anchor.diff:      new_doc = Doc(Vocab()).from_disk('/moby_dick.bin')
   
   +infobox
  -    |  #[strong API:] #[+api("language") #[code Language]],
  +    |  #[+label-inline API:] #[+api("language") #[code Language]],
       |  #[+api("doc") #[code Doc]]
  -    |  #[strong Usage:] #[+a("/usage/models#saving-loading") Saving and loading models]
  +    |  #[+label-inline Usage:] #[+a("/usage/models#saving-loading") Saving and loading models]
   
   +h(3, "lightning-tour-rule-matcher") Match text with token rules
   
  
positive.code:     displacy.serve(doc_ent, style='ent')
  
  +infobox
      |  #[strong API:] #[+api("displacy") #[code displacy]]
      |  #[strong Usage:] #[+a("/usage/visualizers") Visualizers]
  
  +h(3, "lightning-tour-word-vectors") Get word vectors and similarity
      +tag-model("word vectors")
  
  p
      |  For the best results, you should run this example using the
      |  #[+a("/models/en#en_vectors_web_lg") #[code en_vectors_web_lg]] model
  
  +code.
      doc = nlp(u"Apple and banana are similar. Pasta and hippo aren't.")
  
negative.code:     doc_de = nlp_de(u'Ich bin ein Berliner.')
  
  +infobox
      |  #[+label-inline API:] #[+api("spacy#load") #[code spacy.load()]]
      |  #[+label-inline Usage:] #[+a("/usage/models") Models],
      |  #[+a("/usage/spacy-101") spaCy 101]
  
  +h(3, "lightning-tour-tokens-sentences") Get tokens, noun chunks & sentences
  