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Allow overriding meta from spacy.blank
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@ -47,13 +47,17 @@ def load(
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def blank(
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name: str, *, config: Union[Dict[str, Any], Config] = util.SimpleFrozenDict()
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name: str,
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*,
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config: Union[Dict[str, Any], Config] = util.SimpleFrozenDict(),
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meta: Dict[str, Any] = util.SimpleFrozenDict()
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) -> Language:
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"""Create a blank nlp object for a given language code.
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name (str): The language code, e.g. "en".
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config (Dict[str, Any] / Config): Optional config overrides.
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meta (Dict[str, Any]): Overrides for nlp.meta.
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RETURNS (Language): The nlp object.
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"""
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LangClass = util.get_lang_class(name)
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return LangClass.from_config(config)
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return LangClass.from_config(config, meta=meta)
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@ -1458,6 +1458,7 @@ class Language:
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vocab: Union[Vocab, bool] = True,
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disable: Iterable[str] = SimpleFrozenList(),
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exclude: Iterable[str] = SimpleFrozenList(),
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meta: Dict[str, Any] = SimpleFrozenDict(),
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auto_fill: bool = True,
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validate: bool = True,
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) -> "Language":
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@ -1472,6 +1473,7 @@ class Language:
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explicitly enable them by calling nlp.enable_pipe.
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exclude (Iterable[str]): Names of pipeline components to exclude.
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Excluded components won't be loaded.
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meta (Dict[str, Any]): Meta overrides for nlp.meta.
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auto_fill (bool): Automatically fill in missing values in config based
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on defaults and function argument annotations.
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validate (bool): Validate the component config and arguments against
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@ -1525,7 +1527,7 @@ class Language:
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# inside stuff like the spacy train function. If we loaded them here,
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# then we would load them twice at runtime: once when we make from config,
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# and then again when we load from disk.
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nlp = lang_cls(vocab=vocab, create_tokenizer=create_tokenizer)
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nlp = lang_cls(vocab=vocab, create_tokenizer=create_tokenizer, meta=meta)
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if after_creation is not None:
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nlp = after_creation(nlp)
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if not isinstance(nlp, cls):
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@ -6,6 +6,7 @@ from spacy.vocab import Vocab
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from spacy.training import Example
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from spacy.lang.en import English
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from spacy.util import registry
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import spacy
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from .util import add_vecs_to_vocab, assert_docs_equal
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@ -266,3 +267,13 @@ def test_language_custom_tokenizer():
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assert [t.text for t in doc] == ["_hello", "_world"]
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doc = list(nlp.pipe(["hello world"]))[0]
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assert [t.text for t in doc] == ["_hello", "_world"]
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def test_spacy_blank():
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nlp = spacy.blank("en")
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assert nlp.config["training"]["dropout"] == 0.1
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config = {"training": {"dropout": 0.2}}
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meta = {"name": "my_custom_model"}
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nlp = spacy.blank("en", config=config, meta=meta)
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assert nlp.config["training"]["dropout"] == 0.2
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assert nlp.meta["name"] == "my_custom_model"
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