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README.md
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---
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license: bsd-2-clause
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---
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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 section describes suitable use cases for the dataset. -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
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[More Information Needed]
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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` - 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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[More Information Needed]
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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.
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