Data augmentation is an important component in the robustness evaluation of\nmodels in natural language processing (NLP) and in enhancing the diversity of\nthe data they are trained on. In this paper, we present NL-Augmenter, a new\nparticipatory Python-based natural language augmentation framework which\nsupports the creation of both transformations (modifications to the data) and\nfilters (data splits according to specific features). We describe the framework\nand an initial set of 117 transformations and 23 filters for a variety of\nnatural language tasks. We demonstrate the efficacy of NL-Augmenter by using\nseveral of its transformations to analyze the robustness of popular natural\nlanguage models. The infrastructure, datacards and robustness analysis results\nare available publicly on the NL-Augmenter repository\n(https://github.com/GEM-benchmark/NL-Augmenter).\n