final_pipelines.py

# First we get the streams from TFDS
train_stream = trax.data.TFDS('imdb_reviews', keys=('text', 'label'), train=True)()
eval_stream = trax.data.TFDS('imdb_reviews', keys=('text', 'label'), train=False)()

# Next, we build the pipeline
data_pipeline = trax.data.Serial(
    trax.data.Tokenize(vocab_file='en_8k.subword', keys=[0]),
    trax.data.Shuffle(),
    trax.data.FilterByLength(max_length=2048, length_keys=[0]),
    trax.data.BucketByLength(boundaries=[  32, 128, 512, 2048],
                             batch_sizes=[512, 128,  32,    8, 1],
                             length_keys=[0]),
    trax.data.AddLossWeights()
  )

# Finally, we get the generators
train_batches_stream = data_pipeline(train_stream)
eval_batches_stream = data_pipeline(eval_stream)