https://atheros.ai/blog/text-classification-with-transformers-in-tensorflow-2

info_output.py

tfds.core.DatasetInfo(
    name='imdb_reviews',
    version=1.0.0,
    description='Large Movie Review Dataset.
This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well.',
    homepage='http://ai.stanford.edu/~amaas/data/sentiment/',
    features=FeaturesDict({
        'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
        'text': Text(shape=(), dtype=tf.string),
    }),
    total_num_examples=100000,
    splits={
        'test': 25000,
        'train': 25000,
        'unsupervised': 50000,
    },
    supervised_keys=('text', 'label'),
    citation=InProceedings{maas-EtAl:2011:ACL-HLT2011,
      author    = {Maas, Andrew L.  and  Daly, Raymond E.  and  Pham, Peter T.  and  Huang, Dan  and  Ng, Andrew Y.  and  Potts, Christopher},
      title     = {Learning Word Vectors for Sentiment Analysis},
      booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
      month     = {June},
      year      = {2011},
      address   = {Portland, Oregon, USA},
      publisher = {Association for Computational Linguistics},
      pages     = {142--150},
      url       = {http:\/\/www.aclweb.org\/anthology\/P11-1015}
    },
    redistribution_info=,
)