      return batch, None
  
  
  def build_text_classifier(nr_class, width=64, **cfg):
      nr_vector = cfg.get('nr_vector', 5000)
      pretrained_dims = cfg.get('pretrained_dims', 0)
      with Model.define_operators({'>>': chain, '+': add, '|': concatenate,
                                   '**': clone}):
          if cfg.get('low_data') and pretrained_dims:
              model = (
 
</s> remove         #model = linear_model >> logistic

 </s> add  </s> remove             self._phrase_key[i] = doc.c[j].lex.orth
        cdef hash_t key = hash64(self._phrase_key,
                                 self.max_length * sizeof(attr_t), 0)
 </s> add             phrase_key[i] = doc.c[j].lex.orth
        cdef hash_t key = hash64(phrase_key,
                                 (end-start) * sizeof(attr_t), 0) </s> remove     max_doc_len = util.env_opt('max_doc_len', 5000)
    corpus = GoldCorpus(train_path, dev_path, limit=n_sents)
    n_train_words = corpus.count_train()

 </s> add  </s> remove         with loc.open('r', encoding='utf8') as file_:
            docs = ujson.load(file_)
        if limit is not None:
            docs = docs[:limit]
        for doc in docs:
 </s> add         for doc in _json_iterate(loc): </s> remove     if not output_path.exists():
        output_path.mkdir()
 </s> add 
    depth = cfg.get('depth', 2)
      return batch, None
  
  
  def build_text_classifier(nr_class, width=64, **cfg):
    depth = cfg.get('depth', 2)
      nr_vector = cfg.get('nr_vector', 5000)
      pretrained_dims = cfg.get('pretrained_dims', 0)
      with Model.define_operators({'>>': chain, '+': add, '|': concatenate,
                                   '**': clone}):
          if cfg.get('low_data') and pretrained_dims:
              model = (
keep keep keep add keep keep keep keep keep keep
 Revert "Merge branch 'develop' of https://github.com/explosion/spaCy into develop"

This reverts commit c9ba3d3c2dc7067cf8bd55f878cec45a8c6d73d4, reversing
changes made to 92c26a35d425d4e8ca1b805ea776ea10f5ded3df. 
spacy/_ml.py