This repo contains the code to attack SAM Model.
The SAM model is trained by ourselves.
We put the code in root directory, which is based on this github repo.
You can reproduce the results by running:
python train.py \
--emsize [word embedding size default 300] \
--nhid [hidden layer size, default 300] \
--nlayers [hidden layer numbers in Bi-LSTM, default 2] \
--attention-unit [attention unit number, d_a in the paper, default 350] \
--attention-hops [hop number, r in the paper, default 1] \
--dropout [dropout ratio, default 0.5] \
--nfc [hidden layer size for MLP in the classifier, default 512] \
--lr [learning rate, default 0.001] \
--epochs [epoch number for training, default 40] \
--seed [initial seed for reproduction, default 1111] \
--log-interval [the interval for reporting training loss, default 200] \
--batch-size [size of a batch in training procedure, default 32] \
--optimizer [type of the optimizer, default Adam] \
--penalization-coeff [coefficient of the Frobenius Norm penalization term, default 1.0] \
--class-number [number of class for the last step of classification] \
--save [path to save model] \
--dictionary [location of the dictionary generated by the tokenizer] \
--word-vector [location of the initial word vector, e.g. GloVe, should be a torch .pt model] \
--train-data [location of training data, should be in the same format with tokenized productions] \
--val-data [development set] \
--test-data [location of testing dataset] \
--cuda [whether using GPU for training, remove this when using CPU]
We are using the following hyper-parameters:
attention_hops = 10
nfc = 512
batch_size = 256
seed = 2334
You may read our attack.py for more information. You can try different attack methods (word-level/sentence-level).