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Developing documentation guidelines and easy-to-use templates for datasetsand models is a challenging task, especially given the variety of backgrounds,skills, and incentives of the people involved in the building of naturallanguage processing (NLP) tools. Nevertheless, the adoption of standarddocumentation practices across the field of NLP promotes more accessible anddetailed descriptions of NLP datasets and models, while supporting researchersand developers in reflecting on their work. To help with the standardization ofdocumentation, we present two case studies of efforts that aim to developreusable documentation templates -- the HuggingFace data card, a generalpurpose card for datasets in NLP, and the GEM benchmark data and model cardswith a focus on natural language generation. We describe our process fordeveloping these templates, including the identification of relevantstakeholder groups, the definition of a set of guiding principles, the use ofexisting templates as our foundation, and iterative revisions based onfeedback.

Reusable Templates and Guides For Documenting Datasets and Models for  Natural Language Processing and Generation: A Case Study of the HuggingFace  and GEM Data and Model Cards