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Merge branch 'add/exclusive-spancat' of github.com:ljvmiranda921/spaCy into exclusive-spancat

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This commit is contained in:
kadarakos 2023-03-08 11:36:45 +00:00
commit 95206efe95
21 changed files with 199 additions and 69 deletions
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5
.github/azure-steps.yml vendored
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@ -59,6 +59,11 @@ steps:
displayName: 'Test download CLI'
condition: eq(variables['python_version'], '3.8')
- script: |
python -W error -m spacy info ca_core_news_sm | grep -q download_url
displayName: 'Test download_url in info CLI'
condition: eq(variables['python_version'], '3.8')
- script: |
python -W error -c "import ca_core_news_sm; nlp = ca_core_news_sm.load(); doc=nlp('test')"
displayName: 'Test no warnings on load (#11713)'

2
pyproject.toml
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@ -5,7 +5,7 @@ requires = [
"cymem>=2.0.2,<2.1.0",
"preshed>=3.0.2,<3.1.0",
"murmurhash>=0.28.0,<1.1.0",
"thinc>=8.1.6,<8.2.0",
"thinc>=8.1.8,<8.2.0",
"numpy>=1.15.0",
]
build-backend = "setuptools.build_meta"

2
requirements.txt
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@ -3,7 +3,7 @@ spacy-legacy>=3.0.11,<3.1.0
spacy-loggers>=1.0.0,<2.0.0
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0
thinc>=8.1.6,<8.2.0
thinc>=8.1.8,<8.2.0
ml_datasets>=0.2.0,<0.3.0
murmurhash>=0.28.0,<1.1.0
wasabi>=0.9.1,<1.2.0

4
setup.cfg
View File

@ -39,7 +39,7 @@ setup_requires =
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0
murmurhash>=0.28.0,<1.1.0
thinc>=8.1.6,<8.2.0
thinc>=8.1.8,<8.2.0
install_requires =
# Our libraries
spacy-legacy>=3.0.11,<3.1.0
@ -47,7 +47,7 @@ install_requires =
murmurhash>=0.28.0,<1.1.0
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0
thinc>=8.1.6,<8.2.0
thinc>=8.1.8,<8.2.0
wasabi>=0.9.1,<1.2.0
srsly>=2.4.3,<3.0.0
catalogue>=2.0.6,<2.1.0

17
spacy/cli/info.py
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@ -1,6 +1,5 @@
from typing import Optional, Dict, Any, Union, List
import platform
import pkg_resources
import json
from pathlib import Path
from wasabi import Printer, MarkdownRenderer
@ -10,6 +9,7 @@ from ._util import app, Arg, Opt, string_to_list
from .download import get_model_filename, get_latest_version
from .. import util
from .. import about
from ..compat import importlib_metadata
@app.command("info")
@ -137,15 +137,14 @@ def info_installed_model_url(model: str) -> Optional[str]:
dist-info available.
"""
try:
dist = pkg_resources.get_distribution(model)
data = json.loads(dist.get_metadata("direct_url.json"))
return data["url"]
except pkg_resources.DistributionNotFound:
# no such package
return None
dist = importlib_metadata.distribution(model)
text = dist.read_text("direct_url.json")
if isinstance(text, str):
data = json.loads(text)
return data["url"]
except Exception:
# something else, like no file or invalid JSON
return None
pass
return None
def info_model_url(model: str) -> Dict[str, Any]:

2
spacy/cli/project/run.py
View File

@ -2,7 +2,6 @@ from typing import Optional, List, Dict, Sequence, Any, Iterable, Tuple
import os.path
from pathlib import Path
import pkg_resources
from wasabi import msg
from wasabi.util import locale_escape
import sys
@ -331,6 +330,7 @@ def _check_requirements(requirements: List[str]) -> Tuple[bool, bool]:
RETURNS (Tuple[bool, bool]): Whether (1) any packages couldn't be imported, (2) any packages with version conflicts
exist.
"""
import pkg_resources
failed_pkgs_msgs: List[str] = []
conflicting_pkgs_msgs: List[str] = []

31
spacy/cli/templates/quickstart_training.jinja
View File

@ -24,8 +24,11 @@ gpu_allocator = null
lang = "{{ lang }}"
{%- set has_textcat = ("textcat" in components or "textcat_multilabel" in components) -%}
{%- set with_accuracy = optimize == "accuracy" -%}
{%- set has_accurate_textcat = has_textcat and with_accuracy -%}
{%- if ("tagger" in components or "morphologizer" in components or "parser" in components or "ner" in components or "spancat" in components or "trainable_lemmatizer" in components or "entity_linker" in components or has_accurate_textcat) -%}
{# The BOW textcat doesn't need a source of features, so it can omit the
tok2vec/transformer. #}
{%- set with_accuracy_or_transformer = (use_transformer or with_accuracy) -%}
{%- set textcat_needs_features = has_textcat and with_accuracy_or_transformer -%}
{%- if ("tagger" in components or "morphologizer" in components or "parser" in components or "ner" in components or "spancat" in components or "trainable_lemmatizer" in components or "entity_linker" in components or textcat_needs_features) -%}
{%- set full_pipeline = ["transformer" if use_transformer else "tok2vec"] + components -%}
{%- else -%}
{%- set full_pipeline = components -%}
@ -221,10 +224,16 @@ no_output_layer = false
{% else -%}
[components.textcat.model]
@architectures = "spacy.TextCatBOW.v2"
@architectures = "spacy.TextCatCNN.v2"
exclusive_classes = true
ngram_size = 1
no_output_layer = false
nO = null
[components.textcat.model.tok2vec]
@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0
[components.textcat.model.tok2vec.pooling]
@layers = "reduce_mean.v1"
{%- endif %}
{%- endif %}
@ -252,10 +261,16 @@ no_output_layer = false
{% else -%}
[components.textcat_multilabel.model]
@architectures = "spacy.TextCatBOW.v2"
@architectures = "spacy.TextCatCNN.v2"
exclusive_classes = false
ngram_size = 1
no_output_layer = false
nO = null
[components.textcat_multilabel.model.tok2vec]
@architectures = "spacy-transformers.TransformerListener.v1"
grad_factor = 1.0
[components.textcat_multilabel.model.tok2vec.pooling]
@layers = "reduce_mean.v1"
{%- endif %}
{%- endif %}

2
spacy/errors.py
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@ -549,6 +549,8 @@ class Errors(metaclass=ErrorsWithCodes):
"during training, make sure to include it in 'annotating components'")
# New errors added in v3.x
E850 = ("The PretrainVectors objective currently only supports default "
"vectors, not {mode} vectors.")
E851 = ("The 'textcat' component labels should only have values of 0 or 1, "
"but found value of '{val}'.")
E852 = ("The tar file pulled from the remote attempted an unsafe path "

3
spacy/lexeme.pyi
View File

@ -25,7 +25,8 @@ class Lexeme:
def orth_(self) -> str: ...
@property
def text(self) -> str: ...
lower: str
orth: int
lower: int
norm: int
shape: int
prefix: int

2
spacy/lexeme.pyx
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@ -199,7 +199,7 @@ cdef class Lexeme:
return self.orth_
property lower:
"""RETURNS (str): Lowercase form of the lexeme."""
"""RETURNS (uint64): Lowercase form of the lexeme."""
def __get__(self):
return self.c.lower

8
spacy/ml/models/entity_linker.py
View File

@ -89,6 +89,14 @@ def load_kb(
return kb_from_file
@registry.misc("spacy.EmptyKB.v2")
def empty_kb_for_config() -> Callable[[Vocab, int], KnowledgeBase]:
def empty_kb_factory(vocab: Vocab, entity_vector_length: int):
return InMemoryLookupKB(vocab=vocab, entity_vector_length=entity_vector_length)
return empty_kb_factory
@registry.misc("spacy.EmptyKB.v1")
def empty_kb(
entity_vector_length: int,

3
spacy/ml/models/multi_task.py
View File

@ -8,6 +8,7 @@ from thinc.loss import Loss
from ...util import registry, OOV_RANK
from ...errors import Errors
from ...attrs import ID
from ...vectors import Mode as VectorsMode
import numpy
from functools import partial
@ -23,6 +24,8 @@ def create_pretrain_vectors(
maxout_pieces: int, hidden_size: int, loss: str
) -> Callable[["Vocab", Model], Model]:
def create_vectors_objective(vocab: "Vocab", tok2vec: Model) -> Model:
if vocab.vectors.mode != VectorsMode.default:
raise ValueError(Errors.E850.format(mode=vocab.vectors.mode))
if vocab.vectors.shape[1] == 0:
raise ValueError(Errors.E875)
model = build_cloze_multi_task_model(

11
spacy/pipeline/entity_linker.py
View File

@ -54,6 +54,7 @@ DEFAULT_NEL_MODEL = Config().from_str(default_model_config)["model"]
"entity_vector_length": 64,
"get_candidates": {"@misc": "spacy.CandidateGenerator.v1"},
"get_candidates_batch": {"@misc": "spacy.CandidateBatchGenerator.v1"},
"generate_empty_kb": {"@misc": "spacy.EmptyKB.v2"},
"overwrite": True,
"scorer": {"@scorers": "spacy.entity_linker_scorer.v1"},
"use_gold_ents": True,
@ -80,6 +81,7 @@ def make_entity_linker(
get_candidates_batch: Callable[
[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]
],
generate_empty_kb: Callable[[Vocab, int], KnowledgeBase],
overwrite: bool,
scorer: Optional[Callable],
use_gold_ents: bool,
@ -101,6 +103,7 @@ def make_entity_linker(
get_candidates_batch (
Callable[[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]], Iterable[Candidate]]
): Function that produces a list of candidates, given a certain knowledge base and several textual mentions.
generate_empty_kb (Callable[[Vocab, int], KnowledgeBase]): Callable returning empty KnowledgeBase.
scorer (Optional[Callable]): The scoring method.
use_gold_ents (bool): Whether to copy entities from gold docs or not. If false, another
component must provide entity annotations.
@ -135,6 +138,7 @@ def make_entity_linker(
entity_vector_length=entity_vector_length,
get_candidates=get_candidates,
get_candidates_batch=get_candidates_batch,
generate_empty_kb=generate_empty_kb,
overwrite=overwrite,
scorer=scorer,
use_gold_ents=use_gold_ents,
@ -175,6 +179,7 @@ class EntityLinker(TrainablePipe):
get_candidates_batch: Callable[
[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]
],
generate_empty_kb: Callable[[Vocab, int], KnowledgeBase],
overwrite: bool = BACKWARD_OVERWRITE,
scorer: Optional[Callable] = entity_linker_score,
use_gold_ents: bool,
@ -198,6 +203,7 @@ class EntityLinker(TrainablePipe):
Callable[[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]],
Iterable[Candidate]]
): Function that produces a list of candidates, given a certain knowledge base and several textual mentions.
generate_empty_kb (Callable[[Vocab, int], KnowledgeBase]): Callable returning empty KnowledgeBase.
scorer (Optional[Callable]): The scoring method. Defaults to Scorer.score_links.
use_gold_ents (bool): Whether to copy entities from gold docs or not. If false, another
component must provide entity annotations.
@ -220,6 +226,7 @@ class EntityLinker(TrainablePipe):
self.model = model
self.name = name
self.labels_discard = list(labels_discard)
# how many neighbour sentences to take into account
self.n_sents = n_sents
self.incl_prior = incl_prior
self.incl_context = incl_context
@ -227,9 +234,7 @@ class EntityLinker(TrainablePipe):
self.get_candidates_batch = get_candidates_batch
self.cfg: Dict[str, Any] = {"overwrite": overwrite}
self.distance = CosineDistance(normalize=False)
# how many neighbour sentences to take into account
# create an empty KB by default
self.kb = empty_kb(entity_vector_length)(self.vocab)
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

4
spacy/tests/parser/test_ner.py
View File

@ -9,6 +9,8 @@ from spacy.lang.en import English
from spacy.lang.it import Italian
from spacy.language import Language
from spacy.lookups import Lookups
from spacy.pipeline import EntityRecognizer
from spacy.pipeline.ner import DEFAULT_NER_MODEL
from spacy.pipeline._parser_internals.ner import BiluoPushDown
from spacy.training import Example, iob_to_biluo, split_bilu_label
from spacy.tokens import Doc, Span
@ -16,8 +18,6 @@ from spacy.vocab import Vocab
import logging
from ..util import make_tempdir
from ...pipeline import EntityRecognizer
from ...pipeline.ner import DEFAULT_NER_MODEL
TRAIN_DATA = [
("Who is Shaka Khan?", {"entities": [(7, 17, "PERSON")]}),

6
spacy/tests/parser/test_parse.py
View File

@ -8,11 +8,11 @@ from spacy.lang.en import English
from spacy.tokens import Doc
from spacy.training import Example
from spacy.vocab import Vocab
from spacy.pipeline import DependencyParser
from spacy.pipeline.dep_parser import DEFAULT_PARSER_MODEL
from spacy.pipeline.tok2vec import DEFAULT_TOK2VEC_MODEL
from ...pipeline import DependencyParser
from ...pipeline.dep_parser import DEFAULT_PARSER_MODEL
from ..util import apply_transition_sequence, make_tempdir
from ...pipeline.tok2vec import DEFAULT_TOK2VEC_MODEL
TRAIN_DATA = [
(

71
spacy/tests/serialize/test_serialize_kb.py
View File

@ -1,7 +1,10 @@
from typing import Callable
from pathlib import Path
from typing import Callable, Iterable, Any, Dict
from spacy import util
from spacy.util import ensure_path, registry, load_model_from_config
import srsly
from spacy import util, Errors
from spacy.util import ensure_path, registry, load_model_from_config, SimpleFrozenList
from spacy.kb.kb_in_memory import InMemoryLookupKB
from spacy.vocab import Vocab
from thinc.api import Config
@ -91,7 +94,10 @@ def test_serialize_subclassed_kb():
[components.entity_linker]
factory = "entity_linker"
[components.entity_linker.generate_empty_kb]
@misc = "kb_test.CustomEmptyKB.v1"
[initialize]
[initialize.components]
@ -99,7 +105,7 @@ def test_serialize_subclassed_kb():
[initialize.components.entity_linker]
[initialize.components.entity_linker.kb_loader]
@misc = "spacy.CustomKB.v1"
@misc = "kb_test.CustomKB.v1"
entity_vector_length = 342
custom_field = 666
"""
@ -109,10 +115,57 @@ def test_serialize_subclassed_kb():
super().__init__(vocab, entity_vector_length)
self.custom_field = custom_field
@registry.misc("spacy.CustomKB.v1")
def to_disk(self, path, exclude: Iterable[str] = SimpleFrozenList()):
"""We overwrite InMemoryLookupKB.to_disk() to ensure that self.custom_field is stored as well."""
path = ensure_path(path)
if not path.exists():
path.mkdir(parents=True)
if not path.is_dir():
raise ValueError(Errors.E928.format(loc=path))
def serialize_custom_fields(file_path: Path) -> None:
srsly.write_json(file_path, {"custom_field": self.custom_field})
serialize = {
"contents": lambda p: self.write_contents(p),
"strings.json": lambda p: self.vocab.strings.to_disk(p),
"custom_fields": lambda p: serialize_custom_fields(p),
}
util.to_disk(path, serialize, exclude)
def from_disk(self, path, exclude: Iterable[str] = SimpleFrozenList()):
"""We overwrite InMemoryLookupKB.from_disk() to ensure that self.custom_field is loaded as well."""
path = ensure_path(path)
if not path.exists():
raise ValueError(Errors.E929.format(loc=path))
if not path.is_dir():
raise ValueError(Errors.E928.format(loc=path))
def deserialize_custom_fields(file_path: Path) -> None:
self.custom_field = srsly.read_json(file_path)["custom_field"]
deserialize: Dict[str, Callable[[Any], Any]] = {
"contents": lambda p: self.read_contents(p),
"strings.json": lambda p: self.vocab.strings.from_disk(p),
"custom_fields": lambda p: deserialize_custom_fields(p),
}
util.from_disk(path, deserialize, exclude)
@registry.misc("kb_test.CustomEmptyKB.v1")
def empty_custom_kb() -> Callable[[Vocab, int], SubInMemoryLookupKB]:
def empty_kb_factory(vocab: Vocab, entity_vector_length: int):
return SubInMemoryLookupKB(
vocab=vocab,
entity_vector_length=entity_vector_length,
custom_field=0,
)
return empty_kb_factory
@registry.misc("kb_test.CustomKB.v1")
def custom_kb(
entity_vector_length: int, custom_field: int
) -> Callable[[Vocab], InMemoryLookupKB]:
) -> Callable[[Vocab], SubInMemoryLookupKB]:
def custom_kb_factory(vocab):
kb = SubInMemoryLookupKB(
vocab=vocab,
@ -139,6 +192,6 @@ def test_serialize_subclassed_kb():
nlp2 = util.load_model_from_path(tmp_dir)
entity_linker2 = nlp2.get_pipe("entity_linker")
# After IO, the KB is the standard one
assert type(entity_linker2.kb) == InMemoryLookupKB
assert type(entity_linker2.kb) == SubInMemoryLookupKB
assert entity_linker2.kb.entity_vector_length == 342
assert not hasattr(entity_linker2.kb, "custom_field")
assert entity_linker2.kb.custom_field == 666

6
spacy/tests/test_cli.py
View File

@ -2,7 +2,6 @@ import os
import math
from collections import Counter
from typing import Tuple, List, Dict, Any
import pkg_resources
import time
from pathlib import Path
@ -29,6 +28,7 @@ from spacy.cli.debug_data import _print_span_characteristics
from spacy.cli.debug_data import _get_spans_length_freq_dist
from spacy.cli.download import get_compatibility, get_version
from spacy.cli.init_config import RECOMMENDATIONS, init_config, fill_config
from spacy.cli.init_pipeline import _init_labels
from spacy.cli.package import get_third_party_dependencies
from spacy.cli.package import _is_permitted_package_name
from spacy.cli.project.remote_storage import RemoteStorage
@ -47,7 +47,6 @@ from spacy.training.converters import conll_ner_to_docs, conllu_to_docs
from spacy.training.converters import iob_to_docs
from spacy.util import ENV_VARS, get_minor_version, load_model_from_config, load_config
from ..cli.init_pipeline import _init_labels
from .util import make_tempdir
@ -1126,6 +1125,7 @@ def test_cli_find_threshold(capsys):
)
@pytest.mark.filterwarnings("ignore::DeprecationWarning")
@pytest.mark.parametrize(
"reqs,output",
[
@ -1158,6 +1158,8 @@ def test_cli_find_threshold(capsys):
],
)
def test_project_check_requirements(reqs, output):
import pkg_resources
# excessive guard against unlikely package name
try:
pkg_resources.require("spacyunknowndoesnotexist12345")

47
spacy/tests/training/test_pretraining.py
View File

@ -2,17 +2,19 @@ from pathlib import Path
import numpy as np
import pytest
import srsly
from spacy.vocab import Vocab
from thinc.api import Config
from thinc.api import Config, get_current_ops
from spacy import util
from spacy.lang.en import English
from spacy.training.initialize import init_nlp
from spacy.training.loop import train
from spacy.training.pretrain import pretrain
from spacy.tokens import Doc, DocBin
from spacy.language import DEFAULT_CONFIG_PRETRAIN_PATH, DEFAULT_CONFIG_PATH
from spacy.ml.models.multi_task import create_pretrain_vectors
from spacy.vectors import Vectors
from spacy.vocab import Vocab
from ..util import make_tempdir
from ... import util
from ...lang.en import English
from ...training.initialize import init_nlp
from ...training.loop import train
from ...training.pretrain import pretrain
from ...tokens import Doc, DocBin
from ...language import DEFAULT_CONFIG_PRETRAIN_PATH, DEFAULT_CONFIG_PATH
pretrain_string_listener = """
[nlp]
@ -346,3 +348,30 @@ def write_vectors_model(tmp_dir):
nlp = English(vocab)
nlp.to_disk(nlp_path)
return str(nlp_path)
def test_pretrain_default_vectors():
nlp = English()
nlp.add_pipe("tok2vec")
nlp.initialize()
# default vectors are supported
nlp.vocab.vectors = Vectors(shape=(10, 10))
create_pretrain_vectors(1, 1, "cosine")(nlp.vocab, nlp.get_pipe("tok2vec").model)
# error for no vectors
with pytest.raises(ValueError, match="E875"):
nlp.vocab.vectors = Vectors()
create_pretrain_vectors(1, 1, "cosine")(
nlp.vocab, nlp.get_pipe("tok2vec").model
)
# error for floret vectors
with pytest.raises(ValueError, match="E850"):
ops = get_current_ops()
nlp.vocab.vectors = Vectors(
data=ops.xp.zeros((10, 10)), mode="floret", hash_count=1
)
create_pretrain_vectors(1, 1, "cosine")(
nlp.vocab, nlp.get_pipe("tok2vec").model
)

10
website/docs/api/architectures.mdx
View File

@ -899,15 +899,21 @@ The `EntityLinker` model architecture is a Thinc `Model` with a
| `nO` | Output dimension, determined by the length of the vectors encoding each entity in the KB. If the `nO` dimension is not set, the entity linking component will set it when `initialize` is called. ~~Optional[int]~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Doc], Floats2d]~~ |
### spacy.EmptyKB.v1 {id="EmptyKB"}
### spacy.EmptyKB.v1 {id="EmptyKB.v1"}
A function that creates an empty `KnowledgeBase` from a [`Vocab`](/api/vocab)
instance. This is the default when a new entity linker component is created.
instance.
| Name | Description |
| ---------------------- | ----------------------------------------------------------------------------------- |
| `entity_vector_length` | The length of the vectors encoding each entity in the KB. Defaults to `64`. ~~int~~ |
### spacy.EmptyKB.v2 {id="EmptyKB"}
A function that creates an empty `KnowledgeBase` from a [`Vocab`](/api/vocab)
instance. This is the default when a new entity linker component is created. It
returns a `Callable[[Vocab, int], InMemoryLookupKB]`.
### spacy.KBFromFile.v1 {id="KBFromFile"}
A function that reads an existing `KnowledgeBase` from file.

28
website/docs/api/entitylinker.mdx
View File

@ -53,19 +53,21 @@ architectures and their arguments and hyperparameters.
> 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]]~~ |
| `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]~~ |
| `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 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">3.6</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]~~ |
| `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]~~ |
```python
%%GITHUB_SPACY/spacy/pipeline/entity_linker.py

4
website/docs/api/spancategorizer.mdx
View File

@ -68,11 +68,11 @@ architectures and their arguments and hyperparameters.
> "spans_key": "labeled_spans",
> "model": DEFAULT_SPANCAT_SINGLELABEL_MODEL,
> "suggester": {"@misc": "spacy.ngram_suggester.v1", "sizes": [1, 2, 3]},
> # Additional spancat_exclusive parameters
> # Additional spancat_singlelabel parameters
> "negative_weight": 0.8,
> "allow_overlap": True,
> }
> nlp.add_pipe("spancat_exclusive", config=config)
> nlp.add_pipe("spancat_singlelabel", config=config)
> ```
| Setting | Description |