pg19
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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).
Split |
Examples |
'test' |
100 |
'train' |
28,602 |
'validation' |
50 |
FeaturesDict({
'book_id': int32,
'book_link': string,
'book_text': Text(shape=(), dtype=string),
'book_title': string,
'publication_date': string,
})
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 |
|
@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},
}
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Last updated 2022-12-16 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2022-12-16 UTC."],[],[],null,["# pg19\n\n\u003cbr /\u003e\n\n- **Description**:\n\nThis dataset contains the PG-19 language modeling benchmark. It includes a set\nof books extracted from the Project Gutenberg books project\n(\u003chttps://www.gutenberg.org\u003e), that were published before 1919. It also contains\nmetadata of book titles and publication dates. PG-19 is over double the size of\nthe Billion Word benchmark and contains documents that are 20X longer, on\naverage, than the WikiText long-range language modelling benchmark.\n\nBooks are partitioned into a train, validation, and test set. Books metadata is\nstored in metadata.csv which contains (book_id, short_book_title,\npublication_date, book_link).\n\n- **Additional Documentation** :\n [Explore on Papers With Code\n north_east](https://paperswithcode.com/dataset/pg-19)\n\n- **Homepage** :\n \u003chttps://github.com/deepmind/pg19\u003e\n\n- **Source code** :\n [`tfds.datasets.pg19.Builder`](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/datasets/pg19/pg19_dataset_builder.py)\n\n- **Versions**:\n\n - **`0.1.1`** (default): No release notes.\n- **Download size** : `Unknown size`\n\n- **Dataset size** : `10.94 GiB`\n\n- **Auto-cached**\n ([documentation](https://www.tensorflow.org/datasets/performances#auto-caching)):\n No\n\n- **Splits**:\n\n| Split | Examples |\n|----------------|----------|\n| `'test'` | 100 |\n| `'train'` | 28,602 |\n| `'validation'` | 50 |\n\n- **Feature structure**:\n\n FeaturesDict({\n 'book_id': int32,\n 'book_link': string,\n 'book_text': Text(shape=(), dtype=string),\n 'book_title': string,\n 'publication_date': string,\n })\n\n- **Feature documentation**:\n\n| Feature | Class | Shape | Dtype | Description |\n|------------------|--------------|-------|--------|-------------|\n| | FeaturesDict | | | |\n| book_id | Tensor | | int32 | |\n| book_link | Tensor | | string | |\n| book_text | Text | | string | |\n| book_title | Tensor | | string | |\n| publication_date | Tensor | | string | |\n\n- **Supervised keys** (See\n [`as_supervised` doc](https://www.tensorflow.org/datasets/api_docs/python/tfds/load#args)):\n `None`\n\n- **Figure**\n ([tfds.show_examples](https://www.tensorflow.org/datasets/api_docs/python/tfds/visualization/show_examples)):\n Not supported.\n\n- **Examples**\n ([tfds.as_dataframe](https://www.tensorflow.org/datasets/api_docs/python/tfds/as_dataframe)):\n\nDisplay examples... \n\n- **Citation**:\n\n @article{raecompressive2019,\n author = {Rae, Jack W and Potapenko, Anna and Jayakumar, Siddhant M and\n Hillier, Chloe and Lillicrap, Timothy P},\n title = {Compressive Transformers for Long-Range Sequence Modelling},\n journal = {arXiv preprint},\n url = {https://arxiv.org/abs/1911.05507},\n year = {2019},\n }"]]