  TextAttack is an open-source Python toolkit for adversarial attacks,
adversarial training, and data augmentation in NLP. TextAttack unites 15+
papers from the NLP adversarial attack literature into a single framework, with
many components reused across attacks. This framework allows both researchers
and developers to test and study the weaknesses of their NLP models. To build
such an open-source NLP toolkit requires solving some common problems: How do
we enable users to supply models from different deep learning frameworks? How
can we build tools to support as many different datasets as possible? We share
our insights into developing a well-written, well-documented NLP Python
framework in hope that they can aid future development of similar packages.
