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r-three/shakespeare-dialogue

Taken from Andrej Karpathy and repurposed for UofT's CSC 413 Deep Learning 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/. Usage To use for e.g. character modelling: from datasets import load_dataset # Load the dataset dataset = load_dataset("r-three/shakespeare-dialogue") # Access examples for… See the full description on the dataset page: https://huggingface.co/datasets/r-three/shakespeare-dialogue.

sourceHugging Facecc0-1.0updated 1y agoView on Hugging Face
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Taken from Andrej Karpathy and repurposed for UofT's CSC 413 Deep Learning 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/.

Usage

To use for e.g. character modelling:

python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("r-three/shakespeare-dialogue")

# Access examples
for example in dataset['train'][:5]:
    print(f"{example['character']}: {example['text']}")
    print(f"From: {example['work']}\n")

# Filter by play
hamlet = dataset['train'].filter(lambda x: 'HAMLET' in x['work'])

# Filter by character
hamlet_lines = dataset['train'].filter(lambda x: x['character'] == 'Hamlet')

# Get character statistics
from collections import Counter
characters = Counter(dataset['train']['character'])
print(f"Top 10 characters: {characters.most_common(10)}")

Source

Public domain works by William Shakespeare, sourced from the Karpathy char-rnn repository.

To cite:

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

License

This dataset is in the public domain (CC0-1.0) as Shakespeare's works are no longer under copyright.