imdb_reviews

import tensorflow_datasets as tfds

ds = tfds.load('imdb_reviews', split='train')
for ex in ds.take(4):
  print(ex)

• 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/
• Source code: tfds.text.imdb.IMDBReviews
• Versions:
  • 1.0.0 (default): New split API (https://tensorflow.org/datasets/splits)
• Download size: 80.23 MiB
• Dataset size: Unknown size
• Auto-cached (documentation): No
• Splits:

• Supervised keys (See as_supervised doc): ('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}
}

imdb_reviews/plain_text (default config)

• Config description: Plain text
• Features:

FeaturesDict({
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    'text': Text(shape=(), dtype=tf.string),
})

imdb_reviews/bytes

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

FeaturesDict({
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    'text': Text(shape=(None,), dtype=tf.int64, encoder=<bytetextencoder vocab_size="257">),
})

imdb_reviews/subwords8k

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

FeaturesDict({
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    'text': Text(shape=(None,), dtype=tf.int64, encoder=<subwordtextencoder vocab_size="8185">),
})

imdb_reviews/subwords32k

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

FeaturesDict({
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    'text': Text(shape=(None,), dtype=tf.int64, encoder=<subwordtextencoder vocab_size="32650">),
})
```</subwordtextencoder></subwordtextencoder></bytetextencoder>