(benchmark_id, t5_tokenization=False)
return data.Serial(
      _t5_glue_data_split(benchmark_id)
      if t5_tokenization else _t5_glue_data_split_no_token(benchmark_id),
      data.Tokenize(),
      data.Shuffle(),
      data.PadToLength(),
      data.TruncateToLength(),
      data.Batch(),
  )
Returns a T5-preprocessed data stream for ``benchmark_id``.

  Args:
    benchmark_id: String that indicates the name and data split of a GLUE
        benchmark. Data splits are indicated as underscore suffixes, e.g.,
        ``'cola_t'`` (Cola benchmark, training split), ``'rte_e'`` (RTE
        benchmark, eval/validation split), and ``'mnli_e2'`` (MNLI benchmark,
        alternate "mismatched" eval/validation split).
    t5_tokenization: if true, then use t5_tokenization.
Returns a T5-preprocessed data stream for ``benchmark_id``.
Returns a T5 - preprocessed data stream for benchmark_id .
def _T5GlueDataStream(benchmark_id, t5_tokenization=False):
  """Returns a T5-preprocessed data stream for ``benchmark_id``.

  Args:
    benchmark_id: String that indicates the name and data split of a GLUE
        benchmark. Data splits are indicated as underscore suffixes, e.g.,
        ``'cola_t'`` (Cola benchmark, training split), ``'rte_e'`` (RTE
        benchmark, eval/validation split), and ``'mnli_e2'`` (MNLI benchmark,
        alternate "mismatched" eval/validation split).
    t5_tokenization: if true, then use t5_tokenization.
  """
  return data.Serial(
      _t5_glue_data_split(benchmark_id)
      if t5_tokenization else _t5_glue_data_split_no_token(benchmark_id),
      data.Tokenize(),
      data.Shuffle(),
      data.PadToLength(),
      data.TruncateToLength(),
      data.Batch(),
  )
def _T5GlueDataStream ( benchmark_id , t5_tokenization = False ) : return data . Serial ( _t5_glue_data_split ( benchmark_id ) if t5_tokenization else _t5_glue_data_split_no_token ( benchmark_id ) , data . Tokenize ( ) , data . Shuffle ( ) , data . PadToLength ( ) , data . TruncateToLength ( ) , data . Batch ( ) , )