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""First Citizen:\nBefore we proceed any further, hear me speak.\n\nAll:\nSpeak, speak.\n\nFirst Citiz"(...TRUNCATED)

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':

To use for e.g. character modelling:

d = datasets.load_dataset(name='tiny_shakespeare')['train']
d = 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 = 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 and Leaderboards

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Dataset Structure

Data Instances


  • 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.


  • text: a string feature.

Data Splits

name train validation test
default 1 1 1

Dataset Creation

Curation Rationale

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Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotation process

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Who are the annotators?

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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

  author={Karpathy, Andrej},


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

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