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Python 3.13 support (#13823)

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In order to support Python 3.13, we had to migrate to Cython 3.0. This caused some tricky interaction with our Pydantic usage, because Cython 3 uses the from __future__ import annotations semantics, which causes type annotations to be saved as strings.

The end result is that we can't have Language.factory decorated functions in Cython modules anymore, as the Language.factory decorator expects to inspect the signature of the functions and build a Pydantic model. If the function is implemented in Cython, an error is raised because the type is not resolved.

To address this I've moved the factory functions into a new module, spacy.pipeline.factories. I've added __getattr__ importlib hooks to the previous locations, in case anyone was importing these functions directly. The change should have no backwards compatibility implications.

Along the way I've also refactored the registration of functions for the config. Previously these ran as import-time side-effects, using the registry decorator. I've created instead a new module spacy.registrations. When the registry is accessed it calls a function ensure_populated(), which cases the registrations to occur.

I've made a similar change to the Language.factory registrations in the new spacy.pipeline.factories module.

I want to remove these import-time side-effects so that we can speed up the loading time of the library, which can be especially painful on the CLI. I also find that I'm often working to track down the implementations of functions referenced by strings in the config. Having the registrations all happen in one place will make this easier.

With these changes I've fortunately avoided the need to migrate to Pydantic v2 properly --- we're still using the v1 compatibility shim. We might not be able to hold out forever though: Pydantic (reasonably) aren't actively supporting the v1 shims. I put a lot of work into v2 migration when investigating the 3.13 support, and it's definitely challenging. In any case, it's a relief that we don't have to do the v2 migration at the same time as the Cython 3.0/Python 3.13 support.
This commit is contained in:
Matthew Honnibal 2025-05-22 13:47:21 +02:00 committed by GitHub
parent 911539e9a4
commit 5bebbf7550
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84 changed files with 2226 additions and 1272 deletions
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13
.github/workflows/tests.yml vendored
View File

@ -45,11 +45,12 @@ jobs:
run: |
python -m pip install flake8==5.0.4
python -m flake8 spacy --count --select=E901,E999,F821,F822,F823,W605 --show-source --statistics
- name: cython-lint
run: |
python -m pip install cython-lint -c requirements.txt
# E501: line too log, W291: trailing whitespace, E266: too many leading '#' for block comment
cython-lint spacy --ignore E501,W291,E266
# Unfortunately cython-lint isn't working after the shift to Cython 3.
#- name: cython-lint
# run: |
# python -m pip install cython-lint -c requirements.txt
# # E501: line too log, W291: trailing whitespace, E266: too many leading '#' for block comment
# cython-lint spacy --ignore E501,W291,E266
tests:
name: Test
@ -58,7 +59,7 @@ jobs:
fail-fast: true
matrix:
os: [ubuntu-latest, windows-latest, macos-latest]
python_version: ["3.9", "3.12"]
python_version: ["3.9", "3.12", "3.13"]
runs-on: ${{ matrix.os }}

1
MANIFEST.in
View File

@ -4,5 +4,6 @@ include README.md
include pyproject.toml
include spacy/py.typed
recursive-include spacy/cli *.yml
recursive-include spacy/tests *.json
recursive-include licenses *
recursive-exclude spacy *.cpp

2
pyproject.toml
View File

@ -1,7 +1,7 @@
[build-system]
requires = [
"setuptools",
"cython>=0.25,<3.0",
"cython>=3.0,<4.0",
"cymem>=2.0.2,<2.1.0",
"preshed>=3.0.2,<3.1.0",
"murmurhash>=0.28.0,<1.1.0",

2
requirements.txt
View File

@ -23,7 +23,7 @@ setuptools
packaging>=20.0
# Development dependencies
pre-commit>=2.13.0
cython>=0.25,<3.0
cython>=3.0,<4.0
pytest>=5.2.0,!=7.1.0
pytest-timeout>=1.3.0,<2.0.0
mock>=2.0.0,<3.0.0

4
setup.cfg
View File

@ -30,11 +30,11 @@ project_urls =
[options]
zip_safe = false
include_package_data = true
python_requires = >=3.9,<3.13
python_requires = >=3.9,<3.14
# NOTE: This section is superseded by pyproject.toml and will be removed in
# spaCy v4
setup_requires =
cython>=0.25,<3.0
cython>=3.0,<4.0
numpy>=2.0.0,<3.0.0; python_version < "3.9"
numpy>=2.0.0,<3.0.0; python_version >= "3.9"
# We also need our Cython packages here to compile against

1
spacy/__init__.py
View File

@ -17,6 +17,7 @@ from .cli.info import info # noqa: F401
from .errors import Errors
from .glossary import explain # noqa: F401
from .language import Language
from .registrations import REGISTRY_POPULATED, populate_registry
from .util import logger, registry # noqa: F401
from .vocab import Vocab

1
spacy/lang/ja/__init__.py
View File

@ -32,7 +32,6 @@ split_mode = null
"""
@registry.tokenizers("spacy.ja.JapaneseTokenizer")
def create_tokenizer(split_mode: Optional[str] = None):
def japanese_tokenizer_factory(nlp):
return JapaneseTokenizer(nlp.vocab, split_mode=split_mode)

1
spacy/lang/ko/__init__.py
View File

@ -20,7 +20,6 @@ DEFAULT_CONFIG = """
"""
@registry.tokenizers("spacy.ko.KoreanTokenizer")
def create_tokenizer():
def korean_tokenizer_factory(nlp):
return KoreanTokenizer(nlp.vocab)

1
spacy/lang/th/__init__.py
View File

@ -13,7 +13,6 @@ DEFAULT_CONFIG = """
"""
@registry.tokenizers("spacy.th.ThaiTokenizer")
def create_thai_tokenizer():
def thai_tokenizer_factory(nlp):
return ThaiTokenizer(nlp.vocab)

1
spacy/lang/vi/__init__.py
View File

@ -22,7 +22,6 @@ use_pyvi = true
"""
@registry.tokenizers("spacy.vi.VietnameseTokenizer")
def create_vietnamese_tokenizer(use_pyvi: bool = True):
def vietnamese_tokenizer_factory(nlp):
return VietnameseTokenizer(nlp.vocab, use_pyvi=use_pyvi)

1
spacy/lang/zh/__init__.py
View File

@ -46,7 +46,6 @@ class Segmenter(str, Enum):
return list(cls.__members__.keys())
@registry.tokenizers("spacy.zh.ChineseTokenizer")
def create_chinese_tokenizer(segmenter: Segmenter = Segmenter.char):
def chinese_tokenizer_factory(nlp):
return ChineseTokenizer(nlp.vocab, segmenter=segmenter)

5
spacy/language.py
View File

@ -104,7 +104,6 @@ class BaseDefaults:
writing_system = {"direction": "ltr", "has_case": True, "has_letters": True}
@registry.tokenizers("spacy.Tokenizer.v1")
def create_tokenizer() -> Callable[["Language"], Tokenizer]:
"""Registered function to create a tokenizer. Returns a factory that takes
the nlp object and returns a Tokenizer instance using the language detaults.
@ -130,7 +129,6 @@ def create_tokenizer() -> Callable[["Language"], Tokenizer]:
return tokenizer_factory
@registry.misc("spacy.LookupsDataLoader.v1")
def load_lookups_data(lang, tables):
util.logger.debug("Loading lookups from spacy-lookups-data: %s", tables)
lookups = load_lookups(lang=lang, tables=tables)
@ -185,6 +183,9 @@ class Language:
DOCS: https://spacy.io/api/language#init
"""
from .pipeline.factories import register_factories
register_factories()
# We're only calling this to import all factories provided via entry
# points. The factory decorator applied to these functions takes care
# of the rest.

8
spacy/lexeme.pxd
View File

@ -35,7 +35,7 @@ cdef class Lexeme:
return self
@staticmethod
cdef inline void set_struct_attr(LexemeC* lex, attr_id_t name, attr_t value) nogil:
cdef inline void set_struct_attr(LexemeC* lex, attr_id_t name, attr_t value) noexcept nogil:
if name < (sizeof(flags_t) * 8):
Lexeme.c_set_flag(lex, name, value)
elif name == ID:
@ -54,7 +54,7 @@ cdef class Lexeme:
lex.lang = value
@staticmethod
cdef inline attr_t get_struct_attr(const LexemeC* lex, attr_id_t feat_name) nogil:
cdef inline attr_t get_struct_attr(const LexemeC* lex, attr_id_t feat_name) noexcept nogil:
if feat_name < (sizeof(flags_t) * 8):
if Lexeme.c_check_flag(lex, feat_name):
return 1
@ -82,7 +82,7 @@ cdef class Lexeme:
return 0
@staticmethod
cdef inline bint c_check_flag(const LexemeC* lexeme, attr_id_t flag_id) nogil:
cdef inline bint c_check_flag(const LexemeC* lexeme, attr_id_t flag_id) noexcept nogil:
cdef flags_t one = 1
if lexeme.flags & (one << flag_id):
return True
@ -90,7 +90,7 @@ cdef class Lexeme:
return False
@staticmethod
cdef inline bint c_set_flag(LexemeC* lex, attr_id_t flag_id, bint value) nogil:
cdef inline bint c_set_flag(LexemeC* lex, attr_id_t flag_id, bint value) noexcept nogil:
cdef flags_t one = 1
if value:
lex.flags |= one << flag_id

4
spacy/lexeme.pyx
View File

@ -70,7 +70,7 @@ cdef class Lexeme:
if isinstance(other, Lexeme):
a = self.orth
b = other.orth
elif isinstance(other, long):
elif isinstance(other, int):
a = self.orth
b = other
elif isinstance(other, str):
@ -104,7 +104,7 @@ cdef class Lexeme:
# skip PROB, e.g. from lexemes.jsonl
if isinstance(value, float):
continue
elif isinstance(value, (int, long)):
elif isinstance(value, int):
Lexeme.set_struct_attr(self.c, attr, value)
else:
Lexeme.set_struct_attr(self.c, attr, self.vocab.strings.add(value))

3
spacy/matcher/levenshtein.pyx
View File

@ -1,4 +1,4 @@
# cython: binding=True, infer_types=True
# cython: binding=True, infer_types=True, language_level=3
from cpython.object cimport PyObject
from libc.stdint cimport int64_t
@ -27,6 +27,5 @@ cpdef bint levenshtein_compare(input_text: str, pattern_text: str, fuzzy: int =
return levenshtein(input_text, pattern_text, max_edits) <= max_edits
@registry.misc("spacy.levenshtein_compare.v1")
def make_levenshtein_compare():
return levenshtein_compare

10
spacy/matcher/matcher.pyx
View File

@ -625,7 +625,7 @@ cdef action_t get_action(
const TokenC * token,
const attr_t * extra_attrs,
const int8_t * predicate_matches
) nogil:
) noexcept nogil:
"""We need to consider:
a) Does the token match the specification? [Yes, No]
b) What's the quantifier? [1, 0+, ?]
@ -740,7 +740,7 @@ cdef int8_t get_is_match(
const TokenC* token,
const attr_t* extra_attrs,
const int8_t* predicate_matches
) nogil:
) noexcept nogil:
for i in range(state.pattern.nr_py):
if predicate_matches[state.pattern.py_predicates[i]] == -1:
return 0
@ -755,14 +755,14 @@ cdef int8_t get_is_match(
return True
cdef inline int8_t get_is_final(PatternStateC state) nogil:
cdef inline int8_t get_is_final(PatternStateC state) noexcept nogil:
if state.pattern[1].quantifier == FINAL_ID:
return 1
else:
return 0
cdef inline int8_t get_quantifier(PatternStateC state) nogil:
cdef inline int8_t get_quantifier(PatternStateC state) noexcept nogil:
return state.pattern.quantifier
@ -805,7 +805,7 @@ cdef TokenPatternC* init_pattern(Pool mem, attr_t entity_id, object token_specs)
return pattern
cdef attr_t get_ent_id(const TokenPatternC* pattern) nogil:
cdef attr_t get_ent_id(const TokenPatternC* pattern) noexcept nogil:
while pattern.quantifier != FINAL_ID:
pattern += 1
id_attr = pattern[0].attrs[0]

2
spacy/matcher/phrasematcher.pyx
View File

@ -47,7 +47,7 @@ cdef class PhraseMatcher:
self._terminal_hash = 826361138722620965
map_init(self.mem, self.c_map, 8)
if isinstance(attr, (int, long)):
if isinstance(attr, int):
self.attr = attr
else:
if attr is None:

1
spacy/ml/_character_embed.py
View File

@ -7,7 +7,6 @@ from ..tokens import Doc
from ..util import registry
@registry.layers("spacy.CharEmbed.v1")
def CharacterEmbed(nM: int, nC: int) -> Model[List[Doc], List[Floats2d]]:
# nM: Number of dimensions per character. nC: Number of characters.
return Model(

1
spacy/ml/_precomputable_affine.py
View File

@ -3,7 +3,6 @@ from thinc.api import Model, normal_init
from ..util import registry
@registry.layers("spacy.PrecomputableAffine.v1")
def PrecomputableAffine(nO, nI, nF, nP, dropout=0.1):
model = Model(
"precomputable_affine",

2
spacy/ml/callbacks.py
View File

@ -50,7 +50,6 @@ def models_with_nvtx_range(nlp, forward_color: int, backprop_color: int):
return nlp
@registry.callbacks("spacy.models_with_nvtx_range.v1")
def create_models_with_nvtx_range(
forward_color: int = -1, backprop_color: int = -1
) -> Callable[["Language"], "Language"]:
@ -110,7 +109,6 @@ def pipes_with_nvtx_range(
return nlp
@registry.callbacks("spacy.models_and_pipes_with_nvtx_range.v1")
def create_models_and_pipes_with_nvtx_range(
forward_color: int = -1,
backprop_color: int = -1,

1
spacy/ml/extract_ngrams.py
View File

@ -4,7 +4,6 @@ from ..attrs import LOWER
from ..util import registry
@registry.layers("spacy.extract_ngrams.v1")
def extract_ngrams(ngram_size: int, attr: int = LOWER) -> Model:
model: Model = Model("extract_ngrams", forward)
model.attrs["ngram_size"] = ngram_size

1
spacy/ml/extract_spans.py
View File

@ -6,7 +6,6 @@ from thinc.types import Ints1d, Ragged
from ..util import registry
@registry.layers("spacy.extract_spans.v1")
def extract_spans() -> Model[Tuple[Ragged, Ragged], Ragged]:
"""Extract spans from a sequence of source arrays, as specified by an array
of (start, end) indices. The output is a ragged array of the

5
spacy/ml/featureextractor.py
View File

@ -6,8 +6,9 @@ from thinc.types import Ints2d
from ..tokens import Doc
@registry.layers("spacy.FeatureExtractor.v1")
def FeatureExtractor(columns: List[Union[int, str]]) -> Model[List[Doc], List[Ints2d]]:
def FeatureExtractor(
columns: Union[List[str], List[int], List[Union[int, str]]]
) -> Model[List[Doc], List[Ints2d]]:
return Model("extract_features", forward, attrs={"columns": columns})

6
spacy/ml/models/entity_linker.py
View File

@ -28,7 +28,6 @@ from ...vocab import Vocab
from ..extract_spans import extract_spans
@registry.architectures("spacy.EntityLinker.v2")
def build_nel_encoder(
tok2vec: Model, nO: Optional[int] = None
) -> Model[List[Doc], Floats2d]:
@ -92,7 +91,6 @@ def span_maker_forward(model, docs: List[Doc], is_train) -> Tuple[Ragged, Callab
return out, lambda x: []
@registry.misc("spacy.KBFromFile.v1")
def load_kb(
kb_path: Path,
) -> Callable[[Vocab], KnowledgeBase]:
@ -104,7 +102,6 @@ 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)
@ -112,7 +109,6 @@ def empty_kb_for_config() -> Callable[[Vocab, int], KnowledgeBase]:
return empty_kb_factory
@registry.misc("spacy.EmptyKB.v1")
def empty_kb(
entity_vector_length: int,
) -> Callable[[Vocab], KnowledgeBase]:
@ -122,12 +118,10 @@ def empty_kb(
return empty_kb_factory
@registry.misc("spacy.CandidateGenerator.v1")
def create_candidates() -> Callable[[KnowledgeBase, Span], Iterable[Candidate]]:
return get_candidates
@registry.misc("spacy.CandidateBatchGenerator.v1")
def create_candidates_batch() -> Callable[
[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]
]:

2
spacy/ml/models/multi_task.py
View File

@ -30,7 +30,6 @@ if TYPE_CHECKING:
from ...vocab import Vocab # noqa: F401
@registry.architectures("spacy.PretrainVectors.v1")
def create_pretrain_vectors(
maxout_pieces: int, hidden_size: int, loss: str
) -> Callable[["Vocab", Model], Model]:
@ -57,7 +56,6 @@ def create_pretrain_vectors(
return create_vectors_objective
@registry.architectures("spacy.PretrainCharacters.v1")
def create_pretrain_characters(
maxout_pieces: int, hidden_size: int, n_characters: int
) -> Callable[["Vocab", Model], Model]:

1
spacy/ml/models/parser.py
View File

@ -11,7 +11,6 @@ from .._precomputable_affine import PrecomputableAffine
from ..tb_framework import TransitionModel
@registry.architectures("spacy.TransitionBasedParser.v2")
def build_tb_parser_model(
tok2vec: Model[List[Doc], List[Floats2d]],
state_type: Literal["parser", "ner"],

1
spacy/ml/models/span_finder.py
View File

@ -10,7 +10,6 @@ InT = List[Doc]
OutT = Floats2d
@registry.architectures("spacy.SpanFinder.v1")
def build_finder_model(
tok2vec: Model[InT, List[Floats2d]], scorer: Model[OutT, OutT]
) -> Model[InT, OutT]:

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

@ -22,7 +22,6 @@ from ...util import registry
from ..extract_spans import extract_spans
@registry.layers("spacy.LinearLogistic.v1")
def build_linear_logistic(nO=None, nI=None) -> Model[Floats2d, Floats2d]:
"""An output layer for multi-label classification. It uses a linear layer
followed by a logistic activation.
@ -30,7 +29,6 @@ def build_linear_logistic(nO=None, nI=None) -> Model[Floats2d, Floats2d]:
return chain(Linear(nO=nO, nI=nI, init_W=glorot_uniform_init), Logistic())
@registry.layers("spacy.mean_max_reducer.v1")
def build_mean_max_reducer(hidden_size: int) -> Model[Ragged, Floats2d]:
"""Reduce sequences by concatenating their mean and max pooled vectors,
and then combine the concatenated vectors with a hidden layer.
@ -46,7 +44,6 @@ def build_mean_max_reducer(hidden_size: int) -> Model[Ragged, Floats2d]:
)
@registry.architectures("spacy.SpanCategorizer.v1")
def build_spancat_model(
tok2vec: Model[List[Doc], List[Floats2d]],
reducer: Model[Ragged, Floats2d],

1
spacy/ml/models/tagger.py
View File

@ -7,7 +7,6 @@ from ...tokens import Doc
from ...util import registry
@registry.architectures("spacy.Tagger.v2")
def build_tagger_model(
tok2vec: Model[List[Doc], List[Floats2d]], nO: Optional[int] = None, normalize=False
) -> Model[List[Doc], List[Floats2d]]:

7
spacy/ml/models/textcat.py
View File

@ -44,7 +44,6 @@ from .tok2vec import get_tok2vec_width
NEG_VALUE = -5000
@registry.architectures("spacy.TextCatCNN.v2")
def build_simple_cnn_text_classifier(
tok2vec: Model, exclusive_classes: bool, nO: Optional[int] = None
) -> Model[List[Doc], Floats2d]:
@ -72,7 +71,6 @@ def resize_and_set_ref(model, new_nO, resizable_layer):
return model
@registry.architectures("spacy.TextCatBOW.v2")
def build_bow_text_classifier(
exclusive_classes: bool,
ngram_size: int,
@ -88,7 +86,6 @@ def build_bow_text_classifier(
)
@registry.architectures("spacy.TextCatBOW.v3")
def build_bow_text_classifier_v3(
exclusive_classes: bool,
ngram_size: int,
@ -142,7 +139,6 @@ def _build_bow_text_classifier(
return model
@registry.architectures("spacy.TextCatEnsemble.v2")
def build_text_classifier_v2(
tok2vec: Model[List[Doc], List[Floats2d]],
linear_model: Model[List[Doc], Floats2d],
@ -200,7 +196,6 @@ def init_ensemble_textcat(model, X, Y) -> Model:
return model
@registry.architectures("spacy.TextCatLowData.v1")
def build_text_classifier_lowdata(
width: int, dropout: Optional[float], nO: Optional[int] = None
) -> Model[List[Doc], Floats2d]:
@ -221,7 +216,6 @@ def build_text_classifier_lowdata(
return model
@registry.architectures("spacy.TextCatParametricAttention.v1")
def build_textcat_parametric_attention_v1(
tok2vec: Model[List[Doc], List[Floats2d]],
exclusive_classes: bool,
@ -294,7 +288,6 @@ def _init_parametric_attention_with_residual_nonlinear(model, X, Y) -> Model:
return model
@registry.architectures("spacy.TextCatReduce.v1")
def build_reduce_text_classifier(
tok2vec: Model,
exclusive_classes: bool,

12
spacy/ml/models/tok2vec.py
View File

@ -29,7 +29,6 @@ from ..featureextractor import FeatureExtractor
from ..staticvectors import StaticVectors
@registry.architectures("spacy.Tok2VecListener.v1")
def tok2vec_listener_v1(width: int, upstream: str = "*"):
tok2vec = Tok2VecListener(upstream_name=upstream, width=width)
return tok2vec
@ -46,7 +45,6 @@ def get_tok2vec_width(model: Model):
return nO
@registry.architectures("spacy.HashEmbedCNN.v2")
def build_hash_embed_cnn_tok2vec(
*,
width: int,
@ -102,7 +100,6 @@ def build_hash_embed_cnn_tok2vec(
)
@registry.architectures("spacy.Tok2Vec.v2")
def build_Tok2Vec_model(
embed: Model[List[Doc], List[Floats2d]],
encode: Model[List[Floats2d], List[Floats2d]],
@ -123,10 +120,9 @@ def build_Tok2Vec_model(
return tok2vec
@registry.architectures("spacy.MultiHashEmbed.v2")
def MultiHashEmbed(
width: int,
attrs: List[Union[str, int]],
attrs: Union[List[str], List[int], List[Union[str, int]]],
rows: List[int],
include_static_vectors: bool,
) -> Model[List[Doc], List[Floats2d]]:
@ -192,7 +188,7 @@ def MultiHashEmbed(
)
else:
model = chain(
FeatureExtractor(list(attrs)),
FeatureExtractor(attrs),
cast(Model[List[Ints2d], Ragged], list2ragged()),
with_array(concatenate(*embeddings)),
max_out,
@ -201,7 +197,6 @@ def MultiHashEmbed(
return model
@registry.architectures("spacy.CharacterEmbed.v2")
def CharacterEmbed(
width: int,
rows: int,
@ -278,7 +273,6 @@ def CharacterEmbed(
return model
@registry.architectures("spacy.MaxoutWindowEncoder.v2")
def MaxoutWindowEncoder(
width: int, window_size: int, maxout_pieces: int, depth: int
) -> Model[List[Floats2d], List[Floats2d]]:
@ -310,7 +304,6 @@ def MaxoutWindowEncoder(
return with_array(model, pad=receptive_field)
@registry.architectures("spacy.MishWindowEncoder.v2")
def MishWindowEncoder(
width: int, window_size: int, depth: int
) -> Model[List[Floats2d], List[Floats2d]]:
@ -333,7 +326,6 @@ def MishWindowEncoder(
return with_array(model)
@registry.architectures("spacy.TorchBiLSTMEncoder.v1")
def BiLSTMEncoder(
width: int, depth: int, dropout: float
) -> Model[List[Floats2d], List[Floats2d]]:

16
spacy/ml/parser_model.pyx
View File

@ -52,14 +52,14 @@ cdef SizesC get_c_sizes(model, int batch_size) except *:
return output
cdef ActivationsC alloc_activations(SizesC n) nogil:
cdef ActivationsC alloc_activations(SizesC n) noexcept nogil:
cdef ActivationsC A
memset(&A, 0, sizeof(A))
resize_activations(&A, n)
return A
cdef void free_activations(const ActivationsC* A) nogil:
cdef void free_activations(const ActivationsC* A) noexcept nogil:
free(A.token_ids)
free(A.scores)
free(A.unmaxed)
@ -67,7 +67,7 @@ cdef void free_activations(const ActivationsC* A) nogil:
free(A.is_valid)
cdef void resize_activations(ActivationsC* A, SizesC n) nogil:
cdef void resize_activations(ActivationsC* A, SizesC n) noexcept nogil:
if n.states <= A._max_size:
A._curr_size = n.states
return
@ -100,7 +100,7 @@ cdef void resize_activations(ActivationsC* A, SizesC n) nogil:
cdef void predict_states(
CBlas cblas, ActivationsC* A, StateC** states, const WeightsC* W, SizesC n
) nogil:
) noexcept nogil:
resize_activations(A, n)
for i in range(n.states):
states[i].set_context_tokens(&A.token_ids[i*n.feats], n.feats)
@ -159,7 +159,7 @@ cdef void sum_state_features(
int B,
int F,
int O
) nogil:
) noexcept nogil:
cdef int idx, b, f
cdef const float* feature
padding = cached
@ -183,7 +183,7 @@ cdef void cpu_log_loss(
const int* is_valid,
const float* scores,
int O
) nogil:
) noexcept nogil:
"""Do multi-label log loss"""
cdef double max_, gmax, Z, gZ
best = arg_max_if_gold(scores, costs, is_valid, O)
@ -209,7 +209,7 @@ cdef void cpu_log_loss(
cdef int arg_max_if_gold(
const weight_t* scores, const weight_t* costs, const int* is_valid, int n
) nogil:
) noexcept nogil:
# Find minimum cost
cdef float cost = 1
for i in range(n):
@ -224,7 +224,7 @@ cdef int arg_max_if_gold(
return best
cdef int arg_max_if_valid(const weight_t* scores, const int* is_valid, int n) nogil:
cdef int arg_max_if_valid(const weight_t* scores, const int* is_valid, int n) noexcept nogil:
cdef int best = -1
for i in range(n):
if is_valid[i] >= 1:

1
spacy/ml/staticvectors.py
View File

@ -13,7 +13,6 @@ from ..vectors import Mode, Vectors
from ..vocab import Vocab
@registry.layers("spacy.StaticVectors.v2")
def StaticVectors(
nO: Optional[int] = None,
nM: Optional[int] = None,

1
spacy/ml/tb_framework.py
View File

@ -4,7 +4,6 @@ from ..util import registry
from .parser_model import ParserStepModel
@registry.layers("spacy.TransitionModel.v1")
def TransitionModel(
tok2vec, lower, upper, resize_output, dropout=0.2, unseen_classes=set()
):

5
spacy/parts_of_speech.pyx
View File

@ -25,3 +25,8 @@ IDS = {
NAMES = {value: key for key, value in IDS.items()}
# As of Cython 3.1, the global Python namespace no longer has the enum
# contents by default.
globals().update(IDS)

90
spacy/pipeline/_parser_internals/_state.pxd
View File

@ -17,7 +17,7 @@ from ...typedefs cimport attr_t
from ...vocab cimport EMPTY_LEXEME
cdef inline bint is_space_token(const TokenC* token) nogil:
cdef inline bint is_space_token(const TokenC* token) noexcept nogil:
return Lexeme.c_check_flag(token.lex, IS_SPACE)
cdef struct ArcC:
@ -41,7 +41,7 @@ cdef cppclass StateC:
int offset
int _b_i
__init__(const TokenC* sent, int length) nogil:
inline __init__(const TokenC* sent, int length) noexcept nogil:
this._sent = sent
this._heads = <int*>calloc(length, sizeof(int))
if not (this._sent and this._heads):
@ -57,10 +57,10 @@ cdef cppclass StateC:
memset(&this._empty_token, 0, sizeof(TokenC))
this._empty_token.lex = &EMPTY_LEXEME
__dealloc__():
inline __dealloc__():
free(this._heads)
void set_context_tokens(int* ids, int n) nogil:
inline void set_context_tokens(int* ids, int n) noexcept nogil:
cdef int i, j
if n == 1:
if this.B(0) >= 0:
@ -131,14 +131,14 @@ cdef cppclass StateC:
else:
ids[i] = -1
int S(int i) nogil const:
inline int S(int i) noexcept nogil const:
if i >= this._stack.size():
return -1
elif i < 0:
return -1
return this._stack.at(this._stack.size() - (i+1))
int B(int i) nogil const:
inline int B(int i) noexcept nogil const:
if i < 0:
return -1
elif i < this._rebuffer.size():
@ -150,19 +150,19 @@ cdef cppclass StateC:
else:
return b_i
const TokenC* B_(int i) nogil const:
inline const TokenC* B_(int i) noexcept nogil const:
return this.safe_get(this.B(i))
const TokenC* E_(int i) nogil const:
inline const TokenC* E_(int i) noexcept nogil const:
return this.safe_get(this.E(i))
const TokenC* safe_get(int i) nogil const:
inline const TokenC* safe_get(int i) noexcept nogil const:
if i < 0 or i >= this.length:
return &this._empty_token
else:
return &this._sent[i]
void map_get_arcs(const unordered_map[int, vector[ArcC]] &heads_arcs, vector[ArcC]* out) nogil const:
inline void map_get_arcs(const unordered_map[int, vector[ArcC]] &heads_arcs, vector[ArcC]* out) noexcept nogil const:
cdef const vector[ArcC]* arcs
head_arcs_it = heads_arcs.const_begin()
while head_arcs_it != heads_arcs.const_end():
@ -175,23 +175,23 @@ cdef cppclass StateC:
incr(arcs_it)
incr(head_arcs_it)
void get_arcs(vector[ArcC]* out) nogil const:
inline void get_arcs(vector[ArcC]* out) noexcept nogil const:
this.map_get_arcs(this._left_arcs, out)
this.map_get_arcs(this._right_arcs, out)
int H(int child) nogil const:
inline int H(int child) noexcept nogil const:
if child >= this.length or child < 0:
return -1
else:
return this._heads[child]
int E(int i) nogil const:
inline int E(int i) noexcept nogil const:
if this._ents.size() == 0:
return -1
else:
return this._ents.back().start
int nth_child(const unordered_map[int, vector[ArcC]]& heads_arcs, int head, int idx) nogil const:
inline int nth_child(const unordered_map[int, vector[ArcC]]& heads_arcs, int head, int idx) noexcept nogil const:
if idx < 1:
return -1
@ -215,22 +215,22 @@ cdef cppclass StateC:
return -1
int L(int head, int idx) nogil const:
inline int L(int head, int idx) noexcept nogil const:
return this.nth_child(this._left_arcs, head, idx)
int R(int head, int idx) nogil const:
inline int R(int head, int idx) noexcept nogil const:
return this.nth_child(this._right_arcs, head, idx)
bint empty() nogil const:
inline bint empty() noexcept nogil const:
return this._stack.size() == 0
bint eol() nogil const:
inline bint eol() noexcept nogil const:
return this.buffer_length() == 0
bint is_final() nogil const:
inline bint is_final() noexcept nogil const:
return this.stack_depth() <= 0 and this.eol()
int cannot_sent_start(int word) nogil const:
inline int cannot_sent_start(int word) noexcept nogil const:
if word < 0 or word >= this.length:
return 0
elif this._sent[word].sent_start == -1:
@ -238,7 +238,7 @@ cdef cppclass StateC:
else:
return 0
int is_sent_start(int word) nogil const:
inline int is_sent_start(int word) noexcept nogil const:
if word < 0 or word >= this.length:
return 0
elif this._sent[word].sent_start == 1:
@ -248,20 +248,20 @@ cdef cppclass StateC:
else:
return 0
void set_sent_start(int word, int value) nogil:
inline void set_sent_start(int word, int value) noexcept nogil:
if value >= 1:
this._sent_starts.insert(word)
bint has_head(int child) nogil const:
inline bint has_head(int child) noexcept nogil const:
return this._heads[child] >= 0
int l_edge(int word) nogil const:
inline int l_edge(int word) noexcept nogil const:
return word
int r_edge(int word) nogil const:
inline int r_edge(int word) noexcept nogil const:
return word
int n_arcs(const unordered_map[int, vector[ArcC]] &heads_arcs, int head) nogil const:
inline int n_arcs(const unordered_map[int, vector[ArcC]] &heads_arcs, int head) noexcept nogil const:
cdef int n = 0
head_arcs_it = heads_arcs.const_find(head)
if head_arcs_it == heads_arcs.const_end():
@ -277,28 +277,28 @@ cdef cppclass StateC:
return n
int n_L(int head) nogil const:
inline int n_L(int head) noexcept nogil const:
return n_arcs(this._left_arcs, head)
int n_R(int head) nogil const:
inline int n_R(int head) noexcept nogil const:
return n_arcs(this._right_arcs, head)
bint stack_is_connected() nogil const:
inline bint stack_is_connected() noexcept nogil const:
return False
bint entity_is_open() nogil const:
inline bint entity_is_open() noexcept nogil const:
if this._ents.size() == 0:
return False
else:
return this._ents.back().end == -1
int stack_depth() nogil const:
inline int stack_depth() noexcept nogil const:
return this._stack.size()
int buffer_length() nogil const:
inline int buffer_length() noexcept nogil const:
return (this.length - this._b_i) + this._rebuffer.size()
void push() nogil:
inline void push() noexcept nogil:
b0 = this.B(0)
if this._rebuffer.size():
b0 = this._rebuffer.back()
@ -308,32 +308,32 @@ cdef cppclass StateC:
this._b_i += 1
this._stack.push_back(b0)
void pop() nogil:
inline void pop() noexcept nogil:
this._stack.pop_back()
void force_final() nogil:
inline void force_final() noexcept nogil:
# This should only be used in desperate situations, as it may leave
# the analysis in an unexpected state.
this._stack.clear()
this._b_i = this.length
void unshift() nogil:
inline void unshift() noexcept nogil:
s0 = this._stack.back()
this._unshiftable[s0] = 1
this._rebuffer.push_back(s0)
this._stack.pop_back()
int is_unshiftable(int item) nogil const:
inline int is_unshiftable(int item) noexcept nogil const:
if item >= this._unshiftable.size():
return 0
else:
return this._unshiftable.at(item)
void set_reshiftable(int item) nogil:
inline void set_reshiftable(int item) noexcept nogil:
if item < this._unshiftable.size():
this._unshiftable[item] = 0
void add_arc(int head, int child, attr_t label) nogil:
inline void add_arc(int head, int child, attr_t label) noexcept nogil:
if this.has_head(child):
this.del_arc(this.H(child), child)
cdef ArcC arc
@ -346,7 +346,7 @@ cdef cppclass StateC:
this._right_arcs[arc.head].push_back(arc)
this._heads[child] = head
void map_del_arc(unordered_map[int, vector[ArcC]]* heads_arcs, int h_i, int c_i) nogil:
inline void map_del_arc(unordered_map[int, vector[ArcC]]* heads_arcs, int h_i, int c_i) noexcept nogil:
arcs_it = heads_arcs.find(h_i)
if arcs_it == heads_arcs.end():
return
@ -367,13 +367,13 @@ cdef cppclass StateC:
arc.label = 0
break
void del_arc(int h_i, int c_i) nogil:
inline void del_arc(int h_i, int c_i) noexcept nogil:
if h_i > c_i:
this.map_del_arc(&this._left_arcs, h_i, c_i)
else:
this.map_del_arc(&this._right_arcs, h_i, c_i)
SpanC get_ent() nogil const:
inline SpanC get_ent() noexcept nogil const:
cdef SpanC ent
if this._ents.size() == 0:
ent.start = 0
@ -383,17 +383,17 @@ cdef cppclass StateC:
else:
return this._ents.back()
void open_ent(attr_t label) nogil:
inline void open_ent(attr_t label) noexcept nogil:
cdef SpanC ent
ent.start = this.B(0)
ent.label = label
ent.end = -1
this._ents.push_back(ent)
void close_ent() nogil:
inline void close_ent() noexcept nogil:
this._ents.back().end = this.B(0)+1
void clone(const StateC* src) nogil:
inline void clone(const StateC* src) noexcept nogil:
this.length = src.length
this._sent = src._sent
this._stack = src._stack

58
spacy/pipeline/_parser_internals/arc_eager.pyx
View File

@ -155,7 +155,7 @@ cdef GoldParseStateC create_gold_state(
return gs
cdef void update_gold_state(GoldParseStateC* gs, const StateC* s) nogil:
cdef void update_gold_state(GoldParseStateC* gs, const StateC* s) noexcept nogil:
for i in range(gs.length):
gs.state_bits[i] = set_state_flag(
gs.state_bits[i],
@ -239,12 +239,12 @@ def _get_aligned_sent_starts(example):
return [None] * len(example.x)
cdef int check_state_gold(char state_bits, char flag) nogil:
cdef int check_state_gold(char state_bits, char flag) noexcept nogil:
cdef char one = 1
return 1 if (state_bits & (one << flag)) else 0
cdef int set_state_flag(char state_bits, char flag, int value) nogil:
cdef int set_state_flag(char state_bits, char flag, int value) noexcept nogil:
cdef char one = 1
if value:
return state_bits | (one << flag)
@ -252,27 +252,27 @@ cdef int set_state_flag(char state_bits, char flag, int value) nogil:
return state_bits & ~(one << flag)
cdef int is_head_in_stack(const GoldParseStateC* gold, int i) nogil:
cdef int is_head_in_stack(const GoldParseStateC* gold, int i) noexcept nogil:
return check_state_gold(gold.state_bits[i], HEAD_IN_STACK)
cdef int is_head_in_buffer(const GoldParseStateC* gold, int i) nogil:
cdef int is_head_in_buffer(const GoldParseStateC* gold, int i) noexcept nogil:
return check_state_gold(gold.state_bits[i], HEAD_IN_BUFFER)
cdef int is_head_unknown(const GoldParseStateC* gold, int i) nogil:
cdef int is_head_unknown(const GoldParseStateC* gold, int i) noexcept nogil:
return check_state_gold(gold.state_bits[i], HEAD_UNKNOWN)
cdef int is_sent_start(const GoldParseStateC* gold, int i) nogil:
cdef int is_sent_start(const GoldParseStateC* gold, int i) noexcept nogil:
return check_state_gold(gold.state_bits[i], IS_SENT_START)
cdef int is_sent_start_unknown(const GoldParseStateC* gold, int i) nogil:
cdef int is_sent_start_unknown(const GoldParseStateC* gold, int i) noexcept nogil:
return check_state_gold(gold.state_bits[i], SENT_START_UNKNOWN)
# Helper functions for the arc-eager oracle
cdef weight_t push_cost(const StateC* state, const GoldParseStateC* gold) nogil:
cdef weight_t push_cost(const StateC* state, const GoldParseStateC* gold) noexcept nogil:
cdef weight_t cost = 0
b0 = state.B(0)
if b0 < 0:
@ -285,7 +285,7 @@ cdef weight_t push_cost(const StateC* state, const GoldParseStateC* gold) nogil:
return cost
cdef weight_t pop_cost(const StateC* state, const GoldParseStateC* gold) nogil:
cdef weight_t pop_cost(const StateC* state, const GoldParseStateC* gold) noexcept nogil:
cdef weight_t cost = 0
s0 = state.S(0)
if s0 < 0:
@ -296,7 +296,7 @@ cdef weight_t pop_cost(const StateC* state, const GoldParseStateC* gold) nogil:
return cost
cdef bint arc_is_gold(const GoldParseStateC* gold, int head, int child) nogil:
cdef bint arc_is_gold(const GoldParseStateC* gold, int head, int child) noexcept nogil:
if is_head_unknown(gold, child):
return True
elif gold.heads[child] == head:
@ -305,7 +305,7 @@ cdef bint arc_is_gold(const GoldParseStateC* gold, int head, int child) nogil:
return False
cdef bint label_is_gold(const GoldParseStateC* gold, int child, attr_t label) nogil:
cdef bint label_is_gold(const GoldParseStateC* gold, int child, attr_t label) noexcept nogil:
if is_head_unknown(gold, child):
return True
elif label == 0:
@ -316,7 +316,7 @@ cdef bint label_is_gold(const GoldParseStateC* gold, int child, attr_t label) no
return False
cdef bint _is_gold_root(const GoldParseStateC* gold, int word) nogil:
cdef bint _is_gold_root(const GoldParseStateC* gold, int word) noexcept nogil:
return gold.heads[word] == word or is_head_unknown(gold, word)
@ -336,7 +336,7 @@ cdef class Shift:
* Advance buffer
"""
@staticmethod
cdef bint is_valid(const StateC* st, attr_t label) nogil:
cdef bint is_valid(const StateC* st, attr_t label) noexcept nogil:
if st.stack_depth() == 0:
return 1
elif st.buffer_length() < 2:
@ -349,11 +349,11 @@ cdef class Shift:
return 1
@staticmethod
cdef int transition(StateC* st, attr_t label) nogil:
cdef int transition(StateC* st, attr_t label) noexcept nogil:
st.push()
@staticmethod
cdef weight_t cost(const StateC* state, const void* _gold, attr_t label) nogil:
cdef weight_t cost(const StateC* state, const void* _gold, attr_t label) noexcept nogil:
gold = <const GoldParseStateC*>_gold
return gold.push_cost
@ -375,7 +375,7 @@ cdef class Reduce:
cost by those arcs.
"""
@staticmethod
cdef bint is_valid(const StateC* st, attr_t label) nogil:
cdef bint is_valid(const StateC* st, attr_t label) noexcept nogil:
if st.stack_depth() == 0:
return False
elif st.buffer_length() == 0:
@ -386,14 +386,14 @@ cdef class Reduce:
return True
@staticmethod
cdef int transition(StateC* st, attr_t label) nogil:
cdef int transition(StateC* st, attr_t label) noexcept nogil:
if st.has_head(st.S(0)) or st.stack_depth() == 1:
st.pop()
else:
st.unshift()
@staticmethod
cdef weight_t cost(const StateC* state, const void* _gold, attr_t label) nogil:
cdef weight_t cost(const StateC* state, const void* _gold, attr_t label) noexcept nogil:
gold = <const GoldParseStateC*>_gold
if state.is_sent_start(state.B(0)):
return 0
@ -421,7 +421,7 @@ cdef class LeftArc:
pop_cost - Arc(B[0], S[0], label) + (Arc(S[1], S[0]) if H(S[0]) else Arcs(S, S[0]))
"""
@staticmethod
cdef bint is_valid(const StateC* st, attr_t label) nogil:
cdef bint is_valid(const StateC* st, attr_t label) noexcept nogil:
if st.stack_depth() == 0:
return 0
elif st.buffer_length() == 0:
@ -434,7 +434,7 @@ cdef class LeftArc:
return 1
@staticmethod
cdef int transition(StateC* st, attr_t label) nogil:
cdef int transition(StateC* st, attr_t label) noexcept nogil:
st.add_arc(st.B(0), st.S(0), label)
# If we change the stack, it's okay to remove the shifted mark, as
# we can't get in an infinite loop this way.
@ -442,7 +442,7 @@ cdef class LeftArc:
st.pop()
@staticmethod
cdef inline weight_t cost(const StateC* state, const void* _gold, attr_t label) nogil:
cdef inline weight_t cost(const StateC* state, const void* _gold, attr_t label) noexcept nogil:
gold = <const GoldParseStateC*>_gold
cdef weight_t cost = gold.pop_cost
s0 = state.S(0)
@ -474,7 +474,7 @@ cdef class RightArc:
push_cost + (not shifted[b0] and Arc(B[1:], B[0])) - Arc(S[0], B[0], label)
"""
@staticmethod
cdef bint is_valid(const StateC* st, attr_t label) nogil:
cdef bint is_valid(const StateC* st, attr_t label) noexcept nogil:
if st.stack_depth() == 0:
return 0
elif st.buffer_length() == 0:
@ -488,12 +488,12 @@ cdef class RightArc:
return 1
@staticmethod
cdef int transition(StateC* st, attr_t label) nogil:
cdef int transition(StateC* st, attr_t label) noexcept nogil:
st.add_arc(st.S(0), st.B(0), label)
st.push()
@staticmethod
cdef inline weight_t cost(const StateC* state, const void* _gold, attr_t label) nogil:
cdef inline weight_t cost(const StateC* state, const void* _gold, attr_t label) noexcept nogil:
gold = <const GoldParseStateC*>_gold
cost = gold.push_cost
s0 = state.S(0)
@ -525,7 +525,7 @@ cdef class Break:
* Arcs between S and B[1]
"""
@staticmethod
cdef bint is_valid(const StateC* st, attr_t label) nogil:
cdef bint is_valid(const StateC* st, attr_t label) noexcept nogil:
if st.buffer_length() < 2:
return False
elif st.B(1) != st.B(0) + 1:
@ -538,11 +538,11 @@ cdef class Break:
return True
@staticmethod
cdef int transition(StateC* st, attr_t label) nogil:
cdef int transition(StateC* st, attr_t label) noexcept nogil:
st.set_sent_start(st.B(1), 1)
@staticmethod
cdef weight_t cost(const StateC* state, const void* _gold, attr_t label) nogil:
cdef weight_t cost(const StateC* state, const void* _gold, attr_t label) noexcept nogil:
gold = <const GoldParseStateC*>_gold
cdef int b0 = state.B(0)
cdef int cost = 0
@ -785,7 +785,7 @@ cdef class ArcEager(TransitionSystem):
else:
return False
cdef int set_valid(self, int* output, const StateC* st) nogil:
cdef int set_valid(self, int* output, const StateC* st) noexcept nogil:
cdef int[N_MOVES] is_valid
is_valid[SHIFT] = Shift.is_valid(st, 0)
is_valid[REDUCE] = Reduce.is_valid(st, 0)

40
spacy/pipeline/_parser_internals/ner.pyx
View File

@ -110,7 +110,7 @@ cdef void update_gold_state(GoldNERStateC* gs, const StateC* state) except *:
cdef do_func_t[N_MOVES] do_funcs
cdef bint _entity_is_sunk(const StateC* state, Transition* golds) nogil:
cdef bint _entity_is_sunk(const StateC* state, Transition* golds) noexcept nogil:
if not state.entity_is_open():
return False
@ -238,7 +238,7 @@ cdef class BiluoPushDown(TransitionSystem):
def add_action(self, int action, label_name, freq=None):
cdef attr_t label_id
if not isinstance(label_name, (int, long)):
if not isinstance(label_name, int):
label_id = self.strings.add(label_name)
else:
label_id = label_name
@ -347,21 +347,21 @@ cdef class BiluoPushDown(TransitionSystem):
cdef class Missing:
@staticmethod
cdef bint is_valid(const StateC* st, attr_t label) nogil:
cdef bint is_valid(const StateC* st, attr_t label) noexcept nogil:
return False
@staticmethod
cdef int transition(StateC* s, attr_t label) nogil:
cdef int transition(StateC* s, attr_t label) noexcept nogil:
pass
@staticmethod
cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) nogil:
cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) noexcept nogil:
return 9000
cdef class Begin:
@staticmethod
cdef bint is_valid(const StateC* st, attr_t label) nogil:
cdef bint is_valid(const StateC* st, attr_t label) noexcept nogil:
cdef int preset_ent_iob = st.B_(0).ent_iob
cdef attr_t preset_ent_label = st.B_(0).ent_type
if st.entity_is_open():
@ -400,13 +400,13 @@ cdef class Begin:
return True
@staticmethod
cdef int transition(StateC* st, attr_t label) nogil:
cdef int transition(StateC* st, attr_t label) noexcept nogil:
st.open_ent(label)
st.push()
st.pop()
@staticmethod
cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) nogil:
cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) noexcept nogil:
gold = <GoldNERStateC*>_gold
b0 = s.B(0)
cdef int cost = 0
@ -439,7 +439,7 @@ cdef class Begin:
cdef class In:
@staticmethod
cdef bint is_valid(const StateC* st, attr_t label) nogil:
cdef bint is_valid(const StateC* st, attr_t label) noexcept nogil:
if not st.entity_is_open():
return False
if st.buffer_length() < 2:
@ -475,12 +475,12 @@ cdef class In:
return True
@staticmethod
cdef int transition(StateC* st, attr_t label) nogil:
cdef int transition(StateC* st, attr_t label) noexcept nogil:
st.push()
st.pop()
@staticmethod
cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) nogil:
cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) noexcept nogil:
gold = <GoldNERStateC*>_gold
cdef int next_act = gold.ner[s.B(1)].move if s.B(1) >= 0 else OUT
cdef int g_act = gold.ner[s.B(0)].move
@ -510,7 +510,7 @@ cdef class In:
cdef class Last:
@staticmethod
cdef bint is_valid(const StateC* st, attr_t label) nogil:
cdef bint is_valid(const StateC* st, attr_t label) noexcept nogil:
cdef int preset_ent_iob = st.B_(0).ent_iob
cdef attr_t preset_ent_label = st.B_(0).ent_type
if label == 0:
@ -535,13 +535,13 @@ cdef class Last:
return True
@staticmethod
cdef int transition(StateC* st, attr_t label) nogil:
cdef int transition(StateC* st, attr_t label) noexcept nogil:
st.close_ent()
st.push()
st.pop()
@staticmethod
cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) nogil:
cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) noexcept nogil:
gold = <GoldNERStateC*>_gold
b0 = s.B(0)
ent_start = s.E(0)
@ -581,7 +581,7 @@ cdef class Last:
cdef class Unit:
@staticmethod
cdef bint is_valid(const StateC* st, attr_t label) nogil:
cdef bint is_valid(const StateC* st, attr_t label) noexcept nogil:
cdef int preset_ent_iob = st.B_(0).ent_iob
cdef attr_t preset_ent_label = st.B_(0).ent_type
if label == 0:
@ -609,14 +609,14 @@ cdef class Unit:
return True
@staticmethod
cdef int transition(StateC* st, attr_t label) nogil:
cdef int transition(StateC* st, attr_t label) noexcept nogil:
st.open_ent(label)
st.close_ent()
st.push()
st.pop()
@staticmethod
cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) nogil:
cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) noexcept nogil:
gold = <GoldNERStateC*>_gold
cdef int g_act = gold.ner[s.B(0)].move
cdef attr_t g_tag = gold.ner[s.B(0)].label
@ -646,7 +646,7 @@ cdef class Unit:
cdef class Out:
@staticmethod
cdef bint is_valid(const StateC* st, attr_t label) nogil:
cdef bint is_valid(const StateC* st, attr_t label) noexcept nogil:
cdef int preset_ent_iob = st.B_(0).ent_iob
if st.entity_is_open():
return False
@ -658,12 +658,12 @@ cdef class Out:
return True
@staticmethod
cdef int transition(StateC* st, attr_t label) nogil:
cdef int transition(StateC* st, attr_t label) noexcept nogil:
st.push()
st.pop()
@staticmethod
cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) nogil:
cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) noexcept nogil:
gold = <GoldNERStateC*>_gold
cdef int g_act = gold.ner[s.B(0)].move
cdef weight_t cost = 0

2
spacy/pipeline/_parser_internals/nonproj.pyx
View File

@ -94,7 +94,7 @@ cdef bool _has_head_as_ancestor(int tokenid, int head, const vector[int]& heads)
return False
cdef string heads_to_string(const vector[int]& heads) nogil:
cdef string heads_to_string(const vector[int]& heads) noexcept nogil:
cdef vector[int].const_iterator citer
cdef string cycle_str

16
spacy/pipeline/_parser_internals/transition_system.pxd
View File

@ -15,22 +15,22 @@ cdef struct Transition:
weight_t score
bint (*is_valid)(const StateC* state, attr_t label) nogil
weight_t (*get_cost)(const StateC* state, const void* gold, attr_t label) nogil
int (*do)(StateC* state, attr_t label) nogil
bint (*is_valid)(const StateC* state, attr_t label) noexcept nogil
weight_t (*get_cost)(const StateC* state, const void* gold, attr_t label) noexcept nogil
int (*do)(StateC* state, attr_t label) noexcept nogil
ctypedef weight_t (*get_cost_func_t)(
const StateC* state, const void* gold, attr_tlabel
) nogil
) noexcept nogil
ctypedef weight_t (*move_cost_func_t)(
const StateC* state, const void* gold
) nogil
) noexcept nogil
ctypedef weight_t (*label_cost_func_t)(
const StateC* state, const void* gold, attr_t label
) nogil
) noexcept nogil
ctypedef int (*do_func_t)(StateC* state, attr_t label) nogil
ctypedef int (*do_func_t)(StateC* state, attr_t label) noexcept nogil
ctypedef void* (*init_state_t)(Pool mem, int length, void* tokens) except NULL
@ -53,7 +53,7 @@ cdef class TransitionSystem:
cdef Transition init_transition(self, int clas, int move, attr_t label) except *
cdef int set_valid(self, int* output, const StateC* st) nogil
cdef int set_valid(self, int* output, const StateC* st) noexcept nogil
cdef int set_costs(self, int* is_valid, weight_t* costs,
const StateC* state, gold) except -1

5
spacy/pipeline/_parser_internals/transition_system.pyx
View File

@ -149,7 +149,7 @@ cdef class TransitionSystem:
action = self.lookup_transition(move_name)
return action.is_valid(stcls.c, action.label)
cdef int set_valid(self, int* is_valid, const StateC* st) nogil:
cdef int set_valid(self, int* is_valid, const StateC* st) noexcept nogil:
cdef int i
for i in range(self.n_moves):
is_valid[i] = self.c[i].is_valid(st, self.c[i].label)
@ -191,8 +191,7 @@ cdef class TransitionSystem:
def add_action(self, int action, label_name):
cdef attr_t label_id
if not isinstance(label_name, int) and \
not isinstance(label_name, long):
if not isinstance(label_name, int):
label_id = self.strings.add(label_name)
else:
label_id = label_name

24
spacy/pipeline/attributeruler.py
View File

@ -1,3 +1,5 @@
import importlib
import sys
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
@ -22,19 +24,6 @@ TagMapType = Dict[str, Dict[Union[int, str], Union[int, str]]]
MorphRulesType = Dict[str, Dict[str, Dict[Union[int, str], Union[int, str]]]]
@Language.factory(
"attribute_ruler",
default_config={
"validate": False,
"scorer": {"@scorers": "spacy.attribute_ruler_scorer.v1"},
},
)
def make_attribute_ruler(
nlp: Language, name: str, validate: bool, scorer: Optional[Callable]
):
return AttributeRuler(nlp.vocab, name, validate=validate, scorer=scorer)
def attribute_ruler_score(examples: Iterable[Example], **kwargs) -> Dict[str, Any]:
def morph_key_getter(token, attr):
return getattr(token, attr).key
@ -54,7 +43,6 @@ def attribute_ruler_score(examples: Iterable[Example], **kwargs) -> Dict[str, An
return results
@registry.scorers("spacy.attribute_ruler_scorer.v1")
def make_attribute_ruler_scorer():
return attribute_ruler_score
@ -355,3 +343,11 @@ def _split_morph_attrs(attrs: dict) -> Tuple[dict, dict]:
else:
morph_attrs[k] = v
return other_attrs, morph_attrs
# Setup backwards compatibility hook for factories
def __getattr__(name):
if name == "make_attribute_ruler":
module = importlib.import_module("spacy.pipeline.factories")
return module.make_attribute_ruler
raise AttributeError(f"module {__name__} has no attribute {name}")

196
spacy/pipeline/dep_parser.pyx
View File

@ -1,4 +1,6 @@
# cython: infer_types=True, binding=True
import importlib
import sys
from collections import defaultdict
from typing import Callable, Optional
@ -39,188 +41,6 @@ subword_features = true
DEFAULT_PARSER_MODEL = Config().from_str(default_model_config)["model"]
@Language.factory(
"parser",
assigns=["token.dep", "token.head", "token.is_sent_start", "doc.sents"],
default_config={
"moves": None,
"update_with_oracle_cut_size": 100,
"learn_tokens": False,
"min_action_freq": 30,
"model": DEFAULT_PARSER_MODEL,
"scorer": {"@scorers": "spacy.parser_scorer.v1"},
},
default_score_weights={
"dep_uas": 0.5,
"dep_las": 0.5,
"dep_las_per_type": None,
"sents_p": None,
"sents_r": None,
"sents_f": 0.0,
},
)
def make_parser(
nlp: Language,
name: str,
model: Model,
moves: Optional[TransitionSystem],
update_with_oracle_cut_size: int,
learn_tokens: bool,
min_action_freq: int,
scorer: Optional[Callable],
):
"""Create a transition-based DependencyParser component. The dependency parser
jointly learns sentence segmentation and labelled dependency parsing, and can
optionally learn to merge tokens that had been over-segmented by the tokenizer.
The parser uses a variant of the non-monotonic arc-eager transition-system
described by Honnibal and Johnson (2014), with the addition of a "break"
transition to perform the sentence segmentation. Nivre's pseudo-projective
dependency transformation is used to allow the parser to predict
non-projective parses.
The parser is trained using an imitation learning objective. The parser follows
the actions predicted by the current weights, and at each state, determines
which actions are compatible with the optimal parse that could be reached
from the current state. The weights such that the scores assigned to the
set of optimal actions is increased, while scores assigned to other
actions are decreased. Note that more than one action may be optimal for
a given state.
model (Model): The model for the transition-based parser. The model needs
to have a specific substructure of named components --- see the
spacy.ml.tb_framework.TransitionModel for details.
moves (Optional[TransitionSystem]): This defines how the parse-state is created,
updated and evaluated. If 'moves' is None, a new instance is
created with `self.TransitionSystem()`. Defaults to `None`.
update_with_oracle_cut_size (int): During training, cut long sequences into
shorter segments by creating intermediate states based on the gold-standard
history. The model is not very sensitive to this parameter, so you usually
won't need to change it. 100 is a good default.
learn_tokens (bool): Whether to learn to merge subtokens that are split
relative to the gold standard. Experimental.
min_action_freq (int): The minimum frequency of labelled actions to retain.
Rarer labelled actions have their label backed-off to "dep". While this
primarily affects the label accuracy, it can also affect the attachment
structure, as the labels are used to represent the pseudo-projectivity
transformation.
scorer (Optional[Callable]): The scoring method.
"""
return DependencyParser(
nlp.vocab,
model,
name,
moves=moves,
update_with_oracle_cut_size=update_with_oracle_cut_size,
multitasks=[],
learn_tokens=learn_tokens,
min_action_freq=min_action_freq,
beam_width=1,
beam_density=0.0,
beam_update_prob=0.0,
# At some point in the future we can try to implement support for
# partial annotations, perhaps only in the beam objective.
incorrect_spans_key=None,
scorer=scorer,
)
@Language.factory(
"beam_parser",
assigns=["token.dep", "token.head", "token.is_sent_start", "doc.sents"],
default_config={
"beam_width": 8,
"beam_density": 0.01,
"beam_update_prob": 0.5,
"moves": None,
"update_with_oracle_cut_size": 100,
"learn_tokens": False,
"min_action_freq": 30,
"model": DEFAULT_PARSER_MODEL,
"scorer": {"@scorers": "spacy.parser_scorer.v1"},
},
default_score_weights={
"dep_uas": 0.5,
"dep_las": 0.5,
"dep_las_per_type": None,
"sents_p": None,
"sents_r": None,
"sents_f": 0.0,
},
)
def make_beam_parser(
nlp: Language,
name: str,
model: Model,
moves: Optional[TransitionSystem],
update_with_oracle_cut_size: int,
learn_tokens: bool,
min_action_freq: int,
beam_width: int,
beam_density: float,
beam_update_prob: float,
scorer: Optional[Callable],
):
"""Create a transition-based DependencyParser component that uses beam-search.
The dependency parser jointly learns sentence segmentation and labelled
dependency parsing, and can optionally learn to merge tokens that had been
over-segmented by the tokenizer.
The parser uses a variant of the non-monotonic arc-eager transition-system
described by Honnibal and Johnson (2014), with the addition of a "break"
transition to perform the sentence segmentation. Nivre's pseudo-projective
dependency transformation is used to allow the parser to predict
non-projective parses.
The parser is trained using a global objective. That is, it learns to assign
probabilities to whole parses.
model (Model): The model for the transition-based parser. The model needs
to have a specific substructure of named components --- see the
spacy.ml.tb_framework.TransitionModel for details.
moves (Optional[TransitionSystem]): This defines how the parse-state is created,
updated and evaluated. If 'moves' is None, a new instance is
created with `self.TransitionSystem()`. Defaults to `None`.
update_with_oracle_cut_size (int): During training, cut long sequences into
shorter segments by creating intermediate states based on the gold-standard
history. The model is not very sensitive to this parameter, so you usually
won't need to change it. 100 is a good default.
beam_width (int): The number of candidate analyses to maintain.
beam_density (float): The minimum ratio between the scores of the first and
last candidates in the beam. This allows the parser to avoid exploring
candidates that are too far behind. This is mostly intended to improve
efficiency, but it can also improve accuracy as deeper search is not
always better.
beam_update_prob (float): The chance of making a beam update, instead of a
greedy update. Greedy updates are an approximation for the beam updates,
and are faster to compute.
learn_tokens (bool): Whether to learn to merge subtokens that are split
relative to the gold standard. Experimental.
min_action_freq (int): The minimum frequency of labelled actions to retain.
Rarer labelled actions have their label backed-off to "dep". While this
primarily affects the label accuracy, it can also affect the attachment
structure, as the labels are used to represent the pseudo-projectivity
transformation.
"""
return DependencyParser(
nlp.vocab,
model,
name,
moves=moves,
update_with_oracle_cut_size=update_with_oracle_cut_size,
beam_width=beam_width,
beam_density=beam_density,
beam_update_prob=beam_update_prob,
multitasks=[],
learn_tokens=learn_tokens,
min_action_freq=min_action_freq,
# At some point in the future we can try to implement support for
# partial annotations, perhaps only in the beam objective.
incorrect_spans_key=None,
scorer=scorer,
)
def parser_score(examples, **kwargs):
"""Score a batch of examples.
@ -246,7 +66,6 @@ def parser_score(examples, **kwargs):
return results
@registry.scorers("spacy.parser_scorer.v1")
def make_parser_scorer():
return parser_score
@ -346,3 +165,14 @@ cdef class DependencyParser(Parser):
# because we instead have a label frequency cut-off and back off rare
# labels to 'dep'.
pass
# Setup backwards compatibility hook for factories
def __getattr__(name):
if name == "make_parser":
module = importlib.import_module("spacy.pipeline.factories")
return module.make_parser
elif name == "make_beam_parser":
module = importlib.import_module("spacy.pipeline.factories")
return module.make_beam_parser
raise AttributeError(f"module {__name__} has no attribute {name}")

47
spacy/pipeline/edit_tree_lemmatizer.py
View File

@ -1,3 +1,5 @@
import importlib
import sys
from collections import Counter
from itertools import islice
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, cast
@ -39,43 +41,6 @@ subword_features = true
DEFAULT_EDIT_TREE_LEMMATIZER_MODEL = Config().from_str(default_model_config)["model"]
@Language.factory(
"trainable_lemmatizer",
assigns=["token.lemma"],
requires=[],
default_config={
"model": DEFAULT_EDIT_TREE_LEMMATIZER_MODEL,
"backoff": "orth",
"min_tree_freq": 3,
"overwrite": False,
"top_k": 1,
"scorer": {"@scorers": "spacy.lemmatizer_scorer.v1"},
},
default_score_weights={"lemma_acc": 1.0},
)
def make_edit_tree_lemmatizer(
nlp: Language,
name: str,
model: Model,
backoff: Optional[str],
min_tree_freq: int,
overwrite: bool,
top_k: int,
scorer: Optional[Callable],
):
"""Construct an EditTreeLemmatizer component."""
return EditTreeLemmatizer(
nlp.vocab,
model,
name,
backoff=backoff,
min_tree_freq=min_tree_freq,
overwrite=overwrite,
top_k=top_k,
scorer=scorer,
)
class EditTreeLemmatizer(TrainablePipe):
"""
Lemmatizer that lemmatizes each word using a predicted edit tree.
@ -421,3 +386,11 @@ class EditTreeLemmatizer(TrainablePipe):
self.tree2label[tree_id] = len(self.cfg["labels"])
self.cfg["labels"].append(tree_id)
return self.tree2label[tree_id]
# Setup backwards compatibility hook for factories
def __getattr__(name):
if name == "make_edit_tree_lemmatizer":
module = importlib.import_module("spacy.pipeline.factories")
return module.make_edit_tree_lemmatizer
raise AttributeError(f"module {__name__} has no attribute {name}")

117
spacy/pipeline/entity_linker.py
View File

@ -1,4 +1,6 @@
import importlib
import random
import sys
from itertools import islice
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Optional, Union
@ -40,117 +42,10 @@ subword_features = true
DEFAULT_NEL_MODEL = Config().from_str(default_model_config)["model"]
@Language.factory(
"entity_linker",
requires=["doc.ents", "doc.sents", "token.ent_iob", "token.ent_type"],
assigns=["token.ent_kb_id"],
default_config={
"model": DEFAULT_NEL_MODEL,
"labels_discard": [],
"n_sents": 0,
"incl_prior": True,
"incl_context": True,
"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,
"candidates_batch_size": 1,
"threshold": None,
},
default_score_weights={
"nel_micro_f": 1.0,
"nel_micro_r": None,
"nel_micro_p": None,
},
)
def make_entity_linker(
nlp: Language,
name: str,
model: Model,
*,
labels_discard: Iterable[str],
n_sents: int,
incl_prior: bool,
incl_context: bool,
entity_vector_length: int,
get_candidates: Callable[[KnowledgeBase, Span], Iterable[Candidate]],
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,
candidates_batch_size: int,
threshold: Optional[float] = None,
):
"""Construct an EntityLinker component.
model (Model[List[Doc], Floats2d]): A model that learns document vector
representations. Given a batch of Doc objects, it should return a single
array, with one row per item in the batch.
labels_discard (Iterable[str]): NER labels that will automatically get a "NIL" prediction.
n_sents (int): The number of neighbouring sentences to take into account.
incl_prior (bool): Whether or not to include prior probabilities from the KB in the model.
incl_context (bool): Whether or not to include the local context in the model.
entity_vector_length (int): Size of encoding vectors in the KB.
get_candidates (Callable[[KnowledgeBase, Span], Iterable[Candidate]]): Function that
produces a list of candidates, given a certain knowledge base and a textual mention.
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 during training or not. If false, another
component must provide entity annotations.
candidates_batch_size (int): Size of batches for entity candidate generation.
threshold (Optional[float]): Confidence threshold for entity predictions. If confidence is below the threshold,
prediction is discarded. If None, predictions are not filtered by any threshold.
"""
if not model.attrs.get("include_span_maker", False):
# The only difference in arguments here is that use_gold_ents and threshold aren't available.
return EntityLinker_v1(
nlp.vocab,
model,
name,
labels_discard=labels_discard,
n_sents=n_sents,
incl_prior=incl_prior,
incl_context=incl_context,
entity_vector_length=entity_vector_length,
get_candidates=get_candidates,
overwrite=overwrite,
scorer=scorer,
)
return EntityLinker(
nlp.vocab,
model,
name,
labels_discard=labels_discard,
n_sents=n_sents,
incl_prior=incl_prior,
incl_context=incl_context,
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,
candidates_batch_size=candidates_batch_size,
threshold=threshold,
)
def entity_linker_score(examples, **kwargs):
return Scorer.score_links(examples, negative_labels=[EntityLinker.NIL], **kwargs)
@registry.scorers("spacy.entity_linker_scorer.v1")
def make_entity_linker_scorer():
return entity_linker_score
@ -676,3 +571,11 @@ class EntityLinker(TrainablePipe):
def add_label(self, label):
raise NotImplementedError
# Setup backwards compatibility hook for factories
def __getattr__(name):
if name == "make_entity_linker":
module = importlib.import_module("spacy.pipeline.factories")
return module.make_entity_linker
raise AttributeError(f"module {__name__} has no attribute {name}")

51
spacy/pipeline/entityruler.py
View File

@ -1,3 +1,5 @@
import importlib
import sys
import warnings
from collections import defaultdict
from pathlib import Path
@ -19,51 +21,10 @@ DEFAULT_ENT_ID_SEP = "||"
PatternType = Dict[str, Union[str, List[Dict[str, Any]]]]
@Language.factory(
"entity_ruler",
assigns=["doc.ents", "token.ent_type", "token.ent_iob"],
default_config={
"phrase_matcher_attr": None,
"matcher_fuzzy_compare": {"@misc": "spacy.levenshtein_compare.v1"},
"validate": False,
"overwrite_ents": False,
"ent_id_sep": DEFAULT_ENT_ID_SEP,
"scorer": {"@scorers": "spacy.entity_ruler_scorer.v1"},
},
default_score_weights={
"ents_f": 1.0,
"ents_p": 0.0,
"ents_r": 0.0,
"ents_per_type": None,
},
)
def make_entity_ruler(
nlp: Language,
name: str,
phrase_matcher_attr: Optional[Union[int, str]],
matcher_fuzzy_compare: Callable,
validate: bool,
overwrite_ents: bool,
ent_id_sep: str,
scorer: Optional[Callable],
):
return EntityRuler(
nlp,
name,
phrase_matcher_attr=phrase_matcher_attr,
matcher_fuzzy_compare=matcher_fuzzy_compare,
validate=validate,
overwrite_ents=overwrite_ents,
ent_id_sep=ent_id_sep,
scorer=scorer,
)
def entity_ruler_score(examples, **kwargs):
return get_ner_prf(examples)
@registry.scorers("spacy.entity_ruler_scorer.v1")
def make_entity_ruler_scorer():
return entity_ruler_score
@ -539,3 +500,11 @@ class EntityRuler(Pipe):
srsly.write_jsonl(path, self.patterns)
else:
to_disk(path, serializers, {})
# Setup backwards compatibility hook for factories
def __getattr__(name):
if name == "make_entity_ruler":
module = importlib.import_module("spacy.pipeline.factories")
return module.make_entity_ruler
raise AttributeError(f"module {__name__} has no attribute {name}")

929
spacy/pipeline/factories.py Normal file
View File

@ -0,0 +1,929 @@
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
from thinc.api import Model
from thinc.types import Floats2d, Ragged
from ..kb import Candidate, KnowledgeBase
from ..language import Language
from ..pipeline._parser_internals.transition_system import TransitionSystem
from ..pipeline.attributeruler import AttributeRuler
from ..pipeline.dep_parser import DEFAULT_PARSER_MODEL, DependencyParser
from ..pipeline.edit_tree_lemmatizer import (
DEFAULT_EDIT_TREE_LEMMATIZER_MODEL,
EditTreeLemmatizer,
)
# Import factory default configurations
from ..pipeline.entity_linker import DEFAULT_NEL_MODEL, EntityLinker, EntityLinker_v1
from ..pipeline.entityruler import DEFAULT_ENT_ID_SEP, EntityRuler
from ..pipeline.functions import DocCleaner, TokenSplitter
from ..pipeline.lemmatizer import Lemmatizer
from ..pipeline.morphologizer import DEFAULT_MORPH_MODEL, Morphologizer
from ..pipeline.multitask import DEFAULT_MT_MODEL, MultitaskObjective
from ..pipeline.ner import DEFAULT_NER_MODEL, EntityRecognizer
from ..pipeline.sentencizer import Sentencizer
from ..pipeline.senter import DEFAULT_SENTER_MODEL, SentenceRecognizer
from ..pipeline.span_finder import DEFAULT_SPAN_FINDER_MODEL, SpanFinder
from ..pipeline.span_ruler import DEFAULT_SPANS_KEY as SPAN_RULER_DEFAULT_SPANS_KEY
from ..pipeline.span_ruler import (
SpanRuler,
prioritize_existing_ents_filter,
prioritize_new_ents_filter,
)
from ..pipeline.spancat import (
DEFAULT_SPANCAT_MODEL,
DEFAULT_SPANCAT_SINGLELABEL_MODEL,
DEFAULT_SPANS_KEY,
SpanCategorizer,
Suggester,
)
from ..pipeline.tagger import DEFAULT_TAGGER_MODEL, Tagger
from ..pipeline.textcat import DEFAULT_SINGLE_TEXTCAT_MODEL, TextCategorizer
from ..pipeline.textcat_multilabel import (
DEFAULT_MULTI_TEXTCAT_MODEL,
MultiLabel_TextCategorizer,
)
from ..pipeline.tok2vec import DEFAULT_TOK2VEC_MODEL, Tok2Vec
from ..tokens.doc import Doc
from ..tokens.span import Span
from ..vocab import Vocab
# Global flag to track if factories have been registered
FACTORIES_REGISTERED = False
def register_factories() -> None:
"""Register all factories with the registry.
This function registers all pipeline component factories, centralizing
the registrations that were previously done with @Language.factory decorators.
"""
global FACTORIES_REGISTERED
if FACTORIES_REGISTERED:
return
# Register factories using the same pattern as Language.factory decorator
# We use Language.factory()() pattern which exactly mimics the decorator
# attributeruler
Language.factory(
"attribute_ruler",
default_config={
"validate": False,
"scorer": {"@scorers": "spacy.attribute_ruler_scorer.v1"},
},
)(make_attribute_ruler)
# entity_linker
Language.factory(
"entity_linker",
requires=["doc.ents", "doc.sents", "token.ent_iob", "token.ent_type"],
assigns=["token.ent_kb_id"],
default_config={
"model": DEFAULT_NEL_MODEL,
"labels_discard": [],
"n_sents": 0,
"incl_prior": True,
"incl_context": True,
"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,
"candidates_batch_size": 1,
"threshold": None,
},
default_score_weights={
"nel_micro_f": 1.0,
"nel_micro_r": None,
"nel_micro_p": None,
},
)(make_entity_linker)
# entity_ruler
Language.factory(
"entity_ruler",
assigns=["doc.ents", "token.ent_type", "token.ent_iob"],
default_config={
"phrase_matcher_attr": None,
"matcher_fuzzy_compare": {"@misc": "spacy.levenshtein_compare.v1"},
"validate": False,
"overwrite_ents": False,
"ent_id_sep": DEFAULT_ENT_ID_SEP,
"scorer": {"@scorers": "spacy.entity_ruler_scorer.v1"},
},
default_score_weights={
"ents_f": 1.0,
"ents_p": 0.0,
"ents_r": 0.0,
"ents_per_type": None,
},
)(make_entity_ruler)
# lemmatizer
Language.factory(
"lemmatizer",
assigns=["token.lemma"],
default_config={
"model": None,
"mode": "lookup",
"overwrite": False,
"scorer": {"@scorers": "spacy.lemmatizer_scorer.v1"},
},
default_score_weights={"lemma_acc": 1.0},
)(make_lemmatizer)
# textcat
Language.factory(
"textcat",
assigns=["doc.cats"],
default_config={
"threshold": 0.0,
"model": DEFAULT_SINGLE_TEXTCAT_MODEL,
"scorer": {"@scorers": "spacy.textcat_scorer.v2"},
},
default_score_weights={
"cats_score": 1.0,
"cats_score_desc": None,
"cats_micro_p": None,
"cats_micro_r": None,
"cats_micro_f": None,
"cats_macro_p": None,
"cats_macro_r": None,
"cats_macro_f": None,
"cats_macro_auc": None,
"cats_f_per_type": None,
},
)(make_textcat)
# token_splitter
Language.factory(
"token_splitter",
default_config={"min_length": 25, "split_length": 10},
retokenizes=True,
)(make_token_splitter)
# doc_cleaner
Language.factory(
"doc_cleaner",
default_config={"attrs": {"tensor": None, "_.trf_data": None}, "silent": True},
)(make_doc_cleaner)
# tok2vec
Language.factory(
"tok2vec",
assigns=["doc.tensor"],
default_config={"model": DEFAULT_TOK2VEC_MODEL},
)(make_tok2vec)
# senter
Language.factory(
"senter",
assigns=["token.is_sent_start"],
default_config={
"model": DEFAULT_SENTER_MODEL,
"overwrite": False,
"scorer": {"@scorers": "spacy.senter_scorer.v1"},
},
default_score_weights={"sents_f": 1.0, "sents_p": 0.0, "sents_r": 0.0},
)(make_senter)
# morphologizer
Language.factory(
"morphologizer",
assigns=["token.morph", "token.pos"],
default_config={
"model": DEFAULT_MORPH_MODEL,
"overwrite": True,
"extend": False,
"scorer": {"@scorers": "spacy.morphologizer_scorer.v1"},
"label_smoothing": 0.0,
},
default_score_weights={
"pos_acc": 0.5,
"morph_acc": 0.5,
"morph_per_feat": None,
},
)(make_morphologizer)
# spancat
Language.factory(
"spancat",
assigns=["doc.spans"],
default_config={
"threshold": 0.5,
"spans_key": DEFAULT_SPANS_KEY,
"max_positive": None,
"model": DEFAULT_SPANCAT_MODEL,
"suggester": {"@misc": "spacy.ngram_suggester.v1", "sizes": [1, 2, 3]},
"scorer": {"@scorers": "spacy.spancat_scorer.v1"},
},
default_score_weights={"spans_sc_f": 1.0, "spans_sc_p": 0.0, "spans_sc_r": 0.0},
)(make_spancat)
# spancat_singlelabel
Language.factory(
"spancat_singlelabel",
assigns=["doc.spans"],
default_config={
"spans_key": DEFAULT_SPANS_KEY,
"model": DEFAULT_SPANCAT_SINGLELABEL_MODEL,
"negative_weight": 1.0,
"suggester": {"@misc": "spacy.ngram_suggester.v1", "sizes": [1, 2, 3]},
"scorer": {"@scorers": "spacy.spancat_scorer.v1"},
"allow_overlap": True,
},
default_score_weights={"spans_sc_f": 1.0, "spans_sc_p": 0.0, "spans_sc_r": 0.0},
)(make_spancat_singlelabel)
# future_entity_ruler
Language.factory(
"future_entity_ruler",
assigns=["doc.ents"],
default_config={
"phrase_matcher_attr": None,
"validate": False,
"overwrite_ents": False,
"scorer": {"@scorers": "spacy.entity_ruler_scorer.v1"},
"ent_id_sep": "__unused__",
"matcher_fuzzy_compare": {"@misc": "spacy.levenshtein_compare.v1"},
},
default_score_weights={
"ents_f": 1.0,
"ents_p": 0.0,
"ents_r": 0.0,
"ents_per_type": None,
},
)(make_future_entity_ruler)
# span_ruler
Language.factory(
"span_ruler",
assigns=["doc.spans"],
default_config={
"spans_key": SPAN_RULER_DEFAULT_SPANS_KEY,
"spans_filter": None,
"annotate_ents": False,
"ents_filter": {"@misc": "spacy.first_longest_spans_filter.v1"},
"phrase_matcher_attr": None,
"matcher_fuzzy_compare": {"@misc": "spacy.levenshtein_compare.v1"},
"validate": False,
"overwrite": True,
"scorer": {
"@scorers": "spacy.overlapping_labeled_spans_scorer.v1",
"spans_key": SPAN_RULER_DEFAULT_SPANS_KEY,
},
},
default_score_weights={
f"spans_{SPAN_RULER_DEFAULT_SPANS_KEY}_f": 1.0,
f"spans_{SPAN_RULER_DEFAULT_SPANS_KEY}_p": 0.0,
f"spans_{SPAN_RULER_DEFAULT_SPANS_KEY}_r": 0.0,
f"spans_{SPAN_RULER_DEFAULT_SPANS_KEY}_per_type": None,
},
)(make_span_ruler)
# trainable_lemmatizer
Language.factory(
"trainable_lemmatizer",
assigns=["token.lemma"],
requires=[],
default_config={
"model": DEFAULT_EDIT_TREE_LEMMATIZER_MODEL,
"backoff": "orth",
"min_tree_freq": 3,
"overwrite": False,
"top_k": 1,
"scorer": {"@scorers": "spacy.lemmatizer_scorer.v1"},
},
default_score_weights={"lemma_acc": 1.0},
)(make_edit_tree_lemmatizer)
# textcat_multilabel
Language.factory(
"textcat_multilabel",
assigns=["doc.cats"],
default_config={
"threshold": 0.5,
"model": DEFAULT_MULTI_TEXTCAT_MODEL,
"scorer": {"@scorers": "spacy.textcat_multilabel_scorer.v2"},
},
default_score_weights={
"cats_score": 1.0,
"cats_score_desc": None,
"cats_micro_p": None,
"cats_micro_r": None,
"cats_micro_f": None,
"cats_macro_p": None,
"cats_macro_r": None,
"cats_macro_f": None,
"cats_macro_auc": None,
"cats_f_per_type": None,
},
)(make_multilabel_textcat)
# span_finder
Language.factory(
"span_finder",
assigns=["doc.spans"],
default_config={
"threshold": 0.5,
"model": DEFAULT_SPAN_FINDER_MODEL,
"spans_key": DEFAULT_SPANS_KEY,
"max_length": 25,
"min_length": None,
"scorer": {"@scorers": "spacy.span_finder_scorer.v1"},
},
default_score_weights={
f"spans_{DEFAULT_SPANS_KEY}_f": 1.0,
f"spans_{DEFAULT_SPANS_KEY}_p": 0.0,
f"spans_{DEFAULT_SPANS_KEY}_r": 0.0,
},
)(make_span_finder)
# ner
Language.factory(
"ner",
assigns=["doc.ents", "token.ent_iob", "token.ent_type"],
default_config={
"moves": None,
"update_with_oracle_cut_size": 100,
"model": DEFAULT_NER_MODEL,
"incorrect_spans_key": None,
"scorer": {"@scorers": "spacy.ner_scorer.v1"},
},
default_score_weights={
"ents_f": 1.0,
"ents_p": 0.0,
"ents_r": 0.0,
"ents_per_type": None,
},
)(make_ner)
# beam_ner
Language.factory(
"beam_ner",
assigns=["doc.ents", "token.ent_iob", "token.ent_type"],
default_config={
"moves": None,
"update_with_oracle_cut_size": 100,
"model": DEFAULT_NER_MODEL,
"beam_density": 0.01,
"beam_update_prob": 0.5,
"beam_width": 32,
"incorrect_spans_key": None,
"scorer": {"@scorers": "spacy.ner_scorer.v1"},
},
default_score_weights={
"ents_f": 1.0,
"ents_p": 0.0,
"ents_r": 0.0,
"ents_per_type": None,
},
)(make_beam_ner)
# parser
Language.factory(
"parser",
assigns=["token.dep", "token.head", "token.is_sent_start", "doc.sents"],
default_config={
"moves": None,
"update_with_oracle_cut_size": 100,
"learn_tokens": False,
"min_action_freq": 30,
"model": DEFAULT_PARSER_MODEL,
"scorer": {"@scorers": "spacy.parser_scorer.v1"},
},
default_score_weights={
"dep_uas": 0.5,
"dep_las": 0.5,
"dep_las_per_type": None,
"sents_p": None,
"sents_r": None,
"sents_f": 0.0,
},
)(make_parser)
# beam_parser
Language.factory(
"beam_parser",
assigns=["token.dep", "token.head", "token.is_sent_start", "doc.sents"],
default_config={
"moves": None,
"update_with_oracle_cut_size": 100,
"learn_tokens": False,
"min_action_freq": 30,
"beam_width": 8,
"beam_density": 0.0001,
"beam_update_prob": 0.5,
"model": DEFAULT_PARSER_MODEL,
"scorer": {"@scorers": "spacy.parser_scorer.v1"},
},
default_score_weights={
"dep_uas": 0.5,
"dep_las": 0.5,
"dep_las_per_type": None,
"sents_p": None,
"sents_r": None,
"sents_f": 0.0,
},
)(make_beam_parser)
# tagger
Language.factory(
"tagger",
assigns=["token.tag"],
default_config={
"model": DEFAULT_TAGGER_MODEL,
"overwrite": False,
"scorer": {"@scorers": "spacy.tagger_scorer.v1"},
"neg_prefix": "!",
"label_smoothing": 0.0,
},
default_score_weights={
"tag_acc": 1.0,
"pos_acc": 0.0,
"tag_micro_p": None,
"tag_micro_r": None,
"tag_micro_f": None,
},
)(make_tagger)
# nn_labeller
Language.factory(
"nn_labeller",
default_config={
"labels": None,
"target": "dep_tag_offset",
"model": DEFAULT_MT_MODEL,
},
)(make_nn_labeller)
# sentencizer
Language.factory(
"sentencizer",
assigns=["token.is_sent_start", "doc.sents"],
default_config={
"punct_chars": None,
"overwrite": False,
"scorer": {"@scorers": "spacy.senter_scorer.v1"},
},
default_score_weights={"sents_f": 1.0, "sents_p": 0.0, "sents_r": 0.0},
)(make_sentencizer)
# Set the flag to indicate that all factories have been registered
FACTORIES_REGISTERED = True
# We can't have function implementations for these factories in Cython, because
# we need to build a Pydantic model for them dynamically, reading their argument
# structure from the signature. In Cython 3, this doesn't work because the
# from __future__ import annotations semantics are used, which means the types
# are stored as strings.
def make_sentencizer(
nlp: Language,
name: str,
punct_chars: Optional[List[str]],
overwrite: bool,
scorer: Optional[Callable],
):
return Sentencizer(
name, punct_chars=punct_chars, overwrite=overwrite, scorer=scorer
)
def make_attribute_ruler(
nlp: Language, name: str, validate: bool, scorer: Optional[Callable]
):
return AttributeRuler(nlp.vocab, name, validate=validate, scorer=scorer)
def make_entity_linker(
nlp: Language,
name: str,
model: Model,
*,
labels_discard: Iterable[str],
n_sents: int,
incl_prior: bool,
incl_context: bool,
entity_vector_length: int,
get_candidates: Callable[[KnowledgeBase, Span], Iterable[Candidate]],
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,
candidates_batch_size: int,
threshold: Optional[float] = None,
):
if not model.attrs.get("include_span_maker", False):
# The only difference in arguments here is that use_gold_ents and threshold aren't available.
return EntityLinker_v1(
nlp.vocab,
model,
name,
labels_discard=labels_discard,
n_sents=n_sents,
incl_prior=incl_prior,
incl_context=incl_context,
entity_vector_length=entity_vector_length,
get_candidates=get_candidates,
overwrite=overwrite,
scorer=scorer,
)
return EntityLinker(
nlp.vocab,
model,
name,
labels_discard=labels_discard,
n_sents=n_sents,
incl_prior=incl_prior,
incl_context=incl_context,
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,
candidates_batch_size=candidates_batch_size,
threshold=threshold,
)
def make_lemmatizer(
nlp: Language,
model: Optional[Model],
name: str,
mode: str,
overwrite: bool,
scorer: Optional[Callable],
):
return Lemmatizer(
nlp.vocab, model, name, mode=mode, overwrite=overwrite, scorer=scorer
)
def make_textcat(
nlp: Language,
name: str,
model: Model[List[Doc], List[Floats2d]],
threshold: float,
scorer: Optional[Callable],
) -> TextCategorizer:
return TextCategorizer(nlp.vocab, model, name, threshold=threshold, scorer=scorer)
def make_token_splitter(
nlp: Language, name: str, *, min_length: int = 0, split_length: int = 0
):
return TokenSplitter(min_length=min_length, split_length=split_length)
def make_doc_cleaner(nlp: Language, name: str, *, attrs: Dict[str, Any], silent: bool):
return DocCleaner(attrs, silent=silent)
def make_tok2vec(nlp: Language, name: str, model: Model) -> Tok2Vec:
return Tok2Vec(nlp.vocab, model, name)
def make_spancat(
nlp: Language,
name: str,
suggester: Suggester,
model: Model[Tuple[List[Doc], Ragged], Floats2d],
spans_key: str,
scorer: Optional[Callable],
threshold: float,
max_positive: Optional[int],
) -> SpanCategorizer:
return SpanCategorizer(
nlp.vocab,
model=model,
suggester=suggester,
name=name,
spans_key=spans_key,
negative_weight=None,
allow_overlap=True,
max_positive=max_positive,
threshold=threshold,
scorer=scorer,
add_negative_label=False,
)
def make_spancat_singlelabel(
nlp: Language,
name: str,
suggester: Suggester,
model: Model[Tuple[List[Doc], Ragged], Floats2d],
spans_key: str,
negative_weight: float,
allow_overlap: bool,
scorer: Optional[Callable],
) -> SpanCategorizer:
return SpanCategorizer(
nlp.vocab,
model=model,
suggester=suggester,
name=name,
spans_key=spans_key,
negative_weight=negative_weight,
allow_overlap=allow_overlap,
max_positive=1,
add_negative_label=True,
threshold=None,
scorer=scorer,
)
def make_future_entity_ruler(
nlp: Language,
name: str,
phrase_matcher_attr: Optional[Union[int, str]],
matcher_fuzzy_compare: Callable,
validate: bool,
overwrite_ents: bool,
scorer: Optional[Callable],
ent_id_sep: str,
):
if overwrite_ents:
ents_filter = prioritize_new_ents_filter
else:
ents_filter = prioritize_existing_ents_filter
return SpanRuler(
nlp,
name,
spans_key=None,
spans_filter=None,
annotate_ents=True,
ents_filter=ents_filter,
phrase_matcher_attr=phrase_matcher_attr,
matcher_fuzzy_compare=matcher_fuzzy_compare,
validate=validate,
overwrite=False,
scorer=scorer,
)
def make_entity_ruler(
nlp: Language,
name: str,
phrase_matcher_attr: Optional[Union[int, str]],
matcher_fuzzy_compare: Callable,
validate: bool,
overwrite_ents: bool,
ent_id_sep: str,
scorer: Optional[Callable],
):
return EntityRuler(
nlp,
name,
phrase_matcher_attr=phrase_matcher_attr,
matcher_fuzzy_compare=matcher_fuzzy_compare,
validate=validate,
overwrite_ents=overwrite_ents,
ent_id_sep=ent_id_sep,
scorer=scorer,
)
def make_span_ruler(
nlp: Language,
name: str,
spans_key: Optional[str],
spans_filter: Optional[Callable[[Iterable[Span], Iterable[Span]], Iterable[Span]]],
annotate_ents: bool,
ents_filter: Callable[[Iterable[Span], Iterable[Span]], Iterable[Span]],
phrase_matcher_attr: Optional[Union[int, str]],
matcher_fuzzy_compare: Callable,
validate: bool,
overwrite: bool,
scorer: Optional[Callable],
):
return SpanRuler(
nlp,
name,
spans_key=spans_key,
spans_filter=spans_filter,
annotate_ents=annotate_ents,
ents_filter=ents_filter,
phrase_matcher_attr=phrase_matcher_attr,
matcher_fuzzy_compare=matcher_fuzzy_compare,
validate=validate,
overwrite=overwrite,
scorer=scorer,
)
def make_edit_tree_lemmatizer(
nlp: Language,
name: str,
model: Model,
backoff: Optional[str],
min_tree_freq: int,
overwrite: bool,
top_k: int,
scorer: Optional[Callable],
):
return EditTreeLemmatizer(
nlp.vocab,
model,
name,
backoff=backoff,
min_tree_freq=min_tree_freq,
overwrite=overwrite,
top_k=top_k,
scorer=scorer,
)
def make_multilabel_textcat(
nlp: Language,
name: str,
model: Model[List[Doc], List[Floats2d]],
threshold: float,
scorer: Optional[Callable],
) -> MultiLabel_TextCategorizer:
return MultiLabel_TextCategorizer(
nlp.vocab, model, name, threshold=threshold, scorer=scorer
)
def make_span_finder(
nlp: Language,
name: str,
model: Model[Iterable[Doc], Floats2d],
spans_key: str,
threshold: float,
max_length: Optional[int],
min_length: Optional[int],
scorer: Optional[Callable],
) -> SpanFinder:
return SpanFinder(
nlp,
model=model,
threshold=threshold,
name=name,
scorer=scorer,
max_length=max_length,
min_length=min_length,
spans_key=spans_key,
)
def make_ner(
nlp: Language,
name: str,
model: Model,
moves: Optional[TransitionSystem],
update_with_oracle_cut_size: int,
incorrect_spans_key: Optional[str],
scorer: Optional[Callable],
):
return EntityRecognizer(
nlp.vocab,
model,
name=name,
moves=moves,
update_with_oracle_cut_size=update_with_oracle_cut_size,
incorrect_spans_key=incorrect_spans_key,
scorer=scorer,
)
def make_beam_ner(
nlp: Language,
name: str,
model: Model,
moves: Optional[TransitionSystem],
update_with_oracle_cut_size: int,
beam_width: int,
beam_density: float,
beam_update_prob: float,
incorrect_spans_key: Optional[str],
scorer: Optional[Callable],
):
return EntityRecognizer(
nlp.vocab,
model,
name=name,
moves=moves,
update_with_oracle_cut_size=update_with_oracle_cut_size,
beam_width=beam_width,
beam_density=beam_density,
beam_update_prob=beam_update_prob,
incorrect_spans_key=incorrect_spans_key,
scorer=scorer,
)
def make_parser(
nlp: Language,
name: str,
model: Model,
moves: Optional[TransitionSystem],
update_with_oracle_cut_size: int,
learn_tokens: bool,
min_action_freq: int,
scorer: Optional[Callable],
):
return DependencyParser(
nlp.vocab,
model,
name=name,
moves=moves,
update_with_oracle_cut_size=update_with_oracle_cut_size,
learn_tokens=learn_tokens,
min_action_freq=min_action_freq,
scorer=scorer,
)
def make_beam_parser(
nlp: Language,
name: str,
model: Model,
moves: Optional[TransitionSystem],
update_with_oracle_cut_size: int,
learn_tokens: bool,
min_action_freq: int,
beam_width: int,
beam_density: float,
beam_update_prob: float,
scorer: Optional[Callable],
):
return DependencyParser(
nlp.vocab,
model,
name=name,
moves=moves,
update_with_oracle_cut_size=update_with_oracle_cut_size,
learn_tokens=learn_tokens,
min_action_freq=min_action_freq,
beam_width=beam_width,
beam_density=beam_density,
beam_update_prob=beam_update_prob,
scorer=scorer,
)
def make_tagger(
nlp: Language,
name: str,
model: Model,
overwrite: bool,
scorer: Optional[Callable],
neg_prefix: str,
label_smoothing: float,
):
return Tagger(
nlp.vocab,
model,
name=name,
overwrite=overwrite,
scorer=scorer,
neg_prefix=neg_prefix,
label_smoothing=label_smoothing,
)
def make_nn_labeller(
nlp: Language, name: str, model: Model, labels: Optional[dict], target: str
):
return MultitaskObjective(nlp.vocab, model, name, target=target)
def make_morphologizer(
nlp: Language,
model: Model,
name: str,
overwrite: bool,
extend: bool,
label_smoothing: float,
scorer: Optional[Callable],
):
return Morphologizer(
nlp.vocab,
model,
name,
overwrite=overwrite,
extend=extend,
label_smoothing=label_smoothing,
scorer=scorer,
)
def make_senter(
nlp: Language, name: str, model: Model, overwrite: bool, scorer: Optional[Callable]
):
return SentenceRecognizer(
nlp.vocab, model, name, overwrite=overwrite, scorer=scorer
)

32
spacy/pipeline/functions.py
View File

@ -1,3 +1,5 @@
import importlib
import sys
import warnings
from typing import Any, Dict
@ -73,17 +75,6 @@ def merge_subtokens(doc: Doc, label: str = "subtok") -> Doc:
return doc
@Language.factory(
"token_splitter",
default_config={"min_length": 25, "split_length": 10},
retokenizes=True,
)
def make_token_splitter(
nlp: Language, name: str, *, min_length: int = 0, split_length: int = 0
):
return TokenSplitter(min_length=min_length, split_length=split_length)
class TokenSplitter:
def __init__(self, min_length: int = 0, split_length: int = 0):
self.min_length = min_length
@ -141,14 +132,6 @@ class TokenSplitter:
util.from_disk(path, serializers, [])
@Language.factory(
"doc_cleaner",
default_config={"attrs": {"tensor": None, "_.trf_data": None}, "silent": True},
)
def make_doc_cleaner(nlp: Language, name: str, *, attrs: Dict[str, Any], silent: bool):
return DocCleaner(attrs, silent=silent)
class DocCleaner:
def __init__(self, attrs: Dict[str, Any], *, silent: bool = True):
self.cfg: Dict[str, Any] = {"attrs": dict(attrs), "silent": silent}
@ -201,3 +184,14 @@ class DocCleaner:
"cfg": lambda p: self.cfg.update(srsly.read_json(p)),
}
util.from_disk(path, serializers, [])
# Setup backwards compatibility hook for factories
def __getattr__(name):
if name == "make_doc_cleaner":
module = importlib.import_module("spacy.pipeline.factories")
return module.make_doc_cleaner
elif name == "make_token_splitter":
module = importlib.import_module("spacy.pipeline.factories")
return module.make_token_splitter
raise AttributeError(f"module {__name__} has no attribute {name}")

35
spacy/pipeline/lemmatizer.py
View File

@ -1,3 +1,5 @@
import importlib
import sys
import warnings
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
@ -16,35 +18,10 @@ from ..vocab import Vocab
from .pipe import Pipe
@Language.factory(
"lemmatizer",
assigns=["token.lemma"],
default_config={
"model": None,
"mode": "lookup",
"overwrite": False,
"scorer": {"@scorers": "spacy.lemmatizer_scorer.v1"},
},
default_score_weights={"lemma_acc": 1.0},
)
def make_lemmatizer(
nlp: Language,
model: Optional[Model],
name: str,
mode: str,
overwrite: bool,
scorer: Optional[Callable],
):
return Lemmatizer(
nlp.vocab, model, name, mode=mode, overwrite=overwrite, scorer=scorer
)
def lemmatizer_score(examples: Iterable[Example], **kwargs) -> Dict[str, Any]:
return Scorer.score_token_attr(examples, "lemma", **kwargs)
@registry.scorers("spacy.lemmatizer_scorer.v1")
def make_lemmatizer_scorer():
return lemmatizer_score
@ -334,3 +311,11 @@ class Lemmatizer(Pipe):
util.from_bytes(bytes_data, deserialize, exclude)
self._validate_tables()
return self
# Setup backwards compatibility hook for factories
def __getattr__(name):
if name == "make_lemmatizer":
module = importlib.import_module("spacy.pipeline.factories")
return module.make_lemmatizer
raise AttributeError(f"module {__name__} has no attribute {name}")

30
spacy/pipeline/morphologizer.pyx
View File

@ -1,4 +1,6 @@
# cython: infer_types=True, binding=True
import importlib
import sys
from itertools import islice
from typing import Callable, Dict, Optional, Union
@ -47,25 +49,6 @@ maxout_pieces = 3
DEFAULT_MORPH_MODEL = Config().from_str(default_model_config)["model"]
@Language.factory(
"morphologizer",
assigns=["token.morph", "token.pos"],
default_config={"model": DEFAULT_MORPH_MODEL, "overwrite": True, "extend": False,
"scorer": {"@scorers": "spacy.morphologizer_scorer.v1"}, "label_smoothing": 0.0},
default_score_weights={"pos_acc": 0.5, "morph_acc": 0.5, "morph_per_feat": None},
)
def make_morphologizer(
nlp: Language,
model: Model,
name: str,
overwrite: bool,
extend: bool,
label_smoothing: float,
scorer: Optional[Callable],
):
return Morphologizer(nlp.vocab, model, name, overwrite=overwrite, extend=extend, label_smoothing=label_smoothing, scorer=scorer)
def morphologizer_score(examples, **kwargs):
def morph_key_getter(token, attr):
return getattr(token, attr).key
@ -81,7 +64,6 @@ def morphologizer_score(examples, **kwargs):
return results
@registry.scorers("spacy.morphologizer_scorer.v1")
def make_morphologizer_scorer():
return morphologizer_score
@ -309,3 +291,11 @@ class Morphologizer(Tagger):
if self.model.ops.xp.isnan(loss):
raise ValueError(Errors.E910.format(name=self.name))
return float(loss), d_scores
# Setup backwards compatibility hook for factories
def __getattr__(name):
if name == "make_morphologizer":
module = importlib.import_module("spacy.pipeline.factories")
return module.make_morphologizer
raise AttributeError(f"module {__name__} has no attribute {name}")

18
spacy/pipeline/multitask.pyx
View File

@ -1,4 +1,6 @@
# cython: infer_types=True, binding=True
import importlib
import sys
from typing import Optional
import numpy
@ -30,14 +32,6 @@ subword_features = true
DEFAULT_MT_MODEL = Config().from_str(default_model_config)["model"]
@Language.factory(
"nn_labeller",
default_config={"labels": None, "target": "dep_tag_offset", "model": DEFAULT_MT_MODEL}
)
def make_nn_labeller(nlp: Language, name: str, model: Model, labels: Optional[dict], target: str):
return MultitaskObjective(nlp.vocab, model, name)
class MultitaskObjective(Tagger):
"""Experimental: Assist training of a parser or tagger, by training a
side-objective.
@ -213,3 +207,11 @@ class ClozeMultitask(TrainablePipe):
def add_label(self, label):
raise NotImplementedError
# Setup backwards compatibility hook for factories
def __getattr__(name):
if name == "make_nn_labeller":
module = importlib.import_module("spacy.pipeline.factories")
return module.make_nn_labeller
raise AttributeError(f"module {__name__} has no attribute {name}")

157
spacy/pipeline/ner.pyx
View File

@ -1,4 +1,6 @@
# cython: infer_types=True, binding=True
import importlib
import sys
from collections import defaultdict
from typing import Callable, Optional
@ -36,154 +38,10 @@ subword_features = true
DEFAULT_NER_MODEL = Config().from_str(default_model_config)["model"]
@Language.factory(
"ner",
assigns=["doc.ents", "token.ent_iob", "token.ent_type"],
default_config={
"moves": None,
"update_with_oracle_cut_size": 100,
"model": DEFAULT_NER_MODEL,
"incorrect_spans_key": None,
"scorer": {"@scorers": "spacy.ner_scorer.v1"},
},
default_score_weights={"ents_f": 1.0, "ents_p": 0.0, "ents_r": 0.0, "ents_per_type": None},
)
def make_ner(
nlp: Language,
name: str,
model: Model,
moves: Optional[TransitionSystem],
update_with_oracle_cut_size: int,
incorrect_spans_key: Optional[str],
scorer: Optional[Callable],
):
"""Create a transition-based EntityRecognizer component. The entity recognizer
identifies non-overlapping labelled spans of tokens.
The transition-based algorithm used encodes certain assumptions that are
effective for "traditional" named entity recognition tasks, but may not be
a good fit for every span identification problem. Specifically, the loss
function optimizes for whole entity accuracy, so if your inter-annotator
agreement on boundary tokens is low, the component will likely perform poorly
on your problem. The transition-based algorithm also assumes that the most
decisive information about your entities will be close to their initial tokens.
If your entities are long and characterised by tokens in their middle, the
component will likely do poorly on your task.
model (Model): The model for the transition-based parser. The model needs
to have a specific substructure of named components --- see the
spacy.ml.tb_framework.TransitionModel for details.
moves (Optional[TransitionSystem]): This defines how the parse-state is created,
updated and evaluated. If 'moves' is None, a new instance is
created with `self.TransitionSystem()`. Defaults to `None`.
update_with_oracle_cut_size (int): During training, cut long sequences into
shorter segments by creating intermediate states based on the gold-standard
history. The model is not very sensitive to this parameter, so you usually
won't need to change it. 100 is a good default.
incorrect_spans_key (Optional[str]): Identifies spans that are known
to be incorrect entity annotations. The incorrect entity annotations
can be stored in the span group, under this key.
scorer (Optional[Callable]): The scoring method.
"""
return EntityRecognizer(
nlp.vocab,
model,
name,
moves=moves,
update_with_oracle_cut_size=update_with_oracle_cut_size,
incorrect_spans_key=incorrect_spans_key,
multitasks=[],
beam_width=1,
beam_density=0.0,
beam_update_prob=0.0,
scorer=scorer,
)
@Language.factory(
"beam_ner",
assigns=["doc.ents", "token.ent_iob", "token.ent_type"],
default_config={
"moves": None,
"update_with_oracle_cut_size": 100,
"model": DEFAULT_NER_MODEL,
"beam_density": 0.01,
"beam_update_prob": 0.5,
"beam_width": 32,
"incorrect_spans_key": None,
"scorer": None,
},
default_score_weights={"ents_f": 1.0, "ents_p": 0.0, "ents_r": 0.0, "ents_per_type": None},
)
def make_beam_ner(
nlp: Language,
name: str,
model: Model,
moves: Optional[TransitionSystem],
update_with_oracle_cut_size: int,
beam_width: int,
beam_density: float,
beam_update_prob: float,
incorrect_spans_key: Optional[str],
scorer: Optional[Callable],
):
"""Create a transition-based EntityRecognizer component that uses beam-search.
The entity recognizer identifies non-overlapping labelled spans of tokens.
The transition-based algorithm used encodes certain assumptions that are
effective for "traditional" named entity recognition tasks, but may not be
a good fit for every span identification problem. Specifically, the loss
function optimizes for whole entity accuracy, so if your inter-annotator
agreement on boundary tokens is low, the component will likely perform poorly
on your problem. The transition-based algorithm also assumes that the most
decisive information about your entities will be close to their initial tokens.
If your entities are long and characterised by tokens in their middle, the
component will likely do poorly on your task.
model (Model): The model for the transition-based parser. The model needs
to have a specific substructure of named components --- see the
spacy.ml.tb_framework.TransitionModel for details.
moves (Optional[TransitionSystem]): This defines how the parse-state is created,
updated and evaluated. If 'moves' is None, a new instance is
created with `self.TransitionSystem()`. Defaults to `None`.
update_with_oracle_cut_size (int): During training, cut long sequences into
shorter segments by creating intermediate states based on the gold-standard
history. The model is not very sensitive to this parameter, so you usually
won't need to change it. 100 is a good default.
beam_width (int): The number of candidate analyses to maintain.
beam_density (float): The minimum ratio between the scores of the first and
last candidates in the beam. This allows the parser to avoid exploring
candidates that are too far behind. This is mostly intended to improve
efficiency, but it can also improve accuracy as deeper search is not
always better.
beam_update_prob (float): The chance of making a beam update, instead of a
greedy update. Greedy updates are an approximation for the beam updates,
and are faster to compute.
incorrect_spans_key (Optional[str]): Optional key into span groups of
entities known to be non-entities.
scorer (Optional[Callable]): The scoring method.
"""
return EntityRecognizer(
nlp.vocab,
model,
name,
moves=moves,
update_with_oracle_cut_size=update_with_oracle_cut_size,
multitasks=[],
beam_width=beam_width,
beam_density=beam_density,
beam_update_prob=beam_update_prob,
incorrect_spans_key=incorrect_spans_key,
scorer=scorer,
)
def ner_score(examples, **kwargs):
return get_ner_prf(examples, **kwargs)
@registry.scorers("spacy.ner_scorer.v1")
def make_ner_scorer():
return ner_score
@ -261,3 +119,14 @@ cdef class EntityRecognizer(Parser):
score_dict[(start, end, label)] += score
entity_scores.append(score_dict)
return entity_scores
# Setup backwards compatibility hook for factories
def __getattr__(name):
if name == "make_ner":
module = importlib.import_module("spacy.pipeline.factories")
return module.make_ner
elif name == "make_beam_ner":
module = importlib.import_module("spacy.pipeline.factories")
return module.make_beam_ner
raise AttributeError(f"module {__name__} has no attribute {name}")

7
spacy/pipeline/pipe.pyx
View File

@ -21,13 +21,6 @@ cdef class Pipe:
DOCS: https://spacy.io/api/pipe
"""
@classmethod
def __init_subclass__(cls, **kwargs):
"""Raise a warning if an inheriting class implements 'begin_training'
(from v2) instead of the new 'initialize' method (from v3)"""
if hasattr(cls, "begin_training"):
warnings.warn(Warnings.W088.format(name=cls.__name__))
def __call__(self, Doc doc) -> Doc:
"""Apply the pipe to one document. The document is modified in place,
and returned. This usually happens under the hood when the nlp object

26
spacy/pipeline/sentencizer.pyx
View File

@ -1,4 +1,6 @@
# cython: infer_types=True, binding=True
import importlib
import sys
from typing import Callable, List, Optional
import srsly
@ -14,22 +16,6 @@ from .senter import senter_score
BACKWARD_OVERWRITE = False
@Language.factory(
"sentencizer",
assigns=["token.is_sent_start", "doc.sents"],
default_config={"punct_chars": None, "overwrite": False, "scorer": {"@scorers": "spacy.senter_scorer.v1"}},
default_score_weights={"sents_f": 1.0, "sents_p": 0.0, "sents_r": 0.0},
)
def make_sentencizer(
nlp: Language,
name: str,
punct_chars: Optional[List[str]],
overwrite: bool,
scorer: Optional[Callable],
):
return Sentencizer(name, punct_chars=punct_chars, overwrite=overwrite, scorer=scorer)
class Sentencizer(Pipe):
"""Segment the Doc into sentences using a rule-based strategy.
@ -181,3 +167,11 @@ class Sentencizer(Pipe):
self.punct_chars = set(cfg.get("punct_chars", self.default_punct_chars))
self.overwrite = cfg.get("overwrite", self.overwrite)
return self
# Setup backwards compatibility hook for factories
def __getattr__(name):
if name == "make_sentencizer":
module = importlib.import_module("spacy.pipeline.factories")
return module.make_sentencizer
raise AttributeError(f"module {__name__} has no attribute {name}")

21
spacy/pipeline/senter.pyx
View File

@ -1,4 +1,6 @@
# cython: infer_types=True, binding=True
import importlib
import sys
from itertools import islice
from typing import Callable, Optional
@ -34,16 +36,6 @@ subword_features = true
DEFAULT_SENTER_MODEL = Config().from_str(default_model_config)["model"]
@Language.factory(
"senter",
assigns=["token.is_sent_start"],
default_config={"model": DEFAULT_SENTER_MODEL, "overwrite": False, "scorer": {"@scorers": "spacy.senter_scorer.v1"}},
default_score_weights={"sents_f": 1.0, "sents_p": 0.0, "sents_r": 0.0},
)
def make_senter(nlp: Language, name: str, model: Model, overwrite: bool, scorer: Optional[Callable]):
return SentenceRecognizer(nlp.vocab, model, name, overwrite=overwrite, scorer=scorer)
def senter_score(examples, **kwargs):
def has_sents(doc):
return doc.has_annotation("SENT_START")
@ -53,7 +45,6 @@ def senter_score(examples, **kwargs):
return results
@registry.scorers("spacy.senter_scorer.v1")
def make_senter_scorer():
return senter_score
@ -185,3 +176,11 @@ class SentenceRecognizer(Tagger):
def add_label(self, label, values=None):
raise NotImplementedError
# Setup backwards compatibility hook for factories
def __getattr__(name):
if name == "make_senter":
module = importlib.import_module("spacy.pipeline.factories")
return module.make_senter
raise AttributeError(f"module {__name__} has no attribute {name}")

67
spacy/pipeline/span_finder.py
View File

@ -1,3 +1,5 @@
import importlib
import sys
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
from thinc.api import Config, Model, Optimizer, set_dropout_rate
@ -41,63 +43,6 @@ depth = 4
DEFAULT_SPAN_FINDER_MODEL = Config().from_str(span_finder_default_config)["model"]
@Language.factory(
"span_finder",
assigns=["doc.spans"],
default_config={
"threshold": 0.5,
"model": DEFAULT_SPAN_FINDER_MODEL,
"spans_key": DEFAULT_SPANS_KEY,
"max_length": 25,
"min_length": None,
"scorer": {"@scorers": "spacy.span_finder_scorer.v1"},
},
default_score_weights={
f"spans_{DEFAULT_SPANS_KEY}_f": 1.0,
f"spans_{DEFAULT_SPANS_KEY}_p": 0.0,
f"spans_{DEFAULT_SPANS_KEY}_r": 0.0,
},
)
def make_span_finder(
nlp: Language,
name: str,
model: Model[Iterable[Doc], Floats2d],
spans_key: str,
threshold: float,
max_length: Optional[int],
min_length: Optional[int],
scorer: Optional[Callable],
) -> "SpanFinder":
"""Create a SpanFinder component. The component predicts whether a token is
the start or the end of a potential span.
model (Model[List[Doc], Floats2d]): A model instance that
is given a list of documents and predicts a probability for each token.
spans_key (str): Key of the doc.spans dict to save the spans under. During
initialization and training, the component will look for spans on the
reference document under the same key.
threshold (float): Minimum probability to consider a prediction positive.
max_length (Optional[int]): Maximum length of the produced spans, defaults
to None meaning unlimited length.
min_length (Optional[int]): Minimum length of the produced spans, defaults
to None meaning shortest span length is 1.
scorer (Optional[Callable]): The scoring method. Defaults to
Scorer.score_spans for the Doc.spans[spans_key] with overlapping
spans allowed.
"""
return SpanFinder(
nlp,
model=model,
threshold=threshold,
name=name,
scorer=scorer,
max_length=max_length,
min_length=min_length,
spans_key=spans_key,
)
@registry.scorers("spacy.span_finder_scorer.v1")
def make_span_finder_scorer():
return span_finder_score
@ -333,3 +278,11 @@ class SpanFinder(TrainablePipe):
self.model.initialize(X=docs, Y=Y)
else:
self.model.initialize()
# Setup backwards compatibility hook for factories
def __getattr__(name):
if name == "make_span_finder":
module = importlib.import_module("spacy.pipeline.factories")
return module.make_span_finder
raise AttributeError(f"module {__name__} has no attribute {name}")

115
spacy/pipeline/span_ruler.py
View File

@ -1,3 +1,5 @@
import importlib
import sys
import warnings
from functools import partial
from pathlib import Path
@ -32,105 +34,6 @@ PatternType = Dict[str, Union[str, List[Dict[str, Any]]]]
DEFAULT_SPANS_KEY = "ruler"
@Language.factory(
"future_entity_ruler",
assigns=["doc.ents"],
default_config={
"phrase_matcher_attr": None,
"validate": False,
"overwrite_ents": False,
"scorer": {"@scorers": "spacy.entity_ruler_scorer.v1"},
"ent_id_sep": "__unused__",
"matcher_fuzzy_compare": {"@misc": "spacy.levenshtein_compare.v1"},
},
default_score_weights={
"ents_f": 1.0,
"ents_p": 0.0,
"ents_r": 0.0,
"ents_per_type": None,
},
)
def make_entity_ruler(
nlp: Language,
name: str,
phrase_matcher_attr: Optional[Union[int, str]],
matcher_fuzzy_compare: Callable,
validate: bool,
overwrite_ents: bool,
scorer: Optional[Callable],
ent_id_sep: str,
):
if overwrite_ents:
ents_filter = prioritize_new_ents_filter
else:
ents_filter = prioritize_existing_ents_filter
return SpanRuler(
nlp,
name,
spans_key=None,
spans_filter=None,
annotate_ents=True,
ents_filter=ents_filter,
phrase_matcher_attr=phrase_matcher_attr,
matcher_fuzzy_compare=matcher_fuzzy_compare,
validate=validate,
overwrite=False,
scorer=scorer,
)
@Language.factory(
"span_ruler",
assigns=["doc.spans"],
default_config={
"spans_key": DEFAULT_SPANS_KEY,
"spans_filter": None,
"annotate_ents": False,
"ents_filter": {"@misc": "spacy.first_longest_spans_filter.v1"},
"phrase_matcher_attr": None,
"matcher_fuzzy_compare": {"@misc": "spacy.levenshtein_compare.v1"},
"validate": False,
"overwrite": True,
"scorer": {
"@scorers": "spacy.overlapping_labeled_spans_scorer.v1",
"spans_key": DEFAULT_SPANS_KEY,
},
},
default_score_weights={
f"spans_{DEFAULT_SPANS_KEY}_f": 1.0,
f"spans_{DEFAULT_SPANS_KEY}_p": 0.0,
f"spans_{DEFAULT_SPANS_KEY}_r": 0.0,
f"spans_{DEFAULT_SPANS_KEY}_per_type": None,
},
)
def make_span_ruler(
nlp: Language,
name: str,
spans_key: Optional[str],
spans_filter: Optional[Callable[[Iterable[Span], Iterable[Span]], Iterable[Span]]],
annotate_ents: bool,
ents_filter: Callable[[Iterable[Span], Iterable[Span]], Iterable[Span]],
phrase_matcher_attr: Optional[Union[int, str]],
matcher_fuzzy_compare: Callable,
validate: bool,
overwrite: bool,
scorer: Optional[Callable],
):
return SpanRuler(
nlp,
name,
spans_key=spans_key,
spans_filter=spans_filter,
annotate_ents=annotate_ents,
ents_filter=ents_filter,
phrase_matcher_attr=phrase_matcher_attr,
matcher_fuzzy_compare=matcher_fuzzy_compare,
validate=validate,
overwrite=overwrite,
scorer=scorer,
)
def prioritize_new_ents_filter(
entities: Iterable[Span], spans: Iterable[Span]
) -> List[Span]:
@ -157,7 +60,6 @@ def prioritize_new_ents_filter(
return entities + new_entities
@registry.misc("spacy.prioritize_new_ents_filter.v1")
def make_prioritize_new_ents_filter():
return prioritize_new_ents_filter
@ -188,7 +90,6 @@ def prioritize_existing_ents_filter(
return entities + new_entities
@registry.misc("spacy.prioritize_existing_ents_filter.v1")
def make_preserve_existing_ents_filter():
return prioritize_existing_ents_filter
@ -208,7 +109,6 @@ def overlapping_labeled_spans_score(
return Scorer.score_spans(examples, **kwargs)
@registry.scorers("spacy.overlapping_labeled_spans_scorer.v1")
def make_overlapping_labeled_spans_scorer(spans_key: str = DEFAULT_SPANS_KEY):
return partial(overlapping_labeled_spans_score, spans_key=spans_key)
@ -595,3 +495,14 @@ class SpanRuler(Pipe):
"patterns": lambda p: srsly.write_jsonl(p, self.patterns),
}
util.to_disk(path, serializers, {})
# Setup backwards compatibility hook for factories
def __getattr__(name):
if name == "make_span_ruler":
module = importlib.import_module("spacy.pipeline.factories")
return module.make_span_ruler
elif name == "make_entity_ruler":
module = importlib.import_module("spacy.pipeline.factories")
return module.make_future_entity_ruler
raise AttributeError(f"module {__name__} has no attribute {name}")

147
spacy/pipeline/spancat.py
View File

@ -1,3 +1,5 @@
import importlib
import sys
from dataclasses import dataclass
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union, cast
@ -134,7 +136,6 @@ def preset_spans_suggester(
return output
@registry.misc("spacy.ngram_suggester.v1")
def build_ngram_suggester(sizes: List[int]) -> Suggester:
"""Suggest all spans of the given lengths. Spans are returned as a ragged
array of integers. The array has two columns, indicating the start and end
@ -143,7 +144,6 @@ def build_ngram_suggester(sizes: List[int]) -> Suggester:
return partial(ngram_suggester, sizes=sizes)
@registry.misc("spacy.ngram_range_suggester.v1")
def build_ngram_range_suggester(min_size: int, max_size: int) -> Suggester:
"""Suggest all spans of the given lengths between a given min and max value - both inclusive.
Spans are returned as a ragged array of integers. The array has two columns,
@ -152,7 +152,6 @@ def build_ngram_range_suggester(min_size: int, max_size: int) -> Suggester:
return build_ngram_suggester(sizes)
@registry.misc("spacy.preset_spans_suggester.v1")
def build_preset_spans_suggester(spans_key: str) -> Suggester:
"""Suggest all spans that are already stored in doc.spans[spans_key].
This is useful when an upstream component is used to set the spans
@ -160,136 +159,6 @@ def build_preset_spans_suggester(spans_key: str) -> Suggester:
return partial(preset_spans_suggester, spans_key=spans_key)
@Language.factory(
"spancat",
assigns=["doc.spans"],
default_config={
"threshold": 0.5,
"spans_key": DEFAULT_SPANS_KEY,
"max_positive": None,
"model": DEFAULT_SPANCAT_MODEL,
"suggester": {"@misc": "spacy.ngram_suggester.v1", "sizes": [1, 2, 3]},
"scorer": {"@scorers": "spacy.spancat_scorer.v1"},
},
default_score_weights={"spans_sc_f": 1.0, "spans_sc_p": 0.0, "spans_sc_r": 0.0},
)
def make_spancat(
nlp: Language,
name: str,
suggester: Suggester,
model: Model[Tuple[List[Doc], Ragged], Floats2d],
spans_key: str,
scorer: Optional[Callable],
threshold: float,
max_positive: Optional[int],
) -> "SpanCategorizer":
"""Create a SpanCategorizer component and configure it for multi-label
classification to be able to assign multiple labels for each span.
The span categorizer consists of two
parts: a suggester function that proposes candidate spans, and a labeller
model that predicts one or more labels for each span.
name (str): The component instance name, used to add entries to the
losses during training.
suggester (Callable[[Iterable[Doc], Optional[Ops]], Ragged]): A function that suggests spans.
Spans are returned as a ragged array with two integer columns, for the
start and end positions.
model (Model[Tuple[List[Doc], Ragged], Floats2d]): A model instance that
is given a list of documents and (start, end) indices representing
candidate span offsets. The model predicts a probability for each category
for each span.
spans_key (str): Key of the doc.spans dict to save the spans under. During
initialization and training, the component will look for spans on the
reference document under the same key.
scorer (Optional[Callable]): The scoring method. Defaults to
Scorer.score_spans for the Doc.spans[spans_key] with overlapping
spans allowed.
threshold (float): Minimum probability to consider a prediction positive.
Spans with a positive prediction will be saved on the Doc. Defaults to
0.5.
max_positive (Optional[int]): Maximum number of labels to consider positive
per span. Defaults to None, indicating no limit.
"""
return SpanCategorizer(
nlp.vocab,
model=model,
suggester=suggester,
name=name,
spans_key=spans_key,
negative_weight=None,
allow_overlap=True,
max_positive=max_positive,
threshold=threshold,
scorer=scorer,
add_negative_label=False,
)
@Language.factory(
"spancat_singlelabel",
assigns=["doc.spans"],
default_config={
"spans_key": DEFAULT_SPANS_KEY,
"model": DEFAULT_SPANCAT_SINGLELABEL_MODEL,
"negative_weight": 1.0,
"suggester": {"@misc": "spacy.ngram_suggester.v1", "sizes": [1, 2, 3]},
"scorer": {"@scorers": "spacy.spancat_scorer.v1"},
"allow_overlap": True,
},
default_score_weights={"spans_sc_f": 1.0, "spans_sc_p": 0.0, "spans_sc_r": 0.0},
)
def make_spancat_singlelabel(
nlp: Language,
name: str,
suggester: Suggester,
model: Model[Tuple[List[Doc], Ragged], Floats2d],
spans_key: str,
negative_weight: float,
allow_overlap: bool,
scorer: Optional[Callable],
) -> "SpanCategorizer":
"""Create a SpanCategorizer component and configure it for multi-class
classification. With this configuration each span can get at most one
label. The span categorizer consists of two
parts: a suggester function that proposes candidate spans, and a labeller
model that predicts one or more labels for each span.
name (str): The component instance name, used to add entries to the
losses during training.
suggester (Callable[[Iterable[Doc], Optional[Ops]], Ragged]): A function that suggests spans.
Spans are returned as a ragged array with two integer columns, for the
start and end positions.
model (Model[Tuple[List[Doc], Ragged], Floats2d]): A model instance that
is given a list of documents and (start, end) indices representing
candidate span offsets. The model predicts a probability for each category
for each span.
spans_key (str): Key of the doc.spans dict to save the spans under. During
initialization and training, the component will look for spans on the
reference document under the same key.
scorer (Optional[Callable]): The scoring method. Defaults to
Scorer.score_spans for the Doc.spans[spans_key] with overlapping
spans allowed.
negative_weight (float): Multiplier for the loss terms.
Can be used to downweight the negative samples if there are too many.
allow_overlap (bool): If True the data is assumed to contain overlapping spans.
Otherwise it produces non-overlapping spans greedily prioritizing
higher assigned label scores.
"""
return SpanCategorizer(
nlp.vocab,
model=model,
suggester=suggester,
name=name,
spans_key=spans_key,
negative_weight=negative_weight,
allow_overlap=allow_overlap,
max_positive=1,
add_negative_label=True,
threshold=None,
scorer=scorer,
)
def spancat_score(examples: Iterable[Example], **kwargs) -> Dict[str, Any]:
kwargs = dict(kwargs)
attr_prefix = "spans_"
@ -303,7 +172,6 @@ def spancat_score(examples: Iterable[Example], **kwargs) -> Dict[str, Any]:
return Scorer.score_spans(examples, **kwargs)
@registry.scorers("spacy.spancat_scorer.v1")
def make_spancat_scorer():
return spancat_score
@ -785,3 +653,14 @@ class SpanCategorizer(TrainablePipe):
spans.attrs["scores"] = numpy.array(attrs_scores)
return spans
# Setup backwards compatibility hook for factories
def __getattr__(name):
if name == "make_spancat":
module = importlib.import_module("spacy.pipeline.factories")
return module.make_spancat
elif name == "make_spancat_singlelabel":
module = importlib.import_module("spacy.pipeline.factories")
return module.make_spancat_singlelabel
raise AttributeError(f"module {__name__} has no attribute {name}")

36
spacy/pipeline/tagger.pyx
View File

@ -1,4 +1,6 @@
# cython: infer_types=True, binding=True
import importlib
import sys
from itertools import islice
from typing import Callable, Optional
@ -35,36 +37,10 @@ subword_features = true
DEFAULT_TAGGER_MODEL = Config().from_str(default_model_config)["model"]
@Language.factory(
"tagger",
assigns=["token.tag"],
default_config={"model": DEFAULT_TAGGER_MODEL, "overwrite": False, "scorer": {"@scorers": "spacy.tagger_scorer.v1"}, "neg_prefix": "!", "label_smoothing": 0.0},
default_score_weights={"tag_acc": 1.0},
)
def make_tagger(
nlp: Language,
name: str,
model: Model,
overwrite: bool,
scorer: Optional[Callable],
neg_prefix: str,
label_smoothing: float,
):
"""Construct a part-of-speech tagger component.
model (Model[List[Doc], List[Floats2d]]): A model instance that predicts
the tag probabilities. The output vectors should match the number of tags
in size, and be normalized as probabilities (all scores between 0 and 1,
with the rows summing to 1).
"""
return Tagger(nlp.vocab, model, name, overwrite=overwrite, scorer=scorer, neg_prefix=neg_prefix, label_smoothing=label_smoothing)
def tagger_score(examples, **kwargs):
return Scorer.score_token_attr(examples, "tag", **kwargs)
@registry.scorers("spacy.tagger_scorer.v1")
def make_tagger_scorer():
return tagger_score
@ -317,3 +293,11 @@ class Tagger(TrainablePipe):
self.cfg["labels"].append(label)
self.vocab.strings.add(label)
return 1
# Setup backwards compatibility hook for factories
def __getattr__(name):
if name == "make_tagger":
module = importlib.import_module("spacy.pipeline.factories")
return module.make_tagger
raise AttributeError(f"module {__name__} has no attribute {name}")

51
spacy/pipeline/textcat.py
View File

@ -1,3 +1,5 @@
import importlib
import sys
from itertools import islice
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
@ -74,46 +76,6 @@ subword_features = true
"""
@Language.factory(
"textcat",
assigns=["doc.cats"],
default_config={
"threshold": 0.0,
"model": DEFAULT_SINGLE_TEXTCAT_MODEL,
"scorer": {"@scorers": "spacy.textcat_scorer.v2"},
},
default_score_weights={
"cats_score": 1.0,
"cats_score_desc": None,
"cats_micro_p": None,
"cats_micro_r": None,
"cats_micro_f": None,
"cats_macro_p": None,
"cats_macro_r": None,
"cats_macro_f": None,
"cats_macro_auc": None,
"cats_f_per_type": None,
},
)
def make_textcat(
nlp: Language,
name: str,
model: Model[List[Doc], List[Floats2d]],
threshold: float,
scorer: Optional[Callable],
) -> "TextCategorizer":
"""Create a TextCategorizer component. The text categorizer predicts categories
over a whole document. It can learn one or more labels, and the labels are considered
to be mutually exclusive (i.e. one true label per doc).
model (Model[List[Doc], List[Floats2d]]): A model instance that predicts
scores for each category.
threshold (float): Cutoff to consider a prediction "positive".
scorer (Optional[Callable]): The scoring method.
"""
return TextCategorizer(nlp.vocab, model, name, threshold=threshold, scorer=scorer)
def textcat_score(examples: Iterable[Example], **kwargs) -> Dict[str, Any]:
return Scorer.score_cats(
examples,
@ -123,7 +85,6 @@ def textcat_score(examples: Iterable[Example], **kwargs) -> Dict[str, Any]:
)
@registry.scorers("spacy.textcat_scorer.v2")
def make_textcat_scorer():
return textcat_score
@ -412,3 +373,11 @@ class TextCategorizer(TrainablePipe):
for val in vals:
if not (val == 1.0 or val == 0.0):
raise ValueError(Errors.E851.format(val=val))
# Setup backwards compatibility hook for factories
def __getattr__(name):
if name == "make_textcat":
module = importlib.import_module("spacy.pipeline.factories")
return module.make_textcat
raise AttributeError(f"module {__name__} has no attribute {name}")

54
spacy/pipeline/textcat_multilabel.py
View File

@ -1,3 +1,5 @@
import importlib
import sys
from itertools import islice
from typing import Any, Callable, Dict, Iterable, List, Optional
@ -72,49 +74,6 @@ subword_features = true
"""
@Language.factory(
"textcat_multilabel",
assigns=["doc.cats"],
default_config={
"threshold": 0.5,
"model": DEFAULT_MULTI_TEXTCAT_MODEL,
"scorer": {"@scorers": "spacy.textcat_multilabel_scorer.v2"},
},
default_score_weights={
"cats_score": 1.0,
"cats_score_desc": None,
"cats_micro_p": None,
"cats_micro_r": None,
"cats_micro_f": None,
"cats_macro_p": None,
"cats_macro_r": None,
"cats_macro_f": None,
"cats_macro_auc": None,
"cats_f_per_type": None,
},
)
def make_multilabel_textcat(
nlp: Language,
name: str,
model: Model[List[Doc], List[Floats2d]],
threshold: float,
scorer: Optional[Callable],
) -> "MultiLabel_TextCategorizer":
"""Create a MultiLabel_TextCategorizer component. The text categorizer predicts categories
over a whole document. It can learn one or more labels, and the labels are considered
to be non-mutually exclusive, which means that there can be zero or more labels
per doc).
model (Model[List[Doc], List[Floats2d]]): A model instance that predicts
scores for each category.
threshold (float): Cutoff to consider a prediction "positive".
scorer (Optional[Callable]): The scoring method.
"""
return MultiLabel_TextCategorizer(
nlp.vocab, model, name, threshold=threshold, scorer=scorer
)
def textcat_multilabel_score(examples: Iterable[Example], **kwargs) -> Dict[str, Any]:
return Scorer.score_cats(
examples,
@ -124,7 +83,6 @@ def textcat_multilabel_score(examples: Iterable[Example], **kwargs) -> Dict[str,
)
@registry.scorers("spacy.textcat_multilabel_scorer.v2")
def make_textcat_multilabel_scorer():
return textcat_multilabel_score
@ -212,3 +170,11 @@ class MultiLabel_TextCategorizer(TextCategorizer):
for val in ex.reference.cats.values():
if not (val == 1.0 or val == 0.0):
raise ValueError(Errors.E851.format(val=val))
# Setup backwards compatibility hook for factories
def __getattr__(name):
if name == "make_multilabel_textcat":
module = importlib.import_module("spacy.pipeline.factories")
return module.make_multilabel_textcat
raise AttributeError(f"module {__name__} has no attribute {name}")

17
spacy/pipeline/tok2vec.py
View File

@ -1,3 +1,5 @@
import importlib
import sys
from itertools import islice
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence
@ -24,13 +26,6 @@ subword_features = true
DEFAULT_TOK2VEC_MODEL = Config().from_str(default_model_config)["model"]
@Language.factory(
"tok2vec", assigns=["doc.tensor"], default_config={"model": DEFAULT_TOK2VEC_MODEL}
)
def make_tok2vec(nlp: Language, name: str, model: Model) -> "Tok2Vec":
return Tok2Vec(nlp.vocab, model, name)
class Tok2Vec(TrainablePipe):
"""Apply a "token-to-vector" model and set its outputs in the doc.tensor
attribute. This is mostly useful to share a single subnetwork between multiple
@ -320,3 +315,11 @@ def forward(model: Tok2VecListener, inputs, is_train: bool):
def _empty_backprop(dX): # for pickling
return []
# Setup backwards compatibility hook for factories
def __getattr__(name):
if name == "make_tok2vec":
module = importlib.import_module("spacy.pipeline.factories")
return module.make_tok2vec
raise AttributeError(f"module {__name__} has no attribute {name}")

4
spacy/pipeline/transition_parser.pxd
View File

@ -19,7 +19,7 @@ cdef class Parser(TrainablePipe):
StateC** states,
WeightsC weights,
SizesC sizes
) nogil
) noexcept nogil
cdef void c_transition_batch(
self,
@ -27,4 +27,4 @@ cdef class Parser(TrainablePipe):
const float* scores,
int nr_class,
int batch_size
) nogil
) noexcept nogil

4
spacy/pipeline/transition_parser.pyx
View File

@ -316,7 +316,7 @@ cdef class Parser(TrainablePipe):
cdef void _parseC(
self, CBlas cblas, StateC** states, WeightsC weights, SizesC sizes
) nogil:
) noexcept nogil:
cdef int i
cdef vector[StateC*] unfinished
cdef ActivationsC activations = alloc_activations(sizes)
@ -359,7 +359,7 @@ cdef class Parser(TrainablePipe):
const float* scores,
int nr_class,
int batch_size
) nogil:
) noexcept nogil:
# n_moves should not be zero at this point, but make sure to avoid zero-length mem alloc
with gil:
assert self.moves.n_moves > 0, Errors.E924.format(name=self.name)

245
spacy/registrations.py Normal file
View File

@ -0,0 +1,245 @@
"""Centralized registry population for spaCy config
This module centralizes registry decorations to prevent circular import issues
with Cython annotation changes from __future__ import annotations. Functions
remain in their original locations, but decoration is moved here.
Component definitions and registrations are in spacy/pipeline/factories.py
"""
# Global flag to track if registry has been populated
REGISTRY_POPULATED = False
def populate_registry() -> None:
"""Populate the registry with all necessary components.
This function should be called before accessing the registry, to ensure
it's populated. The function uses a global flag to prevent repopulation.
"""
global REGISTRY_POPULATED
if REGISTRY_POPULATED:
return
# Import all necessary modules
from .lang.ja import create_tokenizer as create_japanese_tokenizer
from .lang.ko import create_tokenizer as create_korean_tokenizer
from .lang.th import create_thai_tokenizer
from .lang.vi import create_vietnamese_tokenizer
from .lang.zh import create_chinese_tokenizer
from .language import load_lookups_data
from .matcher.levenshtein import make_levenshtein_compare
from .ml.models.entity_linker import (
create_candidates,
create_candidates_batch,
empty_kb,
empty_kb_for_config,
load_kb,
)
from .pipeline.attributeruler import make_attribute_ruler_scorer
from .pipeline.dep_parser import make_parser_scorer
# Import the functions we refactored by removing direct registry decorators
from .pipeline.entity_linker import make_entity_linker_scorer
from .pipeline.entityruler import (
make_entity_ruler_scorer as make_entityruler_scorer,
)
from .pipeline.lemmatizer import make_lemmatizer_scorer
from .pipeline.morphologizer import make_morphologizer_scorer
from .pipeline.ner import make_ner_scorer
from .pipeline.senter import make_senter_scorer
from .pipeline.span_finder import make_span_finder_scorer
from .pipeline.span_ruler import (
make_overlapping_labeled_spans_scorer,
make_preserve_existing_ents_filter,
make_prioritize_new_ents_filter,
)
from .pipeline.spancat import (
build_ngram_range_suggester,
build_ngram_suggester,
build_preset_spans_suggester,
make_spancat_scorer,
)
# Import all pipeline components that were using registry decorators
from .pipeline.tagger import make_tagger_scorer
from .pipeline.textcat import make_textcat_scorer
from .pipeline.textcat_multilabel import make_textcat_multilabel_scorer
from .util import make_first_longest_spans_filter, registry
# Register miscellaneous components
registry.misc("spacy.first_longest_spans_filter.v1")(
make_first_longest_spans_filter
)
registry.misc("spacy.ngram_suggester.v1")(build_ngram_suggester)
registry.misc("spacy.ngram_range_suggester.v1")(build_ngram_range_suggester)
registry.misc("spacy.preset_spans_suggester.v1")(build_preset_spans_suggester)
registry.misc("spacy.prioritize_new_ents_filter.v1")(
make_prioritize_new_ents_filter
)
registry.misc("spacy.prioritize_existing_ents_filter.v1")(
make_preserve_existing_ents_filter
)
registry.misc("spacy.levenshtein_compare.v1")(make_levenshtein_compare)
# KB-related registrations
registry.misc("spacy.KBFromFile.v1")(load_kb)
registry.misc("spacy.EmptyKB.v2")(empty_kb_for_config)
registry.misc("spacy.EmptyKB.v1")(empty_kb)
registry.misc("spacy.CandidateGenerator.v1")(create_candidates)
registry.misc("spacy.CandidateBatchGenerator.v1")(create_candidates_batch)
registry.misc("spacy.LookupsDataLoader.v1")(load_lookups_data)
# Need to get references to the existing functions in registry by importing the function that is there
# For the registry that was previously decorated
# Import ML components that use registry
from .language import create_tokenizer
from .ml._precomputable_affine import PrecomputableAffine
from .ml.callbacks import (
create_models_and_pipes_with_nvtx_range,
create_models_with_nvtx_range,
)
from .ml.extract_ngrams import extract_ngrams
from .ml.extract_spans import extract_spans
# Import decorator-removed ML components
from .ml.featureextractor import FeatureExtractor
from .ml.models.entity_linker import build_nel_encoder
from .ml.models.multi_task import (
create_pretrain_characters,
create_pretrain_vectors,
)
from .ml.models.parser import build_tb_parser_model
from .ml.models.span_finder import build_finder_model
from .ml.models.spancat import (
build_linear_logistic,
build_mean_max_reducer,
build_spancat_model,
)
from .ml.models.tagger import build_tagger_model
from .ml.models.textcat import (
build_bow_text_classifier,
build_bow_text_classifier_v3,
build_reduce_text_classifier,
build_simple_cnn_text_classifier,
build_text_classifier_lowdata,
build_text_classifier_v2,
build_textcat_parametric_attention_v1,
)
from .ml.models.tok2vec import (
BiLSTMEncoder,
CharacterEmbed,
MaxoutWindowEncoder,
MishWindowEncoder,
MultiHashEmbed,
build_hash_embed_cnn_tok2vec,
build_Tok2Vec_model,
tok2vec_listener_v1,
)
from .ml.staticvectors import StaticVectors
from .ml.tb_framework import TransitionModel
from .training.augment import (
create_combined_augmenter,
create_lower_casing_augmenter,
create_orth_variants_augmenter,
)
from .training.batchers import (
configure_minibatch,
configure_minibatch_by_padded_size,
configure_minibatch_by_words,
)
from .training.callbacks import create_copy_from_base_model
from .training.loggers import console_logger, console_logger_v3
# Register scorers
registry.scorers("spacy.tagger_scorer.v1")(make_tagger_scorer)
registry.scorers("spacy.ner_scorer.v1")(make_ner_scorer)
# span_ruler_scorer removed as it's not in span_ruler.py
registry.scorers("spacy.entity_ruler_scorer.v1")(make_entityruler_scorer)
registry.scorers("spacy.senter_scorer.v1")(make_senter_scorer)
registry.scorers("spacy.textcat_scorer.v1")(make_textcat_scorer)
registry.scorers("spacy.textcat_scorer.v2")(make_textcat_scorer)
registry.scorers("spacy.textcat_multilabel_scorer.v1")(
make_textcat_multilabel_scorer
)
registry.scorers("spacy.textcat_multilabel_scorer.v2")(
make_textcat_multilabel_scorer
)
registry.scorers("spacy.lemmatizer_scorer.v1")(make_lemmatizer_scorer)
registry.scorers("spacy.span_finder_scorer.v1")(make_span_finder_scorer)
registry.scorers("spacy.spancat_scorer.v1")(make_spancat_scorer)
registry.scorers("spacy.entity_linker_scorer.v1")(make_entity_linker_scorer)
registry.scorers("spacy.overlapping_labeled_spans_scorer.v1")(
make_overlapping_labeled_spans_scorer
)
registry.scorers("spacy.attribute_ruler_scorer.v1")(make_attribute_ruler_scorer)
registry.scorers("spacy.parser_scorer.v1")(make_parser_scorer)
registry.scorers("spacy.morphologizer_scorer.v1")(make_morphologizer_scorer)
# Register tokenizers
registry.tokenizers("spacy.Tokenizer.v1")(create_tokenizer)
registry.tokenizers("spacy.ja.JapaneseTokenizer")(create_japanese_tokenizer)
registry.tokenizers("spacy.zh.ChineseTokenizer")(create_chinese_tokenizer)
registry.tokenizers("spacy.ko.KoreanTokenizer")(create_korean_tokenizer)
registry.tokenizers("spacy.vi.VietnameseTokenizer")(create_vietnamese_tokenizer)
registry.tokenizers("spacy.th.ThaiTokenizer")(create_thai_tokenizer)
# Register tok2vec architectures we've modified
registry.architectures("spacy.Tok2VecListener.v1")(tok2vec_listener_v1)
registry.architectures("spacy.HashEmbedCNN.v2")(build_hash_embed_cnn_tok2vec)
registry.architectures("spacy.Tok2Vec.v2")(build_Tok2Vec_model)
registry.architectures("spacy.MultiHashEmbed.v2")(MultiHashEmbed)
registry.architectures("spacy.CharacterEmbed.v2")(CharacterEmbed)
registry.architectures("spacy.MaxoutWindowEncoder.v2")(MaxoutWindowEncoder)
registry.architectures("spacy.MishWindowEncoder.v2")(MishWindowEncoder)
registry.architectures("spacy.TorchBiLSTMEncoder.v1")(BiLSTMEncoder)
registry.architectures("spacy.EntityLinker.v2")(build_nel_encoder)
registry.architectures("spacy.TextCatCNN.v2")(build_simple_cnn_text_classifier)
registry.architectures("spacy.TextCatBOW.v2")(build_bow_text_classifier)
registry.architectures("spacy.TextCatBOW.v3")(build_bow_text_classifier_v3)
registry.architectures("spacy.TextCatEnsemble.v2")(build_text_classifier_v2)
registry.architectures("spacy.TextCatLowData.v1")(build_text_classifier_lowdata)
registry.architectures("spacy.TextCatParametricAttention.v1")(
build_textcat_parametric_attention_v1
)
registry.architectures("spacy.TextCatReduce.v1")(build_reduce_text_classifier)
registry.architectures("spacy.SpanCategorizer.v1")(build_spancat_model)
registry.architectures("spacy.SpanFinder.v1")(build_finder_model)
registry.architectures("spacy.TransitionBasedParser.v2")(build_tb_parser_model)
registry.architectures("spacy.PretrainVectors.v1")(create_pretrain_vectors)
registry.architectures("spacy.PretrainCharacters.v1")(create_pretrain_characters)
registry.architectures("spacy.Tagger.v2")(build_tagger_model)
# Register layers
registry.layers("spacy.FeatureExtractor.v1")(FeatureExtractor)
registry.layers("spacy.extract_spans.v1")(extract_spans)
registry.layers("spacy.extract_ngrams.v1")(extract_ngrams)
registry.layers("spacy.LinearLogistic.v1")(build_linear_logistic)
registry.layers("spacy.mean_max_reducer.v1")(build_mean_max_reducer)
registry.layers("spacy.StaticVectors.v2")(StaticVectors)
registry.layers("spacy.PrecomputableAffine.v1")(PrecomputableAffine)
registry.layers("spacy.CharEmbed.v1")(CharacterEmbed)
registry.layers("spacy.TransitionModel.v1")(TransitionModel)
# Register callbacks
registry.callbacks("spacy.copy_from_base_model.v1")(create_copy_from_base_model)
registry.callbacks("spacy.models_with_nvtx_range.v1")(create_models_with_nvtx_range)
registry.callbacks("spacy.models_and_pipes_with_nvtx_range.v1")(
create_models_and_pipes_with_nvtx_range
)
# Register loggers
registry.loggers("spacy.ConsoleLogger.v2")(console_logger)
registry.loggers("spacy.ConsoleLogger.v3")(console_logger_v3)
# Register batchers
registry.batchers("spacy.batch_by_padded.v1")(configure_minibatch_by_padded_size)
registry.batchers("spacy.batch_by_words.v1")(configure_minibatch_by_words)
registry.batchers("spacy.batch_by_sequence.v1")(configure_minibatch)
# Register augmenters
registry.augmenters("spacy.combined_augmenter.v1")(create_combined_augmenter)
registry.augmenters("spacy.lower_case.v1")(create_lower_casing_augmenter)
registry.augmenters("spacy.orth_variants.v1")(create_orth_variants_augmenter)
# Set the flag to indicate that the registry has been populated
REGISTRY_POPULATED = True

1
spacy/symbols.pyx
View File

@ -479,3 +479,4 @@ NAMES = [it[0] for it in sorted(IDS.items(), key=sort_nums)]
# (which is generating an enormous amount of C++ in Cython 0.24+)
# We keep the enum cdef, and just make sure the names are available to Python
locals().update(IDS)

132
spacy/tests/factory_registrations.json Normal file
View File

@ -0,0 +1,132 @@
{
"attribute_ruler": {
"name": "attribute_ruler",
"module": "spacy.pipeline.attributeruler",
"function": "make_attribute_ruler"
},
"beam_ner": {
"name": "beam_ner",
"module": "spacy.pipeline.ner",
"function": "make_beam_ner"
},
"beam_parser": {
"name": "beam_parser",
"module": "spacy.pipeline.dep_parser",
"function": "make_beam_parser"
},
"doc_cleaner": {
"name": "doc_cleaner",
"module": "spacy.pipeline.functions",
"function": "make_doc_cleaner"
},
"entity_linker": {
"name": "entity_linker",
"module": "spacy.pipeline.entity_linker",
"function": "make_entity_linker"
},
"entity_ruler": {
"name": "entity_ruler",
"module": "spacy.pipeline.entityruler",
"function": "make_entity_ruler"
},
"future_entity_ruler": {
"name": "future_entity_ruler",
"module": "spacy.pipeline.span_ruler",
"function": "make_entity_ruler"
},
"lemmatizer": {
"name": "lemmatizer",
"module": "spacy.pipeline.lemmatizer",
"function": "make_lemmatizer"
},
"merge_entities": {
"name": "merge_entities",
"module": "spacy.language",
"function": "Language.component.<locals>.add_component.<locals>.factory_func"
},
"merge_noun_chunks": {
"name": "merge_noun_chunks",
"module": "spacy.language",
"function": "Language.component.<locals>.add_component.<locals>.factory_func"
},
"merge_subtokens": {
"name": "merge_subtokens",
"module": "spacy.language",
"function": "Language.component.<locals>.add_component.<locals>.factory_func"
},
"morphologizer": {
"name": "morphologizer",
"module": "spacy.pipeline.morphologizer",
"function": "make_morphologizer"
},
"ner": {
"name": "ner",
"module": "spacy.pipeline.ner",
"function": "make_ner"
},
"parser": {
"name": "parser",
"module": "spacy.pipeline.dep_parser",
"function": "make_parser"
},
"sentencizer": {
"name": "sentencizer",
"module": "spacy.pipeline.sentencizer",
"function": "make_sentencizer"
},
"senter": {
"name": "senter",
"module": "spacy.pipeline.senter",
"function": "make_senter"
},
"span_finder": {
"name": "span_finder",
"module": "spacy.pipeline.span_finder",
"function": "make_span_finder"
},
"span_ruler": {
"name": "span_ruler",
"module": "spacy.pipeline.span_ruler",
"function": "make_span_ruler"
},
"spancat": {
"name": "spancat",
"module": "spacy.pipeline.spancat",
"function": "make_spancat"
},
"spancat_singlelabel": {
"name": "spancat_singlelabel",
"module": "spacy.pipeline.spancat",
"function": "make_spancat_singlelabel"
},
"tagger": {
"name": "tagger",
"module": "spacy.pipeline.tagger",
"function": "make_tagger"
},
"textcat": {
"name": "textcat",
"module": "spacy.pipeline.textcat",
"function": "make_textcat"
},
"textcat_multilabel": {
"name": "textcat_multilabel",
"module": "spacy.pipeline.textcat_multilabel",
"function": "make_multilabel_textcat"
},
"tok2vec": {
"name": "tok2vec",
"module": "spacy.pipeline.tok2vec",
"function": "make_tok2vec"
},
"token_splitter": {
"name": "token_splitter",
"module": "spacy.pipeline.functions",
"function": "make_token_splitter"
},
"trainable_lemmatizer": {
"name": "trainable_lemmatizer",
"module": "spacy.pipeline.edit_tree_lemmatizer",
"function": "make_edit_tree_lemmatizer"
}
}

11
spacy/tests/pipeline/test_pipe_methods.py
View File

@ -529,17 +529,6 @@ def test_pipe_label_data_no_labels(pipe):
assert "labels" not in get_arg_names(initialize)
def test_warning_pipe_begin_training():
with pytest.warns(UserWarning, match="begin_training"):
class IncompatPipe(TrainablePipe):
def __init__(self):
...
def begin_training(*args, **kwargs):
...
def test_pipe_methods_initialize():
"""Test that the [initialize] config reflects the components correctly."""
nlp = Language()

284
spacy/tests/registry_contents.json Normal file
View File

@ -0,0 +1,284 @@
{
"architectures": [
"spacy-legacy.CharacterEmbed.v1",
"spacy-legacy.EntityLinker.v1",
"spacy-legacy.HashEmbedCNN.v1",
"spacy-legacy.MaxoutWindowEncoder.v1",
"spacy-legacy.MishWindowEncoder.v1",
"spacy-legacy.MultiHashEmbed.v1",
"spacy-legacy.Tagger.v1",
"spacy-legacy.TextCatBOW.v1",
"spacy-legacy.TextCatCNN.v1",
"spacy-legacy.TextCatEnsemble.v1",
"spacy-legacy.Tok2Vec.v1",
"spacy-legacy.TransitionBasedParser.v1",
"spacy.CharacterEmbed.v2",
"spacy.EntityLinker.v2",
"spacy.HashEmbedCNN.v2",
"spacy.MaxoutWindowEncoder.v2",
"spacy.MishWindowEncoder.v2",
"spacy.MultiHashEmbed.v2",
"spacy.PretrainCharacters.v1",
"spacy.PretrainVectors.v1",
"spacy.SpanCategorizer.v1",
"spacy.SpanFinder.v1",
"spacy.Tagger.v2",
"spacy.TextCatBOW.v2",
"spacy.TextCatBOW.v3",
"spacy.TextCatCNN.v2",
"spacy.TextCatEnsemble.v2",
"spacy.TextCatLowData.v1",
"spacy.TextCatParametricAttention.v1",
"spacy.TextCatReduce.v1",
"spacy.Tok2Vec.v2",
"spacy.Tok2VecListener.v1",
"spacy.TorchBiLSTMEncoder.v1",
"spacy.TransitionBasedParser.v2"
],
"augmenters": [
"spacy.combined_augmenter.v1",
"spacy.lower_case.v1",
"spacy.orth_variants.v1"
],
"batchers": [
"spacy.batch_by_padded.v1",
"spacy.batch_by_sequence.v1",
"spacy.batch_by_words.v1"
],
"callbacks": [
"spacy.copy_from_base_model.v1",
"spacy.models_and_pipes_with_nvtx_range.v1",
"spacy.models_with_nvtx_range.v1"
],
"cli": [],
"datasets": [],
"displacy_colors": [],
"factories": [
"attribute_ruler",
"beam_ner",
"beam_parser",
"doc_cleaner",
"entity_linker",
"entity_ruler",
"future_entity_ruler",
"lemmatizer",
"merge_entities",
"merge_noun_chunks",
"merge_subtokens",
"morphologizer",
"ner",
"parser",
"sentencizer",
"senter",
"span_finder",
"span_ruler",
"spancat",
"spancat_singlelabel",
"tagger",
"textcat",
"textcat_multilabel",
"tok2vec",
"token_splitter",
"trainable_lemmatizer"
],
"initializers": [
"glorot_normal_init.v1",
"glorot_uniform_init.v1",
"he_normal_init.v1",
"he_uniform_init.v1",
"lecun_normal_init.v1",
"lecun_uniform_init.v1",
"normal_init.v1",
"uniform_init.v1",
"zero_init.v1"
],
"languages": [],
"layers": [
"CauchySimilarity.v1",
"ClippedLinear.v1",
"Dish.v1",
"Dropout.v1",
"Embed.v1",
"Gelu.v1",
"HardSigmoid.v1",
"HardSwish.v1",
"HardSwishMobilenet.v1",
"HardTanh.v1",
"HashEmbed.v1",
"LSTM.v1",
"LayerNorm.v1",
"Linear.v1",
"Logistic.v1",
"MXNetWrapper.v1",
"Maxout.v1",
"Mish.v1",
"MultiSoftmax.v1",
"ParametricAttention.v1",
"ParametricAttention.v2",
"PyTorchLSTM.v1",
"PyTorchRNNWrapper.v1",
"PyTorchWrapper.v1",
"PyTorchWrapper.v2",
"PyTorchWrapper.v3",
"Relu.v1",
"ReluK.v1",
"Sigmoid.v1",
"Softmax.v1",
"Softmax.v2",
"SparseLinear.v1",
"SparseLinear.v2",
"Swish.v1",
"add.v1",
"bidirectional.v1",
"chain.v1",
"clone.v1",
"concatenate.v1",
"expand_window.v1",
"list2array.v1",
"list2padded.v1",
"list2ragged.v1",
"noop.v1",
"padded2list.v1",
"premap_ids.v1",
"ragged2list.v1",
"reduce_first.v1",
"reduce_last.v1",
"reduce_max.v1",
"reduce_mean.v1",
"reduce_sum.v1",
"remap_ids.v1",
"remap_ids.v2",
"residual.v1",
"resizable.v1",
"siamese.v1",
"sigmoid_activation.v1",
"softmax_activation.v1",
"spacy-legacy.StaticVectors.v1",
"spacy.CharEmbed.v1",
"spacy.FeatureExtractor.v1",
"spacy.LinearLogistic.v1",
"spacy.PrecomputableAffine.v1",
"spacy.StaticVectors.v2",
"spacy.TransitionModel.v1",
"spacy.extract_ngrams.v1",
"spacy.extract_spans.v1",
"spacy.mean_max_reducer.v1",
"strings2arrays.v1",
"tuplify.v1",
"uniqued.v1",
"with_array.v1",
"with_array2d.v1",
"with_cpu.v1",
"with_flatten.v1",
"with_flatten.v2",
"with_getitem.v1",
"with_list.v1",
"with_padded.v1",
"with_ragged.v1",
"with_reshape.v1"
],
"lemmatizers": [],
"loggers": [
"spacy-legacy.ConsoleLogger.v1",
"spacy-legacy.ConsoleLogger.v2",
"spacy-legacy.WandbLogger.v1",
"spacy.ChainLogger.v1",
"spacy.ClearMLLogger.v1",
"spacy.ClearMLLogger.v2",
"spacy.ConsoleLogger.v2",
"spacy.ConsoleLogger.v3",
"spacy.CupyLogger.v1",
"spacy.LookupLogger.v1",
"spacy.MLflowLogger.v1",
"spacy.MLflowLogger.v2",
"spacy.PyTorchLogger.v1",
"spacy.WandbLogger.v1",
"spacy.WandbLogger.v2",
"spacy.WandbLogger.v3",
"spacy.WandbLogger.v4",
"spacy.WandbLogger.v5"
],
"lookups": [],
"losses": [
"CategoricalCrossentropy.v1",
"CategoricalCrossentropy.v2",
"CategoricalCrossentropy.v3",
"CosineDistance.v1",
"L2Distance.v1",
"SequenceCategoricalCrossentropy.v1",
"SequenceCategoricalCrossentropy.v2",
"SequenceCategoricalCrossentropy.v3"
],
"misc": [
"spacy.CandidateBatchGenerator.v1",
"spacy.CandidateGenerator.v1",
"spacy.EmptyKB.v1",
"spacy.EmptyKB.v2",
"spacy.KBFromFile.v1",
"spacy.LookupsDataLoader.v1",
"spacy.first_longest_spans_filter.v1",
"spacy.levenshtein_compare.v1",
"spacy.ngram_range_suggester.v1",
"spacy.ngram_suggester.v1",
"spacy.preset_spans_suggester.v1",
"spacy.prioritize_existing_ents_filter.v1",
"spacy.prioritize_new_ents_filter.v1"
],
"models": [],
"ops": [
"CupyOps",
"MPSOps",
"NumpyOps"
],
"optimizers": [
"Adam.v1",
"RAdam.v1",
"SGD.v1"
],
"readers": [
"ml_datasets.cmu_movies.v1",
"ml_datasets.dbpedia.v1",
"ml_datasets.imdb_sentiment.v1",
"spacy.Corpus.v1",
"spacy.JsonlCorpus.v1",
"spacy.PlainTextCorpus.v1",
"spacy.read_labels.v1",
"srsly.read_json.v1",
"srsly.read_jsonl.v1",
"srsly.read_msgpack.v1",
"srsly.read_yaml.v1"
],
"schedules": [
"compounding.v1",
"constant.v1",
"constant_then.v1",
"cyclic_triangular.v1",
"decaying.v1",
"slanted_triangular.v1",
"warmup_linear.v1"
],
"scorers": [
"spacy-legacy.textcat_multilabel_scorer.v1",
"spacy-legacy.textcat_scorer.v1",
"spacy.attribute_ruler_scorer.v1",
"spacy.entity_linker_scorer.v1",
"spacy.entity_ruler_scorer.v1",
"spacy.lemmatizer_scorer.v1",
"spacy.morphologizer_scorer.v1",
"spacy.ner_scorer.v1",
"spacy.overlapping_labeled_spans_scorer.v1",
"spacy.parser_scorer.v1",
"spacy.senter_scorer.v1",
"spacy.span_finder_scorer.v1",
"spacy.spancat_scorer.v1",
"spacy.tagger_scorer.v1",
"spacy.textcat_multilabel_scorer.v2",
"spacy.textcat_scorer.v2"
],
"tokenizers": [
"spacy.Tokenizer.v1"
],
"vectors": [
"spacy.Vectors.v1"
]
}

17
spacy/tests/serialize/test_resource_warning.py
View File

@ -87,7 +87,7 @@ def entity_linker():
objects_to_test = (
[nlp(), vectors(), custom_pipe(), tagger(), entity_linker()],
[nlp, vectors, custom_pipe, tagger, entity_linker],
["nlp", "vectors", "custom_pipe", "tagger", "entity_linker"],
)
@ -101,8 +101,9 @@ def write_obj_and_catch_warnings(obj):
return list(filter(lambda x: isinstance(x, ResourceWarning), warnings_list))
@pytest.mark.parametrize("obj", objects_to_test[0], ids=objects_to_test[1])
def test_to_disk_resource_warning(obj):
@pytest.mark.parametrize("obj_factory", objects_to_test[0], ids=objects_to_test[1])
def test_to_disk_resource_warning(obj_factory):
obj = obj_factory()
warnings_list = write_obj_and_catch_warnings(obj)
assert len(warnings_list) == 0
@ -139,9 +140,11 @@ def test_save_and_load_knowledge_base():
class TestToDiskResourceWarningUnittest(TestCase):
def test_resource_warning(self):
scenarios = zip(*objects_to_test)
items = [x() for x in objects_to_test[0]]
names = objects_to_test[1]
scenarios = zip(items, names)
for scenario in scenarios:
with self.subTest(msg=scenario[1]):
warnings_list = write_obj_and_catch_warnings(scenario[0])
for item, name in scenarios:
with self.subTest(msg=name):
warnings_list = write_obj_and_catch_warnings(item)
self.assertEqual(len(warnings_list), 0)

85
spacy/tests/test_factory_imports.py Normal file
View File

@ -0,0 +1,85 @@
# coding: utf-8
"""Test factory import compatibility from original and new locations."""
import importlib
import pytest
@pytest.mark.parametrize(
"factory_name,original_module,compat_module",
[
("make_tagger", "spacy.pipeline.factories", "spacy.pipeline.tagger"),
("make_sentencizer", "spacy.pipeline.factories", "spacy.pipeline.sentencizer"),
("make_ner", "spacy.pipeline.factories", "spacy.pipeline.ner"),
("make_parser", "spacy.pipeline.factories", "spacy.pipeline.dep_parser"),
("make_tok2vec", "spacy.pipeline.factories", "spacy.pipeline.tok2vec"),
("make_spancat", "spacy.pipeline.factories", "spacy.pipeline.spancat"),
(
"make_spancat_singlelabel",
"spacy.pipeline.factories",
"spacy.pipeline.spancat",
),
("make_lemmatizer", "spacy.pipeline.factories", "spacy.pipeline.lemmatizer"),
("make_entity_ruler", "spacy.pipeline.factories", "spacy.pipeline.entityruler"),
("make_span_ruler", "spacy.pipeline.factories", "spacy.pipeline.span_ruler"),
(
"make_edit_tree_lemmatizer",
"spacy.pipeline.factories",
"spacy.pipeline.edit_tree_lemmatizer",
),
(
"make_attribute_ruler",
"spacy.pipeline.factories",
"spacy.pipeline.attributeruler",
),
(
"make_entity_linker",
"spacy.pipeline.factories",
"spacy.pipeline.entity_linker",
),
("make_textcat", "spacy.pipeline.factories", "spacy.pipeline.textcat"),
("make_token_splitter", "spacy.pipeline.factories", "spacy.pipeline.functions"),
("make_doc_cleaner", "spacy.pipeline.factories", "spacy.pipeline.functions"),
(
"make_morphologizer",
"spacy.pipeline.factories",
"spacy.pipeline.morphologizer",
),
("make_senter", "spacy.pipeline.factories", "spacy.pipeline.senter"),
("make_span_finder", "spacy.pipeline.factories", "spacy.pipeline.span_finder"),
(
"make_multilabel_textcat",
"spacy.pipeline.factories",
"spacy.pipeline.textcat_multilabel",
),
("make_beam_ner", "spacy.pipeline.factories", "spacy.pipeline.ner"),
("make_beam_parser", "spacy.pipeline.factories", "spacy.pipeline.dep_parser"),
("make_nn_labeller", "spacy.pipeline.factories", "spacy.pipeline.multitask"),
# This one's special because the function was named make_span_ruler, so
# the name in the registrations.py doesn't match the name we make the import hook
# point to. We could make a test just for this but shrug
# ("make_future_entity_ruler", "spacy.pipeline.factories", "spacy.pipeline.span_ruler"),
],
)
def test_factory_import_compatibility(factory_name, original_module, compat_module):
"""Test that factory functions can be imported from both original and compatibility locations."""
# Import from the original module (registrations.py)
original_module_obj = importlib.import_module(original_module)
original_factory = getattr(original_module_obj, factory_name)
assert (
original_factory is not None
), f"Could not import {factory_name} from {original_module}"
# Import from the compatibility module (component file)
compat_module_obj = importlib.import_module(compat_module)
compat_factory = getattr(compat_module_obj, factory_name)
assert (
compat_factory is not None
), f"Could not import {factory_name} from {compat_module}"
# Test that they're the same function (identity)
assert original_factory is compat_factory, (
f"Factory {factory_name} imported from {original_module} is not the same object "
f"as the one imported from {compat_module}"
)

97
spacy/tests/test_factory_registrations.py Normal file
View File

@ -0,0 +1,97 @@
import inspect
import json
from pathlib import Path
import pytest
from spacy.language import Language
from spacy.util import registry
# Path to the reference factory registrations, relative to this file
REFERENCE_FILE = Path(__file__).parent / "factory_registrations.json"
# Monkey patch the util.is_same_func to handle Cython functions
import inspect
from spacy import util
original_is_same_func = util.is_same_func
def patched_is_same_func(func1, func2):
# Handle Cython functions
try:
return original_is_same_func(func1, func2)
except TypeError:
# For Cython functions, just compare the string representation
return str(func1) == str(func2)
util.is_same_func = patched_is_same_func
@pytest.fixture
def reference_factory_registrations():
"""Load reference factory registrations from JSON file"""
if not REFERENCE_FILE.exists():
pytest.fail(
f"Reference file {REFERENCE_FILE} not found. Run export_factory_registrations.py first."
)
with REFERENCE_FILE.open("r") as f:
return json.load(f)
def test_factory_registrations_preserved(reference_factory_registrations):
"""Test that all factory registrations from the reference file are still present."""
# Ensure the registry is populated
registry.ensure_populated()
# Get all factory registrations
all_factories = registry.factories.get_all()
# Initialize our data structure to store current factory registrations
current_registrations = {}
# Process factory registrations
for name, func in all_factories.items():
# Store information about each factory
try:
module_name = func.__module__
except (AttributeError, TypeError):
# For Cython functions, just use a placeholder
module_name = str(func).split()[1].split(".")[0]
try:
func_name = func.__qualname__
except (AttributeError, TypeError):
# For Cython functions, use the function's name
func_name = (
func.__name__
if hasattr(func, "__name__")
else str(func).split()[1].split(".")[-1]
)
current_registrations[name] = {
"name": name,
"module": module_name,
"function": func_name,
}
# Check for missing registrations
missing_registrations = set(reference_factory_registrations.keys()) - set(
current_registrations.keys()
)
assert (
not missing_registrations
), f"Missing factory registrations: {', '.join(sorted(missing_registrations))}"
# Check for new registrations (not an error, but informative)
new_registrations = set(current_registrations.keys()) - set(
reference_factory_registrations.keys()
)
if new_registrations:
# This is not an error, just informative
print(
f"New factory registrations found: {', '.join(sorted(new_registrations))}"
)

55
spacy/tests/test_registry_population.py Normal file
View File

@ -0,0 +1,55 @@
import json
import os
from pathlib import Path
import pytest
from spacy.util import registry
# Path to the reference registry contents, relative to this file
REFERENCE_FILE = Path(__file__).parent / "registry_contents.json"
@pytest.fixture
def reference_registry():
"""Load reference registry contents from JSON file"""
if not REFERENCE_FILE.exists():
pytest.fail(f"Reference file {REFERENCE_FILE} not found.")
with REFERENCE_FILE.open("r") as f:
return json.load(f)
def test_registry_types(reference_registry):
"""Test that all registry types match the reference"""
# Get current registry types
current_registry_types = set(registry.get_registry_names())
expected_registry_types = set(reference_registry.keys())
# Check for missing registry types
missing_types = expected_registry_types - current_registry_types
assert not missing_types, f"Missing registry types: {', '.join(missing_types)}"
def test_registry_entries(reference_registry):
"""Test that all registry entries are present"""
# Check each registry's entries
for registry_name, expected_entries in reference_registry.items():
# Skip if this registry type doesn't exist
if not hasattr(registry, registry_name):
pytest.fail(f"Registry '{registry_name}' does not exist.")
# Get current entries
reg = getattr(registry, registry_name)
current_entries = sorted(list(reg.get_all().keys()))
# Compare entries
expected_set = set(expected_entries)
current_set = set(current_entries)
# Check for missing entries - these would indicate our new registry population
# mechanism is missing something
missing_entries = expected_set - current_set
assert (
not missing_entries
), f"Registry '{registry_name}' missing entries: {', '.join(missing_entries)}"

4
spacy/tests/training/test_readers.py
View File

@ -101,7 +101,11 @@ def test_cat_readers(reader, additional_config):
nlp = load_model_from_config(config, auto_fill=True)
T = registry.resolve(nlp.config["training"], schema=ConfigSchemaTraining)
dot_names = [T["train_corpus"], T["dev_corpus"]]
print("T", T)
print("dot names", dot_names)
train_corpus, dev_corpus = resolve_dot_names(nlp.config, dot_names)
data = list(train_corpus(nlp))
print(len(data))
optimizer = T["optimizer"]
# simulate a training loop
nlp.initialize(lambda: train_corpus(nlp), sgd=optimizer)

4
spacy/tokenizer.pyx
View File

@ -867,11 +867,11 @@ cdef extern from "<algorithm>" namespace "std" nogil:
bint (*)(SpanC, SpanC))
cdef bint len_start_cmp(SpanC a, SpanC b) nogil:
cdef bint len_start_cmp(SpanC a, SpanC b) noexcept nogil:
if a.end - a.start == b.end - b.start:
return b.start < a.start
return a.end - a.start < b.end - b.start
cdef bint start_cmp(SpanC a, SpanC b) nogil:
cdef bint start_cmp(SpanC a, SpanC b) noexcept nogil:
return a.start < b.start

4
spacy/tokens/doc.pxd
View File

@ -7,8 +7,8 @@ from ..typedefs cimport attr_t
from ..vocab cimport Vocab
cdef attr_t get_token_attr(const TokenC* token, attr_id_t feat_name) nogil
cdef attr_t get_token_attr_for_matcher(const TokenC* token, attr_id_t feat_name) nogil
cdef attr_t get_token_attr(const TokenC* token, attr_id_t feat_name) noexcept nogil
cdef attr_t get_token_attr_for_matcher(const TokenC* token, attr_id_t feat_name) noexcept nogil
ctypedef const LexemeC* const_Lexeme_ptr

4
spacy/tokens/doc.pyx
View File

@ -71,7 +71,7 @@ cdef int bounds_check(int i, int length, int padding) except -1:
raise IndexError(Errors.E026.format(i=i, length=length))
cdef attr_t get_token_attr(const TokenC* token, attr_id_t feat_name) nogil:
cdef attr_t get_token_attr(const TokenC* token, attr_id_t feat_name) noexcept nogil:
if feat_name == LEMMA:
return token.lemma
elif feat_name == NORM:
@ -106,7 +106,7 @@ cdef attr_t get_token_attr(const TokenC* token, attr_id_t feat_name) nogil:
return Lexeme.get_struct_attr(token.lex, feat_name)
cdef attr_t get_token_attr_for_matcher(const TokenC* token, attr_id_t feat_name) nogil:
cdef attr_t get_token_attr_for_matcher(const TokenC* token, attr_id_t feat_name) noexcept nogil:
if feat_name == SENT_START:
if token.sent_start == 1:
return True

8
spacy/tokens/token.pxd
View File

@ -33,7 +33,7 @@ cdef class Token:
cpdef bint check_flag(self, attr_id_t flag_id) except -1
@staticmethod
cdef inline attr_t get_struct_attr(const TokenC* token, attr_id_t feat_name) nogil:
cdef inline attr_t get_struct_attr(const TokenC* token, attr_id_t feat_name) noexcept nogil:
if feat_name < (sizeof(flags_t) * 8):
return Lexeme.c_check_flag(token.lex, feat_name)
elif feat_name == LEMMA:
@ -70,7 +70,7 @@ cdef class Token:
@staticmethod
cdef inline attr_t set_struct_attr(TokenC* token, attr_id_t feat_name,
attr_t value) nogil:
attr_t value) noexcept nogil:
if feat_name == LEMMA:
token.lemma = value
elif feat_name == NORM:
@ -99,9 +99,9 @@ cdef class Token:
token.sent_start = value
@staticmethod
cdef inline int missing_dep(const TokenC* token) nogil:
cdef inline int missing_dep(const TokenC* token) noexcept nogil:
return token.dep == MISSING_DEP
@staticmethod
cdef inline int missing_head(const TokenC* token) nogil:
cdef inline int missing_head(const TokenC* token) noexcept nogil:
return Token.missing_dep(token)

3
spacy/training/augment.py
View File

@ -11,7 +11,6 @@ if TYPE_CHECKING:
from ..language import Language # noqa: F401
@registry.augmenters("spacy.combined_augmenter.v1")
def create_combined_augmenter(
lower_level: float,
orth_level: float,
@ -84,7 +83,6 @@ def combined_augmenter(
yield example
@registry.augmenters("spacy.orth_variants.v1")
def create_orth_variants_augmenter(
level: float, lower: float, orth_variants: Dict[str, List[Dict]]
) -> Callable[["Language", Example], Iterator[Example]]:
@ -102,7 +100,6 @@ def create_orth_variants_augmenter(
)
@registry.augmenters("spacy.lower_case.v1")
def create_lower_casing_augmenter(
level: float,
) -> Callable[["Language", Example], Iterator[Example]]:

3
spacy/training/batchers.py
View File

@ -19,7 +19,6 @@ ItemT = TypeVar("ItemT")
BatcherT = Callable[[Iterable[ItemT]], Iterable[List[ItemT]]]
@registry.batchers("spacy.batch_by_padded.v1")
def configure_minibatch_by_padded_size(
*,
size: Sizing,
@ -54,7 +53,6 @@ def configure_minibatch_by_padded_size(
)
@registry.batchers("spacy.batch_by_words.v1")
def configure_minibatch_by_words(
*,
size: Sizing,
@ -82,7 +80,6 @@ def configure_minibatch_by_words(
)
@registry.batchers("spacy.batch_by_sequence.v1")
def configure_minibatch(
size: Sizing, get_length: Optional[Callable[[ItemT], int]] = None
) -> BatcherT:

1
spacy/training/callbacks.py
View File

@ -7,7 +7,6 @@ if TYPE_CHECKING:
from ..language import Language
@registry.callbacks("spacy.copy_from_base_model.v1")
def create_copy_from_base_model(
tokenizer: Optional[str] = None,
vocab: Optional[str] = None,

2
spacy/training/loggers.py
View File

@ -29,7 +29,6 @@ def setup_table(
# We cannot rename this method as it's directly imported
# and used by external packages such as spacy-loggers.
@registry.loggers("spacy.ConsoleLogger.v2")
def console_logger(
progress_bar: bool = False,
console_output: bool = True,
@ -47,7 +46,6 @@ def console_logger(
)
@registry.loggers("spacy.ConsoleLogger.v3")
def console_logger_v3(
progress_bar: Optional[str] = None,
console_output: bool = True,

13
spacy/util.py
View File

@ -132,9 +132,18 @@ class registry(thinc.registry):
models = catalogue.create("spacy", "models", entry_points=True)
cli = catalogue.create("spacy", "cli", entry_points=True)
@classmethod
def ensure_populated(cls) -> None:
"""Ensure the registry is populated with all necessary components."""
from .registrations import REGISTRY_POPULATED, populate_registry
if not REGISTRY_POPULATED:
populate_registry()
@classmethod
def get_registry_names(cls) -> List[str]:
"""List all available registries."""
cls.ensure_populated()
names = []
for name, value in inspect.getmembers(cls):
if not name.startswith("_") and isinstance(value, Registry):
@ -144,6 +153,7 @@ class registry(thinc.registry):
@classmethod
def get(cls, registry_name: str, func_name: str) -> Callable:
"""Get a registered function from the registry."""
cls.ensure_populated()
# We're overwriting this classmethod so we're able to provide more
# specific error messages and implement a fallback to spacy-legacy.
if not hasattr(cls, registry_name):
@ -179,6 +189,7 @@ class registry(thinc.registry):
func_name (str): Name of the registered function.
RETURNS (Dict[str, Optional[Union[str, int]]]): The function info.
"""
cls.ensure_populated()
# We're overwriting this classmethod so we're able to provide more
# specific error messages and implement a fallback to spacy-legacy.
if not hasattr(cls, registry_name):
@ -205,6 +216,7 @@ class registry(thinc.registry):
@classmethod
def has(cls, registry_name: str, func_name: str) -> bool:
"""Check whether a function is available in a registry."""
cls.ensure_populated()
if not hasattr(cls, registry_name):
return False
reg = getattr(cls, registry_name)
@ -1323,7 +1335,6 @@ def filter_chain_spans(*spans: Iterable["Span"]) -> List["Span"]:
return filter_spans(itertools.chain(*spans))
@registry.misc("spacy.first_longest_spans_filter.v1")
def make_first_longest_spans_filter():
return filter_chain_spans

2
spacy/vectors.pyx
View File

@ -177,7 +177,7 @@ cdef class Vectors(BaseVectors):
self.hash_seed = hash_seed
self.bow = bow
self.eow = eow
if isinstance(attr, (int, long)):
if isinstance(attr, int):
self.attr = attr
else:
attr = attr.upper()