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CONTRIBUTING.md

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If you are a **user of a dataset**, the main source of information should be the dataset paper if it is available: we recommend pulling information from there into the relevant paragraphs of the template. We also eagerly welcome discussions on the [Considerations for Using the Data](https://github.com/huggingface/datasets/blob/main/templates/README_guide.md#considerations-for-using-the-data) based on existing scholarship or personal experience that would benefit the whole community.
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Finally, if you want more information on the how and why of dataset cards, we strongly recommend reading the foundational works [Datasheets for Datasets](https://arxiv.org/abs/1803.09010) and [Data Statements for NLP](https://www.aclweb.org/anthology/Q18-1041/).
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Finally, if you want more information on the how and why of dataset cards, we strongly recommend reading the foundational works [Datasheets for Datasets](https://huggingface.co/papers/1803.09010) and [Data Statements for NLP](https://www.aclweb.org/anthology/Q18-1041/).
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Thank you for your contribution!
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README.md

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## BibTeX
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If you want to cite our 🤗 Datasets library, you can use our [paper](https://arxiv.org/abs/2109.02846):
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If you want to cite our 🤗 Datasets library, you can use our [paper](https://huggingface.co/papers/2109.02846):
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```bibtex
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@inproceedings{lhoest-etal-2021-datasets,

docs/source/dataset_card.mdx

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# Create a dataset card
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Each dataset should have a dataset card to promote responsible usage and inform users of any potential biases within the dataset.
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This idea was inspired by the Model Cards proposed by [Mitchell, 2018](https://arxiv.org/abs/1810.03993).
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This idea was inspired by the Model Cards proposed by [Mitchell, 2018](https://huggingface.co/papers/1810.03993).
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Dataset cards help users understand a dataset's contents, the context for using the dataset, how it was created, and any other considerations a user should be aware of.
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Creating a dataset card is easy and can be done in just a few steps:

templates/README_guide.md

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Provide descriptions of specific biases that are likely to be reflected in the data, and state whether any steps were taken to reduce their impact.
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For Wikipedia text, see for example [Dinan et al 2020 on biases in Wikipedia (esp. Table 1)](https://arxiv.org/abs/2005.00614), or [Blodgett et al 2020](https://www.aclweb.org/anthology/2020.acl-main.485/) for a more general discussion of the topic.
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For Wikipedia text, see for example [Dinan et al 2020 on biases in Wikipedia (esp. Table 1)](https://huggingface.co/papers/2005.00614), or [Blodgett et al 2020](https://www.aclweb.org/anthology/2020.acl-main.485/) for a more general discussion of the topic.
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If analyses have been run quantifying these biases, please add brief summaries and links to the studies here.
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