      nlp._optimizer = None
  
      print("Itn.\tP.Loss\tN.Loss\tUAS\tNER P.\tNER R.\tNER F.\tTag %\tToken %")
      try:
          train_docs = corpus.train_docs(nlp, projectivize=True, noise_level=0.0,
                                         gold_preproc=gold_preproc, max_length=0)
          train_docs = list(train_docs)
          for i in range(n_iter):
              with tqdm.tqdm(total=n_train_words, leave=False) as pbar:
                  losses = {}
                  for batch in minibatch(train_docs, size=batch_sizes):
                      batch = [(d, g) for (d, g) in batch if len(d) < max_doc_len]
 
</s> remove                 for batch in minibatch(train_docs, size=batch_sizes):
                    batch = [(d, g) for (d, g) in batch if len(d) < max_doc_len]
 </s> add                 for batch in minibatch_by_words(train_docs, size=batch_sizes): </s> remove         states, golds, max_steps = self._init_gold_batch(docs, golds)
 </s> add         # Chop sequences into lengths of this many transitions, to make the
        # batch uniform length.
        cut_gold = numpy.random.choice(range(20, 100))
        states, golds, max_steps = self._init_gold_batch(docs, golds, max_length=cut_gold) </s> remove                        noise_level=0.0):
 </s> add                        noise_level=0.0, make_projective=False): </s> remove         for loc in self.train_locs:
            gold_tuples = read_json_file(loc)
 </s> add         for loc in locs:
            loc = util.ensure_path(loc)
            if loc.parts[-1].endswith('json'):
                gold_tuples = read_json_file(loc)
            elif loc.parts[-1].endswith('msg'):
                with loc.open('rb') as file_:
                    gold_tuples = msgpack.load(file_, encoding='utf8')
            else:
                msg = "Cannot read from file: %s. Supported formats: .json, .msg"
                raise ValueError(msg % loc)
      nlp._optimizer = None
  
      print("Itn.\tP.Loss\tN.Loss\tUAS\tNER P.\tNER R.\tNER F.\tTag %\tToken %")
      try:



          for i in range(n_iter):
              with tqdm.tqdm(total=n_train_words, leave=False) as pbar:
                  losses = {}
                  for batch in minibatch(train_docs, size=batch_sizes):
                      batch = [(d, g) for (d, g) in batch if len(d) < max_doc_len]
keep keep keep keep replace replace replace 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/cli/train.py