anchor.diff:          |  #[a(href="#brown-clusters") Brown clusters] and
           |  #[a(href="#word-vectors") word vectors].
   
  -p
  -    |  Once you have the tokenizer and vocabulary, you can
  -    |  #[+a("/docs/usage/training") train the tagger, parser and entity recognizer].
  +    +item
  +        |  #[strong Set up] a #[a(href="#model-directory") model direcory] and #[strong train] the #[a(href="#train-tagger-parser") tagger and parser].
  +
  +p
       |  For some languages, you may also want to develop a solution for
       |  lemmatization and morphological analysis.
   
  
positive.code:     |  by linear models, while the word vectors are useful for lexical
      |  similarity models and deep learning.
  
  p
      |  Once you've collected the word frequencies, Brown clusters and word
      |  vectors files, you can use the
      |  #[+src(gh("spacy-dev-resources", "training/init.py")) init.py]
      |  script from our
      |  #[+a(gh("spacy-dev-resources")) developer resources], or use the new
      |  #[+a("/docs/usage/cli#model") #[code model] command] to create a data
      |  directory:
  
  
negative.code:     |  loaded. By default, the command expects to be able to find your language
      |  class using #[code spacy.util.get_lang_class(lang_id)].
  
  
  +h(2, "train-tagger-parser") Training the tagger and parser
  
  p
      |  You can now train the model using a corpus for your language annotated
      |  with #[+a("http://universaldependencies.org/") Universal Dependencies].
      |  If your corpus uses the connlu format, you can use the
      |  #[+a("/docs/usage/cli#convert") #[code convert] command] to convert it to
      |  spaCy's #[+a("/docs/api/annotation#json-input") JSON format] for training.
  
  p
      |  Once you have your UD corpus transformed into JSON, you can train your
      |  model use the using spaCy's
      |  #[+a("/docs/usage/cli#train") #[code train] command]:
  
  +code(false, "bash").
      python -m spacy train [lang] [output_dir] [train_data] [dev_data] [--n_iter] [--parser_L1] [--no_tagger] [--no_parser] [--no_ner]
  