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---
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 dataset is designed to be used in FL settings. We recommend using [Flower Dataset](https://flower.ai/docs/datasets/) (flwr-datasets) and [Flower](https://flower.ai/docs/framework/) (flwr).
To partition the dataset, do the following.
1. Install the package.
```bash
pip install flwr-datasets
```
2. Use the HF Dataset under the hood in Flower Datasets.
```python
from flwr_datasets import FederatedDataset
from flwr_datasets.partitioner import NaturalIdPartitioner
fds = FederatedDataset(
dataset="flwrlabs/shakespeare",
partitioners={"train": NaturalIdPartitioner(partition_by="character_id")}
)
partition = fds.load_partition(node_id=0)
```
## 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](https://flower.ai/). |