      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_by_words(train_docs, size=batch_sizes):
                      if not batch:
                          continue
 
</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                 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         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     @property
    def train_tuples(self):
 </s> add         # Write temp directory with one doc per file, so we can shuffle
        # and stream
        self.tmp_dir = Path(tempfile.mkdtemp())
        self.write_msgpack(self.tmp_dir / 'train', train)
        self.write_msgpack(self.tmp_dir / 'dev', dev)

    def __del__(self):
        shutil.rmtree(self.tmp_dir)

    @staticmethod
    def write_msgpack(directory, doc_tuples):
        if not directory.exists():
            directory.mkdir()
        for i, doc_tuple in enumerate(doc_tuples):
            with open(directory / '{}.msg'.format(i), 'wb') as file_:
                msgpack.dump([doc_tuple], file_, use_bin_type=True, encoding='utf8')
    
    @staticmethod
    def walk_corpus(path):
        path = util.ensure_path(path)
        if not path.is_dir():
            return [path]
        paths = [path]
        locs = []
        seen = set()
        for path in paths:
            if str(path) in seen:
                continue
            seen.add(str(path))
            if path.parts[-1].startswith('.'):
                continue
            elif path.is_dir():
                paths.extend(path.iterdir())
            elif path.parts[-1].endswith('.json'):
                locs.append(path)
        return locs

    @staticmethod
    def read_tuples(locs, limit=0): </s> remove                 buf.append(iterable.next())
 </s> add                 buf.append(next(iterable))
            train_docs = corpus.train_docs(nlp, noise_level=0.0,
                                           gold_preproc=gold_preproc, max_length=0)
            words_seen = 0
      print("Itn.\tP.Loss\tN.Loss\tUAS\tNER P.\tNER R.\tNER F.\tTag %\tToken %")
      try:
          for i in range(n_iter):
            train_docs = corpus.train_docs(nlp, noise_level=0.0,
                                           gold_preproc=gold_preproc, max_length=0)
            words_seen = 0
              with tqdm.tqdm(total=n_train_words, leave=False) as pbar:
                  losses = {}
                  for batch in minibatch_by_words(train_docs, size=batch_sizes):
                      if not batch:
                          continue
keep keep add 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