pg19

• Description:

This dataset contains the PG-19 language modeling benchmark. It includes a set of books extracted from the Project Gutenberg books project (https://www.gutenberg.org), that were published before 1919. It also contains metadata of book titles and publication dates. PG-19 is over double the size of the Billion Word benchmark and contains documents that are 20X longer, on average, than the WikiText long-range language modelling benchmark.

Books are partitioned into a train, validation, and test set. Books metadata is stored in metadata.csv which contains (book_id, short_book_title, publication_date, book_link).

• Additional Documentation: Explore on Papers With Code

• Homepage: https://github.com/deepmind/pg19

• Source code: tfds.datasets.pg19.Builder

• Versions:

  • 0.1.1 (default): No release notes.

• Download size: Unknown size

• Dataset size: 10.94 GiB

• Auto-cached (documentation): No

• Splits:

Split	Examples
'test'	100
'train'	28,602
'validation'	50

• Feature structure:

FeaturesDict({
    'book_id': int32,
    'book_link': string,
    'book_text': Text(shape=(), dtype=string),
    'book_title': string,
    'publication_date': string,
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
book_id	Tensor		int32
book_link	Tensor		string
book_text	Text		string
book_title	Tensor		string
publication_date	Tensor		string

• Supervised keys (See as_supervised doc): None

• Figure (tfds.show_examples): Not supported.

• Examples (tfds.as_dataframe):

• Citation:

@article{raecompressive2019,
author = {Rae, Jack W and Potapenko, Anna and Jayakumar, Siddhant M and
          Hillier, Chloe and Lillicrap, Timothy P},
title = {Compressive Transformers for Long-Range Sequence Modelling},
journal = {arXiv preprint},
url = {https://arxiv.org/abs/1911.05507},
year = {2019},
}