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