def _t5_glue_data_split(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 = generic_text_dataset_preprocess_fn(
      dataset,
      spm_path=t5_data().DEFAULT_SPM_PATH,
      text_preprocess_fns=[
          lambda ds, training: t5_data().preprocessors.glue(  # pylint: disable=g-long-lambda
              ds,
              benchmark_name=benchmark,
              label_names=_GLUE_LABELS[benchmark])
      ],
      copy_pretokenized=True,
      debug_print_examples=True,
      debug_print_examples_rate=0.05)
  dataset_as_numpy = tfds.as_numpy(processed_dataset)

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