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Improve landing example [ci skip]
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@ -23,32 +23,29 @@ import Link from '../components/link'
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import BenchmarksChoi from 'usage/_benchmarks-choi.md'
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const CODE_EXAMPLE = `# pip install spacy
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# python -m spacy download en_core_web_md
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# python -m spacy download en_core_web_sm
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import spacy
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# Load English tokenizer, tagger, parser, NER and word vectors
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nlp = spacy.load("en_core_web_md")
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nlp = spacy.load("en_core_web_sm")
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# Process whole documents
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text = (u"When Sebastian Thrun started working on self-driving cars at "
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u"Google in 2007, few people outside of the company took him "
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u"seriously. “I can tell you very senior CEOs of major American "
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u"car companies would shake my hand and turn away because I wasn’t "
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u"worth talking to,” said Thrun, now the co-founder and CEO of "
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u"online higher education startup Udacity, in an interview with "
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u"Recode earlier this week.")
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text = ("When Sebastian Thrun started working on self-driving cars at "
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"Google in 2007, few people outside of the company took him "
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"seriously. “I can tell you very senior CEOs of major American "
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"car companies would shake my hand and turn away because I wasn’t "
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"worth talking to,” said Thrun, in an interview with Recode earlier "
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"this week.")
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doc = nlp(text)
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# Analyze syntax
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print("Noun phrases:", [chunk.text for chunk in doc.noun_chunks])
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print("Verbs:", [token.lemma_ for token in doc if token.pos_ == "VERB"])
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# Find named entities, phrases and concepts
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for entity in doc.ents:
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print(entity.text, entity.label_)
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# Determine semantic similarities
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doc1 = nlp(u"my fries were super gross")
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doc2 = nlp(u"such disgusting fries")
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similarity = doc1.similarity(doc2)
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print(doc1.text, doc2.text, similarity)
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`
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/**
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