spaCy/spacy/__init__.py

85 lines
2.9 KiB
Python

import sys
from pathlib import Path
from typing import Any, Dict, Iterable, Union
# set library-specific custom warning handling before doing anything else
from .errors import setup_default_warnings
setup_default_warnings() # noqa: E402
# These are imported as part of the API
from thinc.api import Config, prefer_gpu, require_cpu, require_gpu # noqa: F401
from . import pipeline # noqa: F401
from . import util
from .about import __version__ # noqa: F401
from .errors import Errors
from .glossary import explain # noqa: F401
from .language import Language
from .util import logger, registry # noqa: F401
from .vocab import Vocab
if sys.maxunicode == 65535:
raise SystemError(Errors.E130)
def load(
name: Union[str, Path],
*,
vocab: Union[Vocab, bool] = True,
disable: Union[str, Iterable[str]] = util._DEFAULT_EMPTY_PIPES,
enable: Union[str, Iterable[str]] = util._DEFAULT_EMPTY_PIPES,
exclude: Union[str, Iterable[str]] = util._DEFAULT_EMPTY_PIPES,
config: Union[Dict[str, Any], Config] = util.SimpleFrozenDict(),
) -> Language:
"""Load a spaCy model from an installed package or a local path.
name (str): Package name or model path.
vocab (Vocab): A Vocab object. If True, a vocab is created.
disable (Union[str, Iterable[str]]): Name(s) of pipeline component(s) to disable. Disabled
pipes will be loaded but they won't be run unless you explicitly
enable them by calling nlp.enable_pipe.
enable (Union[str, Iterable[str]]): Name(s) of pipeline component(s) to enable. All other
pipes will be disabled (but can be enabled later using nlp.enable_pipe).
exclude (Union[str, Iterable[str]]): Name(s) of pipeline component(s) to exclude. Excluded
components won't be loaded.
config (Dict[str, Any] / Config): Config overrides as nested dict or dict
keyed by section values in dot notation.
RETURNS (Language): The loaded nlp object.
"""
return util.load_model(
name,
vocab=vocab,
disable=disable,
enable=enable,
exclude=exclude,
config=config,
)
def blank(
name: str,
*,
vocab: Union[Vocab, bool] = True,
config: Union[Dict[str, Any], Config] = util.SimpleFrozenDict(),
meta: Dict[str, Any] = util.SimpleFrozenDict(),
) -> Language:
"""Create a blank nlp object for a given language code.
name (str): The language code, e.g. "en".
vocab (Vocab): A Vocab object. If True, a vocab is created.
config (Dict[str, Any] / Config): Optional config overrides.
meta (Dict[str, Any]): Overrides for nlp.meta.
RETURNS (Language): The nlp object.
"""
LangClass = util.get_lang_class(name)
# We should accept both dot notation and nested dict here for consistency
config = util.dot_to_dict(config)
return LangClass.from_config(config, vocab=vocab, meta=meta)
def info(*args, **kwargs):
from .cli.info import info as cli_info
return cli_info(*args, **kwargs)