mirror of
https://github.com/explosion/spaCy.git
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ff91e6dac7
* Show warning if entity_ruler runs without patterns * Show warning if matcher runs without patterns * fix wording * unit test for warning once (WIP) * warn W036 only once * cleanup * create filter_warning helper
74 lines
2.5 KiB
Python
74 lines
2.5 KiB
Python
from typing import Union, Iterable, Dict, Any
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from pathlib import Path
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import sys
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# set library-specific custom warning handling before doing anything else
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from .errors import setup_default_warnings
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setup_default_warnings()
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# These are imported as part of the API
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from thinc.api import prefer_gpu, require_gpu, require_cpu # noqa: F401
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from thinc.api import Config
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from . import pipeline # noqa: F401
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from .cli.info import info # noqa: F401
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from .glossary import explain # noqa: F401
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from .about import __version__ # noqa: F401
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from .util import registry, logger # noqa: F401
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from .errors import Errors
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from .language import Language
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from .vocab import Vocab
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from . import util
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if sys.maxunicode == 65535:
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raise SystemError(Errors.E130)
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def load(
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name: Union[str, Path],
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*,
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vocab: Union[Vocab, bool] = True,
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disable: Iterable[str] = util.SimpleFrozenList(),
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exclude: Iterable[str] = util.SimpleFrozenList(),
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config: Union[Dict[str, Any], Config] = util.SimpleFrozenDict(),
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) -> Language:
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"""Load a spaCy model from an installed package or a local path.
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name (str): Package name or model path.
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vocab (Vocab): A Vocab object. If True, a vocab is created.
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disable (Iterable[str]): Names of pipeline components to disable. Disabled
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pipes will be loaded but they won't be run unless you explicitly
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enable them by calling nlp.enable_pipe.
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exclude (Iterable[str]): Names of pipeline components to exclude. Excluded
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components won't be loaded.
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config (Dict[str, Any] / Config): Config overrides as nested dict or dict
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keyed by section values in dot notation.
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RETURNS (Language): The loaded nlp object.
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"""
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return util.load_model(
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name, vocab=vocab, disable=disable, exclude=exclude, config=config
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)
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def blank(
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name: str,
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*,
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vocab: Union[Vocab, bool] = True,
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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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vocab (Vocab): A Vocab object. If True, a vocab is created.
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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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# We should accept both dot notation and nested dict here for consistency
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config = util.dot_to_dict(config)
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return LangClass.from_config(config, vocab=vocab, meta=meta)
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