github.com/QData/TextAttack @v0.3.11 sqlite
README
TextAttack 🐙
Generating adversarial examples for NLP models
[TextAttack Documentation on ReadTheDocs]
About • Setup • Usage • Design
About
TextAttack is a Python framework for adversarial attacks, data augmentation, and model training in NLP.
If you're looking for information about TextAttack's menagerie of pre-trained models, you might want the TextAttack Model Zoo page.
Slack Channel
For help and realtime updates related to TextAttack, please join the TextAttack Slack!
Why TextAttack?
There are lots of reasons to use TextAttack:
- Understand NLP models better by running different adversarial attacks on them and examining the output
- Research and develop different NLP adversarial attacks using the TextAttack framework and library of components
- Augment your dataset to increase model generalization and robustness downstream
- Train NLP models using just a single command (all downloads included!)
Setup
Installation
You should be running Python 3.6+ to use this package. A CUDA-compatible GPU is optional but will greatly improve code speed. TextAttack is available through pip:
pip install textattack
Once TextAttack is installed, you can run it via command-line (textattack ...)
or via python module (python -m textattack ...).
Tip: TextAttack downloads files to
~/.cache/textattack/by default. This includes pretrained models, dataset samples, and the configuration fileconfig.yaml. To change the cache path, set the environment variableTA_CACHE_DIR. (for example:TA_CACHE_DIR=/tmp/ textattack attack ...).
Usage
Help: textattack --help
TextAttack's main features can all be accessed via the textattack command. Two very
common commands are textattack attack <args>, and textattack augment <args>. You can see more
information about all commands using
textattack --help
or a specific command using, for example,
textattack attack --help
The examples/ folder includes scripts showing common TextAttack usage for training models, running attacks, and augmenting a CSV file.
The documentation website contains walkthroughs explaining basic usage of TextAttack, including building a custom transformation and a custom constraint..
Running Attacks: textattack attack --help
The easiest way to try out an attack is via the command-line interface, textattack attack.
Tip: If your machine has multiple GPUs, you can distribute the attack across them using the
--paralleloption. For some attacks, this can really help performance. (If you want to attack Keras models in parallel, please check outexamples/attack/attack_keras_parallel.pyinstead)
Here are some concrete examples:
TextFooler on BERT trained on the MR sentiment classification dataset:
textattack attack --recipe textfooler --model bert-base-uncased-mr --num-examples 100
DeepWordBug on DistilBERT trained on the Quora Question Pairs paraphrase identification dataset:
textattack attack --model distilbert-base-uncased-cola --recipe deepwordbug --num-examples 100
Beam search with beam width 4 and word embedding transformation and untargeted goal function on an LSTM:
textattack attack --model lstm-mr --num-examples 20 \
--search-method beam-search^beam_width=4 --transformation word-swap-embedding \
--constraints repeat stopword max-words-perturbed^max_num_words=2 embedding^min_cos_sim=0.8 part-of-speech \
--goal-function untargeted-classification
Tip: Instead of specifying a dataset and number of examples, you can pass
--interactiveto attack samples inputted by the user.
Attacks and Papers Implemented ("Attack Recipes"): textattack attack --recipe [recipe_name]
We include attack recipes which implement attacks from the literature. You can list attack recipes using textattack list attack-recipes.
To run an attack recipe: textattack attack --recipe [recipe_name]
| Attack Recipe Name | Goal Function | ConstraintsEnforced | Transformation | Search Method | Main Idea |
|---|---|---|---|---|---|
| Attacks on classification tasks, like sentiment classification and entailment: | |||||
a2t
|
Untargeted {Classification, Entailment} | Percentage of words perturbed, Word embedding distance, DistilBERT sentence encoding cosine similarity, part-of-speech consistency | Counter-fitted word embedding swap (or) BERT Masked Token Prediction | Greedy-WIR (gradient) | from (["Towards Improving Adversarial Training of NLP Models" (Yoo et al., 2021)](https://arxiv.org/abs/2109.00544)) |
alzantot |
Untargeted {Classification, Entailment} | Percentage of words perturbed, Language Model perplexity, Word embedding distance | Counter-fitted word embedding swap | Genetic Algorithm | from (["Generating Natural Language Adversarial Examples" (Alzantot et al., 2018)](https://arxiv.org/abs/1804.07998)) |
bae |
Untargeted Classification | USE sentence encoding cosine similarity | BERT Masked Token Prediction | Greedy-WIR | BERT masked language model transformation attack from (["BAE: BERT-based Adversarial Examples for Text Classification" (Garg & Ramakrishnan, 2019)](https://arxiv.org/abs/2004.01970)). |
bert-attack |
Untargeted Classification | USE sentence encoding cosine similarity, Maximum number of words perturbed | BERT Masked Token Prediction (with subword expansion) | Greedy-WIR | (["BERT-ATTACK: Adversarial Attack Against BERT Using BERT" (Li et al., 2020)](https://arxiv.org/abs/2004.09984)) |
checklist |
{Untargeted, Targeted} Classification | checklist distance | contract, extend, and substitutes name entities | Greedy-WIR | Invariance testing implemented in CheckList . (["Beyond Accuracy: Behavioral Testing of NLP models with CheckList" (Ribeiro et al., 2020)](https://arxiv.org/abs/2005.04118)) |
clare |
Untargeted {Classification, Entailment} | USE sentence encoding cosine similarity | RoBERTa Masked Prediction for token swap, insert and merge | Greedy | ["Contextualized Perturbation for Textual Adversarial Attack" (Li et al., 2020)](https://arxiv.org/abs/2009.07502)) |
deepwordbug |
{Untargeted, Targeted} Classification | Levenshtein edit distance | {Character Insertion, Character Deletion, Neighboring Character Swap, Character Substitution} | Greedy-WIR | Greedy replace-1 scoring and multi-transformation character-swap attack (["Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers" (Gao et al., 2018)](https://arxiv.org/abs/1801.04354) |
faster-alzantot |
Untargeted {Classification, Entailment} | Percentage of words perturbed, Language Model perplexity, Word embedding distance | Counter-fitted word embedding swap | Genetic Algorithm | Modified, faster version of the Alzantot et al. genetic algorithm, from (["Certified Robustness to Adversarial Word Substitutions" (Jia et al., 2019)](https://arxiv.org/abs/1909.00986)) |
hotflip (word swap) |
Untargeted Classification | Word Embedding Cosine Similarity, Part-of-speech match, Number of words perturbed | Gradient-Based Word Swap | Beam search | (["HotFlip: White-Box Adversarial Examples for Text Classification" (Ebrahimi et al., 2017)](https://arxiv.org/abs/1712.06751)) |
iga |
Untargeted {Classification, Entailment} | Percentage of words perturbed, Word embedding distance | Counter-fitted word embedding swap | Genetic Algorithm | Improved genetic algorithm -based word substitution from (["Natural Language Adversarial Attacks and Defenses in Word Level (Wang et al., 2019)"](https://arxiv.org/abs/1909.06723) |
input-reduction |
Input Reduction | Word deletion | Greedy-WIR | Greedy attack with word importance ranking , Reducing the input while maintaining the prediction through word importance ranking (["Pathologies of Neural Models Make Interpretation Difficult" (Feng et al., 2018)](https://arxiv.org/pdf/1804.07781.pdf)) | |
kuleshov |
Untargeted Classification | Thought vector encoding cosine similarity, Language model similarity probability | Counter-fitted word embedding swap | Greedy word swap | (["Adversarial Examples for Natural Language Classification Problems" (Kuleshov et al., 2018)](https://openreview.net/pdf?id=r1QZ3zbAZ)) |
pruthi |
Untargeted Classification | Minimum word length, Maximum number of words perturbed | {Neighboring Character Swap, Character Deletion, Character Insertion, Keyboard-Based Character Swap} | Greedy search | simulates common typos (["Combating Adversarial Misspellings with Robust Word Recognition" (Pruthi et al., 2019)](https://arxiv.org/abs/1905.11268) |
pso |
Untargeted Classification | HowNet Word Swap | Particle Swarm Optimization | (["Word-level Textual Adversarial Attacking as Combinatorial Optimization" (Zang et al., 2020)](https://www.aclweb.org/anthology/2020.acl-main.540/)) | |
pwws |
Untargeted Classification | WordNet-based synonym swap | Greedy-WIR (saliency) | Greedy attack with word importance ranking based on word saliency and synonym swap scores (["Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency" (Ren et al., 2019)](https://www.aclweb.org/anthology/P19-1103/)) | |
textbugger : (black-box) |
Untargeted Classification | USE sentence encoding cosine similarity | {Character Insertion, Character Deletion, Neighboring Character Swap, Character Substitution} | Greedy-WIR | ([(["TextBugger: Generating Adversarial Text Against Real-world Applications" (Li et al., 2018)](https://arxiv.org/abs/1812.05271)). |
textfooler |
Untargeted {Classification, Entailment} | Word Embedding Distance, Part-of-speech match, USE sentence encoding cosine |
Core symbols most depended-on inside this repo
from_pretrainedShape
Languages
Modules by API surface
Dependencies from manifests, versioned
For agents
$ claude mcp add TextAttack \
-- python -m otcore.mcp_server <graph>