imdb_reviews

  • 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.

Split Examples
'test' 25,000
'train' 25,000
'unsupervised' 50,000
@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}
}

imdb_reviews/plain_text (default config)

  • Config description: Plain text

  • Dataset size: 129.83 MiB

  • Feature structure:

FeaturesDict({
    'label': ClassLabel(shape=(), dtype=int64, num_classes=2),
    'text': Text(shape=(), dtype=string),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
label ClassLabel int64
text Text string

imdb_reviews/bytes

  • Config description: Uses byte-level text encoding with tfds.deprecated.text.ByteTextEncoder

  • Dataset size: 129.88 MiB

  • Feature structure:

FeaturesDict({
    'label': ClassLabel(shape=(), dtype=int64, num_classes=2),
    'text': Text(shape=(None,), dtype=int64, encoder=<ByteTextEncoder vocab_size=257>),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
label ClassLabel int64
text Text (None,) int64

imdb_reviews/subwords8k

  • Config description: Uses tfds.deprecated.text.SubwordTextEncoder with 8k vocab size

  • Dataset size: 54.72 MiB

  • Feature structure:

FeaturesDict({
    'label': ClassLabel(shape=(), dtype=int64, num_classes=2),
    'text': Text(shape=(None,), dtype=int64, encoder=<SubwordTextEncoder vocab_size=8185>),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
label ClassLabel int64
text Text (None,) int64

imdb_reviews/subwords32k

  • Config description: Uses tfds.deprecated.text.SubwordTextEncoder with 32k vocab size

  • Dataset size: 50.33 MiB

  • Feature structure:

FeaturesDict({
    'label': ClassLabel(shape=(), dtype=int64, num_classes=2),
    'text': Text(shape=(None,), dtype=int64, encoder=<SubwordTextEncoder vocab_size=32650>),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
label ClassLabel int64
text Text (None,) int64