anchor.diff:      assert apple.similarity(banana) > pasta.similarity(hippo)
       assert apple.has_vector, banana.has_vector, pasta.has_vector, hippo.has_vector
   
  -+infobox
  -    |  #[strong Usage:] #[+a("/usage/vectors-similarity") Word vectors and similarity]
  +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.
  +
  ++infobox
  +    |  #[+label-inline Usage:] #[+a("/usage/vectors-similarity") Word vectors and similarity]
   
   +h(3, "lightning-tour-serialization") Simple and efficient serialization
   
  
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:     unicorn_text = doc.vocab.strings[unicorn_hash] # '🦄 '
  
  +infobox
      |  #[+label-inline API:] #[+api("stringstore") #[code stringstore]]
      |  #[+label-inline Usage:] #[+a("/usage/spacy-101#vocab") Vocab, hashes and lexemes 101]
  
  +h(3, "lightning-tour-entities") Recongnise and update named entities
      +tag-model("NER")
  