This folder contains the code for the paper 《Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation》
pytorch >= 0.4.0
torchtext >= 0.4.0
nltk
fasttext == 0.8.3
kenlm
The hyperparameters for the Style Transformer can be found in ''main.py''.
The most of them are listed below:
data_path : the path of the datasets
log_dir : where to save the logging info
save_path = where to save the checkpoing
discriminator_method : the type of discriminator ('Multi' or 'Cond')
min_freq : the minimun frequency for building vocabulary
max_length : the maximun sentence length
embed_size : the dimention of the token embedding
d_model : the dimention of Transformer d_model parameter
h : the number of Transformer attention head
num_layers : the number of Transformer layer
batch_size : the training batch size
lr_F : the learning rate for the Style Transformer
lr_D : the learning rate for the discriminator
L2 : the L2 norm regularization factor
iter_D : the number of the discriminator update step pre training interation
iter_F : the number of the Style Transformer update step pre training interation
dropout : the dropout factor for the whole model
log_steps : the number of steps to log model info
eval_steps : the number of steps to evaluate model info
slf_factor : the weight factor for the self reconstruction loss
cyc_factor : the weight factor for the cycle reconstruction loss
adv_factor : the weight factor for the style controlling loss
You can adjust them in the Config class from the ''main.py''.
If you want to run the model, use the command:
python main.pyTo evaluation the model, we used Fasttext, NLTK and KenLM toolkit to evaluate the style control, content preservation and fluency respectively. The evaluation related files for the Yelp dataset are placed in the ''evaluator'' folder.
Because the file "ppl_yelp.binary" is too big to upload, we exclude it from the "evaluator" folder. As a result, you can not evaluate the ppl score via evaluator. To solve this problem, you can use the KenLM toolkit to train a language model by yourself or use other script to evaluate it.
Update: You can find the outputs of our model in the "outputs" folder.
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