//- 💫 DOCS > USAGE > PROCESSING TEXT

include ../../_includes/_mixins

p
    |  Once you have loaded the #[code nlp] object, you can call it as though
    |  it were a function. This allows you to process a single unicode string.

+code.
    doc = nlp(u'Hello, world! A three sentence document.\nWith new lines...')

p
    |  The library should perform equally well with #[strong short or long documents].
    |  All algorithms are linear-time in the length of the string, and once the
    |  data is loaded, there's no significant start-up cost to consider. This
    |  means that you don't have to strategically merge or split your text —
    |  you should feel free to feed in either single tweets or whole novels.

p
    |  If you run #[+api("spacy#load") #[code spacy.load('en')]], spaCy will
    |  load the #[+a("/docs/usage/models") model] associated with the name
    |  #[code 'en']. Each model is a Python package containing an
    |  #[+src(gh("spacy-dev-resources", "templates/model/en_model_name/__init__.py"))__init__.py]

the #[code nlp] object will
    |  be an instance of #[code spacy.en.English]. This means that when you run
    |  #[code doc = nlp(text)], you're executing
    |  #[code spacy.en.English.__call__], which is implemented on its parent
    |  class, #[+api("language") #[code Language]].

+code.
    doc = nlp.make_doc(text)
    for proc in nlp.pipeline:
        proc(doc)

p
    |  I've tried to make sure that the #[code Language.__call__] function
    |  doesn't do any "heavy lifting", so that you won't have complicated logic
    |  to replicate if you need to make your own pipeline class. This is all it
    |  does.

p
    |  The #[code .make_doc()] method and #[code .pipeline] attribute make it
    |  easier to customise spaCy's behaviour. If you're using the default
    |  pipeline, we can desugar one more time.

+code.
    doc = nlp.tokenizer(text)
    nlp.tagger(doc)
    nlp.parser(doc)
    nlp.entity(doc)

p Finally, here's where you can find out about each of those components:

+table(["Name", "Source"])
    +row
        +cell #[code tokenizer]
        +cell #[+src(gh("spacy", "spacy/tokenizer.pyx")) spacy.tokenizer.Tokenizer]

    +row
        +cell #[code tagger]
        +cell #[+src(gh("spacy", "spacy/tagger.pyx")) spacy.pipeline.Tagger]

    +row
        +cell #[code parser]
        +cell #[+src(gh("spacy", "spacy/syntax/parser.pyx")) spacy.pipeline.DependencyParser]

    +row
        +cell #[code entity]
        +cell #[+src(gh("spacy", "spacy/syntax/parser.pyx")) spacy.pipeline.EntityRecognizer]

+h(2, "multithreading") Multi-threading with #[code .pipe()]

p
    |  If you have a sequence of documents to process, you should use the
    |  #[+api("language#pipe") #[code .pipe()]] method. The #[code .pipe()]
    |  method takes an iterator of texts, and accumulates an internal buffer,
    |  which it works on in parallel. It then yields the documents in order,
    |  one-by-one. After a long and bitter struggle, the global interpreter
    |  lock was freed around spaCy's main parsing loop in v0.100.3. This means
    |  that the #[code .pipe()] method will be significantly faster in most
    |  practical situations, because it allows shared memory parallelism.

+code.
    for doc in nlp.pipe(texts, batch_size=10000, n_threads=3):
       pass

p
    |  To make full use of the #[code .pipe()] function, you might want to
    |  brush up on Python generators. Here are a few quick hints:

+list
    +item
        |  Generator comprehensions can be written
        |  (#[code item for item in sequence])

    +item
        |  The #[code itertools] built-in library and the #[code cytoolz]
        |  package provide a lot of handy generator tools

    +item
        |  Often you'll have an input stream that pairs text with some
        |  important metadata, e.g. a JSON document. To pair up the metadata
        |  with the processed #[code Doc] object, you should use the tee
        |  function to split the generator in two, and then #[code izip] the
        |  extra stream to the document stream.

+h(2, "own-annotations") Bringing your own annotations

p
    |  spaCy generally assumes by default that your data is raw text. However,
    |  sometimes your data is partially annotated, e.g. with pre-existing
    |  tokenization, part-of-speech tags, etc. The most common situation is
    |  that you have pre-defined tokenization. If you have a list of strings,
    |  you can create a #[code Doc] object directly. Optionally, you can also
    |  specify a list of boolean values, indicating whether each word has a
    |  subsequent space.

+code.
    doc = Doc(nlp.vocab, words=[u'Hello', u',', u'world', u'!'], spaces=[False, True, False, False])

p
    |  If provided, the spaces list must be the same length as the words list.
    |  The spaces list affects the #[code doc.text], #[code span.text],
    |  #[code token.idx], #[code span.start_char] and #[code span.end_char]
    |  attributes. If you don't provide a #[code spaces] sequence, spaCy will
    |  assume that all words are whitespace delimited.

+code.
    good_spaces = Doc(nlp.vocab, words=[u'Hello', u',', u'world', u'!'], spaces=[False, True, False, False])
    bad_spaces = Doc(nlp.vocab, words=[u'Hello', u',', u'world', u'!'])
    assert bad_spaces.text == u'Hello , world !'
    assert good_spaces.text == u'Hello, world!'

p
    |  Once you have a #[+api("doc") #[code Doc]] object, you can write to its
    |  attributes to set the part-of-speech tags, syntactic dependencies, named
    |  entities and other attributes. For details, see the respective usage
    |  pages.