https://github.com/google-deepmind/pg19

https://github.com/google-deepmind/pg19

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• Host: GitHub
• URL: https://github.com/google-deepmind/pg19
• Owner: google-deepmind
• License: apache-2.0
• Created: 2019-09-30T15:43:25.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2020-02-25T19:43:55.000Z (over 4 years ago)
• Last Synced: 2024-08-01T13:27:41.179Z (3 months ago)
• Size: 759 KB
• Stars: 222
• Watchers: 10
• Forks: 18
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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README

        
# 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

    

        

             

             Train 

             Validation 

             Test 

        

        

             Books 

             28,602 

             50 

             100 

        

        

            Num. Tokens 

             1,973,136,207 

             3,007,061 

             6,966,499 

        

    

### 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.



  

    property

    value

  

  

    name

    The PG-19 Language Modeling Benchmark

  

  

    alternateName

    PG-19

  

  

    url

    https://github.com/deepmind/pg19

  

  

    sameAs

    https://github.com/deepmind/pg19

  

  

    description

    This repository contains the PG-19 dataset.

    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.

  

  

    provider

    

      


        

          

            property

            value

          

          

            name

            DeepMind

          

          

            sameAs

            https://en.wikipedia.org/wiki/DeepMind

          

        

      

    

  

  

    license

    

      


        

          

            property

            value

          

          

            name

            Apache License, Version 2.0

          

          

            url

            https://www.apache.org/licenses/LICENSE-2.0.html

          

        

      

    

  

  

    citation

    https://identifiers.org/arxiv:1911.05507

  




### 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)