PG-19 Language Modelling Benchmark

This repository contains the PG-19 language modeling benchmark. It includes a set of books extracted from the Project Gutenberg books library [1], that were published before 1919. It also contains metadata of book titles and publication dates.

Full dataset download link

PG-19 is over double the size of the Billion Word benchmark [2] and contains documents that are 20X longer, on average, than the WikiText long-range language modelling benchmark [3].

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

Unlike prior benchmarks, we do not constrain the vocabulary size --- i.e. mapping rare words to an UNK token --- but instead release the data as an open-vocabulary benchmark. The only processing of the text that has been applied is the removal of boilerplate license text, and the mapping of offensive discriminatory words as specified by Ofcom [4] to placeholder tokens. Users are free to model the data at the character-level, subword-level, or via any mechanism that can model an arbitrary string of text.

To compare models we propose to continue measuring the word-level perplexity, by calculating the total likelihood of the dataset (via any chosen subword vocabulary or character-based scheme) divided by the number of tokens --- specified below in the dataset statistics table.

One could use this dataset for benchmarking long-range language models, or use it to pre-train for other natural language processing tasks which require long-range reasoning, such as LAMBADA [5] or NarrativeQA [6]. We would not recommend using this dataset to train a general-purpose language model, e.g. for applications to a production-system dialogue agent, due to the dated linguistic style of old texts and the inherent biases present in historical writing.

Dataset Statistics

Bibtex

@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},
}

Dataset Metadata

The following table is necessary for this dataset to be indexed by search engines such as Google Dataset Search.

Contact

If you have any questions, please contact Jack Rae.

References

• [1] https://www.gutenberg.org
• [2] Chelba et al. "One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling" (2013)
• [3] Merity et al. "Pointer Sentinel Mixture Models" (2016)
• [4] Ofcom offensive language guide
• [5] Paperno et al. "The LAMBADA dataset: Word prediction requiring a broad discourse context" (2016)
• [6] Kočiský et al. "The narrativeqa reading comprehension challenge" (2018)