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‎CONTRIBUTING.md

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This repository is not currently accepting contributions.

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‎README.md

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# PG-19 Language Modelling Benchmark
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This repository contains the PG-19 language modeling benchmark. It includes a
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set of books extracted rom the Project Gutenberg books library [1], that were
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published before 1919. It also contains metadata of book titles and publication
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dates.
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<b><a href="https://console.cloud.google.com/storage/browser/deepmind-gutenberg">Full dataset download link</a></b>
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PG-19 is over double the size of the Billion Word benchmark [2] and contains
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documents that are 20X longer, on average, than the WikiText long-range language
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modelling benchmark [3].
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Books are partitioned into a `train`, `validation`, and `test` set. Book
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metadata is stored in `metadata.csv` which contains
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`(book_id, short_book_title, publication_date)`.
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Unlike prior benchmarks, we do not constrain the vocabulary size ---
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i.e. mapping rare words to an UNK token --- but instead release the data as an
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open-vocabulary benchmark. The only processing of the text that has been applied
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is the removal of boilerplate license text, and the mapping of offensive
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discriminatory words as specified by Ofcom [4] to placeholder <DW> tokens. Users
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are free to model the data at the character-level, subword-level, or via any
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mechanism that can model an arbitrary string of text.
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To compare models we propose to continue measuring the word-level perplexity,
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by calculating the total likelihood of the dataset (via any chosen subword
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vocabulary or character-based scheme) divided by the number of tokens ---
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specified below in the dataset statistics table.
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One could use this dataset for benchmarking long-range language models, or
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use it to pre-train for other natural language processing tasks which require
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long-range reasoning, such as LAMBADA [5] or NarrativeQA [6]. We would not
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recommend using this dataset to train a general-purpose language model, e.g.
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for applications to a production-system dialogue agent, due to the dated
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linguistic style of old texts and the inherent biases present in historical
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writing.
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### Dataset Statistics
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<table >
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<tbody>
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<tr>
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<td> </td>
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<td> Train </td>
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<td> Validation </td>
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<td> Test </td>
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</tr>
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<tr>
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<td> Books </td>
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<td> 28,602 </td>
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<td> 50 </td>
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<td> 100 </td>
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</tr>
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<tr>
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<td>Num. Tokens </td>
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<td> 1,973,136,207 </td>
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<td> 3,007,061 </td>
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<td> 6,966,499 </td>
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</tr>
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</tbody>
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</table>
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### Bibtex
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```
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@article{raecompressive2019,
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author = {Rae, Jack W and Potapenko, Anna and Jayakumar, Siddhant M and
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Hillier, Chloe and Lillicrap, Timothy P},
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title = {Compressive Transformers for Long-Range Sequence Modelling},
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journal = {arXiv preprint},
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url = {https://arxiv.org/abs/1911.05507},
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year = {2019},
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}
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```
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### Dataset Metadata
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The following table is necessary for this dataset to be indexed by search
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engines such as <a href="https://g.co/datasetsearch">Google Dataset Search</a>.
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<div itemscope itemtype="http://schema.org/Dataset">
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<table>
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<tr>
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<th>property</th>
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<th>value</th>
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</tr>
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<tr>
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<td>name</td>
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<td><code itemprop="name">The PG-19 Language Modeling Benchmark</code></td>
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</tr>
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<tr>
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<td>alternateName</td>
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<td><code itemprop="alternateName">PG-19</code></td>
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</tr>
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<tr>
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<td>url</td>
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<td><code itemprop="url">https://github.com/deepmind/pg19</code></td>
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</tr>
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<tr>
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<td>sameAs</td>
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<td><code itemprop="sameAs">https://github.com/deepmind/pg19</code></td>
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</tr>
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<tr>
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<td>description</td>
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<td><code itemprop="description">This repository contains the PG-19 dataset.
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It includes a set of books extracted from the Project Gutenberg
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books project (https://www.gutenberg.org), that were published before
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1919. It also contains metadata of book titles and publication dates.</code></td>
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</tr>
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<tr>
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<td>provider</td>
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<td>
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<div itemscope itemtype="http://schema.org/Organization" itemprop="provider">
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<table>
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<tr>
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<th>property</th>
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<th>value</th>
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</tr>
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<tr>
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<td>name</td>
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<td><code itemprop="name">DeepMind</code></td>
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</tr>
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<tr>
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<td>sameAs</td>
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<td><code itemprop="sameAs">https://en.wikipedia.org/wiki/DeepMind</code></td>
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</tr>
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</table>
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</div>
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</td>
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</tr>
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<tr>
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<td>license</td>
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<td>
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<div itemscope itemtype="http://schema.org/CreativeWork" itemprop="license">
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<table>
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<tr>
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<th>property</th>
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<th>value</th>
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</tr>
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<tr>
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<td>name</td>
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<td><code itemprop="name">Apache License, Version 2.0</code></td>
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</tr>
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<tr>
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<td>url</td>
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<td><code itemprop="url">https://www.apache.org/licenses/LICENSE-2.0.html</code></td>
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</tr>
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</table>
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</div>
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</td>
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</tr>
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<tr>
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<td>citation</td>
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<td><code itemprop="citation">https://identifiers.org/arxiv:1911.05507</code></td>
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</tr>
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</table>
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</div>
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### Contact
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If you have any questions, please contact <a href="mailto:[email protected]">Jack Rae</a>.
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### References
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<ul style="list-style: none;">
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<li>[1] <a href="https://www.gutenberg.org/">https://www.gutenberg.org</a></li>
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<li>[2] Chelba et al. "One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling" (2013)</li>
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<li>[3] Merity et al. "Pointer Sentinel Mixture Models" (2016)</li>
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<li>[4] <a href="https://www.ofcom.org.uk/__data/assets/pdf_file/0023/91625/OfcomQRG-AOC.pdf">Ofcom offensive language guide</a></li>
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<li>[5] Paperno et al. "The LAMBADA dataset: Word prediction requiring a broad discourse context" (2016)</li>
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<li>[6] Kočiský et al. "The narrativeqa reading comprehension challenge" (2018)</li>
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</ul>
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