mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-26 18:06:29 +03:00
598 lines
19 KiB
Python
598 lines
19 KiB
Python
import pytest
|
|
|
|
import spacy
|
|
from spacy.language import Language
|
|
from spacy.lang.en import English
|
|
from spacy.lang.de import German
|
|
from spacy.pipeline.tok2vec import DEFAULT_TOK2VEC_MODEL
|
|
from spacy.tokens import Doc
|
|
from spacy.util import registry, SimpleFrozenDict, combine_score_weights
|
|
from thinc.api import Model, Linear, ConfigValidationError
|
|
from pydantic import StrictInt, StrictStr
|
|
|
|
from ..util import make_tempdir
|
|
|
|
|
|
@pytest.mark.issue(5137)
|
|
def test_issue5137():
|
|
factory_name = "test_issue5137"
|
|
pipe_name = "my_component"
|
|
|
|
@Language.factory(factory_name)
|
|
class MyComponent:
|
|
def __init__(self, nlp, name=pipe_name, categories="all_categories"):
|
|
self.nlp = nlp
|
|
self.categories = categories
|
|
self.name = name
|
|
|
|
def __call__(self, doc):
|
|
pass
|
|
|
|
def to_disk(self, path, **kwargs):
|
|
pass
|
|
|
|
def from_disk(self, path, **cfg):
|
|
pass
|
|
|
|
nlp = English()
|
|
my_component = nlp.add_pipe(factory_name, name=pipe_name)
|
|
assert my_component.categories == "all_categories"
|
|
with make_tempdir() as tmpdir:
|
|
nlp.to_disk(tmpdir)
|
|
overrides = {"components": {pipe_name: {"categories": "my_categories"}}}
|
|
nlp2 = spacy.load(tmpdir, config=overrides)
|
|
assert nlp2.get_pipe(pipe_name).categories == "my_categories"
|
|
|
|
|
|
def test_pipe_function_component():
|
|
name = "test_component"
|
|
|
|
@Language.component(name)
|
|
def component(doc: Doc) -> Doc:
|
|
return doc
|
|
|
|
assert name in registry.factories
|
|
nlp = Language()
|
|
with pytest.raises(ValueError):
|
|
nlp.add_pipe(component)
|
|
nlp.add_pipe(name)
|
|
assert name in nlp.pipe_names
|
|
assert nlp.pipe_factories[name] == name
|
|
assert Language.get_factory_meta(name)
|
|
assert nlp.get_pipe_meta(name)
|
|
pipe = nlp.get_pipe(name)
|
|
assert pipe == component
|
|
pipe = nlp.create_pipe(name)
|
|
assert pipe == component
|
|
|
|
|
|
def test_pipe_class_component_init():
|
|
name1 = "test_class_component1"
|
|
name2 = "test_class_component2"
|
|
|
|
@Language.factory(name1)
|
|
class Component1:
|
|
def __init__(self, nlp: Language, name: str):
|
|
self.nlp = nlp
|
|
|
|
def __call__(self, doc: Doc) -> Doc:
|
|
return doc
|
|
|
|
class Component2:
|
|
def __init__(self, nlp: Language, name: str):
|
|
self.nlp = nlp
|
|
|
|
def __call__(self, doc: Doc) -> Doc:
|
|
return doc
|
|
|
|
@Language.factory(name2)
|
|
def factory(nlp: Language, name=name2):
|
|
return Component2(nlp, name)
|
|
|
|
nlp = Language()
|
|
for name, Component in [(name1, Component1), (name2, Component2)]:
|
|
assert name in registry.factories
|
|
with pytest.raises(ValueError):
|
|
nlp.add_pipe(Component(nlp, name))
|
|
nlp.add_pipe(name)
|
|
assert name in nlp.pipe_names
|
|
assert nlp.pipe_factories[name] == name
|
|
assert Language.get_factory_meta(name)
|
|
assert nlp.get_pipe_meta(name)
|
|
pipe = nlp.get_pipe(name)
|
|
assert isinstance(pipe, Component)
|
|
assert isinstance(pipe.nlp, Language)
|
|
pipe = nlp.create_pipe(name)
|
|
assert isinstance(pipe, Component)
|
|
assert isinstance(pipe.nlp, Language)
|
|
|
|
|
|
def test_pipe_class_component_config():
|
|
name = "test_class_component_config"
|
|
|
|
@Language.factory(name)
|
|
class Component:
|
|
def __init__(
|
|
self, nlp: Language, name: str, value1: StrictInt, value2: StrictStr
|
|
):
|
|
self.nlp = nlp
|
|
self.value1 = value1
|
|
self.value2 = value2
|
|
self.is_base = True
|
|
self.name = name
|
|
|
|
def __call__(self, doc: Doc) -> Doc:
|
|
return doc
|
|
|
|
@English.factory(name)
|
|
class ComponentEN:
|
|
def __init__(
|
|
self, nlp: Language, name: str, value1: StrictInt, value2: StrictStr
|
|
):
|
|
self.nlp = nlp
|
|
self.value1 = value1
|
|
self.value2 = value2
|
|
self.is_base = False
|
|
|
|
def __call__(self, doc: Doc) -> Doc:
|
|
return doc
|
|
|
|
nlp = Language()
|
|
with pytest.raises(ConfigValidationError): # no config provided
|
|
nlp.add_pipe(name)
|
|
with pytest.raises(ConfigValidationError): # invalid config
|
|
nlp.add_pipe(name, config={"value1": "10", "value2": "hello"})
|
|
with pytest.warns(UserWarning):
|
|
nlp.add_pipe(
|
|
name, config={"value1": 10, "value2": "hello", "name": "wrong_name"}
|
|
)
|
|
pipe = nlp.get_pipe(name)
|
|
assert isinstance(pipe.nlp, Language)
|
|
assert pipe.value1 == 10
|
|
assert pipe.value2 == "hello"
|
|
assert pipe.is_base is True
|
|
assert pipe.name == name
|
|
|
|
nlp_en = English()
|
|
with pytest.raises(ConfigValidationError): # invalid config
|
|
nlp_en.add_pipe(name, config={"value1": "10", "value2": "hello"})
|
|
nlp_en.add_pipe(name, config={"value1": 10, "value2": "hello"})
|
|
pipe = nlp_en.get_pipe(name)
|
|
assert isinstance(pipe.nlp, English)
|
|
assert pipe.value1 == 10
|
|
assert pipe.value2 == "hello"
|
|
assert pipe.is_base is False
|
|
|
|
|
|
def test_pipe_class_component_defaults():
|
|
name = "test_class_component_defaults"
|
|
|
|
@Language.factory(name)
|
|
class Component:
|
|
def __init__(
|
|
self,
|
|
nlp: Language,
|
|
name: str,
|
|
value1: StrictInt = StrictInt(10),
|
|
value2: StrictStr = StrictStr("hello"),
|
|
):
|
|
self.nlp = nlp
|
|
self.value1 = value1
|
|
self.value2 = value2
|
|
|
|
def __call__(self, doc: Doc) -> Doc:
|
|
return doc
|
|
|
|
nlp = Language()
|
|
nlp.add_pipe(name)
|
|
pipe = nlp.get_pipe(name)
|
|
assert isinstance(pipe.nlp, Language)
|
|
assert pipe.value1 == 10
|
|
assert pipe.value2 == "hello"
|
|
|
|
|
|
def test_pipe_class_component_model():
|
|
name = "test_class_component_model"
|
|
default_config = {
|
|
"model": {
|
|
"@architectures": "spacy.TextCatEnsemble.v2",
|
|
"tok2vec": DEFAULT_TOK2VEC_MODEL,
|
|
"linear_model": {
|
|
"@architectures": "spacy.TextCatBOW.v2",
|
|
"exclusive_classes": False,
|
|
"ngram_size": 1,
|
|
"no_output_layer": False,
|
|
},
|
|
},
|
|
"value1": 10,
|
|
}
|
|
|
|
@Language.factory(name, default_config=default_config)
|
|
class Component:
|
|
def __init__(self, nlp: Language, model: Model, name: str, value1: StrictInt):
|
|
self.nlp = nlp
|
|
self.model = model
|
|
self.value1 = value1
|
|
self.name = name
|
|
|
|
def __call__(self, doc: Doc) -> Doc:
|
|
return doc
|
|
|
|
nlp = Language()
|
|
nlp.add_pipe(name)
|
|
pipe = nlp.get_pipe(name)
|
|
assert isinstance(pipe.nlp, Language)
|
|
assert pipe.value1 == 10
|
|
assert isinstance(pipe.model, Model)
|
|
|
|
|
|
def test_pipe_class_component_model_custom():
|
|
name = "test_class_component_model_custom"
|
|
arch = f"{name}.arch"
|
|
default_config = {"value1": 1, "model": {"@architectures": arch, "nO": 0, "nI": 0}}
|
|
|
|
@Language.factory(name, default_config=default_config)
|
|
class Component:
|
|
def __init__(
|
|
self,
|
|
nlp: Language,
|
|
model: Model,
|
|
name: str,
|
|
value1: StrictInt = StrictInt(10),
|
|
):
|
|
self.nlp = nlp
|
|
self.model = model
|
|
self.value1 = value1
|
|
self.name = name
|
|
|
|
def __call__(self, doc: Doc) -> Doc:
|
|
return doc
|
|
|
|
@registry.architectures(arch)
|
|
def make_custom_arch(nO: StrictInt, nI: StrictInt):
|
|
return Linear(nO, nI)
|
|
|
|
nlp = Language()
|
|
config = {"value1": 20, "model": {"@architectures": arch, "nO": 1, "nI": 2}}
|
|
nlp.add_pipe(name, config=config)
|
|
pipe = nlp.get_pipe(name)
|
|
assert isinstance(pipe.nlp, Language)
|
|
assert pipe.value1 == 20
|
|
assert isinstance(pipe.model, Model)
|
|
assert pipe.model.name == "linear"
|
|
|
|
nlp = Language()
|
|
with pytest.raises(ConfigValidationError):
|
|
config = {"value1": "20", "model": {"@architectures": arch, "nO": 1, "nI": 2}}
|
|
nlp.add_pipe(name, config=config)
|
|
with pytest.raises(ConfigValidationError):
|
|
config = {"value1": 20, "model": {"@architectures": arch, "nO": 1.0, "nI": 2.0}}
|
|
nlp.add_pipe(name, config=config)
|
|
|
|
|
|
def test_pipe_factories_wrong_formats():
|
|
with pytest.raises(ValueError):
|
|
# Decorator is not called
|
|
@Language.component
|
|
def component(foo: int, bar: str):
|
|
...
|
|
|
|
with pytest.raises(ValueError):
|
|
# Decorator is not called
|
|
@Language.factory
|
|
def factory1(foo: int, bar: str):
|
|
...
|
|
|
|
with pytest.raises(ValueError):
|
|
# Factory function is missing "nlp" and "name" arguments
|
|
@Language.factory("test_pipe_factories_missing_args")
|
|
def factory2(foo: int, bar: str):
|
|
...
|
|
|
|
|
|
def test_pipe_factory_meta_config_cleanup():
|
|
"""Test that component-specific meta and config entries are represented
|
|
correctly and cleaned up when pipes are removed, replaced or renamed."""
|
|
nlp = Language()
|
|
nlp.add_pipe("ner", name="ner_component")
|
|
nlp.add_pipe("textcat")
|
|
assert nlp.get_factory_meta("ner")
|
|
assert nlp.get_pipe_meta("ner_component")
|
|
assert nlp.get_pipe_config("ner_component")
|
|
assert nlp.get_factory_meta("textcat")
|
|
assert nlp.get_pipe_meta("textcat")
|
|
assert nlp.get_pipe_config("textcat")
|
|
nlp.rename_pipe("textcat", "tc")
|
|
assert nlp.get_pipe_meta("tc")
|
|
assert nlp.get_pipe_config("tc")
|
|
with pytest.raises(ValueError):
|
|
nlp.remove_pipe("ner")
|
|
nlp.remove_pipe("ner_component")
|
|
assert "ner_component" not in nlp._pipe_meta
|
|
assert "ner_component" not in nlp._pipe_configs
|
|
with pytest.raises(ValueError):
|
|
nlp.replace_pipe("textcat", "parser")
|
|
nlp.replace_pipe("tc", "parser")
|
|
assert nlp.get_factory_meta("parser")
|
|
assert nlp.get_pipe_meta("tc").factory == "parser"
|
|
|
|
|
|
def test_pipe_factories_empty_dict_default():
|
|
"""Test that default config values can be empty dicts and that no config
|
|
validation error is raised."""
|
|
# TODO: fix this
|
|
name = "test_pipe_factories_empty_dict_default"
|
|
|
|
@Language.factory(name, default_config={"foo": {}})
|
|
def factory(nlp: Language, name: str, foo: dict):
|
|
...
|
|
|
|
nlp = Language()
|
|
nlp.create_pipe(name)
|
|
|
|
|
|
def test_pipe_factories_language_specific():
|
|
"""Test that language sub-classes can have their own factories, with
|
|
fallbacks to the base factories."""
|
|
name1 = "specific_component1"
|
|
name2 = "specific_component2"
|
|
Language.component(name1, func=lambda: "base")
|
|
English.component(name1, func=lambda: "en")
|
|
German.component(name2, func=lambda: "de")
|
|
|
|
assert Language.has_factory(name1)
|
|
assert not Language.has_factory(name2)
|
|
assert English.has_factory(name1)
|
|
assert not English.has_factory(name2)
|
|
assert German.has_factory(name1)
|
|
assert German.has_factory(name2)
|
|
|
|
nlp = Language()
|
|
assert nlp.create_pipe(name1)() == "base"
|
|
with pytest.raises(ValueError):
|
|
nlp.create_pipe(name2)
|
|
nlp_en = English()
|
|
assert nlp_en.create_pipe(name1)() == "en"
|
|
with pytest.raises(ValueError):
|
|
nlp_en.create_pipe(name2)
|
|
nlp_de = German()
|
|
assert nlp_de.create_pipe(name1)() == "base"
|
|
assert nlp_de.create_pipe(name2)() == "de"
|
|
|
|
|
|
def test_language_factories_invalid():
|
|
"""Test that assigning directly to Language.factories is now invalid and
|
|
raises a custom error."""
|
|
assert isinstance(Language.factories, SimpleFrozenDict)
|
|
with pytest.raises(NotImplementedError):
|
|
Language.factories["foo"] = "bar"
|
|
nlp = Language()
|
|
assert isinstance(nlp.factories, SimpleFrozenDict)
|
|
assert len(nlp.factories)
|
|
with pytest.raises(NotImplementedError):
|
|
nlp.factories["foo"] = "bar"
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"weights,override,expected",
|
|
[
|
|
([{"a": 1.0}, {"b": 1.0}, {"c": 1.0}], {}, {"a": 0.33, "b": 0.33, "c": 0.33}),
|
|
([{"a": 1.0}, {"b": 50}, {"c": 100}], {}, {"a": 0.01, "b": 0.33, "c": 0.66}),
|
|
(
|
|
[{"a": 0.7, "b": 0.3}, {"c": 1.0}, {"d": 0.5, "e": 0.5}],
|
|
{},
|
|
{"a": 0.23, "b": 0.1, "c": 0.33, "d": 0.17, "e": 0.17},
|
|
),
|
|
(
|
|
[{"a": 100, "b": 300}, {"c": 50, "d": 50}],
|
|
{},
|
|
{"a": 0.2, "b": 0.6, "c": 0.1, "d": 0.1},
|
|
),
|
|
([{"a": 0.5, "b": 0.5}, {"b": 1.0}], {}, {"a": 0.33, "b": 0.67}),
|
|
([{"a": 0.5, "b": 0.0}], {}, {"a": 1.0, "b": 0.0}),
|
|
([{"a": 0.5, "b": 0.5}, {"b": 1.0}], {"a": 0.0}, {"a": 0.0, "b": 1.0}),
|
|
([{"a": 0.0, "b": 0.0}, {"c": 0.0}], {}, {"a": 0.0, "b": 0.0, "c": 0.0}),
|
|
([{"a": 0.0, "b": 0.0}, {"c": 1.0}], {}, {"a": 0.0, "b": 0.0, "c": 1.0}),
|
|
(
|
|
[{"a": 0.0, "b": 0.0}, {"c": 0.0}],
|
|
{"c": 0.2},
|
|
{"a": 0.0, "b": 0.0, "c": 1.0},
|
|
),
|
|
(
|
|
[{"a": 0.5, "b": 0.5, "c": 1.0, "d": 1.0}],
|
|
{"a": 0.0, "b": 0.0},
|
|
{"a": 0.0, "b": 0.0, "c": 0.5, "d": 0.5},
|
|
),
|
|
(
|
|
[{"a": 0.5, "b": 0.5, "c": 1.0, "d": 1.0}],
|
|
{"a": 0.0, "b": 0.0, "f": 0.0},
|
|
{"a": 0.0, "b": 0.0, "c": 0.5, "d": 0.5, "f": 0.0},
|
|
),
|
|
],
|
|
)
|
|
def test_language_factories_combine_score_weights(weights, override, expected):
|
|
result = combine_score_weights(weights, override)
|
|
assert sum(result.values()) in (0.99, 1.0, 0.0)
|
|
assert result == expected
|
|
|
|
|
|
def test_language_factories_scores():
|
|
name = "test_language_factories_scores"
|
|
func = lambda nlp, name: lambda doc: doc
|
|
weights1 = {"a1": 0.5, "a2": 0.5}
|
|
weights2 = {"b1": 0.2, "b2": 0.7, "b3": 0.1}
|
|
Language.factory(f"{name}1", default_score_weights=weights1, func=func)
|
|
Language.factory(f"{name}2", default_score_weights=weights2, func=func)
|
|
meta1 = Language.get_factory_meta(f"{name}1")
|
|
assert meta1.default_score_weights == weights1
|
|
meta2 = Language.get_factory_meta(f"{name}2")
|
|
assert meta2.default_score_weights == weights2
|
|
nlp = Language()
|
|
nlp._config["training"]["score_weights"] = {}
|
|
nlp.add_pipe(f"{name}1")
|
|
nlp.add_pipe(f"{name}2")
|
|
cfg = nlp.config["training"]
|
|
expected_weights = {"a1": 0.25, "a2": 0.25, "b1": 0.1, "b2": 0.35, "b3": 0.05}
|
|
assert cfg["score_weights"] == expected_weights
|
|
# Test with custom defaults
|
|
config = nlp.config.copy()
|
|
config["training"]["score_weights"]["a1"] = 0.0
|
|
config["training"]["score_weights"]["b3"] = 1.3
|
|
nlp = English.from_config(config)
|
|
score_weights = nlp.config["training"]["score_weights"]
|
|
expected = {"a1": 0.0, "a2": 0.12, "b1": 0.05, "b2": 0.17, "b3": 0.65}
|
|
assert score_weights == expected
|
|
# Test with null values
|
|
config = nlp.config.copy()
|
|
config["training"]["score_weights"]["a1"] = None
|
|
nlp = English.from_config(config)
|
|
score_weights = nlp.config["training"]["score_weights"]
|
|
expected = {"a1": None, "a2": 0.12, "b1": 0.05, "b2": 0.17, "b3": 0.66}
|
|
assert score_weights == expected
|
|
|
|
|
|
def test_pipe_factories_from_source():
|
|
"""Test adding components from a source model."""
|
|
source_nlp = English()
|
|
source_nlp.add_pipe("tagger", name="my_tagger")
|
|
nlp = English()
|
|
with pytest.raises(ValueError):
|
|
nlp.add_pipe("my_tagger", source="en_core_web_sm")
|
|
nlp.add_pipe("my_tagger", source=source_nlp)
|
|
assert "my_tagger" in nlp.pipe_names
|
|
with pytest.raises(KeyError):
|
|
nlp.add_pipe("custom", source=source_nlp)
|
|
|
|
|
|
def test_pipe_factories_from_source_language_subclass():
|
|
class CustomEnglishDefaults(English.Defaults):
|
|
stop_words = set(["custom", "stop"])
|
|
|
|
@registry.languages("custom_en")
|
|
class CustomEnglish(English):
|
|
lang = "custom_en"
|
|
Defaults = CustomEnglishDefaults
|
|
|
|
source_nlp = English()
|
|
source_nlp.add_pipe("tagger")
|
|
|
|
# custom subclass
|
|
nlp = CustomEnglish()
|
|
nlp.add_pipe("tagger", source=source_nlp)
|
|
assert "tagger" in nlp.pipe_names
|
|
|
|
# non-subclass
|
|
nlp = German()
|
|
nlp.add_pipe("tagger", source=source_nlp)
|
|
assert "tagger" in nlp.pipe_names
|
|
|
|
# mismatched vectors
|
|
nlp = English()
|
|
nlp.vocab.vectors.resize((1, 4))
|
|
nlp.vocab.vectors.add("cat", vector=[1, 2, 3, 4])
|
|
with pytest.warns(UserWarning):
|
|
nlp.add_pipe("tagger", source=source_nlp)
|
|
|
|
|
|
def test_pipe_factories_from_source_custom():
|
|
"""Test adding components from a source model with custom components."""
|
|
name = "test_pipe_factories_from_source_custom"
|
|
|
|
@Language.factory(name, default_config={"arg": "hello"})
|
|
def test_factory(nlp, name, arg: str):
|
|
return lambda doc: doc
|
|
|
|
source_nlp = English()
|
|
source_nlp.add_pipe("tagger")
|
|
source_nlp.add_pipe(name, config={"arg": "world"})
|
|
nlp = English()
|
|
nlp.add_pipe(name, source=source_nlp)
|
|
assert name in nlp.pipe_names
|
|
assert nlp.get_pipe_meta(name).default_config["arg"] == "hello"
|
|
config = nlp.config["components"][name]
|
|
assert config["factory"] == name
|
|
assert config["arg"] == "world"
|
|
|
|
|
|
def test_pipe_factories_from_source_config():
|
|
name = "test_pipe_factories_from_source_config"
|
|
|
|
@Language.factory(name, default_config={"arg": "hello"})
|
|
def test_factory(nlp, name, arg: str):
|
|
return lambda doc: doc
|
|
|
|
source_nlp = English()
|
|
source_nlp.add_pipe("tagger")
|
|
source_nlp.add_pipe(name, name="yolo", config={"arg": "world"})
|
|
dest_nlp_cfg = {"lang": "en", "pipeline": ["parser", "custom"]}
|
|
with make_tempdir() as tempdir:
|
|
source_nlp.to_disk(tempdir)
|
|
dest_components_cfg = {
|
|
"parser": {"factory": "parser"},
|
|
"custom": {"source": str(tempdir), "component": "yolo"},
|
|
}
|
|
dest_config = {"nlp": dest_nlp_cfg, "components": dest_components_cfg}
|
|
nlp = English.from_config(dest_config)
|
|
assert nlp.pipe_names == ["parser", "custom"]
|
|
assert nlp.pipe_factories == {"parser": "parser", "custom": name}
|
|
meta = nlp.get_pipe_meta("custom")
|
|
assert meta.factory == name
|
|
assert meta.default_config["arg"] == "hello"
|
|
config = nlp.config["components"]["custom"]
|
|
assert config["factory"] == name
|
|
assert config["arg"] == "world"
|
|
|
|
|
|
class PipeFactoriesIdempotent:
|
|
def __init__(self, nlp, name):
|
|
...
|
|
|
|
def __call__(self, doc):
|
|
...
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"i,func,func2",
|
|
[
|
|
(0, lambda nlp, name: lambda doc: doc, lambda doc: doc),
|
|
(1, PipeFactoriesIdempotent, PipeFactoriesIdempotent(None, None)),
|
|
],
|
|
)
|
|
def test_pipe_factories_decorator_idempotent(i, func, func2):
|
|
"""Check that decorator can be run multiple times if the function is the
|
|
same. This is especially relevant for live reloading because we don't
|
|
want spaCy to raise an error if a module registering components is reloaded.
|
|
"""
|
|
name = f"test_pipe_factories_decorator_idempotent_{i}"
|
|
for i in range(5):
|
|
Language.factory(name, func=func)
|
|
nlp = Language()
|
|
nlp.add_pipe(name)
|
|
Language.factory(name, func=func)
|
|
# Make sure it also works for component decorator, which creates the
|
|
# factory function
|
|
name2 = f"{name}2"
|
|
for i in range(5):
|
|
Language.component(name2, func=func2)
|
|
nlp = Language()
|
|
nlp.add_pipe(name)
|
|
Language.component(name2, func=func2)
|
|
|
|
|
|
def test_pipe_factories_config_excludes_nlp():
|
|
"""Test that the extra values we temporarily add to component config
|
|
blocks/functions are removed and not copied around.
|
|
"""
|
|
name = "test_pipe_factories_config_excludes_nlp"
|
|
func = lambda nlp, name: lambda doc: doc
|
|
Language.factory(name, func=func)
|
|
config = {
|
|
"nlp": {"lang": "en", "pipeline": [name]},
|
|
"components": {name: {"factory": name}},
|
|
}
|
|
nlp = English.from_config(config)
|
|
assert nlp.pipe_names == [name]
|
|
pipe_cfg = nlp.get_pipe_config(name)
|
|
pipe_cfg == {"factory": name}
|
|
assert nlp._pipe_configs[name] == {"factory": name}
|