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79 lines
3.3 KiB
Plaintext
79 lines
3.3 KiB
Plaintext
//- 💫 DOCS > USAGE > PROCESSING TEXT
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include ../../_includes/_mixins
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+under-construction
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+h(2, "multithreading") Multi-threading with #[code .pipe()]
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p
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| If you have a sequence of documents to process, you should use the
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| #[+api("language#pipe") #[code Language.pipe()]] method. The method takes
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| an iterator of texts, and accumulates an internal buffer,
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| which it works on in parallel. It then yields the documents in order,
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| one-by-one. After a long and bitter struggle, the global interpreter
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| lock was freed around spaCy's main parsing loop in v0.100.3. This means
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| that #[code .pipe()] will be significantly faster in most
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| practical situations, because it allows shared memory parallelism.
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+code.
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for doc in nlp.pipe(texts, batch_size=10000, n_threads=3):
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pass
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p
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| To make full use of the #[code .pipe()] function, you might want to
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| brush up on #[strong Python generators]. Here are a few quick hints:
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+list
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+item
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| Generator comprehensions can be written as
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| #[code (item for item in sequence)].
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+item
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| The
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| #[+a("https://docs.python.org/2/library/itertools.html") #[code itertools] built-in library]
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| and the
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| #[+a("https://github.com/pytoolz/cytoolz") #[code cytoolz] package]
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| provide a lot of handy #[strong generator tools].
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+item
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| Often you'll have an input stream that pairs text with some
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| important meta data, e.g. a JSON document. To
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| #[strong pair up the meta data] with the processed #[code Doc]
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| object, you should use the #[code itertools.tee] function to split
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| the generator in two, and then #[code izip] the extra stream to the
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| document stream.
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+h(2, "own-annotations") Bringing your own annotations
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p
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| spaCy generally assumes by default that your data is raw text. However,
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| sometimes your data is partially annotated, e.g. with pre-existing
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| tokenization, part-of-speech tags, etc. The most common situation is
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| that you have pre-defined tokenization. If you have a list of strings,
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| you can create a #[code Doc] object directly. Optionally, you can also
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| specify a list of boolean values, indicating whether each word has a
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| subsequent space.
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+code.
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doc = Doc(nlp.vocab, words=[u'Hello', u',', u'world', u'!'], spaces=[False, True, False, False])
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p
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| If provided, the spaces list must be the same length as the words list.
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| The spaces list affects the #[code doc.text], #[code span.text],
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| #[code token.idx], #[code span.start_char] and #[code span.end_char]
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| attributes. If you don't provide a #[code spaces] sequence, spaCy will
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| assume that all words are whitespace delimited.
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+code.
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good_spaces = Doc(nlp.vocab, words=[u'Hello', u',', u'world', u'!'], spaces=[False, True, False, False])
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bad_spaces = Doc(nlp.vocab, words=[u'Hello', u',', u'world', u'!'])
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assert bad_spaces.text == u'Hello , world !'
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assert good_spaces.text == u'Hello, world!'
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p
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| Once you have a #[+api("doc") #[code Doc]] object, you can write to its
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| attributes to set the part-of-speech tags, syntactic dependencies, named
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| entities and other attributes. For details, see the respective usage
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| pages.
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