          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]
                      if not batch:
                          continue
                      docs, golds = zip(*batch)
                      nlp.update(docs, golds, sgd=optimizer,
                                 drop=next(dropout_rates), losses=losses)
 
</s> remove         train_docs = corpus.train_docs(nlp, projectivize=True, noise_level=0.0,
                                       gold_preproc=gold_preproc, max_length=0)
        train_docs = list(train_docs)
 </s> add  </s> remove 
 </s> add                     words_seen += sum(len(doc) for doc in docs) </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         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):
                for batch in minibatch_by_words(train_docs, size=batch_sizes):
          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_by_words(train_docs, size=batch_sizes):
                for batch in minibatch_by_words(train_docs, size=batch_sizes):
                      if not batch:
                          continue
                      docs, golds = zip(*batch)
                      nlp.update(docs, golds, sgd=optimizer,
                                 drop=next(dropout_rates), losses=losses)
keep keep keep keep 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