wordcnn.sh

python3 attack_classification.py \
        --dataset_path data/imdb  \
        --word_embeddings_path glove.6B.200d.txt \
        --target_model wordCNN \
        --counter_fitting_cos_sim_path mat.txt \
        --target_dataset imdb \
        --target_model_path cnn/imdb \
        --counter_fitting_embeddings_path  counter-fitted-vectors.txt \
        --USE_cache_path " " \
        --max_seq_length 256 \
        --sim_score_window 40 \
        --sim_score_threshold 0.8 \
        --top_k_words 60 \
        --fuzz 70 \
        --nclasses 2 &&
python3 attack_classification.py \
        --dataset_path data/yelp  \
        --word_embeddings_path glove.6B.200d.txt \
        --target_model wordCNN \
        --counter_fitting_cos_sim_path mat.txt \
        --target_dataset yelp \
        --target_model_path cnn/yelp \
        --counter_fitting_embeddings_path  counter-fitted-vectors.txt \
        --USE_cache_path " " \
        --max_seq_length 256 \
        --sim_score_window 40 \
        --sim_score_threshold 0.8 \
        --top_k_words 60 \
        --fuzz 70 \
        --nclasses 2 &&
python3 attack_classification_yahoo.py \
        --dataset_path data/yahoo.csv  \
        --word_embeddings_path glove.6B.200d.txt \
        --target_model wordCNN \
        --counter_fitting_cos_sim_path mat.txt \
        --target_dataset yahoo \
        --target_model_path cnn/yahoo \
        --counter_fitting_embeddings_path  counter-fitted-vectors.txt \
        --USE_cache_path " " \
        --max_seq_length 256 \
        --sim_score_window 40 \
        --sim_score_threshold 0.8 \
        --top_k_words 60 \
        --fuzz 70 \
        --nclasses 10 &&
python3 attack_classification.py \
        --dataset_path data/ag  \
        --word_embeddings_path glove.6B.200d.txt \
        --target_model wordCNN \
        --counter_fitting_cos_sim_path mat.txt \
        --target_dataset ag \
        --target_model_path cnn/ag \
        --counter_fitting_embeddings_path  counter-fitted-vectors.txt \
        --USE_cache_path " " \
        --max_seq_length 256 \
        --sim_score_window 40 \
        --sim_score_threshold 0.8 \
        --top_k_words 60 \
        --fuzz 70 \
        --nclasses 4 &&
python3 attack_classification.py \
        --dataset_path data/mr  \
        --word_embeddings_path glove.6B.200d.txt \
        --target_model wordCNN \
        --counter_fitting_cos_sim_path mat.txt \
        --target_dataset mr \
        --target_model_path cnn/mr \
        --counter_fitting_embeddings_path  counter-fitted-vectors.txt \
        --USE_cache_path " " \
        --max_seq_length 256 \
        --sim_score_window 40 \
        --sim_score_threshold 0.8 \
        --top_k_words 60 \
        --fuzz 70 \
        --nclasses 2