tensor2tensor/data_generators/lambada.py [133:176]:
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  @property
  def is_generate_per_split(self):
    """If true, a single call to generate_samples generates for a single split.

    Returns:
      Boolean.
    """
    return True

  @property
  def dataset_splits(self):
    """Splits of data to produce and number of output shards for each.

    Returns:
      A dict containing splits information.
    """
    return [{
        "split": problem.DatasetSplit.TRAIN,
        "shards": 10,
    }, {
        "split": problem.DatasetSplit.EVAL,
        "shards": 1,
    }, {
        "split": problem.DatasetSplit.TEST,
        "shards": 1,
    }]

  @property
  def vocab_type(self):
    return text_problems.VocabType.TOKEN

  @property
  def vocab_size(self):
    # Similar to the setup of the main paper
    return 60000

  @property
  def oov_token(self):
    return _UNK

  @property
  def use_control_set(self):
    """If evaluate on control set."""
    return False
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tensor2tensor/data_generators/lambada.py [224:267]:
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  @property
  def is_generate_per_split(self):
    """If true, a single call to generate_samples generates for a single split.

    Returns:
      Boolean.
    """
    return True

  @property
  def dataset_splits(self):
    """Splits of data to produce and number of output shards for each.

    Returns:
      A dict containing splits information.
    """
    return [{
        "split": problem.DatasetSplit.TRAIN,
        "shards": 10,
    }, {
        "split": problem.DatasetSplit.EVAL,
        "shards": 1,
    }, {
        "split": problem.DatasetSplit.TEST,
        "shards": 1,
    }]

  @property
  def vocab_type(self):
    return text_problems.VocabType.TOKEN

  @property
  def vocab_size(self):
    # Similar to the setup of the main paper
    return 60000

  @property
  def oov_token(self):
    return _UNK

  @property
  def use_control_set(self):
    """If test on control set."""
    return False
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