---
license: bsd-2-clause
task_categories:
- text-generation
language:
- en
size_categories:
- 1M<n<10M
configs:
- config_name: default
  data_files:
  - split: train
    path: "shakespeare.csv"
---
# Dataset Card for Dataset Name

This dataset is a part of the [LEAF](https://leaf.cmu.edu/) benchmark. 
The Shakespeare dataset is built from [The Complete Works of William Shakespeare](https://www.gutenberg.org/ebooks/100) with the goal of the next character prediction.

## Dataset Details

### Dataset Description

Each sample is comprised of a text of 80 characters (x) and a next character (y). 

- **Curated by:** [LEAF](https://leaf.cmu.edu/)
- **Language(s) (NLP):** English
- **License:** BSD 2-Clause License

### Dataset Sources

The code from the original repository was adopted to post it here. 

- **Repository:** https://github.com/TalwalkarLab/leaf
- **Paper:** https://arxiv.org/abs/1812.01097

## Uses

This dataset is intended to be used in Federated Learning settings. 
A pair of a character and a play  denotes a unique user in the federation.

### Direct Use

<!-- This section describes suitable use cases for the dataset. -->

[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->

[More Information Needed]

## Dataset Structure

The dataset contains only train split. The split in the paper happens at each node only (no centralized dataset). 
The dataset is comprised of columns: 
* `character_id`: str - id denoting a pair of character + play (node in federated learning settings)
* `x`: str - text of 80 characters
* `y`: str - single character following the `x`

Please note that the data is temporal. Therefore, caution is needed when dividing it so as not to leak the information from the train set.

## Dataset Creation

### Curation Rationale

This dataset was created as a part of the [LEAF](https://leaf.cmu.edu/) benchmark.

### Source Data

[The Complete Works of William Shakespeare](https://www.gutenberg.org/ebooks/100)

#### Data Collection and Processing

For the preprocessing details, please refer to the original paper and the source code.

#### Who are the source data producers?

William Shakespeare

## Citation

When working on the LEAF benchmark, please cite the original paper. If you're using this dataset with Flower Datasets, you can cite Flower.

**BibTeX:**
```
@article{DBLP:journals/corr/abs-1812-01097,
  author       = {Sebastian Caldas and
                  Peter Wu and
                  Tian Li and
                  Jakub Kone{\v{c}}n{\'y} and
                  H. Brendan McMahan and
                  Virginia Smith and
                  Ameet Talwalkar},
  title        = {{LEAF:} {A} Benchmark for Federated Settings},
  journal      = {CoRR},
  volume       = {abs/1812.01097},
  year         = {2018},
  url          = {http://arxiv.org/abs/1812.01097},
  eprinttype    = {arXiv},
  eprint       = {1812.01097},
  timestamp    = {Wed, 23 Dec 2020 09:35:18 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1812-01097.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
```
```
@article{DBLP:journals/corr/abs-2007-14390,
  author       = {Daniel J. Beutel and
                  Taner Topal and
                  Akhil Mathur and
                  Xinchi Qiu and
                  Titouan Parcollet and
                  Nicholas D. Lane},
  title        = {Flower: {A} Friendly Federated Learning Research Framework},
  journal      = {CoRR},
  volume       = {abs/2007.14390},
  year         = {2020},
  url          = {https://arxiv.org/abs/2007.14390},
  eprinttype    = {arXiv},
  eprint       = {2007.14390},
  timestamp    = {Mon, 03 Aug 2020 14:32:13 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2007-14390.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
```

## Dataset Card Contact

In case of any doubts, please contact Flower Labs.