title: tensorflow.python.framework.errors_impl.OutOfRangeError: Read less bytes than requested [[{{node checkpoint_initializer_196}}]]
body: <p>I used BERT released from Google research to classify my own data set. I created a new class in the source code to specify my own classification tasks and I run my code on Colab. The error occurs when run the run_classifier.py in the file. Here is my code</p>
  
  <p><strong>create my own task</strong></p>
  
  <pre><code>class MyProcessor(DataProcessor):
    """Base class for data converters for sequence classification data sets."""
    def __init__(self):
      self.labels = ['货物', '服务', '工程']
  
    def get_train_examples(self, data_dir):
      """Gets a collection of `InputExample`s for the train set."""
      return self._create_examples(
              self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
  
  
    def get_dev_examples(self, data_dir):
      """Gets a collection of `InputExample`s for the dev set."""
      return self._create_examples(
              self._read_tsv(os.path.join(data_dir, "val.tsv")), "val")
  
  
    def get_test_examples(self, data_dir):
      """Gets a collection of `InputExample`s for prediction."""
      return self._create_examples(
              self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
  
  
    def get_labels(self):
      return self.labels
  
  
    def _create_examples(self, lines, set_type):
      examples = []
      for (i, line) in enumerate(lines):
        guid = "%s-%s" % (set_type, i)
        if set_type == "test":
          text_a = tokenization.convert_to_unicode(line[0])
          label = "货物"
        else:
          text_a = tokenization.convert_to_unicode(line[1])
          label = tokenization.convert_to_unicode(line[0])
        examples.append(InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
      return examples
  
    @classmethod
    def _read_tsv(cls, input_file, quotechar=None):
      """Reads a tab separated value file."""
      with tf.gfile.Open(input_file, "r") as f:
        reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
        lines = []
        for line in reader:
          lines.append(line)
        return lines
  </code></pre>
  
  <p><strong>run</strong></p>
  
  <pre><code>!python run_classifier.py \
          --task_name=mytask \
                  --data_dir=data/ \
                  --bert_config_file=checkpoint/bert_config.json \
                  --init_checkpoint=checkpoint/bert_model.ckpt \
                  --vocab_file=checkpoint/vocab.txt \
                  --output_dir=./output/result_dir/ \
          --max_seq_length=50 \
          --do_train=true \
          --do_eval=true \
          ----train_batch_size=128
  </code></pre>