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Use an example from an old issue https://github.com/explosion/spaCy/issues/172#issuecomment-183963403.
136 lines
5.4 KiB
Plaintext
136 lines
5.4 KiB
Plaintext
//- 💫 DOCS > USAGE > PROCESSING TEXT
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include ../../_includes/_mixins
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p
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| Once you have loaded the #[code nlp] object, you can call it as though
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| it were a function. This allows you to process a single unicode string.
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+code.
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doc = nlp(u'Hello, world! A three sentence document.\nWith new lines...')
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p
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| The library should perform equally well with short or long documents.
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| All algorithms are linear-time in the length of the string, and once the
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| data is loaded, there's no significant start-up cost to consider. This
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| means that you don't have to strategically merge or split your text —
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| you should feel free to feed in either single tweets or whole novels.
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p
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| If you run #[code nlp = spacy.load('en')], the #[code nlp] object will
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| be an instance of #[code spacy.en.English]. This means that when you run
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| #[code doc = nlp(text)], you're executing
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| #[code spacy.en.English.__call__], which is implemented on its parent
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| class, #[+api("language") #[code Language]].
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+code.
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doc = nlp.make_doc(text)
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for proc in nlp.pipeline:
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proc(doc)
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p
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| I've tried to make sure that the #[code Language.__call__] function
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| doesn't do any "heavy lifting", so that you won't have complicated logic
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| to replicate if you need to make your own pipeline class. This is all it
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| does.
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p
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| The #[code .make_doc()] method and #[code .pipeline] attribute make it
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| easier to customise spaCy's behaviour. If you're using the default
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| pipeline, we can desugar one more time.
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+code.
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doc = nlp.tokenizer(text)
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nlp.tagger(doc)
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nlp.parser(doc)
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nlp.entity(doc)
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p Finally, here's where you can find out about each of those components:
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+table(["Name", "Source"])
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+row
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+cell #[code tokenizer]
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+cell #[+src(gh("spacy", "spacy/tokenizer.pyx")) spacy.tokenizer.Tokenizer]
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+row
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+cell #[code tagger]
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+cell #[+src(gh("spacy", "spacy/tagger.pyx")) spacy.pipeline.Tagger]
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+row
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+cell #[code parser]
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+cell #[+src(gh("spacy", "spacy/syntax/parser.pyx")) spacy.pipeline.DependencyParser]
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+row
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+cell #[code entity]
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+cell #[+src(gh("spacy", "spacy/syntax/parser.pyx")) spacy.pipeline.EntityRecognizer]
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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 .pipe()]] method. The #[code .pipe()]
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| method takes 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 the #[code .pipe()] method 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 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
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| (#[code item for item in sequence])
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+item
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| The #[code itertools] built-in library and the #[code cytoolz]
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| package provide a lot of handy 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 metadata, e.g. a JSON document. To pair up the metadata
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| with the processed #[code Doc] object, you should use the tee
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| function to split the generator in two, and then #[code izip] the
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| extra stream to the document stream. Here's an
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| #[a(href="https://github.com/explosion/spaCy/issues/172#issuecomment-183963403")= "example"]
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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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