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			69 lines
		
	
	
		
			2.4 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
			
		
		
	
	
			69 lines
		
	
	
		
			2.4 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
from typing import Union, Iterable, Dict, Any
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from pathlib import Path
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import warnings
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import sys
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warnings.filterwarnings("ignore", message="numpy.dtype size changed")  # noqa
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warnings.filterwarnings("ignore", message="numpy.ufunc size changed")  # noqa
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# These are imported as part of the API
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from thinc.api import prefer_gpu, require_gpu  # 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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    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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    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(name, disable=disable, exclude=exclude, config=config)
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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, meta=meta)
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