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  • Description:

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 = tfds.load(name='tiny_shakespeare')['train']
d = d.map(lambda x: tf.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)
Split Examples
'test' 1
'train' 1
'validation' 1
  • Feature structure:
    'text': Text(shape=(), dtype=string),
  • Feature documentation:
Feature Class Shape Dtype Description
text Text string
  • Citation:
  author={Karpathy, Andrej},
  howpublished={\url{https://github.com/karpathy/char-rnn} }