File size: 4,442 Bytes
3f1c548 abf98c4 3f1c548 abf98c4 a3ee264 abf98c4 a3ee264 2cfe3a5 a3ee264 abf98c4 cbc2e3c abf98c4 81e0d93 abf98c4 a3ee264 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 |
---
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(partition_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/). |