new release v0.2.14

Improvements

Bug fixing
Matching documentation in Readme.md and the files in /doc folder
add checklist
add multilingual USE
add gradient-based word importance ranking
update to a more complete API documentation
add cola constraint
add the lazy loader

new release v0.2.12

Big Improvements

• add checklist
• add multilingual USE
• add gradient-based word importance ranking
• update to a more complete API documentation
• add cola constraint
• add the lazy loader

0.2.0: 70+ pre-trained models, BERT-based attacks, input reduction, maximizing goal functions, and more!

Big Improvements

• Add tons of (over 70!) pre-trained models (#192, see Model Zoo page!)
• Data augmentation integrated into training! (#195, thanks @jakegrigsby)
• Allow for maximization goal functions (#151, thanks @uvafan )

New Attacks

• Add the Improved Genetic Algorithm (#183, thanks @sherlockyyc!)
• Add BAE and BERT-Attack attack recipes (#160)
• Add PWWS attack (#168, thanks @jakegrigsby)
• Add typo-based attack from Pruthi et al. (#191, thanks @jakegrigsby )
• Easy Data Augmentation augmentation recipe (#168, thanks @jakegrigsby)
• Add input reduction attack from Feng et al. (#161, thanks @uvafan)

Smaller Improvements

• more accurate attack recipes for BAE and TextFooler (#199)
• important fixes to model training code (#186, thanks so much @jind11!!)
• abstract classes, better string representations when printing attacks to console (#202)
• genetic algorithm improvements (#160, thanks @jinyongyoo )
• fixes to parallel attacks (#164, thanks @jinyongyoo )
• datasets to test out T5 on seq2seq attacks (#176)

Bug Fixes

• correctly print attack perturbed words in color, even when words are deleted & inserted (#200)
• fix print_step bug with alzantot recipe (#195, thanks @heytitle for reporting!)
• fix some annoying issues with dependency versioning

`textattack` command, train models and augment CSVs from the command line, better docs, and more

Version 0.1.0 is our biggest release yet! Here's a summary of the changes:

Backwards compatibility note: python -m textattack <args> is renamed to python -m textattack attack <args>. Or, better yet, textattack attack <args>!

Big improvements

• add textattack command (#132)
  • add textattack augment, textattack eval, textattack attack, textattack list (#132)
  • add textattack train, textattack peek-dataset, and lots of infrastructure for training models (#139)
• Move all datasets to nlp format; temporarily remove non-NLP datasets (AGNews, English->German translation) (#134)

Smaller improvements

• Better output formatted -- show labels ("Positive", "Entailment") and confidence score (91%) in output (#142)
• add MaxLengthModification constraint that prevents modifications beyond tokenizer max_length (#143)
• Add pytest tests and code formatting with black; run tests on Python 3.6, 3.7, 3.8 with Travis CI (#127, #136)
• Update NLTK part-of-speech constraint and support part-of-speech tagging with FLAIR instead (#135)
• add BERTScore constrained based on "BERTScore: Evaluating Text Generation with BERT" (Zhang et al, 2019) (#146)
• make logging to file optional (#145)
• Updates to Checkpoint class; track attack results in a worklist; attack resume fixes (#128, #141)
• Silence Weights & Biases warning message when not being used (#130)
• Optionally point all cache directories to a universal cache directory, TA_CACHE_DIR (#150)

Bug fixes

• Fix a bug that can be encountered when resuming attacks from checkpoints (#149)
• Fix a bug in Greedy word-importance-ranking deletion (#152)
• Documentation updates and fixes (#153)

🤗 HuggingFace integration, TextBugger, wider command-line support

big changes:

• load transformers models from the command-line using the --model-from-huggingface option
• load nlp datasets from the command-line using the --dataset-from-nlp option
• command-line support for custom attacks, models, and datasets: --attack-from-file, --model-from-file, --dataset-from-file
• implement attack recipe for TextBugger attack
• add WordDeletion transformation

small changes:

• support white-box transformations via the command-line
• allow Greedy-WIR to rank things in order of ascending importance
• use fast tokenizers behind the scenes
• fix some bugs with the attack Checkpoint class
• some abbreviated syntax (textattack.shared.utils.get_logger() -> textattack.shared.logger, textattack.shared.utils.get_device() -> textattack.shared.utils.device)
• substantially decrease overall TokenizedText memory usage

Attack checkpoints, PreTransformationConstraints, Weights & Biases, and Docs

0.0.2: Better documentation, attack checkpoints, PreTransformationConstraints, and more

• Major documentation restructure (check it out)
• Some refactoring and variable renames to make it easier to jump right in and start working with TextAttack
• Introduction of PreTransformationConstraints: constraints now can be applied before the transformation to prevent word modifications at certain indices. This abstraction allowed us to remove the notion of modified_indices from search methods, which paves the way for us to introduce attacks that insert or delete words and phrases, as opposed to simply swapping words.
• Separation of Attack and SearchMethod: search methods are now a parameter to the attack instead of different subclasses of Attack. This syntax fits better with our framework and enforces a clearer sense of separation between the responsibilities of the attack and those of the search method.
• Transformation and constraint compatibility: Constraints now ensure they're compatible with a specific transformation via a check_compatibility method
• Goal function scores are now normalized between 0 and 1: UntargetedClassification and NonOverlappingOutput now return scores between 0 and 1.
• Attack Checkpoints: Attacks can now save and resume their progress. This is really useful for running long, expensive attacks. python-m textattack supports new checkpoint-related arguments: --checkpoint-interval and --checkpoint-dir
• Weights & Biases: Log attack results to Weights & Biases by adding the --enable-wandb flag