          "next": "customizing-tokenizer"
      },
  
      "customizing-tokenizer": {
          "title": "Customizing the tokenizer",
          "next": "rule-based-matching"
      },
  
      "rule-based-matching": {
          "title": "Rule-based matching",
 
</s> remove +code("Example").
    # TODO

p
    |  You can load new word vectors from a file-like buffer using the
    |  #[code vocab.load_vectors()] method. The file should be a
    |  whitespace-delimited text file, where the word is in the first column,
    |  and subsequent columns provide the vector data.  For faster loading, you
    |  can use the #[code vocab.vectors_from_bin_loc()] method, which accepts a
    |  path to a binary file written by #[code vocab.dump_vectors()].

+code("Example").
    # TODO

p
    |  You can also load vectors from memory by writing to the
    |  #[+api("lexeme#vector") #[code Lexeme.vector]] property. If the vectors
    |  you are writing are of different dimensionality
    |  from the ones currently loaded, you should first call
    |  #[code vocab.resize_vectors(new_size)].

 </s> add  </s> remove     |  vectors of their tokens.

p
    |  You can customize these
 </s> add     |  vectors of their tokens. You can customize these
        "title": "Customising the tokenizer",
          "next": "customizing-tokenizer"
      },
  
      "customizing-tokenizer": {
        "title": "Customising the tokenizer",
          "next": "rule-based-matching"
      },
  
      "rule-based-matching": {
          "title": "Rule-based matching",
keep keep keep keep replace keep keep keep keep keep
 Update usage and 101 docs 
website/docs/usage/_data.json