(benchmark_list=gin.REQUIRED,
                               counters=None,
                               reweight_by_minimum=False,
                               gradually_reweight=False)
return data.Parallel(
      stream_list,
      counters=counters,
      reweight_by_minimum=reweight_by_minimum,
      gradually_reweight=gradually_reweight)()
Returns a parallel set of training streams, based on ``benchmark_list``.

  Args:
    benchmark_list: List of simple lower-case names of GLUE benchmarks, e.g.,
        ``'cola'``, ``'mnli'``, ``'rte'``.
    counters: a list of counters to be passed to data.Parallel, e.g.,
    [8551, 392702, 2490] would be a reasonable counterpart to
    benchmark_list = ["cola", "mnli", "rte"], see
    https://github.com/google-research/text-to-text-transfer-transformer/blob/master/t5/data/glue_utils.py#L42
    for more details on counters.
    reweight_by_minimum: divide by the minimal counter.
    gradually_reweight: a more refined reweighting policy, see inputs.py
      for more details.
Returns a parallel set of training streams, based on ``benchmark_list``.
Returns a parallel set of training streams based on benchmark_list .
def T5GlueTrainStreamsParallel(benchmark_list=gin.REQUIRED,
                               counters=None,
                               reweight_by_minimum=False,
                               gradually_reweight=False):
  """Returns a parallel set of training streams, based on ``benchmark_list``.

  Args:
    benchmark_list: List of simple lower-case names of GLUE benchmarks, e.g.,
        ``'cola'``, ``'mnli'``, ``'rte'``.
    counters: a list of counters to be passed to data.Parallel, e.g.,
    [8551, 392702, 2490] would be a reasonable counterpart to
    benchmark_list = ["cola", "mnli", "rte"], see
    https://github.com/google-research/text-to-text-transfer-transformer/blob/master/t5/data/glue_utils.py#L42
    for more details on counters.
    reweight_by_minimum: divide by the minimal counter.
    gradually_reweight: a more refined reweighting policy, see inputs.py
      for more details.
  """
  stream_list = list(map(T5GlueTrainStream, benchmark_list))
  return data.Parallel(
      stream_list,
      counters=counters,
      reweight_by_minimum=reweight_by_minimum,
      gradually_reweight=gradually_reweight)()
def T5GlueTrainStreamsParallel ( benchmark_list = gin . REQUIRED , counters = None , reweight_by_minimum = False , gradually_reweight = False ) : stream_list = list ( map ( T5GlueTrainStream , benchmark_list ) ) return data . Parallel ( stream_list , counters = counters , reweight_by_minimum = reweight_by_minimum , gradually_reweight = gradually_reweight ) ( )