A Model for Natural Language Attack on Text Classification and Inference

Overview

TextFooler

A Model for Natural Language Attack on Text Classification and Inference

This is the source code for the paper: Jin, Di, et al. "Is BERT Really Robust? Natural Language Attack on Text Classification and Entailment." arXiv preprint arXiv:1907.11932 (2019). If you use the code, please cite the paper:

@article{jin2019bert,
  title={Is BERT Really Robust? Natural Language Attack on Text Classification and Entailment},
  author={Jin, Di and Jin, Zhijing and Zhou, Joey Tianyi and Szolovits, Peter},
  journal={arXiv preprint arXiv:1907.11932},
  year={2019}
}

Data

Our 7 datasets are here.

Prerequisites:

Required packages are listed in the requirements.txt file:

pip install -r requirements.txt

How to use

  • Run the following code to install the esim package:
cd ESIM
python setup.py install
cd ..
python comp_cos_sim_mat.py [PATH_TO_COUNTER_FITTING_WORD_EMBEDDINGS]
  • Run the following code to generate the adversaries for text classification:
python attack_classification.py

For Natural langauge inference:

python attack_nli.py

Examples of run code for these two files are in run_attack_classification.py and run_attack_nli.py. Here we explain each required argument in details:

  • --dataset_path: The path to the dataset. We put the 1000 examples for each dataset we used in the paper in the folder data.
  • --target_model: Name of the target model such as ''bert''.
  • --target_model_path: The path to the trained parameters of the target model. For ease of replication, we shared the trained BERT model parameters, the trained LSTM model parameters, and the trained CNN model parameters on each dataset we used.
  • --counter_fitting_embeddings_path: The path to the counter-fitting word embeddings.
  • --counter_fitting_cos_sim_path: This is optional. If given, then the pre-computed cosine similarity scores based on the counter-fitting word embeddings will be loaded to save time. If not, it will be calculated.
  • --USE_cache_path: The path to save the USE model file (Downloading is automatic if this path is empty).

Two more things to share with you:

  1. In case someone wants to replicate our experiments for training the target models, we shared the used seven datasets we have processed for you!

  2. In case someone may want to use our generated adversary results towards the benchmark data directly, here it is.

Owner
Di Jin
Di Jin
Building a real-time environment using webcam frame division in OpenCV and classify cropped images using a fine-tuned vision transformers on hybryd datasets samples for facial emotion recognition.

Visual Transformer for Facial Emotion Recognition (FER) This project has the aim to build an efficient Visual Transformer for the Facial Emotion Recog

Mario Sessa 8 Dec 12, 2022
Object tracking implemented with YOLOv4, DeepSort, and TensorFlow.

Object tracking implemented with YOLOv4, DeepSort, and TensorFlow. YOLOv4 is a state of the art algorithm that uses deep convolutional neural networks to perform object detections. We can take the ou

The AI Guy 1.1k Dec 29, 2022
Bio-OFC gym implementation and Gym-Fly environment

Bio-OFC gym implementation and Gym-Fly environment This repository includes the gym compatible implementation of the Bio-OFC algorithm from the paper

Siavash Golkar 1 Nov 16, 2021
Official Pytorch Implementation of 3DV2021 paper: SAFA: Structure Aware Face Animation.

SAFA: Structure Aware Face Animation (3DV2021) Official Pytorch Implementation of 3DV2021 paper: SAFA: Structure Aware Face Animation. Getting Started

QiulinW 122 Dec 23, 2022
DECAF: Deep Extreme Classification with Label Features

DECAF DECAF: Deep Extreme Classification with Label Features @InProceedings{Mittal21, author = "Mittal, A. and Dahiya, K. and Agrawal, S. and Sain

46 Nov 06, 2022
Uncertain natural language inference

Uncertain Natural Language Inference This repository hosts the code for the following paper: Tongfei Chen*, Zhengping Jiang*, Adam Poliak, Keisuke Sak

Tongfei Chen 14 Sep 01, 2022
A mini library for Policy Gradients with Parameter-based Exploration, with reference implementation of the ClipUp optimizer from NNAISENSE.

PGPElib A mini library for Policy Gradients with Parameter-based Exploration [1] and friends. This library serves as a clean re-implementation of the

NNAISENSE 56 Jan 01, 2023
Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices

Intro Real-time object detection and classification. Paper: version 1, version 2. Read more about YOLO (in darknet) and download weight files here. In

Trieu 6.1k Dec 30, 2022
Implementation of DocFormer: End-to-End Transformer for Document Understanding, a multi-modal transformer based architecture for the task of Visual Document Understanding (VDU)

DocFormer - PyTorch Implementation of DocFormer: End-to-End Transformer for Document Understanding, a multi-modal transformer based architecture for t

171 Jan 06, 2023
This is the official pytorch implementation for the paper: Instance Similarity Learning for Unsupervised Feature Representation.

ISL This is the official pytorch implementation for the paper: Instance Similarity Learning for Unsupervised Feature Representation, which is accepted

19 May 04, 2022
Deep Watershed Transform for Instance Segmentation

Deep Watershed Transform Performs instance level segmentation detailed in the following paper: Min Bai and Raquel Urtasun, Deep Watershed Transformati

193 Nov 20, 2022
Python wrapper to access the amazon selling partner API

PYTHON-AMAZON-SP-API Amazon Selling-Partner API If you have questions, please join on slack Contributions very welcome! Installation pip install pytho

Michael Primke 330 Jan 06, 2023
Code repository for Self-supervised Structure-sensitive Learning, CVPR'17

Self-supervised Structure-sensitive Learning (SSL) Ke Gong, Xiaodan Liang, Xiaohui Shen, Liang Lin, "Look into Person: Self-supervised Structure-sensi

Clay Gong 219 Dec 29, 2022
Code to reproduce the experiments in the paper "Transformer Based Multi-Source Domain Adaptation" (EMNLP 2020)

Transformer Based Multi-Source Domain Adaptation Dustin Wright and Isabelle Augenstein To appear in EMNLP 2020. Read the preprint: https://arxiv.org/a

CopeNLU 36 Dec 05, 2022
Implementation of our paper "Video Playback Rate Perception for Self-supervised Spatio-Temporal Representation Learning".

PRP Introduction This is the implementation of our paper "Video Playback Rate Perception for Self-supervised Spatio-Temporal Representation Learning".

yuanyao366 39 Dec 29, 2022
[CVPR 2021] Official PyTorch Implementation for "Iterative Filter Adaptive Network for Single Image Defocus Deblurring"

IFAN: Iterative Filter Adaptive Network for Single Image Defocus Deblurring Checkout for the demo (GUI/Google Colab)! The GUI version might occasional

Junyong Lee 173 Dec 30, 2022
Yolov3 pytorch implementation

YOLOV3 Pytorch实现 在bubbliiing大佬代码的基础上进行了修改,添加了部分注释。 预训练模型 预训练模型来源于bubbliiing。 链接:https://pan.baidu.com/s/1ncREw6Na9ycZptdxiVMApw 提取码:appk 训练自己的数据集 按照VO

4 Aug 27, 2022
Evaluation toolkit of the informative tracking benchmark comprising 9 scenarios, 180 diverse videos, and new challenges.

Informative-tracking-benchmark Informative tracking benchmark (ITB) higher diversity. It contains 9 representative scenarios and 180 diverse videos. m

Xin Li 15 Nov 26, 2022
Improving Compound Activity Classification via Deep Transfer and Representation Learning

Improving Compound Activity Classification via Deep Transfer and Representation Learning This repository is the official implementation of Improving C

NingLab 2 Nov 24, 2021
Instantaneous Motion Generation for Robots and Machines.

Ruckig Instantaneous Motion Generation for Robots and Machines. Ruckig generates trajectories on-the-fly, allowing robots and machines to react instan

Berscheid 374 Dec 23, 2022