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Modify EL batching to doc-wise streaming approach (#12367)
* Convert Candidate from Cython to Python class. * Format. * Fix .entity_ typo in _add_activations() usage. * Change type for mentions to look up entity candidates for to SpanGroup from Iterable[Span]. * Update docs. * Update spacy/kb/candidate.py Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Update doc string of BaseCandidate.__init__(). * Update spacy/kb/candidate.py Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Rename Candidate to InMemoryCandidate, BaseCandidate to Candidate. * Adjust Candidate to support and mandate numerical entity IDs. * Format. * Fix docstring and docs. * Update website/docs/api/kb.mdx Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Rename alias -> mention. * Refactor Candidate attribute names. Update docs and tests accordingly. * Refacor Candidate attributes and their usage. * Format. * Fix mypy error. * Update error code in line with v4 convention. * Modify EL batching system. * Update leftover get_candidates() mention in docs. * Format docs. * Format. * Update spacy/kb/candidate.py Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Updated error code. * Simplify interface for int/str representations. * Update website/docs/api/kb.mdx Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Rename 'alias' to 'mention'. * Port Candidate and InMemoryCandidate to Cython. * Remove redundant entry in setup.py. * Add abstract class check. * Drop storing mention. * Update spacy/kb/candidate.pxd Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Fix entity_id refactoring problems in docstrings. * Drop unused InMemoryCandidate._entity_hash. * Update docstrings. * Move attributes out of Candidate. * Partially fix alias/mention terminology usage. Convert Candidate to interface. * Remove prior_prob from supported properties in Candidate. Introduce KnowledgeBase.supports_prior_probs(). * Update docstrings related to prior_prob. * Update alias/mention usage in doc(strings). * Update spacy/ml/models/entity_linker.py Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Update spacy/ml/models/entity_linker.py Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Mention -> alias renaming. Drop Candidate.mentions(). Drop InMemoryLookupKB.get_alias_candidates() from docs. * Update docstrings. * Fix InMemoryCandidate attribute names. * Update spacy/kb/kb.pyx Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Update spacy/ml/models/entity_linker.py Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Update W401 test. * Update spacy/errors.py Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Update spacy/kb/kb.pyx Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Use Candidate output type for toy generators in the test suite to mimick best practices * fix docs * fix import * Fix merge leftovers. * Update spacy/kb/kb.pyx Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Update spacy/kb/kb.pyx Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Update website/docs/api/kb.mdx Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Update website/docs/api/entitylinker.mdx Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Update spacy/kb/kb_in_memory.pyx Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Update website/docs/api/inmemorylookupkb.mdx Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Update get_candidates() docstring. * Reformat imports in entity_linker.py. * Drop valid_ent_idx_per_doc. * Update docs. * Format. * Simplify doc loop in predict(). * Remove E1044 comment. * Fix merge errors. * Format. * Format. * Format. * Fix merge error & tests. * Format. * Apply suggestions from code review Co-authored-by: Madeesh Kannan <shadeMe@users.noreply.github.com> * Use type alias. * isort. * isort. * Lint. * Add typedefs.pyx. * Fix typedef import. * Fix type aliases. * Format. * Update docstring and type usage. * Add info on get_candidates(), get_candidates_batched(). * Readd get_candidates info to v3 changelog. * Update website/docs/api/entitylinker.mdx Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Update factory functions for backwards compatibility. * Format. * Ignore mypy error. * Fix mypy error. * Format. * Add test for multiple docs with multiple entities. --------- Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> Co-authored-by: Madeesh Kannan <shadeMe@users.noreply.github.com> Co-authored-by: svlandeg <svlandeg@github.com>
This commit is contained in:
Raphael Mitsch
GitHub
parent
afb22ad491
commit
304b9331e6
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@ -238,7 +238,7 @@ grad_factor = 1.0
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{% if "entity_linker" in components -%}
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[components.entity_linker]
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factory = "entity_linker"
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get_candidates = {"@misc":"spacy.CandidateGenerator.v1"}
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get_candidates = {"@misc":"spacy.CandidateGenerator.v2"}
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incl_context = true
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incl_prior = true
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@ -517,7 +517,7 @@ width = ${components.tok2vec.model.encode.width}
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{% if "entity_linker" in components -%}
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[components.entity_linker]
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factory = "entity_linker"
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get_candidates = {"@misc":"spacy.CandidateGenerator.v1"}
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get_candidates = {"@misc":"spacy.CandidateGenerator.v2"}
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incl_context = true
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incl_prior = true
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@ -950,7 +950,6 @@ class Errors(metaclass=ErrorsWithCodes):
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"case pass an empty list for the previously not specified argument to avoid this error.")
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E1043 = ("Expected None or a value in range [{range_start}, {range_end}] for entity linker threshold, but got "
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"{value}.")
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E1044 = ("Expected `candidates_batch_size` to be >= 1, but got: {value}")
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E1045 = ("Encountered {parent} subclass without `{parent}.{method}` "
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"method in '{name}'. If you want to use this method, make "
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"sure it's overwritten on the subclass.")
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@ -1,14 +1,14 @@
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# cython: infer_types=True
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from pathlib import Path
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from typing import Iterable, Tuple, Union
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from typing import Iterable, Iterator, Tuple, Union
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from cymem.cymem cimport Pool
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from ..errors import Errors
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from ..tokens import Span, SpanGroup
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from ..tokens import SpanGroup
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from ..util import SimpleFrozenList
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from .candidate import Candidate
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from .candidate cimport Candidate
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cdef class KnowledgeBase:
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@ -19,6 +19,8 @@ cdef class KnowledgeBase:
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DOCS: https://spacy.io/api/kb
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"""
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CandidatesForMentionT = Iterable[Candidate]
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CandidatesForDocT = Iterable[CandidatesForMentionT]
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def __init__(self, vocab: Vocab, entity_vector_length: int):
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"""Create a KnowledgeBase."""
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@ -32,27 +34,15 @@ cdef class KnowledgeBase:
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self.entity_vector_length = entity_vector_length
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self.mem = Pool()
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def get_candidates_batch(
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self, mentions: SpanGroup
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) -> Iterable[Iterable[Candidate]]:
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def get_candidates(self, mentions: Iterator[SpanGroup]) -> Iterator[CandidatesForDocT]:
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"""
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Return candidate entities for a specified Span mention. Each candidate defines at least the entity and the
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entity's embedding vector. Depending on the KB implementation, further properties - such as the prior
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probability of the specified mention text resolving to that entity - might be included.
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Return candidate entities for the specified groups of mentions (as SpanGroup) per Doc.
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Each candidate for a mention defines at least the entity and the entity's embedding vector. Depending on the KB
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implementation, further properties - such as the prior probability of the specified mention text resolving to
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that entity - might be included.
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If no candidates are found for a given mention, an empty list is returned.
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mentions (SpanGroup): Mentions for which to get candidates.
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RETURNS (Iterable[Iterable[Candidate]]): Identified candidates.
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"""
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return [self.get_candidates(span) for span in mentions]
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def get_candidates(self, mention: Span) -> Iterable[Candidate]:
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"""
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Return candidate entities for a specific mention. Each candidate defines at least the entity and the
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entity's embedding vector. Depending on the KB implementation, further properties - such as the prior
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probability of the specified mention text resolving to that entity - might be included.
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If no candidate is found for the given mention, an empty list is returned.
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mention (Span): Mention for which to get candidates.
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RETURNS (Iterable[Candidate]): Identified candidates.
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mentions (Iterator[SpanGroup]): Mentions for which to get candidates.
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RETURNS (Iterator[Iterable[Iterable[Candidate]]]): Identified candidates per mention/doc/doc batch.
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"""
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raise NotImplementedError(
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Errors.E1045.format(
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@ -1,5 +1,5 @@
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# cython: infer_types=True
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from typing import Any, Callable, Dict, Iterable
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from typing import Any, Callable, Dict, Iterable, Iterator
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import srsly
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@ -12,7 +12,7 @@ from preshed.maps cimport PreshMap
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import warnings
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from pathlib import Path
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from ..tokens import Span
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from ..tokens import SpanGroup
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from ..typedefs cimport hash_t
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@ -255,8 +255,9 @@ cdef class InMemoryLookupKB(KnowledgeBase):
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alias_entry.probs = probs
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self._aliases_table[alias_index] = alias_entry
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def get_candidates(self, mention: Span) -> Iterable[InMemoryCandidate]:
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return self._get_alias_candidates(mention.text) # type: ignore
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def get_candidates(self, mentions: Iterator[SpanGroup]) -> Iterator[Iterable[Iterable[InMemoryCandidate]]]:
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for mentions_for_doc in mentions:
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yield [self._get_alias_candidates(span.text) for span in mentions_for_doc]
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def _get_alias_candidates(self, str alias) -> Iterable[InMemoryCandidate]:
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"""
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@ -1,5 +1,5 @@
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from pathlib import Path
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from typing import Callable, Iterable, List, Optional, Tuple
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from typing import Callable, Iterable, Iterator, List, Optional, Tuple
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from thinc.api import (
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Linear,
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@ -21,6 +21,9 @@ from ...util import registry
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from ...vocab import Vocab
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from ..extract_spans import extract_spans
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CandidatesForMentionT = Iterable[Candidate]
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CandidatesForDocT = Iterable[CandidatesForMentionT]
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@registry.architectures("spacy.EntityLinker.v2")
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def build_nel_encoder(
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@ -117,34 +120,38 @@ def empty_kb(
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@registry.misc("spacy.CandidateGenerator.v1")
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def create_candidates() -> Callable[[KnowledgeBase, Span], Iterable[Candidate]]:
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def create_get_candidates() -> Callable[[KnowledgeBase, Span], Iterable[Candidate]]:
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return get_candidates
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@registry.misc("spacy.CandidateBatchGenerator.v1")
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def create_candidates_batch() -> Callable[
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[KnowledgeBase, SpanGroup], Iterable[Iterable[Candidate]]
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@registry.misc("spacy.CandidateGenerator.v2")
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def create_get_candidates_v2() -> Callable[
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[KnowledgeBase, Iterator[SpanGroup]], Iterator[CandidatesForDocT]
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]:
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return get_candidates_batch
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return get_candidates_v2
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def get_candidates(kb: KnowledgeBase, mention: Span) -> Iterable[Candidate]:
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"""
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Return candidate entities for a given mention and fetching appropriate entries from the index.
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Return candidate entities for the given mention from the KB.
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kb (KnowledgeBase): Knowledge base to query.
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mention (Span): Entity mention for which to identify candidates.
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RETURNS (Iterable[Candidate]): Identified candidates.
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mention (Span): Entity mention.
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RETURNS (Iterable[Candidate]): Identified candidates for specified mention.
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"""
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return kb.get_candidates(mention)
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cands_per_doc = next(
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get_candidates_v2(kb, iter([SpanGroup(mention.doc, spans=[mention])]))
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)
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assert isinstance(cands_per_doc, list)
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return next(cands_per_doc[0])
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def get_candidates_batch(
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kb: KnowledgeBase, mentions: SpanGroup
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) -> Iterable[Iterable[Candidate]]:
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def get_candidates_v2(
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kb: KnowledgeBase, mentions: Iterator[SpanGroup]
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) -> Iterator[Iterable[Iterable[Candidate]]]:
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"""
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Return candidate entities for the given mentions and fetching appropriate entries from the index.
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Return candidate entities for the given mentions from the KB.
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kb (KnowledgeBase): Knowledge base to query.
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mentions (SpanGroup): Entity mentions for which to identify candidates.
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RETURNS (Iterable[Iterable[Candidate]]): Identified candidates.
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mentions (Iterator[SpanGroup]): Mentions per doc.
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RETURNS (Iterator[Iterable[Iterable[Candidate]]]): Identified candidates per mentions in document/SpanGroup.
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"""
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return kb.get_candidates_batch(mentions)
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return kb.get_candidates(mentions)
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@ -1,8 +1,19 @@
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import random
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import warnings
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from itertools import islice
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from itertools import islice, tee
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from pathlib import Path
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from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Union, cast
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from typing import (
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Any,
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Callable,
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Dict,
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Iterable,
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Iterator,
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List,
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Optional,
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Sequence,
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Union,
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cast,
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)
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import srsly
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from numpy import dtype
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@ -54,13 +65,11 @@ DEFAULT_NEL_MODEL = Config().from_str(default_model_config)["model"]
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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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"get_candidates": {"@misc": "spacy.CandidateGenerator.v2"},
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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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@ -80,15 +89,13 @@ def make_entity_linker(
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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, SpanGroup], Iterable[Iterable[Candidate]]
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get_candidates: Callable[
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[KnowledgeBase, Iterator[SpanGroup]], Iterator[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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@ -102,16 +109,13 @@ def make_entity_linker(
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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, SpanGroup], 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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get_candidates (Callable[[KnowledgeBase, Iterator[SpanGroup]], Iterator[Iterable[Iterable[Candidate]]]]):
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Function producing a list of candidates per document, given a certain knowledge base and several textual
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documents with 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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@ -129,12 +133,10 @@ def make_entity_linker(
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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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@ -168,15 +170,14 @@ class EntityLinker(TrainablePipe):
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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, SpanGroup], Iterable[Iterable[Candidate]]
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get_candidates: Callable[
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[KnowledgeBase, Iterator[SpanGroup]],
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Iterator[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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@ -191,18 +192,14 @@ class EntityLinker(TrainablePipe):
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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, SpanGroup], 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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get_candidates (Callable[[KnowledgeBase, Iterator[SpanGroup]], Iterator[Iterable[Iterable[Candidate]]]]):
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Function producing a list of candidates per document, given a certain knowledge base and several textual
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documents with 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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@ -227,18 +224,14 @@ class EntityLinker(TrainablePipe):
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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
|
||||
self.get_candidates_batch = get_candidates_batch
|
||||
self.cfg: Dict[str, Any] = {"overwrite": overwrite}
|
||||
self.distance = CosineDistance(normalize=False)
|
||||
self.kb = generate_empty_kb(self.vocab, entity_vector_length)
|
||||
self.scorer = scorer
|
||||
self.use_gold_ents = use_gold_ents
|
||||
self.candidates_batch_size = candidates_batch_size
|
||||
self.threshold = threshold
|
||||
self.save_activations = save_activations
|
||||
|
||||
if candidates_batch_size < 1:
|
||||
raise ValueError(Errors.E1044)
|
||||
if self.incl_prior and not self.kb.supports_prior_probs:
|
||||
warnings.warn(Warnings.W401)
|
||||
|
||||
|
|
@ -318,11 +311,12 @@ class EntityLinker(TrainablePipe):
|
|||
|
||||
If one isn't present, then the update step needs to be skipped.
|
||||
"""
|
||||
|
||||
for eg in examples:
|
||||
for ent in eg.predicted.ents:
|
||||
candidates = list(self.get_candidates(self.kb, ent))
|
||||
if candidates:
|
||||
for candidates_for_doc in self.get_candidates(
|
||||
self.kb,
|
||||
(SpanGroup(doc=eg.predicted, spans=eg.predicted.ents) for eg in examples),
|
||||
):
|
||||
for candidates_for_mention in candidates_for_doc:
|
||||
if list(candidates_for_mention):
|
||||
return True
|
||||
|
||||
return False
|
||||
|
|
@ -451,65 +445,70 @@ class EntityLinker(TrainablePipe):
|
|||
}
|
||||
if isinstance(docs, Doc):
|
||||
docs = [docs]
|
||||
for doc in docs:
|
||||
|
||||
docs_iters = tee(docs, 2)
|
||||
|
||||
# Call candidate generator.
|
||||
all_ent_cands = self.get_candidates(
|
||||
self.kb,
|
||||
(
|
||||
SpanGroup(
|
||||
doc,
|
||||
spans=[
|
||||
ent for ent in doc.ents if ent.label_ not in self.labels_discard
|
||||
],
|
||||
)
|
||||
for doc in docs_iters[0]
|
||||
),
|
||||
)
|
||||
|
||||
for doc in docs_iters[1]:
|
||||
doc_ents: List[Ints1d] = []
|
||||
doc_scores: List[Floats1d] = []
|
||||
if len(doc) == 0:
|
||||
if len(doc) == 0 or len(doc.ents) == 0:
|
||||
docs_scores.append(Ragged(ops.alloc1f(0), ops.alloc1i(0)))
|
||||
docs_ents.append(Ragged(xp.zeros(0, dtype="uint64"), ops.alloc1i(0)))
|
||||
continue
|
||||
sentences = [s for s in doc.sents]
|
||||
doc_ent_cands = list(next(all_ent_cands))
|
||||
|
||||
# Loop over entities in batches.
|
||||
for ent_idx in range(0, len(doc.ents), self.candidates_batch_size):
|
||||
ent_batch = doc.ents[ent_idx : ent_idx + self.candidates_batch_size]
|
||||
|
||||
# Look up candidate entities.
|
||||
valid_ent_idx = [
|
||||
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,
|
||||
SpanGroup(doc, spans=[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 over candidate entities for this doc. (TODO: rewrite)
|
||||
for ent_cand_idx, ent in enumerate(doc.ents):
|
||||
assert hasattr(ent, "sents")
|
||||
sents = list(ent.sents)
|
||||
sent_indices = (
|
||||
sentences.index(sents[0]),
|
||||
sentences.index(sents[-1]),
|
||||
)
|
||||
assert sent_indices[1] >= sent_indices[0] >= 0
|
||||
|
||||
# Looping through each entity in batch (TODO: rewrite)
|
||||
for j, ent in enumerate(ent_batch):
|
||||
assert hasattr(ent, "sents")
|
||||
sents = list(ent.sents)
|
||||
sent_indices = (
|
||||
sentences.index(sents[0]),
|
||||
sentences.index(sents[-1]),
|
||||
if self.incl_context:
|
||||
# get n_neighbour sentences, clipped to the length of the document
|
||||
start_sentence = max(0, sent_indices[0] - self.n_sents)
|
||||
end_sentence = min(
|
||||
len(sentences) - 1, sent_indices[1] + self.n_sents
|
||||
)
|
||||
assert sent_indices[1] >= sent_indices[0] >= 0
|
||||
|
||||
if self.incl_context:
|
||||
# get n_neighbour sentences, clipped to the length of the document
|
||||
start_sentence = max(0, sent_indices[0] - self.n_sents)
|
||||
end_sentence = min(
|
||||
len(sentences) - 1, sent_indices[1] + 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
|
||||
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(doc_ent_cands[ent_cand_idx])
|
||||
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,
|
||||
|
|
@ -517,66 +516,56 @@ class EntityLinker(TrainablePipe):
|
|||
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:
|
||||
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],
|
||||
random.shuffle(candidates)
|
||||
# set all prior probabilities to 0 if incl_prior=False
|
||||
scores = prior_probs = xp.asarray(
|
||||
[
|
||||
c.prior_prob if self.incl_prior else 0.0
|
||||
for c in candidates
|
||||
]
|
||||
)
|
||||
# add in similarity from the context
|
||||
if self.incl_context:
|
||||
entity_encodings = xp.asarray(
|
||||
[c.entity_vector for c in candidates]
|
||||
)
|
||||
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",
|
||||
)
|
||||
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],
|
||||
# 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,
|
||||
|
|
@ -588,6 +577,7 @@ class EntityLinker(TrainablePipe):
|
|||
method="predict", msg="result variables not of equal length"
|
||||
)
|
||||
raise RuntimeError(err)
|
||||
|
||||
return {
|
||||
KNOWLEDGE_BASE_IDS: final_kb_ids,
|
||||
"ents": docs_ents,
|
||||
|
|
|
|||
|
|
@ -1,21 +1,21 @@
|
|||
from typing import Any, Callable, Dict, Iterable, cast
|
||||
from typing import Any, Callable, Dict, Iterable, Iterator, cast
|
||||
|
||||
import pytest
|
||||
from numpy.testing import assert_equal
|
||||
from thinc.types import Ragged
|
||||
|
||||
from spacy import Language, registry, util
|
||||
from spacy import registry, util
|
||||
from spacy.attrs import ENT_KB_ID
|
||||
from spacy.compat import pickle
|
||||
from spacy.kb import Candidate, InMemoryLookupKB, KnowledgeBase
|
||||
from spacy.lang.en import English
|
||||
from spacy.ml import load_kb
|
||||
from spacy.ml.models.entity_linker import build_span_maker, get_candidates
|
||||
from spacy.ml.models.entity_linker import build_span_maker, get_candidates_v2
|
||||
from spacy.pipeline import EntityLinker, TrainablePipe
|
||||
from spacy.pipeline.tok2vec import DEFAULT_TOK2VEC_MODEL
|
||||
from spacy.scorer import Scorer
|
||||
from spacy.tests.util import make_tempdir
|
||||
from spacy.tokens import Doc, Span
|
||||
from spacy.tokens import Doc, Span, SpanGroup
|
||||
from spacy.training import Example
|
||||
from spacy.util import ensure_path
|
||||
from spacy.vocab import Vocab
|
||||
|
|
@ -453,11 +453,21 @@ def test_candidate_generation(nlp):
|
|||
mykb.add_alias(alias="adam", entities=["Q2"], probabilities=[0.9])
|
||||
|
||||
# test the size of the relevant candidates
|
||||
adam_ent_cands = get_candidates(mykb, adam_ent)
|
||||
assert len(get_candidates(mykb, douglas_ent)) == 2
|
||||
adam_ent_cands = next(
|
||||
get_candidates_v2(mykb, SpanGroup(doc=doc, spans=[adam_ent]))
|
||||
)[0]
|
||||
assert len(adam_ent_cands) == 1
|
||||
assert len(get_candidates(mykb, Adam_ent)) == 0 # default case sensitive
|
||||
assert len(get_candidates(mykb, shrubbery_ent)) == 0
|
||||
assert (
|
||||
len(next(get_candidates_v2(mykb, SpanGroup(doc=doc, spans=[douglas_ent])))[0])
|
||||
== 2
|
||||
)
|
||||
assert (
|
||||
len(next(get_candidates_v2(mykb, SpanGroup(doc=doc, spans=[Adam_ent])))[0]) == 0
|
||||
) # default case sensitive
|
||||
assert (
|
||||
len(next(get_candidates_v2(mykb, SpanGroup(doc=doc, spans=[shrubbery_ent])))[0])
|
||||
== 0
|
||||
)
|
||||
|
||||
# test the content of the candidates
|
||||
assert adam_ent_cands[0].entity_id_ == "Q2"
|
||||
|
|
@ -466,6 +476,86 @@ def test_candidate_generation(nlp):
|
|||
assert_almost_equal(adam_ent_cands[0].prior_prob, 0.9)
|
||||
|
||||
|
||||
def test_candidate_generation_multiple_docs(nlp):
|
||||
"""Test correct candidate generation with multiple docs."""
|
||||
mykb = InMemoryLookupKB(nlp.vocab, entity_vector_length=1)
|
||||
docs = [nlp("douglas adam Adam shrubbery"), nlp("shrubbery Adam douglas adam")]
|
||||
|
||||
douglas_ents = [docs[0][0:1], docs[1][2:3]]
|
||||
adam_ents = [docs[0][1:2], docs[1][3:4]]
|
||||
Adam_ents = [docs[0][2:3], docs[1][1:2]]
|
||||
shrubbery_ents = [docs[0][3:4], docs[1][0:1]]
|
||||
|
||||
# adding entities
|
||||
mykb.add_entity(entity="Q1", freq=27, entity_vector=[1])
|
||||
mykb.add_entity(entity="Q2", freq=12, entity_vector=[2])
|
||||
mykb.add_entity(entity="Q3", freq=5, entity_vector=[3])
|
||||
|
||||
# adding aliases
|
||||
mykb.add_alias(alias="douglas", entities=["Q2", "Q3"], probabilities=[0.8, 0.1])
|
||||
mykb.add_alias(alias="adam", entities=["Q2"], probabilities=[0.9])
|
||||
|
||||
# test the size of the relevant candidates
|
||||
adam_ent_cands = list(
|
||||
get_candidates_v2(
|
||||
mykb,
|
||||
[
|
||||
SpanGroup(doc=docs[0], spans=[adam_ents[0]]),
|
||||
SpanGroup(doc=docs[1], spans=[adam_ents[1]]),
|
||||
],
|
||||
)
|
||||
)
|
||||
assert len(adam_ent_cands) == 2
|
||||
assert (
|
||||
len(
|
||||
list(
|
||||
get_candidates_v2(
|
||||
mykb,
|
||||
[
|
||||
SpanGroup(doc=docs[0], spans=[douglas_ents[0]]),
|
||||
SpanGroup(doc=docs[1], spans=[douglas_ents[1]]),
|
||||
],
|
||||
)
|
||||
)
|
||||
)
|
||||
== 2
|
||||
)
|
||||
Adam_ent_cands = list(
|
||||
get_candidates_v2(
|
||||
mykb,
|
||||
[
|
||||
SpanGroup(doc=docs[0], spans=[Adam_ents[0]]),
|
||||
SpanGroup(doc=docs[1], spans=[Adam_ents[1]]),
|
||||
],
|
||||
)
|
||||
)
|
||||
assert len(Adam_ent_cands) == 2
|
||||
assert (
|
||||
len(Adam_ent_cands[0][0]) == 0 and len(Adam_ent_cands[1][0]) == 0
|
||||
) # default case sensitive
|
||||
shrubbery_ents_cands = list(
|
||||
get_candidates_v2(
|
||||
mykb,
|
||||
[
|
||||
SpanGroup(doc=docs[0], spans=[shrubbery_ents[0]]),
|
||||
SpanGroup(doc=docs[1], spans=[shrubbery_ents[1]]),
|
||||
],
|
||||
)
|
||||
)
|
||||
assert len(shrubbery_ents_cands) == 2
|
||||
assert len(shrubbery_ents_cands[0][0]) == 0 and len(shrubbery_ents_cands[1][0]) == 0
|
||||
|
||||
# test the content of the candidates
|
||||
assert (
|
||||
adam_ent_cands[0][0][0].entity_id_ == adam_ent_cands[1][0][0].entity_id_ == "Q2"
|
||||
)
|
||||
assert adam_ent_cands[0][0][0].alias == adam_ent_cands[1][0][0].alias == "adam"
|
||||
assert_almost_equal(adam_ent_cands[0][0][0].entity_freq, 12)
|
||||
assert_almost_equal(adam_ent_cands[1][0][0].entity_freq, 12)
|
||||
assert_almost_equal(adam_ent_cands[0][0][0].prior_prob, 0.9)
|
||||
assert_almost_equal(adam_ent_cands[1][0][0].prior_prob, 0.9)
|
||||
|
||||
|
||||
def test_el_pipe_configuration(nlp):
|
||||
"""Test correct candidate generation as part of the EL pipe"""
|
||||
nlp.add_pipe("sentencizer")
|
||||
|
|
@ -490,24 +580,20 @@ def test_el_pipe_configuration(nlp):
|
|||
assert doc[1].ent_kb_id_ == ""
|
||||
assert doc[2].ent_kb_id_ == "Q2"
|
||||
|
||||
def get_lowercased_candidates(kb, span):
|
||||
return kb._get_alias_candidates(span.text.lower())
|
||||
|
||||
def get_lowercased_candidates_batch(kb, spans):
|
||||
return [get_lowercased_candidates(kb, span) for span in spans]
|
||||
def get_lowercased_candidates(kb: InMemoryLookupKB, mentions: Iterator[SpanGroup]):
|
||||
for mentions_for_doc in mentions:
|
||||
yield [
|
||||
kb._get_alias_candidates(ent_span.text.lower())
|
||||
for ent_span in mentions_for_doc
|
||||
]
|
||||
|
||||
@registry.misc("spacy.LowercaseCandidateGenerator.v1")
|
||||
def create_candidates() -> (
|
||||
Callable[[InMemoryLookupKB, "Span"], Iterable[Candidate]]
|
||||
):
|
||||
def create_candidates() -> Callable[
|
||||
[InMemoryLookupKB, Iterator[SpanGroup]],
|
||||
Iterator[Iterable[Iterable[Candidate]]],
|
||||
]:
|
||||
return get_lowercased_candidates
|
||||
|
||||
@registry.misc("spacy.LowercaseCandidateBatchGenerator.v1")
|
||||
def create_candidates_batch() -> (
|
||||
Callable[[InMemoryLookupKB, Iterable["Span"]], Iterable[Iterable[Candidate]]]
|
||||
):
|
||||
return get_lowercased_candidates_batch
|
||||
|
||||
# replace the pipe with a new one with with a different candidate generator
|
||||
entity_linker = nlp.replace_pipe(
|
||||
"entity_linker",
|
||||
|
|
@ -515,9 +601,6 @@ def test_el_pipe_configuration(nlp):
|
|||
config={
|
||||
"incl_context": False,
|
||||
"get_candidates": {"@misc": "spacy.LowercaseCandidateGenerator.v1"},
|
||||
"get_candidates_batch": {
|
||||
"@misc": "spacy.LowercaseCandidateBatchGenerator.v1"
|
||||
},
|
||||
},
|
||||
)
|
||||
entity_linker.set_kb(create_kb)
|
||||
|
|
|
|||
|
|
@ -1255,6 +1255,15 @@ A function that reads an existing `KnowledgeBase` from file.
|
|||
| --------- | -------------------------------------------------------- |
|
||||
| `kb_path` | The location of the KB that was stored to file. ~~Path~~ |
|
||||
|
||||
### spacy.CandidateGenerator.v2 {id="CandidateGenerator-v2"}
|
||||
|
||||
A function that takes as input a [`KnowledgeBase`](/api/kb) and a
|
||||
`Iterator[SpanGroup]` object denoting a collection of named entities for
|
||||
multiple [`Doc`](/api/doc), and returns an iterable of plausible
|
||||
[`Candidate`](/api/kb/#candidate) objects per `Doc`. The default
|
||||
`CandidateGenerator` uses the text of a mention to find its potential aliases in
|
||||
the `KnowledgeBase`. Note that this function is case-dependent.
|
||||
|
||||
### spacy.CandidateGenerator.v1 {id="CandidateGenerator"}
|
||||
|
||||
A function that takes as input a [`KnowledgeBase`](/api/kb) and a
|
||||
|
|
|
|||
|
|
@ -47,28 +47,41 @@ architectures and their arguments and hyperparameters.
|
|||
> "incl_context": True,
|
||||
> "model": DEFAULT_NEL_MODEL,
|
||||
> "entity_vector_length": 64,
|
||||
> "get_candidates": {'@misc': 'spacy.CandidateGenerator.v1'},
|
||||
> "get_candidates": {'@misc': 'spacy.CandidateGenerator.v2'},
|
||||
> "threshold": None,
|
||||
> }
|
||||
> nlp.add_pipe("entity_linker", config=config)
|
||||
> ```
|
||||
|
||||
| Setting | Description |
|
||||
| --------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `labels_discard` | NER labels that will automatically get a "NIL" prediction. Defaults to `[]`. ~~Iterable[str]~~ |
|
||||
| `n_sents` | The number of neighbouring sentences to take into account. Defaults to 0. ~~int~~ |
|
||||
| `incl_prior` | Whether or not to include prior probabilities from the KB in the model. Defaults to `True`. ~~bool~~ |
|
||||
| `incl_context` | Whether or not to include the local context in the model. Defaults to `True`. ~~bool~~ |
|
||||
| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. Defaults to [EntityLinker](/api/architectures#EntityLinker). ~~Model~~ |
|
||||
| `entity_vector_length` | Size of encoding vectors in the KB. Defaults to `64`. ~~int~~ |
|
||||
| `use_gold_ents` | Whether to copy entities from the gold docs or not. Defaults to `True`. If `False`, entities must be set in the training data or by an annotating component in the pipeline. ~~int~~ |
|
||||
| `get_candidates` | Function that generates plausible candidates for a given `Span` object. Defaults to [CandidateGenerator](/api/architectures#CandidateGenerator), a function looking up exact, case-dependent aliases in the KB. ~~Callable[[KnowledgeBase, Span], Iterable[Candidate]]~~ |
|
||||
| `get_candidates_batch` <Tag variant="new">3.5</Tag> | Function that generates plausible candidates for a given batch of `Span` objects. Defaults to [CandidateBatchGenerator](/api/architectures#CandidateBatchGenerator), a function looking up exact, case-dependent aliases in the KB. ~~Callable[[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]]~~ |
|
||||
| `generate_empty_kb` <Tag variant="new">3.5.1</Tag> | Function that generates an empty `KnowledgeBase` object. Defaults to [`spacy.EmptyKB.v2`](/api/architectures#EmptyKB), which generates an empty [`InMemoryLookupKB`](/api/inmemorylookupkb). ~~Callable[[Vocab, int], KnowledgeBase]~~ |
|
||||
| `overwrite` <Tag variant="new">3.2</Tag> | Whether existing annotation is overwritten. Defaults to `True`. ~~bool~~ |
|
||||
| `scorer` <Tag variant="new">3.2</Tag> | The scoring method. Defaults to [`Scorer.score_links`](/api/scorer#score_links). ~~Optional[Callable]~~ |
|
||||
| `save_activations` <Tag variant="new">4.0</Tag> | Save activations in `Doc` when annotating. Saved activations are `"ents"` and `"scores"`. ~~Union[bool, list[str]]~~ |
|
||||
| `threshold` <Tag variant="new">3.4</Tag> | Confidence threshold for entity predictions. The default of `None` implies that all predictions are accepted, otherwise those with a score beneath the treshold are discarded. If there are no predictions with scores above the threshold, the linked entity is `NIL`. ~~Optional[float]~~ |
|
||||
| Setting | Description |
|
||||
| ------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `labels_discard` | NER labels that will automatically get an "NIL" prediction. Defaults to `[]`. ~~Iterable[str]~~ |
|
||||
| `n_sents` | The number of neighbouring sentences to take into account. Defaults to `0`. ~~int~~ |
|
||||
| `incl_prior` | Whether prior probabilities from the KB are included in the model. Defaults to `True`. ~~bool~~ |
|
||||
| `incl_context` | Whether the local context is included in the model. Defaults to `True`. ~~bool~~ |
|
||||
| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. Defaults to [`EntityLinker`](/api/architectures#EntityLinker). ~~Model~~ |
|
||||
| `entity_vector_length` | Size of encoding vectors in the KB. Defaults to `64`. ~~int~~ |
|
||||
| `use_gold_ents` | Whether entities are copied from the gold docs. Defaults to `True`. If `False`, entities must be set in the training data or by an annotating component in the pipeline. ~~int~~ |
|
||||
| `get_candidates` <Tag variant="new">4.0</Tag> | Function that retrieves plausible candidates per entity mention in a given `Iterator[SpanGroup]` (one `SpanGroup` includes all mentions found in a given `Doc` instance). Defaults to [CandidateGenerator](/api/architectures#CandidateGenerator). ~~Callable[[KnowledgeBase, Iterator[SpanGroup]], Iterator[Iterable[Iterable[Candidate]]]]~~ |
|
||||
| `generate_empty_kb` <Tag variant="new">3.6</Tag> | Function that generates an empty `KnowledgeBase` object. Defaults to [`spacy.EmptyKB.v2`](/api/architectures#EmptyKB), which generates an empty [`InMemoryLookupKB`](/api/inmemorylookupkb). ~~Callable[[Vocab, int], KnowledgeBase]~~ |
|
||||
| `overwrite` <Tag variant="new">3.2</Tag> | Whether existing annotation is overwritten. Defaults to `True`. ~~bool~~ |
|
||||
| `scorer` <Tag variant="new">3.2</Tag> | The scoring method. Defaults to [`Scorer.score_links`](/api/scorer#score_links). ~~Optional[Callable]~~ |
|
||||
| `save_activations` <Tag variant="new">4.0</Tag> | Save activations in `Doc` when annotating. Saved activations are `"ents"` and `"scores"`. ~~Union[bool, list[str]]~~ |
|
||||
| `threshold` <Tag variant="new">3.4</Tag> | Confidence threshold for entity predictions. The default of `None` implies that all predictions are accepted, otherwise those with a score beneath the treshold are discarded. If there are no predictions with scores above the threshold, the linked entity is `NIL`. ~~Optional[float]~~ |
|
||||
|
||||
<Infobox variant="warning">
|
||||
|
||||
Prior to spaCy v4.0 `get_candidates()` returns a single `Iterable` of candidates
|
||||
for one specific mention, i. e. the function was typed as
|
||||
`Callable[[KnowledgeBase, Span], Iterable[Candidate]]`. To retrieve candidates
|
||||
batch-wise, spaCy >= 3.5 exposes `get_candidates_batched()`, which identifies
|
||||
candidates for an arbitrary number of spans:
|
||||
`Callable[[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]]`. The
|
||||
main difference between `get_candidates_batched()` and `get_candidates()` in
|
||||
spaCy >= 4.0 is that the latter considers the grouping of provided mention spans
|
||||
per `Doc` instance.
|
||||
|
||||
</Infobox>
|
||||
|
||||
```python
|
||||
%%GITHUB_SPACY/spacy/pipeline/entity_linker.py
|
||||
|
|
@ -108,7 +121,7 @@ custom knowledge base, you should either call
|
|||
| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
|
||||
| _keyword-only_ | |
|
||||
| `entity_vector_length` | Size of encoding vectors in the KB. ~~int~~ |
|
||||
| `get_candidates` | Function that generates plausible candidates for a given `Span` object. ~~Callable[[KnowledgeBase, Span], Iterable[Candidate]]~~ |
|
||||
| `get_candidates` | Function that retrieves plausible candidates per entity mention in a given `SpanGroup`. Defaults to [CandidateGenerator](/api/architectures#CandidateGenerator). ~~Callable[[KnowledgeBase, Iterator[SpanGroup]], Iterator[Iterable[Iterable[Candidate]]]]~~ |
|
||||
| `labels_discard` | NER labels that will automatically get a `"NIL"` prediction. ~~Iterable[str]~~ |
|
||||
| `n_sents` | The number of neighbouring sentences to take into account. ~~int~~ |
|
||||
| `incl_prior` | Whether or not to include prior probabilities from the KB in the model. ~~bool~~ |
|
||||
|
|
|
|||
|
|
@ -155,35 +155,12 @@ Get a list of all aliases in the knowledge base.
|
|||
|
||||
## InMemoryLookupKB.get_candidates {id="get_candidates",tag="method"}
|
||||
|
||||
Given a certain textual mention as input, retrieve a list of candidate entities
|
||||
of type [`InMemoryCandidate`](/api/kb#candidate). Wraps
|
||||
[`get_alias_candidates()`](/api/inmemorylookupkb#get_alias_candidates).
|
||||
|
||||
> #### Example
|
||||
>
|
||||
> ```python
|
||||
> from spacy.lang.en import English
|
||||
> nlp = English()
|
||||
> doc = nlp("Douglas Adams wrote 'The Hitchhiker's Guide to the Galaxy'.")
|
||||
> candidates = kb.get_candidates(doc[0:2])
|
||||
> ```
|
||||
|
||||
| Name | Description |
|
||||
| ----------- | ------------------------------------------------------------------------------------ |
|
||||
| `mention` | The textual mention or alias. ~~Span~~ |
|
||||
| **RETURNS** | An iterable of relevant `InMemoryCandidate` objects. ~~Iterable[InMemoryCandidate]~~ |
|
||||
|
||||
## InMemoryLookupKB.get_candidates_batch {id="get_candidates_batch",tag="method"}
|
||||
|
||||
Same as [`get_candidates()`](/api/inmemorylookupkb#get_candidates), but for an
|
||||
arbitrary number of mentions. The [`EntityLinker`](/api/entitylinker) component
|
||||
will call `get_candidates_batch()` instead of `get_candidates()`, if the config
|
||||
parameter `candidates_batch_size` is greater or equal than 1.
|
||||
|
||||
The default implementation of `get_candidates_batch()` executes
|
||||
`get_candidates()` in a loop. We recommend implementing a more efficient way to
|
||||
retrieve candidates for multiple mentions at once, if performance is of concern
|
||||
to you.
|
||||
Given textual mentions for an arbitrary number of documents as input, retrieve a
|
||||
list of candidate entities of type [`InMemoryCandidate`](/api/kb#candidate) for
|
||||
each mention. The [`EntityLinker`](/api/entitylinker) component passes a
|
||||
generator that yields mentions as [`SpanGroup`](/api/spangroup))s per document. The decision of how to batch
|
||||
candidate retrieval lookups over multiple documents is left up to the
|
||||
implementation of `KnowledgeBase.get_candidates()`.
|
||||
|
||||
> #### Example
|
||||
>
|
||||
|
|
@ -192,13 +169,13 @@ to you.
|
|||
> from spacy.tokens import SpanGroup
|
||||
> nlp = English()
|
||||
> doc = nlp("Douglas Adams wrote 'The Hitchhiker's Guide to the Galaxy'.")
|
||||
> candidates = kb.get_candidates_batch([SpanGroup(doc, spans=[doc[0:2], doc[3:]]])
|
||||
> candidates = kb.get_candidates([SpanGroup(doc, spans=[doc[0:2], doc[3:]]])
|
||||
> ```
|
||||
|
||||
| Name | Description |
|
||||
| ----------- | ------------------------------------------------------------------------------------------------------------ |
|
||||
| `mentions` | The textual mentions. ~~SpanGroup~~ |
|
||||
| **RETURNS** | An iterable of iterable with relevant `InMemoryCandidate` objects. ~~Iterable[Iterable[InMemoryCandidate]]~~ |
|
||||
| Name | Description |
|
||||
| ----------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `mentions` | The textual mentions or aliases (one `SpanGroup` per `Doc` instance). ~~Iterator[SpanGroup]~~ |
|
||||
| **RETURNS** | An iterator over iterables of iterables with relevant [`InMemoryCandidate`](/api/kb#candidate) objects (per mention and doc). ~~Iterator[Iterable[Iterable[InMemoryCandidate]]]~~ |
|
||||
|
||||
## InMemoryLookupKB.get_vector {id="get_vector",tag="method"}
|
||||
|
||||
|
|
|
|||
|
|
@ -60,34 +60,13 @@ The length of the fixed-size entity vectors in the knowledge base.
|
|||
|
||||
## KnowledgeBase.get_candidates {id="get_candidates",tag="method"}
|
||||
|
||||
Given a certain textual mention as input, retrieve a list of candidate entities
|
||||
of type [`Candidate`](/api/kb#candidate).
|
||||
|
||||
> #### Example
|
||||
>
|
||||
> ```python
|
||||
> from spacy.lang.en import English
|
||||
> nlp = English()
|
||||
> doc = nlp("Douglas Adams wrote 'The Hitchhiker's Guide to the Galaxy'.")
|
||||
> candidates = kb.get_candidates(doc[0:2])
|
||||
> ```
|
||||
|
||||
| Name | Description |
|
||||
| ----------- | -------------------------------------------------------------------- |
|
||||
| `mention` | The textual mention or alias. ~~Span~~ |
|
||||
| **RETURNS** | An iterable of relevant `Candidate` objects. ~~Iterable[Candidate]~~ |
|
||||
|
||||
## KnowledgeBase.get_candidates_batch {id="get_candidates_batch",tag="method"}
|
||||
|
||||
Same as [`get_candidates()`](/api/kb#get_candidates), but for an arbitrary
|
||||
number of mentions. The [`EntityLinker`](/api/entitylinker) component will call
|
||||
`get_candidates_batch()` instead of `get_candidates()`, if the config parameter
|
||||
`candidates_batch_size` is greater or equal than 1.
|
||||
|
||||
The default implementation of `get_candidates_batch()` executes
|
||||
`get_candidates()` in a loop. We recommend implementing a more efficient way to
|
||||
retrieve candidates for multiple mentions at once, if performance is of concern
|
||||
to you.
|
||||
Given textual mentions for an arbitrary number of documents as input, retrieve a
|
||||
list of candidate entities of type [`Candidate`](/api/kb#candidate) for each
|
||||
mention. The [`EntityLinker`](/api/entitylinker) component passes a generator
|
||||
that yields mentions as [`SpanGroup`](/api/spangroup))s per document.
|
||||
The decision of how to batch
|
||||
candidate retrieval lookups over multiple documents is left up to the
|
||||
implementation of `KnowledgeBase.get_candidates()`.
|
||||
|
||||
> #### Example
|
||||
>
|
||||
|
|
@ -99,10 +78,10 @@ to you.
|
|||
> candidates = kb.get_candidates([SpanGroup(doc, spans=[doc[0:2], doc[3:]]])
|
||||
> ```
|
||||
|
||||
| Name | Description |
|
||||
| ----------- | -------------------------------------------------------------------------------------------- |
|
||||
| `mentions` | The textual mentions. ~~SpanGroup~~ |
|
||||
| **RETURNS** | An iterable of iterable with relevant `Candidate` objects. ~~Iterable[Iterable[Candidate]]~~ |
|
||||
| Name | Description |
|
||||
| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
||||
| `mentions` | The textual mentions or aliases (one `SpanGroup` per `Doc` instance). ~~Iterator[SpanGroup]~~ |
|
||||
| **RETURNS** | An iterator (per document) over iterables (per mention) of iterables (per candidate for this mention) with relevant `Candidate` objects. ~~Iterator[Iterable[Iterable[Candidate]]]~~ |
|
||||
|
||||
## KnowledgeBase.get_vector {id="get_vector",tag="method"}
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user