                      docs, golds = zip(*batch)
                      nlp.update(docs, golds, sgd=optimizer,
                                 drop=next(dropout_rates), losses=losses)
                      pbar.update(sum(len(doc) for doc in docs))
  
              with nlp.use_params(optimizer.averages):
                  util.set_env_log(False)
                  epoch_model_path = output_path / ('model%d' % i)
                  nlp.to_disk(epoch_model_path)
                  nlp_loaded = util.load_model_from_path(epoch_model_path)
 
</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         max_length = max(5, min(50, min([len(doc) for doc in whole_docs])))
 </s> add         max_length = max(min_length, min(max_length, min([len(doc) for doc in whole_docs]))) </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             docs, cuda_stream, 0.0)
 </s> add             docs, cuda_stream, drop)
                    words_seen += sum(len(doc) for doc in docs)
                      docs, golds = zip(*batch)
                      nlp.update(docs, golds, sgd=optimizer,
                                 drop=next(dropout_rates), losses=losses)
                      pbar.update(sum(len(doc) for doc in docs))
                    words_seen += sum(len(doc) for doc in docs)
              with nlp.use_params(optimizer.averages):
                  util.set_env_log(False)
                  epoch_model_path = output_path / ('model%d' % i)
                  nlp.to_disk(epoch_model_path)
                  nlp_loaded = util.load_model_from_path(epoch_model_path)
keep keep keep keep 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