context: bool,
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entity_vector_length: int,
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get_candidates: Callable[[KnowledgeBase, Span], Iterable[Candidate]],
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get_candidates_batch: Callable[
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[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]
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],
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generate_empty_kb: Callable[[Vocab, int], KnowledgeBase],
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overwrite: bool = False,
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scorer: Optional[Callable] = entity_linker_score,
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use_gold_ents: bool,
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candidates_batch_size: int,
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threshold: Optional[float] = None,
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save_activations: bool = False,
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) -> None:
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"""Initialize an entity linker.
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vocab (Vocab): The shared vocabulary.
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model (thinc.api.Model): The Thinc Model powering the pipeline component.
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name (str): The component instance name, used to add entries to the
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losses during training.
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labels_discard (Iterable[str]): NER labels that will automatically get a "NIL" prediction.
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n_sents (int): The number of neighbouring sentences to take into account.
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incl_prior (bool): Whether or not to include prior probabilities from the KB in the model.
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incl_context (bool): Whether or not to include the local context in the model.
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entity_vector_length (int): Size of encoding vectors in the KB.
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get_candidates (Callable[[KnowledgeBase, Span], Iterable[Candidate]]): Function that
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produces a list of candidates, given a certain knowledge base and a textual mention.
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get_candidates_batch (
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Callable[[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]],
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Iterable[Candidate]]
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): Function that produces a list of candidates, given a certain knowledge base and several textual mentions.
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generate_empty_kb (Callable[[Vocab, int], KnowledgeBase]): Callable returning empty KnowledgeBase.
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overwrite (bool): Whether to overwrite existing non-empty annotations.
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scorer (Optional[Callable]): The scoring method. Defaults to Scorer.score_links.
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use_gold_ents (bool): Whether to copy entities from gold docs or not. If false, another
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component must provide entity annotations.
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candidates_batch_size (int): Size of batches for entity candidate generation.
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threshold (Optional[float]): Confidence threshold for entity predictions. If confidence is below the
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threshold, prediction is discarded. If None, predictions are not filtered by any threshold.
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save_activations (bool): save model activations in Doc when annotating.
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DOCS: https://spacy.io/api/entitylinker#init
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"""
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if threshold is not None and not (0 <= threshold <= 1):
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raise ValueError(
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Errors.E1043.format(
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range_start=0,
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range_end=1,
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value=threshold,
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)
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)
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self.vocab = vocab
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self.model = model
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self.name = name
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self.labels_discard = list(labels_discard)
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# how many neighbour sentences to take into account
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self.n_sents = n_sents
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self.incl_prior = incl_prior
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self.incl_context = incl_context
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self.get_candidates = get_candidates
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self.get_candidates_batch = get_candidates_batch
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self.cfg: Dict[str, Any] = {"overwrite": overwrite}
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self.distance = CosineDistance(normalize=False)
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self.kb = generate_empty_kb(self.vocab, entity_vector_length)
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self.scorer = scorer
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self.use_gold_ents = use_gold_ents
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self.candidates_batch_size = candidates_batch_size
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self.threshold = threshold
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self.save_activations = save_activations
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if candidates_batch_size < 1:
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raise ValueError(Errors.E1044)
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if self.incl_prior and not self.kb.supports_prior_probs:
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warnings.warn(Warnings.W401)
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def set_kb(self, kb_loader: Callable[[Vocab], KnowledgeBase]):
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"""Define the KB of this pipe by providing a function that will
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create it using this object's vocab."""
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if not callable(kb_loader):
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raise ValueError(Errors.E885.format(arg_type=type(kb_loader)))
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self.kb = kb_loader(self.vocab) # type: ignore
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def validate_kb(self) -> None:
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# Raise an error if the knowledge base is not initialized.
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if self.kb is None:
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raise ValueError(Errors.E1018.format(name=self.name))
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if hasattr(self.kb, "is_empty") and self.kb.is_empty():
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raise ValueError(Errors.E139.format(name=self.name))
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def initialize(
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self,
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get_examples: Callable[[], Iterable[Example]],
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*,
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nlp: Optional[Language] = None,
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kb_loader: Optional[Callable[[Vocab], KnowledgeBase]] = None,
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):
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"""Initialize the pipe for training, using a representative set
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of data examples.
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get_examples (Callable[[], Iterable[Example]]): Function that
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returns a representative sample of gold-standard Example objects.
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nlp (Language): The current nlp object the component is part of.
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kb_loader (Callable[[Vocab], KnowledgeBase]): A function that creates a KnowledgeBase from a Vocab
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instance. Note that providing this argument will overwrite all data accumulated in the current KB.
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Use this only when loading a KB as-such from file.
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DOCS: https://spacy.io/api/entitylinker#initialize
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"""
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validate_get_examples(get_examples, "EntityLinker.initialize")
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if kb_loader is not None:
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self.set_kb(kb_loader)
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self.validate_kb()
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nO = self.kb.entity_vector_length
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doc_sample = []
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vector_sample = []
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for eg in islice(get_examples(), 10):
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doc = eg.x
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if self.use_gold_ents:
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ents, _ = eg.get_aligned_ents_and_ner()
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doc.ents = ents
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doc_sample.append(doc)
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vector_sample.append(self.model.ops.alloc1f(nO))
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assert len(doc_sample) > 0, Errors.E923.format(name=self.name)
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assert len(vector_sample) > 0, Errors.E923.format(name=self.name)
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# XXX In order for size estimation to work, there has to be at least
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# one entity. It's not used for training so it doesn't have to be real,
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# so we add a fake one if none are present.
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# We can't use Doc.has_annotation here because it can be True for docs
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# that have been through an NER component but got no entities.
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has_annotations = any([doc.ents for doc in doc_sample])
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if not has_annotations:
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doc = doc_sample[0]
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ent = doc[0:1]
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ent.label_ = "XXX"
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doc.ents = (ent,)
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self.model.initialize(
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X=doc_sample, Y=self.model.ops.asarray(vector_sample, dtype="float32")
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)
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if not has_annotations:
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# Clean up dummy annotation
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doc.ents = []
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def batch_has_learnable_example(self, examples):
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"""Check if a batch contains a learnable example.
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If one isn't present, then the update step needs to be skipped.
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"""
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for eg in examples:
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for ent in eg.predicted.ents:
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candidates = list(self.get_candidates(self.kb, ent))
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if candidates:
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return True
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return False
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def update(
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self,
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examples: Iterable[Example],
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*,
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drop: float = 0.0,
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sgd: Optional[Optimizer] = None,
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losses: Optional[Dict[str, float]] = None,
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) -> Dict[str, float]:
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"""Learn from a batch of documents and gold-standard information,
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updating the pipe's model. Delegates to predict and get_loss.
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examples (Iterable[Example]): A batch of Example objects.
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drop (float): The dropout rate.
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sgd (thinc.api.Optimizer): The optimizer.
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losses (Dict[str, float]): Optional record of the loss during training.
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Updated using the component name as the key.
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RETURNS (Dict[str, float]): The updated losses dictionary.
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DOCS: https://spacy.io/api/entitylinker#update
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"""
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self.validate_kb()
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if losses is None:
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losses = {}
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losses.setdefault(self.name, 0.0)
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if not examples:
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return losses
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validate_examples(examples, "EntityLinker.update")
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set_dropout_rate(self.model, drop)
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docs = [eg.predicted for eg in examples]
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# save to restore later
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old_ents = [doc.ents for doc in docs]
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for doc, ex in zip(docs, examples):
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if self.use_gold_ents:
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ents, _ = ex.get_aligned_ents_and_ner()
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doc.ents = ents
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else:
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# only keep matching ents
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doc.ents = ex.get_matching_ents()
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# make sure we have something to learn from, if not, short-circuit
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if not self.batch_has_learnable_example(examples):
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return losses
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sentence_encodings, bp_context = self.model.begin_update(docs)
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# now restore the ents
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for doc, old in zip(docs, old_ents):
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doc.ents = old
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loss, d_scores = self.get_loss(
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sentence_encodings=sentence_encodings, examples=examples
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)
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bp_context(d_scores)
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if sgd is not None:
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self.finish_update(sgd)
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losses[self.name] += loss
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return losses
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def get_loss(self, examples: Iterable[Example], sentence_encodings: Floats2d):
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validate_examples(examples, "EntityLinker.get_loss")
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entity_encodings = []
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eidx = 0 # indices in gold entities to keep
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keep_ents = [] # indices in sentence_encodings to keep
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for eg in examples:
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kb_ids = eg.get_aligned("ENT_KB_ID", as_string=True)
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for ent in eg.get_matching_ents():
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kb_id = kb_ids[ent.start]
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if kb_id:
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entity_encoding = self.kb.get_vector(kb_id)
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entity_encodings.append(entity_encoding)
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keep_ents.append(eidx)
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eidx += 1
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entity_encodings = self.model.ops.asarray2f(entity_encodings, dtype="float32")
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selected_encodings = sentence_encodings[keep_ents]
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# if there are no matches, short circuit
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if not keep_ents:
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out = self.model.ops.alloc2f(*sentence_encodings.shape)
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return 0, out
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if selected_encodings.shape != entity_encodings.shape:
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err = Errors.E147.format(
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method="get_loss", msg="gold entities do not match up"
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)
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raise RuntimeError(err)
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gradients = self.distance.get_grad(selected_encodings, entity_encodings)
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# to match the input size, we need to give a zero gradient for items not in the kb
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out = self.model.ops.alloc2f(*sentence_encodings.shape)
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out[keep_ents] = gradients
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loss = self.distance.get_loss(selected_encodings, entity_encodings)
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loss = loss / len(entity_encodings)
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return float(loss), out
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def predict(self, docs: Iterable[Doc]) -> ActivationsT:
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"""Apply the pipeline's model to a batch of docs, without modifying them.
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Returns the KB IDs for each entity in each doc, including NIL if there is
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no prediction.
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docs (Iterable[Doc]): The documents to predict.
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RETURNS (List[str]): The models prediction for each document.
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DOCS: https://spacy.io/api/entitylinker#predict
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"""
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self.validate_kb()
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entity_count = 0
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final_kb_ids: List[str] = []
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ops = self.model.ops
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xp = ops.xp
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docs_ents: List[Ragged] = []
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docs_scores: List[Ragged] = []
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if not docs:
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return {
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KNOWLEDGE_BASE_IDS: final_kb_ids,
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"ents": docs_ents,
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"scores": docs_scores,
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}
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if isinstance(docs, Doc):
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docs = [docs]
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for doc in docs:
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doc_ents: List[Ints1d] = []
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doc_scores: List[Floats1d] = []
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if len(doc) == 0:
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docs_scores.append(Ragged(ops.alloc1f(0), ops.alloc1i(0)))
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docs_ents.append(Ragged(xp.zeros(0, dtype="uint64"), ops.alloc1i(0)))
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continue
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sentences = [s for s in doc.sents]
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# Loop over entities in batches.
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for ent_idx in range(0, len(doc.ents), self.candidates_batch_size):
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ent_batch = doc.ents[ent_idx : ent_idx + self.candidates_batch_size]
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# Look up candidate entities.
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valid_ent_idx = [
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idx
|
|
for idx in range(len(ent_batch))
|
|
if ent_batch[idx].label_ not in self.labels_discard
|
|
]
|
|
|
|
batch_candidates = list(
|
|
self.get_candidates_batch(
|
|
self.kb, [ent_batch[idx] for idx in valid_ent_idx]
|
|
)
|
|
if self.candidates_batch_size > 1
|
|
else [
|
|
self.get_candidates(self.kb, ent_batch[idx])
|
|
for idx in valid_ent_idx
|
|
]
|
|
)
|
|
|
|
# Looping through each entity in batch (TODO: rewrite)
|
|
for j, ent in enumerate(ent_batch):
|
|
sent_index = sentences.index(ent.sent)
|
|
assert sent_index >= 0
|
|
|
|
if self.incl_context:
|
|
# get n_neighbour sentences, clipped to the length of the document
|
|
start_sentence = max(0, sent_index - self.n_sents)
|
|
end_sentence = min(
|
|
len(sentences) - 1, sent_index + self.n_sents
|
|
)
|
|
start_token = sentences[start_sentence].start
|
|
end_token = sentences[end_sentence].end
|
|
sent_doc = doc[start_token:end_token].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]
|
|
sentence_encoding_t = sentence_encoding.T
|
|
sentence_norm = xp.linalg.norm(sentence_encoding_t)
|
|
entity_count += 1
|
|
if ent.label_ in self.labels_discard:
|
|
# ignoring this entity - setting to NIL
|
|
final_kb_ids.append(self.NIL)
|
|
self._add_activations(
|
|
doc_scores=doc_scores,
|
|
doc_ents=doc_ents,
|
|
scores=[0.0],
|
|
ents=[0],
|
|
)
|
|
else:
|
|
candidates = list(batch_candidates[j])
|
|
if not candidates:
|
|
# no prediction possible for this entity - setting to NIL
|
|
final_kb_ids.append(self.NIL)
|
|
self._add_activations(
|
|
doc_scores=doc_scores,
|
|
doc_ents=doc_ents,
|
|
scores=[0.0],
|
|
ents=[0],
|
|
)
|
|
elif len(candidates) == 1 and self.threshold is None:
|
|
# shortcut for efficiency reasons: take the 1 candidate
|
|
final_kb_ids.append(candidates[0].entity_id_)
|
|
self._add_activations(
|
|
doc_scores=doc_scores,
|
|
doc_ents=doc_ents,
|
|
scores=[1.0],
|
|
ents=[candidates[0].entity_id],
|
|
)
|
|
else:
|
|
random.shuffle(candidates)
|
|
# set all prior probabilities to 0 if incl_prior=False
|
|
if self.incl_prior and self.kb.supports_prior_probs:
|
|
prior_probs = xp.asarray([c.prior_prob for c in candidates]) # type: ignore
|
|
else:
|
|
prior_probs = xp.asarray([0.0 for _ in candidates])
|
|
scores = prior_probs
|
|
# add in similarity from the context
|
|
if self.incl_context:
|
|
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)
|
|
final_kb_ids.append(
|
|
candidates[scores.argmax().item()].entity_id_
|
|
if self.threshold is None
|
|
or scores.max() >= self.threshold
|
|
else EntityLinker.NIL
|
|
)
|
|
self._add_activations(
|
|
doc_scores=doc_scores,
|
|
doc_ents=doc_ents,
|
|
scores=scores,
|
|
ents=[c.entity_id for c in candidates],
|
|
)
|
|
self._add_doc_activations(
|
|
docs_scores=docs_scores,
|
|
docs_ents=docs_ents,
|
|
doc_scores=doc_scores,
|
|
doc_ents=doc_ents,
|
|
)
|
|
if not (len(final_kb_ids) == entity_count):
|
|
err = Errors.E147.format(
|
|
method="predict", msg="result variables not of equal length"
|
|
)
|
|
raise RuntimeError(err)
|
|
return {
|
|
KNOWLEDGE_BASE_IDS: final_kb_ids,
|
|
"ents": docs_ents,
|
|
"scores": docs_scores,
|
|
}
|
|
|
|
def set_annotations(self, docs: Iterable[Doc], activations: ActivationsT) -> None:
|
|
"""Modify a batch of documents, using pre-computed scores.
|
|
|
|
docs (Iterable[Doc]): The documents to modify.
|
|
activations (ActivationsT): The activations used for setting annotations, produced
|
|
by EntityLinker.predict.
|
|
|
|
DOCS: https://spacy.io/api/entitylinker#set_annotations
|
|
"""
|
|
kb_ids = cast(List[str], activations[KNOWLEDGE_BASE_IDS])
|
|
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
|
|
overwrite = self.cfg["overwrite"]
|
|
for j, doc in enumerate(docs):
|
|
if self.save_activations:
|
|
doc.activations[self.name] = {}
|
|
for act_name, acts in activations.items():
|
|
if act_name != KNOWLEDGE_BASE_IDS:
|
|
# We only copy activations that are Ragged.
|
|
doc.activations[self.name][act_name] = cast(Ragged, acts[j])
|
|
|
|
for ent in doc.ents:
|
|
kb_id = kb_ids[i]
|
|
i += 1
|
|
for token in ent:
|
|
if token.ent_kb_id == 0 or overwrite:
|
|
token.ent_kb_id_ = kb_id
|
|
|
|
def to_bytes(self, *, exclude=tuple()):
|
|
"""Serialize the pipe to a bytestring.
|
|
|
|
exclude (Iterable[str]): String names of serialization fields to exclude.
|
|
RETURNS (bytes): The serialized object.
|
|
|
|
DOCS: https://spacy.io/api/entitylinker#to_bytes
|
|
"""
|
|
self._validate_serialization_attrs()
|
|
serialize = {}
|
|
if hasattr(self, "cfg") and self.cfg is not None:
|
|
serialize["cfg"] = lambda: srsly.json_dumps(self.cfg)
|
|
serialize["vocab"] = lambda: self.vocab.to_bytes(exclude=exclude)
|
|
serialize["kb"] = self.kb.to_bytes
|
|
serialize["model"] = self.model.to_bytes
|
|
return util.to_bytes(serialize, exclude)
|
|
|
|
def from_bytes(self, bytes_data, *, exclude=tuple()):
|
|
"""Load the pipe from a bytestring.
|
|
|
|
exclude (Iterable[str]): String names of serialization fields to exclude.
|
|
RETURNS (TrainablePipe): The loaded object.
|
|
|
|
DOCS: https://spacy.io/api/entitylinker#from_bytes
|
|
"""
|
|
self._validate_serialization_attrs()
|
|
|
|
def load_model(b):
|
|
try:
|
|
self.model.from_bytes(b)
|
|
except AttributeError:
|
|
raise ValueError(Errors.E149) from None
|
|
|
|
deserialize = {}
|
|
if hasattr(self, "cfg") and self.cfg is not None:
|
|
deserialize["cfg"] = lambda b: self.cfg.update(srsly.json_loads(b))
|
|
deserialize["vocab"] = lambda b: self.vocab.from_bytes(b, exclude=exclude)
|
|
deserialize["kb"] = lambda b: self.kb.from_bytes(b)
|
|
deserialize["model"] = load_model
|
|
util.from_bytes(bytes_data, deserialize, exclude)
|
|
return self
|
|
|
|
def to_disk(
|
|
self, path: Union[str, Path], *, exclude: Iterable[str] = SimpleFrozenList()
|
|
) -> None:
|
|
"""Serialize the pipe to disk.
|
|
|
|
path (str / Path): Path to a directory.
|
|
exclude (Iterable[str]): String names of serialization fields to exclude.
|
|
|
|
DOCS: https://spacy.io/api/entitylinker#to_disk
|
|
"""
|
|
serialize = {}
|
|
serialize["vocab"] = lambda p: self.vocab.to_disk(p, exclude=exclude)
|
|
serialize["cfg"] = lambda p: srsly.write_json(p, self.cfg)
|
|
serialize["kb"] = lambda p: self.kb.to_disk(p)
|
|
serialize["model"] = lambda p: self.model.to_disk(p)
|
|
util.to_disk(path, serialize, exclude)
|
|
|
|
def from_disk(
|
|
self, path: Union[str, Path], *, exclude: Iterable[str] = SimpleFrozenList()
|
|
) -> "EntityLinker":
|
|
"""Load the pipe from disk. Modifies the object in place and returns it.
|
|
|
|
path (str / Path): Path to a directory.
|
|
exclude (Iterable[str]): String names of serialization fields to exclude.
|
|
RETURNS (EntityLinker): The modified EntityLinker object.
|
|
|
|
DOCS: https://spacy.io/api/entitylinker#from_disk
|
|
"""
|
|
|
|
def load_model(p):
|
|
try:
|
|
with p.open("rb") as infile:
|
|
self.model.from_bytes(infile.read())
|
|
except AttributeError:
|
|
raise ValueError(Errors.E149) from None
|
|
|
|
deserialize: Dict[str, Callable[[Any], Any]] = {}
|
|
deserialize["cfg"] = lambda p: self.cfg.update(deserialize_config(p))
|
|
deserialize["vocab"] = lambda p: self.vocab.from_disk(p, exclude=exclude)
|
|
deserialize["kb"] = lambda p: self.kb.from_disk(p)
|
|
deserialize["model"] = load_model
|
|
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
|
|
|
|
def _add_doc_activations(
|
|
self,
|
|
*,
|
|
docs_scores: List[Ragged],
|
|
docs_ents: List[Ragged],
|
|
doc_scores: List[Floats1d],
|
|
doc_ents: List[Ints1d],
|
|
):
|
|
if not self.save_activations:
|
|
return
|
|
ops = self.model.ops
|
|
lengths = ops.asarray1i([s.shape[0] for s in doc_scores])
|
|
docs_scores.append(Ragged(ops.flatten(doc_scores), lengths))
|
|
docs_ents.append(Ragged(ops.flatten(doc_ents), lengths))
|
|
|
|
def _add_activations(
|
|
self,
|
|
*,
|
|
doc_scores: List[Floats1d],
|
|
doc_ents: List[Ints1d],
|
|
scores: Sequence[float],
|
|
ents: Sequence[int],
|
|
):
|
|
if not self.save_activations:
|
|
return
|
|
ops = self.model.ops
|
|
doc_scores.append(ops.asarray1f(scores))
|
|
doc_ents.append(ops.asarray1i(ents, dtype="uint64"))
|