# TextAttack: Lessons learned in designing Python frameworks for NLP

arXiv: https://arxiv.org/abs/2010.01724 · HTML: https://mlsanity.com/paper/2010.01724/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [Multi-granularity Textual Adversarial Attack with Behavior Cloning](https://mlsanity.com/paper/2109.04367/) (2021)
- [PTP: Boosting Stability and Performance of Prompt Tuning with Perturbation-Based Regularizer](https://mlsanity.com/paper/2305.02423/) (2023)
- [ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation](https://mlsanity.com/paper/2210.12396/) (2022)

## Abstract (arXiv)

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.