Dataset:



Dataset Card for "tiny_shakespeare"

Dataset Summary

40,000 lines of Shakespeare from a variety of Shakespeare's plays. Featured in Andrej Karpathy's blog post 'The Unreasonable Effectiveness of Recurrent Neural Networks': http://karpathy.github.io/2015/05/21/rnn-effectiveness/.

To use for e.g. character modelling:

d = datasets.load_dataset(name='tiny_shakespeare')['train']
d = d.map(lambda x: datasets.Value('strings').unicode_split(x['text'], 'UTF-8'))
# train split includes vocabulary for other splits
vocabulary = sorted(set(next(iter(d)).numpy()))
d = d.map(lambda x: {'cur_char': x[:-1], 'next_char': x[1:]})
d = d.unbatch()
seq_len = 100
batch_size = 2
d = d.batch(seq_len)
d = d.batch(batch_size)

Supported Tasks

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Languages

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Dataset Structure

We show detailed information for up to 5 configurations of the dataset.

Data Instances

default

  • Size of downloaded dataset files: 1.06 MB
  • Size of the generated dataset: 1.06 MB
  • Total amount of disk used: 2.13 MB

An example of 'train' looks as follows.

{
    "text": "First Citizen:\nBefore we proceed any further, hear me "
}

Data Fields

The data fields are the same among all splits.

default

  • text: a string feature.

Data Splits Sample Size

name train validation test
default 1 1 1

Dataset Creation

Curation Rationale

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Source Data

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Annotations

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

@misc{
  author={Karpathy, Andrej},
  title={char-rnn},
  year={2015},
  howpublished={\url{https://github.com/karpathy/char-rnn}}
}

Contributions

Thanks to @thomwolf, @lewtun, @patrickvonplaten for adding this dataset.

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