Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
< 1K
Tags:
shakespeare
License:
File size: 1,780 Bytes
02f25e7 d7e2380 484e29a b0cb689 af5226c b0cb689 a5a9a71 b0cb689 3cbcc74 b0cb689 |
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---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 4077942
num_examples: 30
- name: validation
num_bytes: 245785
num_examples: 2
- name: test
num_bytes: 506679
num_examples: 4
download_size: 3073023
dataset_size: 4830406
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
task_categories:
- text-generation
language:
- en
tags:
- shakespeare
size_categories:
- n<1K
license: mit
pretty_name: shakespearefirstfolio
---
# shakespearefirstfolio
## About
🎭 Shakespeare's First Folio (a collection of 36 of Shakespeare's plays) as a Hugging Face dataset!
## Description
In 2015, Andrej Karpathy wrote a post called "The Unreasonable Effectiveness of Recurrent Neural Networks" in his blog. For the needs of this post, he created tinyshakespeare, a subset of Shakespeare's works in a single 40,000 lines file. Surprisingly, language models trained from scratch on this tiny dataset can produce samples that look very close to those written by Shakespeare himself.
Since then, tinyshakespeare has been the defacto dataset used as a first test while developing language models. Unfortunately, it has some problems:
1) It is a single file, which makes further processing difficult
2) It does not contain all of Shakespeare's works
3) It is not clear exactly what works and to what extend are included
This dataset tries to address these problems. It is ~4 times bigger than tinyshakespeare.
It was manually collected from [Folger Shakespeare Library](https://www.folger.edu/).
## Usage
import datasets
dataset = datasets.load_dataset("gvlassis/shakespearefirstfolio") |