def _t5_glue_data_split_no_token(benchmark_id):
  """Returns a GLUE data split prepared with the standard T5 preprocessor."""
  benchmark, split = _t5_glue_benchmark_and_split(benchmark_id)
  dataset = tfds.load(name=f'glue/{benchmark}', split=split)
  processed_dataset = t5_data().preprocessors.glue(  # pylint: disable=g-long-lambda
      dataset,
      benchmark_name=benchmark,
      label_names=_GLUE_LABELS[benchmark])

  def stream_of_inputs_targets_weights(generator=None):
    del generator
    while True:
      for example in processed_dataset:
        input_values = example['inputs'].numpy()
        target_values = example['targets'].numpy()
        yield (input_values,
               target_values,
               jnp.array([1] * len(target_values)))

  return stream_of_inputs_targets_weights
Returns a GLUE data split prepared with the standard T5 preprocessor.
trax/data/tf_inputs.py