spaCy/docs/source/reference/loading.rst

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=================
Loading Resources
=================
99\% of the time, you will load spaCy's resources using a language pipeline class,
e.g. `spacy.en.English`. The pipeline class reads the data from disk, from a
specified directory. By default, spaCy installs data into each language's
package directory, and loads it from there.
Usually, this is all you will need:
>>> from spacy.en import English
>>> nlp = English()
If you need to replace some of the components, you may want to just make your
own pipeline class --- the English class itself does almost no work; it just
applies the modules in order. You can also provide a function or class that
produces a tokenizer, tagger, parser or entity recognizer to :code:`English.__init__`,
to customize the pipeline:
>>> from spacy.en import English
>>> from my_module import MyTagger
>>> nlp = English(Tagger=MyTagger)
In more detail:
.. code::
class English(object):
def __init__(self,
data_dir=path.join(path.dirname(__file__), 'data'),
Tokenizer=Tokenizer.from_dir,
Tagger=EnPosTagger,
Parser=Createarser(ArcEager),
Entity=CreateParser(BiluoNER),
load_vectors=True
):
:code:`data_dir`
:code:`unicode path`
The data directory. May be None, to disable any data loading (including
the vocabulary).
:code:`Tokenizer`
:code:`(Vocab vocab, unicode data_dir)(unicode) --> Doc`
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A class/function that creates the tokenizer.
:code:`Tagger` / :code:`Parser` / :code:`Entity`
:code:`(Vocab vocab, unicode data_dir)(Doc) --> None`
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A class/function that creates the part-of-speech tagger /
syntactic dependency parser / named entity recogniser.
May be None or False, to disable tagging.
:code:`load_vectors`
:code:`bool`
A boolean value to control whether the word vectors are loaded.