TextAttack | TextAttack 🐙 is a Python framework | Machine Learning library

 by   QData Python Version: v0.3.8 License: MIT

kandi X-RAY | TextAttack Summary

kandi X-RAY | TextAttack Summary

TextAttack is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. TextAttack has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.

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.

            kandi-support Support

              TextAttack has a medium active ecosystem.
              It has 2377 star(s) with 321 fork(s). There are 33 watchers for this library.
              It had no major release in the last 12 months.
              There are 38 open issues and 211 have been closed. On average issues are closed in 117 days. There are 7 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of TextAttack is v0.3.8

            kandi-Quality Quality

              TextAttack has 0 bugs and 0 code smells.

            kandi-Security Security

              TextAttack has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              TextAttack code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              TextAttack is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              TextAttack releases are available to install and integrate.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              TextAttack saves you 5416 person hours of effort in developing the same functionality from scratch.
              It has 13138 lines of code, 863 functions and 228 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed TextAttack and discovered the below as its top functions. This is intended to give you an instant insight into TextAttack implemented functionality, and help decide if they suit your requirements.
            • Performs a search
            • Get the best neighbors of the given result
            • Perturb a single member
            • Return the difference between two words
            • Get the gradient of the given text input
            • Builds the training dataset
            • Generate adversarial training sets
            • Train the text attack
            • Return whether the transformation is applied to the embedding
            • Download a text attachment
            • Return the transformed transformations
            • Create an attack from a queue
            • Run text attack
            • Download a text file from S3
            • Returns a list of transformed texts
            • Returns a list of transformed words
            • Create a pretrained model from pretrained
            • Load a pretrained model from pretrained
            • Encodes sentences
            • Calculates the accuracy of the attack
            • Calculate the performance of the results
            • Calculate the average attack score
            • Get transformed transformations
            • Perform a search on the target
            • Get gradient for given text_input
            • Perform a search using the search method
            Get all kandi verified functions for this library.

            TextAttack Key Features

            No Key Features are available at this moment for TextAttack.

            TextAttack Examples and Code Snippets

            copy iconCopy
            python3 classification_attack.py \
                    --dataset_path path_to_data_samples_to_attack  \
                    --target_model Type_of_taget_model (bert,wordCNN,wordLSTM) \
                    --counter_fitting_cos_sim_path path_to_top_50_synonym_file \
            Reevaluating Adversarial Examples in Natural Language,Citation
            Jupyter Notebookdot img2Lines of Code : 6dot img2no licencesLicense : No License
            copy iconCopy
              title={Reevaluating Adversarial Examples in Natural Language},
              author={Morris, John X and Lifland, Eli and Lanchantin, Jack and Ji, Yangfeng and Qi, Yanjun},
              journal={arXiv preprint arXiv:2004.14174},
            Running the attacks with adjusted thresholds
            Jupyter Notebookdot img3Lines of Code : 2dot img3no licencesLicense : No License
            copy iconCopy
            textattack attack --model bert-base-uncased-mr --attack-from-file section_6_adjusted_attacks/recipes/alzantot_2018_adjusted.py --num-examples 5
            textattack attack --model lstm-ag-news --attack-from-file section_6_adjusted_attacks/recipes/textfooler_j  

            Community Discussions


            onnx.load() | ALBert throws DecodeError: Error parsing message
            Asked 2022-Jan-31 at 18:53

            Goal: re-develop this BERT Notebook to use textattack/albert-base-v2-MRPC.

            Kernel: conda_pytorch_p36. PyTorch 1.8.1+cpu.

            I convert a PyTorch / HuggingFace Transformers model to ONNX and store it. DecodeError occurs on onnx.load().

            Are my ONNX files corrupted? This seems to be a common solution; but I don't know how to check for this.

            ALBert Notebook and model files on Google Colab.

            I've also this Git Issue, detailing debugging.

            Problem isn't...
            • Quantisation - any Quantisation code I try, throws the same error.
            • Optimisation - error occurs with or without Optimisation.

            Section 2.2 Quantize ONNX model:



            Answered 2022-Jan-31 at 18:53

            The problem was with updating the config variables for my new model.


            Source https://stackoverflow.com/questions/70787151


            OSError: You seem to have cloned a repository without having git-lfs installed. Please install git-lfs and run git lfs install followed by git lfs pul
            Asked 2022-Jan-25 at 15:12

            I'm using Jupyter Labs on AWS SageMaker.

            Kernel: conda_pytorch_p36 and did Restart & Run All.

            I git cloned this repo.

            Attempt at installing git-lfs:



            Answered 2022-Jan-25 at 15:12

            I've now installed and initialised GIT LFS in cloned folder.


            Source https://stackoverflow.com/questions/70850015


            HuggingFace - 'optimum' ModuleNotFoundError
            Asked 2022-Jan-11 at 12:49

            I want to run the 3 code snippets from this webpage.

            I've made all 3 one post, as I am assuming it all stems from the same problem of optimum not having been imported correctly?

            Kernel: conda_pytorch_p36




            Answered 2022-Jan-11 at 12:49

            Pointed out by a Contributor of HuggingFace, on this Git Issue,

            The library previously named LPOT has been renamed to Intel Neural Compressor (INC), which resulted in a change in the name of our subpackage from lpot to neural_compressor. The correct way to import would now be from optimum.intel.neural_compressor.quantization import IncQuantizerForSequenceClassification Concerning the graphcore subpackage, you need to install it first with pip install optimum[graphcore] Furthermore you'll need to have access to an IPU in order to use it.


            Source https://stackoverflow.com/questions/70607224


            No module named certifi
            Asked 2021-Apr-11 at 08:15

            When executing python3 (Python 3.6.8) script on a local directory, it works well, but when running sbatch job in slurm, complains about certifi.



            Answered 2021-Apr-10 at 12:42

            This could mean that /usr/local/lib/python3.6/site-packages/ is not your PYTHONPATH environment variable that sbatch job in slurm has access to. You can either add it or append it during runtime:

            Source https://stackoverflow.com/questions/67034437


            HuggingFace Bert Sentiment analysis
            Asked 2021-Jan-25 at 10:02

            I am getting the following error :

            AssertionError: text input must of type str (single example), List[str] (batch or single pretokenized example) or List[List[str]] (batch of pretokenized examples)., when I run classifier(encoded). My text type is str so I am not sure what I am doing wrong. Any help is very appreciated.



            Answered 2021-Jan-25 at 10:02

            The pipeline already includes the encoder. Instead of

            Source https://stackoverflow.com/questions/65881820

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install TextAttack

            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:. 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 file config.yaml. To change the cache path, set the environment variable TA_CACHE_DIR. (for example: TA_CACHE_DIR=/tmp/ textattack attack ...).


            see example code: https://github.com/QData/TextAttack/blob/master/examples/attack/attack_camembert.py for using our framework to attack French-BERT. see tutorial notebook: https://textattack.readthedocs.io/en/latest/2notebook/Example_4_CamemBERT.html for using our framework to attack French-BERT. See README_ZH.md for our README in Chinese.
            Find more information at:

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