- 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)
Homepage: https://github.com/karpathy/char-rnn/blob/master/data/tinyshakespeare/input.txt
Source code:
tfds.datasets.tiny_shakespeare.Builder
Versions:
1.0.0
(default): No release notes.
Download size:
1.06 MiB
Dataset size:
1.06 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'test' |
1 |
'train' |
1 |
'validation' |
1 |
- Feature structure:
FeaturesDict({
'text': Text(shape=(), dtype=string),
})
- Feature documentation:
Feature | Class | Shape | Dtype | Description |
---|---|---|---|---|
FeaturesDict | ||||
text | Text | string |
Supervised keys (See
as_supervised
doc):None
Figure (tfds.show_examples): Not supported.
Examples (tfds.as_dataframe):
- Citation:
@misc{
author={Karpathy, Andrej},
title={char-rnn},
year={2015},
howpublished={\url{https://github.com/karpathy/char-rnn} }
}