anchor.diff:      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 mark-up on original string
  ++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. (Yes, I know HTML
  -        won't display it. But the point is no information is lost, so you can
  -        calculate what you need, e.g. <br /> tags, <p> tags, etc.)
  -        '''
  +        """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. &lt;br /&gt;, &lt;p&gt; etc.)
  +        """
           output = []
  -        template = '<span classes="{classes}">{word}</span>{space}'
  +        html = '&lt;span class="{classes}"&gt;{word}&lt;/span&gt;{space}'
           for token in doc:
               if token.is_space:
  -                output.append(token.orth_)
  +                output.append(token.text)
               else:
  -                output.append(
  -                  template.format(
  -                    classes=' '.join(get_classes(token)),
  -                    word=token.orth_,
  -                    space=token.whitespace_))
  +                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', '    ')
  
positive.code: 
  +infobox
      |  #[strong Usage:] #[+a("/docs/usage/saving-loading") Saving and loading]
  
  +h(2, "multi-threaded") Multi-threaded generator
  
  
negative.code:     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.
  