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--- |
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license: bsd-2-clause |
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task_categories: |
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- text-generation |
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language: |
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- en |
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size_categories: |
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- 1M<n<10M |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: "shakespeare.csv" |
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--- |
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# Dataset Card for Dataset Name |
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This dataset is a part of the [LEAF](https://leaf.cmu.edu/) benchmark. |
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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. |
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## Dataset Details |
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### Dataset Description |
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Each sample is comprised of a text of 80 characters (x) and a next character (y). |
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- **Curated by:** [LEAF](https://leaf.cmu.edu/) |
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- **Language(s) (NLP):** English |
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- **License:** BSD 2-Clause License |
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### Dataset Sources |
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The code from the original repository was adopted to post it here. |
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- **Repository:** https://github.com/TalwalkarLab/leaf |
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- **Paper:** https://arxiv.org/abs/1812.01097 |
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## Uses |
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This dataset is intended to be used in Federated Learning settings. |
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A pair of a character and a play denotes a unique user in the federation. |
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### Direct Use |
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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). |
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To partition the dataset, do the following. |
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1. Install the package. |
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```bash |
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pip install flwr-datasets |
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``` |
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2. Use the HF Dataset under the hood in Flower Datasets. |
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```python |
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from flwr_datasets import FederatedDataset |
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from flwr_datasets.partitioner import NaturalIdPartitioner |
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fds = FederatedDataset( |
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dataset="flwrlabs/shakespeare", |
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partitioners={"train": NaturalIdPartitioner(partition_by="character_id")} |
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) |
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partition = fds.load_partition(partition_id=0) |
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``` |
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## Dataset Structure |
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The dataset contains only train split. The split in the paper happens at each node only (no centralized dataset). |
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The dataset is comprised of columns: |
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* `character_id`: str - id denoting a pair of character + play (node in federated learning settings) |
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* `x`: str - text of 80 characters |
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* `y`: str - single character following the `x` |
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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. |
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## Dataset Creation |
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### Curation Rationale |
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This dataset was created as a part of the [LEAF](https://leaf.cmu.edu/) benchmark. |
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### Source Data |
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[The Complete Works of William Shakespeare](https://www.gutenberg.org/ebooks/100) |
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#### Data Collection and Processing |
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For the preprocessing details, please refer to the original paper and the source code. |
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#### Who are the source data producers? |
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William Shakespeare |
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## Citation |
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When working on the LEAF benchmark, please cite the original paper. If you're using this dataset with Flower Datasets, you can cite Flower. |
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**BibTeX:** |
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``` |
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@article{DBLP:journals/corr/abs-1812-01097, |
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author = {Sebastian Caldas and |
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Peter Wu and |
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Tian Li and |
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Jakub Kone{\v{c}}n{\'y} and |
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H. Brendan McMahan and |
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Virginia Smith and |
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Ameet Talwalkar}, |
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title = {{LEAF:} {A} Benchmark for Federated Settings}, |
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journal = {CoRR}, |
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volume = {abs/1812.01097}, |
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year = {2018}, |
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url = {http://arxiv.org/abs/1812.01097}, |
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eprinttype = {arXiv}, |
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eprint = {1812.01097}, |
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timestamp = {Wed, 23 Dec 2020 09:35:18 +0100}, |
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biburl = {https://dblp.org/rec/journals/corr/abs-1812-01097.bib}, |
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bibsource = {dblp computer science bibliography, https://dblp.org} |
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} |
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``` |
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``` |
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@article{DBLP:journals/corr/abs-2007-14390, |
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author = {Daniel J. Beutel and |
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Taner Topal and |
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Akhil Mathur and |
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Xinchi Qiu and |
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Titouan Parcollet and |
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Nicholas D. Lane}, |
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title = {Flower: {A} Friendly Federated Learning Research Framework}, |
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journal = {CoRR}, |
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volume = {abs/2007.14390}, |
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year = {2020}, |
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url = {https://arxiv.org/abs/2007.14390}, |
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eprinttype = {arXiv}, |
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eprint = {2007.14390}, |
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timestamp = {Mon, 03 Aug 2020 14:32:13 +0200}, |
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biburl = {https://dblp.org/rec/journals/corr/abs-2007-14390.bib}, |
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bibsource = {dblp computer science bibliography, https://dblp.org} |
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} |
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``` |
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## Dataset Card Contact |
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In case of any doubts, please contact [Flower Labs](https://flower.ai/). |
