lm1b

• Description:

A benchmark corpus to be used for measuring progress in statistical language modeling. This has almost one billion words in the training data.

• Homepage: http://www.statmt.org/lm-benchmark/

• Source code: tfds.text.Lm1b

• Versions:

  • 1.0.0 (default): New split API (https://tensorflow.org/datasets/splits)

• Download size: 1.67 GiB

• Dataset size: Unknown size

• Auto-cached (documentation): Unknown

• Splits:

Split	Examples
'test'	306,688
'train'	30,301,028

• Supervised keys (See as_supervised doc): ('text', 'text')
• Citation:

@article{DBLP:journals/corr/ChelbaMSGBK13,
  author    = {Ciprian Chelba and
               Tomas Mikolov and
               Mike Schuster and
               Qi Ge and
               Thorsten Brants and
               Phillipp Koehn},
  title     = {One Billion Word Benchmark for Measuring Progress in Statistical Language
               Modeling},
  journal   = {CoRR},
  volume    = {abs/1312.3005},
  year      = {2013},
  url       = {http://arxiv.org/abs/1312.3005},
  archivePrefix = {arXiv},
  eprint    = {1312.3005},
  timestamp = {Mon, 13 Aug 2018 16:46:16 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/ChelbaMSGBK13},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

• Visualization (tfds.show_examples): Not supported.

lm1b/plain_text (default config)

• Config description: Plain text

• Features:

FeaturesDict({
    'text': Text(shape=(), dtype=tf.string),
})

lm1b/bytes

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

• Features:

FeaturesDict({
    'text': Text(shape=(None,), dtype=tf.int64, encoder=<ByteTextEncoder vocab_size=257>),
})

lm1b/subwords8k

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

• Features:

FeaturesDict({
    'text': Text(shape=(None,), dtype=tf.int64, encoder=<SubwordTextEncoder vocab_size=8189>),
})

lm1b/subwords32k

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

• Features:

FeaturesDict({
    'text': Text(shape=(None,), dtype=tf.int64, encoder=<SubwordTextEncoder vocab_size=32711>),
})