QData/TextAttack

Health v2 · maintenance only

Adoption not part of the score

3469 stars · 458 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

TextAttack is a Python framework for generating adversarial examples against NLP models, as well as for data augmentation and model training in NLP. It is usable both as a command-line tool and as a Python library, with a large library of attack recipes and pre-trained models.

Use cases

• run adversarial attacks on NLP models to test robustness
• generate adversarial examples for text classifiers
• augment a dataset to improve model generalization
• train NLP models with a single command
• research and develop new NLP adversarial attack methods
• evaluate sentiment analysis models against perturbations

When to choose

• you need to benchmark or stress-test NLP model robustness
• you want a ready-made library of adversarial attack algorithms for text
• you need automated data augmentation for NLP datasets
• you want to train text classification models quickly from the CLI

When to avoid

• you need adversarial attacks for images or other non-text modalities
• you only need general-purpose NLP pipelines without adversarial features
• you require a lightweight dependency-free solution

Facets

framework · maturity active

machine-learning nlp security data-science cli machine-learning security python cli cross-platform adversarial-attacks adversarial-examples data-augmentation robustness adversarial-machine-learning natural-language-processing

Member repositories

For agents

markdown · JSON · MCP: product_card(name="QData/TextAttack")