TextAttack | TextAttack 🐙 is a Python framework | Machine Learning library
kandi X-RAY | TextAttack Summary
kandi X-RAY | TextAttack Summary
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.
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Top functions reviewed by kandi - BETA
- 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
TextAttack Key Features
TextAttack Examples and Code Snippets
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 \
--target_dat
@article{morris2020reevaluating,
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},
year={20
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
Trending Discussions on TextAttack
QUESTION
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:
...ANSWER
Answered 2022-Jan-31 at 18:53The problem was with updating the config
variables for my new model.
Changes:
QUESTION
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
:
ANSWER
Answered 2022-Jan-25 at 15:12I've now installed and initialised GIT LFS in cloned folder.
Terminal:
QUESTION
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
Installations:
...ANSWER
Answered 2022-Jan-11 at 12:49Pointed 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
toneural_compressor
. The correct way to import would now be fromoptimum.intel.neural_compressor.quantization import IncQuantizerForSequenceClassification
Concerning thegraphcore
subpackage, you need to install it first withpip install optimum[graphcore]
Furthermore you'll need to have access to an IPU in order to use it.
Solution
QUESTION
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.
...ANSWER
Answered 2021-Apr-10 at 12:42This 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:
QUESTION
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.
ANSWER
Answered 2021-Jan-25 at 10:02The pipeline already includes the encoder. Instead of
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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