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Implementation code for the paper "Generating Natural Language Adversarial Examples"

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1) Download the Imdb dataset


2) Download the glove vector embeddings (used by the model)


3) Download the counter-fitted vectors (used by our attack)


4) Build the vocabulary and embeddings matrix.


That will take like a minute, and it will tokenize the dataset and save it to a pickle file. It will also compute some auxiliary files like the matrix of the vector embeddings for words in our dictionary. All files will be saved under

directory created by this script.

5) Train the sentiment analysis model.


6)Download the Google language model.


7) Pre-compute the distances between embeddings of different words (required to do the attack) and save the distance matrix.


8) Now, we are ready to try some attacks ! You can do so by running the

Jupyter notebook !

Attacking Textual Entailment model

The model we are using for our experiment is the SNLI model of Keras SNLI Model .

First, Download the dataset using


Download the Glove and Counter-fitted Glove embedding vectors

bash ./
bash ./

Train the NLI model


Pre-compute the embedding matrix


Now, you are ready to run the attack using example code provided in

Jupyter notebook.

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