Update on GitHub

			

			from nlp import load_dataset

			dataset = load_dataset("tiny_shakespeare")
			
		

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 = nlp.load_dataset(name='tiny_shakespeare')['train'] d = d.map(lambda x: nlp.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) ```

Citation

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

Models trained or fine-tuned on tiny_shakespeare

None yet. Start fine-tuning now =)