spaCy/website/docs/usage/lightning-tour.jade

//- 💫 DOCS > USAGE > LIGHTNING TOUR

include ../../_includes/_mixins

p
    |  The following examples and code snippets give you an overview of spaCy's
    |  functionality and its usage.

+h(2, "models") Install models and process text

+code(false, "bash").
    python -m spacy download en
    python -m spacy download de

+code.
    import spacy
    nlp = spacy.load('en')
    doc = nlp(u'Hello, world. Here are two sentences.')

    nlp_de = spacy.load('de')
    doc_de = nlp_de(u'Ich bin ein Berliner.')

+infobox
    |  #[strong API:] #[+api("spacy#load") #[code spacy.load()]]
    |  #[strong Usage:] #[+a("/docs/usage/models") Models],
    |  #[+a("/docs/usage/spacy-101") spaCy 101]

+h(2, "examples-tokens-sentences") Get tokens, noun chunks & sentences
    +tag-model("dependency parse")

+code.
    doc = nlp(u"Peach emoji is where it has always been. Peach is the superior "
              u"emoji. It's outranking eggplant 🍑 ")

    assert doc[0].text == u'Peach'
    assert doc[1].text == u'emoji'
    assert doc[-1].text == u'🍑'
    assert doc[17:19].text == u'outranking eggplant'
    assert doc.noun_chunks[0].text == u'Peach emoji'

    sentences = list(doc.sents)
    assert len(sentences) == 3
    assert sentences[0].text == u'Peach is the superior emoji.'

+infobox
    |  #[strong API:] #[+api("doc") #[code Doc]], #[+api("token") #[code Token]]
    |  #[strong Usage:] #[+a("/docs/usage/spacy-101") spaCy 101]

+h(2, "examples-pos-tags") Get part-of-speech tags and flags
    +tag-model("tagger")

+code.
    doc = nlp(u'Apple is looking at buying U.K. startup for $1 billion')
    apple = doc[0]
    assert [apple.pos_, apple.pos] == [u'PROPN', 94]
    assert [apple.tag_, apple.tag] == [u'NNP', 475]
    assert [apple.shape_, apple.shape] == [u'Xxxxx', 684]
    assert apple.is_alpha == True
    assert apple.is_punct == False

    billion = doc[10]
    assert billion.is_digit == False
    assert billion.like_num == True
    assert billion.like_email == False

+infobox
    |  #[strong API:] #[+api("token") #[code Token]]
    |  #[strong Usage:] #[+a("/docs/usage/pos-tagging") Part-of-speech tagging]

+h(2, "examples-integer-ids") Use integer IDs for any string

+code.
    hello_id = nlp.vocab.strings['Hello']
    hello_str = nlp.vocab.strings[hello_id]
    assert token.text  == hello_id  == 3125
    assert token.text == hello_str == 'Hello'

+h(2, "examples-entities") Recongnise and update named entities
    +tag-model("NER")

+code.
    doc = nlp(u'San Francisco considers banning sidewalk delivery robots')
    ents = [(e.text, e.start_char, e.end_char, e.label_) for e in doc.ents]
    assert ents == [(u'San Francisco', 0, 13, u'GPE')]

    from spacy.tokens import Span
    doc = nlp(u'Netflix is hiring a new VP of global policy')
    doc.ents = [Span(doc, 0, 1, label=doc.vocab.strings[u'ORG'])]
    ents = [(e.start_char, e.end_char, e.label_) for ent in doc.ents]
    assert ents == [(0, 7, u'ORG')]

+infobox
    |  #[strong Usage:] #[+a("/docs/usage/entity-recognition") Named entity recognition]

+h(2, "displacy") Visualize a dependency parse and named entities in your browser
    +tag-model("dependency parse", "NER")

+code.
    from spacy import displacy

    doc_dep = nlp(u'This is a sentence.')
    displacy.serve(doc_dep, style='dep')

    doc_ent = nlp(u'When Sebastian Thrun started working on self-driving cars at Google '
                  u'in 2007, few people outside of the company took him seriously.')
    displacy.serve(doc_ent, style='ent')

+infobox
    |  #[strong API:] #[+api("displacy") #[code displacy]]
    |  #[strong Usage:] #[+a("/docs/usage/visualizers") Visualizers]

+h(2, "examples-word-vectors") Get word vectors and similarity
    +tag-model("word vectors")

+code.
    doc = nlp(u"Apple and banana are similar. Pasta and hippo aren't.")
    apple = doc[0]
    banana = doc[2]
    pasta = doc[6]
    hippo = doc[8]
    assert apple.similarity(banana) > pasta.similarity(hippo)
    assert apple.has_vector, banana.has_vector, pasta.has_vector, hippo.has_vector

+infobox
    |  #[strong Usage:] #[+a("/docs/usage/word-vectors-similarities") Word vectors and similarity]

+h(2, "examples-serialization") Simple and efficient serialization

+code.
    import spacy
    from spacy.tokens.doc import Doc
    from spacy.vocab import Vocab

    nlp = spacy.load('en')
    moby_dick = open('moby_dick.txt', 'r')
    doc = nlp(moby_dick)
    doc.to_disk('/moby_dick.bin')

    new_doc = Doc(Vocab()).from_disk('/moby_dick.bin')

+infobox
    |  #[strong API:] #[+api("language") #[code Language]],
    |  #[+api("doc") #[code Doc]]
    |  #[strong Usage:] #[+a("/docs/usage/saving-loading") Saving and loading]

+h(2, "rule-matcher") Match text with token rules

+code.
    import spacy
    from spacy.matcher import Matcher

    nlp = spacy.load('en')
    matcher = Matcher(nlp.vocab)

    def set_sentiment(matcher, doc, i, matches):
        doc.sentiment += 0.1

    pattern1 = [{'ORTH': 'Google'}, {'UPPER': 'I'}, {'ORTH': '/'}, {'UPPER': 'O'}]
    pattern2 = [[{'ORTH': emoji, 'OP': '+'}] for emoji in ['😀', '😂', '🤣', '😍']]
    matcher.add('GoogleIO', None, pattern1) # match "Google I/O" or "Google i/o"
    matcher.add('HAPPY', set_sentiment, pattern2) # match one or more happy emoji
    matches = nlp(LOTS_OF TEXT)

+infobox
    |  #[strong API:] #[+api("matcher") #[code Matcher]]
    |  #[strong Usage:] #[+a("/docs/usage/rule-based-matching") Rule-based matching]

+h(2, "multi-threaded") Multi-threaded generator

+code.
    texts = [u'One document.', u'...', u'Lots of documents']
    # .pipe streams input, and produces streaming output
    iter_texts = (texts[i % 3] for i in xrange(100000000))
    for i, doc in enumerate(nlp.pipe(iter_texts, batch_size=50, n_threads=4)):
        assert doc.is_parsed
        if i == 100:
            break

+infobox
    |  #[strong API:] #[+api("doc") #[code Doc]]
    |  #[strong Usage:] #[+a("/docs/usage/production-usage") Production usage]

+h(2, "examples-dependencies") Get syntactic dependencies
    +tag-model("dependency parse")

+code.
    def dependency_labels_to_root(token):
        """Walk up the syntactic tree, collecting the arc labels."""
        dep_labels = []
        while token.head is not token:
            dep_labels.append(token.dep)
            token = token.head
        return dep_labels

+infobox
    |  #[strong API:] #[+api("token") #[code Token]]
    |  #[strong Usage:] #[+a("/docs/usage/dependency-parse") Using the dependency parse]

+h(2, "examples-numpy-arrays") Export to numpy arrays

+code.
    from spacy.attrs import ORTH, LIKE_URL, IS_OOV

    attr_ids = [ORTH, LIKE_URL, IS_OOV]
    doc_array = doc.to_array(attr_ids)
    assert doc_array.shape == (len(doc), len(attr_ids))
    assert doc[0].orth == doc_array[0, 0]
    assert doc[1].orth == doc_array[1, 0]
    assert doc[0].like_url == doc_array[0, 1]
    assert list(doc_array[:, 1]) == [t.like_url for t in doc]

+h(2, "examples-inline") Calculate inline markup on original string

+code.
    def put_spans_around_tokens(doc, get_classes):
        """Given some function to compute class names, put each token in a
        span element, with the appropriate classes computed. All whitespace is
        preserved, outside of the spans. (Of course, HTML won't display more than
        one whitespace character it – but the point is, no information is lost
        and you can calculate what you need, e.g. <br />, <p> etc.)
        """
        output = []
        html = '<span class="{classes}">{word}</span>{space}'
        for token in doc:
            if token.is_space:
                output.append(token.text)
            else:
                classes = ' '.join(get_classes(token))
                output.append(html.format(classes=classes, word=token.text, space=token.whitespace_))
        string = ''.join(output)
        string = string.replace('\n', '')
        string = string.replace('\t', '    ')
        return string