          prints("Expected dict but got: {}".format(type(meta)),
                 title="Not a valid meta.json format", exits=1)
      meta.setdefault('lang', lang)
      meta.setdefault('name', 'unnamed')
  
      print("Counting training words (limit=%s" % n_sents)
      corpus = GoldCorpus(train_path, dev_path, limit=n_sents)
      n_train_words = corpus.count_train()
 
</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     batch_sizes = util.compounding(util.env_opt('batch_from', 1),
                                   util.env_opt('batch_to', 16),
 </s> add     batch_sizes = util.compounding(util.env_opt('batch_from', 1000),
                                   util.env_opt('batch_to', 1000), </s> remove         try:
            import jieba
        except ImportError:
            raise ImportError("The Chinese tokenizer requires the Jieba library: "
                              "https://github.com/fxsjy/jieba")
        words = list(jieba.cut(text, cut_all=False))
        words = [x for x in words if x]
        return Doc(self.vocab, words=words, spaces=[False]*len(words))
 </s> add         if self.Defaults.use_jieba:
            try:
                import jieba
            except ImportError:
                msg = ("Jieba not installed. Either set Chinese.use_jieba = False, "
                       "or install it https://github.com/fxsjy/jieba")
                raise ImportError(msg)
            words = list(jieba.cut(text, cut_all=False))
            words = [x for x in words if x]
            return Doc(self.vocab, words=words, spaces=[False]*len(words))
        else:
            words = []
            spaces = []
            doc = self.tokenizer(text)
            for token in self.tokenizer(text):
                words.extend(list(token.text))
                spaces.extend([False]*len(token.text))
                spaces[-1] = bool(token.whitespace_)
            return Doc(self.vocab, words=words, spaces=spaces) </s> remove     if not output_path.exists():
        output_path.mkdir()
 </s> add 
    
    if not output_path.exists():
        output_path.mkdir()
          prints("Expected dict but got: {}".format(type(meta)),
                 title="Not a valid meta.json format", exits=1)
      meta.setdefault('lang', lang)
      meta.setdefault('name', 'unnamed')
    
    if not output_path.exists():
        output_path.mkdir()
  
      print("Counting training words (limit=%s" % n_sents)
      corpus = GoldCorpus(train_path, dev_path, limit=n_sents)
      n_train_words = corpus.count_train()
keep keep keep add 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/cli/train.py