$ python train.py -h
usage: train.py [-h] --model_fn MODEL_FN --train TRAIN --valid VALID --lang
                LANG [--gpu_id GPU_ID] [--batch_size BATCH_SIZE]
                [--n_epochs N_EPOCHS] [--verbose VERBOSE]
                [--init_epoch INIT_EPOCH] [--max_length MAX_LENGTH]
                [--dropout DROPOUT] [--word_vec_size WORD_VEC_SIZE]
                [--hidden_size HIDDEN_SIZE] [--n_layers N_LAYERS]
                [--max_grad_norm MAX_GRAD_NORM]
                [--iteration_per_update ITERATION_PER_UPDATE] [--lr LR]
                [--lr_step LR_STEP] [--lr_gamma LR_GAMMA]
                [--lr_decay_start LR_DECAY_START] [--use_radam] [--use_adam]
                [--use_noam_decay] [--lr_warmup_ratio LR_WARMUP_RATIO]
                [--rl_lr RL_LR] [--rl_n_samples RL_N_SAMPLES]
                [--rl_n_epochs RL_N_EPOCHS] [--rl_n_gram RL_N_GRAM]
                [--use_transformer] [--n_splits N_SPLITS]

optional arguments:
  -h, --help            show this help message and exit
  --model_fn MODEL_FN   Model file name to save. Additional information would
                        be annotated to the file name.
  --train TRAIN         Training set file name except the extention. (ex:
                        train.en --> train)
  --valid VALID         Validation set file name except the extention. (ex:
                        valid.en --> valid)
  --lang LANG           Set of extention represents language pair. (ex: en +
                        ko --> enko)
  --gpu_id GPU_ID       GPU ID to train. Currently, GPU parallel is not
                        supported. -1 for CPU. Default=-1
  --batch_size BATCH_SIZE
                        Mini batch size for gradient descent. Default=32
  --n_epochs N_EPOCHS   Number of epochs to train. Default=20
  --verbose VERBOSE     VERBOSE_SILENT, VERBOSE_EPOCH_WISE, VERBOSE_BATCH_WISE
                        = 0, 1, 2. Default=2
  --init_epoch INIT_EPOCH
                        Set initial epoch number, which can be useful in
                        continue training. Default=1
  --max_length MAX_LENGTH
                        Maximum length of the training sequence. Default=100
  --dropout DROPOUT     Dropout rate. Default=0.3
  --word_vec_size WORD_VEC_SIZE
                        Word embedding vector dimension. Default=512
  --hidden_size HIDDEN_SIZE
                        Hidden size of LSTM. Default=768
  --n_layers N_LAYERS   Number of layers in LSTM. Default=4
  --max_grad_norm MAX_GRAD_NORM
                        Threshold for gradient clipping. Default=5.0
  --iteration_per_update ITERATION_PER_UPDATE
                        Number of feed-forward iterations for one parameter
                        update. Default=1
  --lr LR               Initial learning rate. Default=1.0
  --lr_step LR_STEP     Number of epochs for each learning rate decay.
                        Default=1
  --lr_gamma LR_GAMMA   Learning rate decay rate. Default=0.5
  --lr_decay_start LR_DECAY_START
                        Learning rate decay start at. Default=10
  --use_radam           Use rectified Adam as optimizer. Other lr arguments
                        should be changed.
  --use_adam            Use Adam as optimizer instead of SGD. Other lr
                        arguments should be changed.
  --use_noam_decay      Use Noam learning rate decay, which is described in
                        "Attention is All You Need" paper.
  --lr_warmup_ratio LR_WARMUP_RATIO
                        Ratio of warming up steps from total iterations for
                        Noam learning rate decay. Default=0.05
  --rl_lr RL_LR         Learning rate for reinforcement learning. Default=0.01
  --rl_n_samples RL_N_SAMPLES
                        Number of samples to get baseline. Default=1
  --rl_n_epochs RL_N_EPOCHS
                        Number of epochs for reinforcement learning.
                        Default=10
  --rl_n_gram RL_N_GRAM
                        Maximum number of tokens to calculate BLEU for
                        reinforcement learning. Default=6
  --use_transformer     Set model architecture as Transformer.
  --n_splits N_SPLITS   Number of heads in multi-head attention in
                        Transformer. Default=8

$ python train.py --model_fn ./models/enko.pth --train ./data/corpus.train --valid ./data/corpus.valid --lang enko --gpu_id 0  --batch_size 64 --n_epochs 15 --dropout .2 --word_vec_size 512 --hidden_size 768 --n_layers 4 --lr 1. --lr_step 1 --lr_gamma .5 --lr_decay_start 10 --rl_n_epochs 10

$ python train.py --model_fn ./models/enko.pth --train ./data/corpus.train --valid ./data/corpus.valid --lang enko --gpu_id 0 --batch_size 32 --n_epochs 20 --hidden_size 1024 --n_layers 6 --use_adam --lr 1e-4 --lr_step 0 --rl_n_epochs 0 --iteration_per_update 50 --max_grad_norm 1e+4 --use_noam_decay --use_transformer --max_length 64 --lr_warmup_ratio .2

$ python translate.py -h
usage: translate.py [-h] --model MODEL [--gpu_id GPU_ID]
                    [--batch_size BATCH_SIZE] [--max_length MAX_LENGTH]
                    [--n_best N_BEST] [--beam_size BEAM_SIZE] [--lang LANG]
                    [--length_penalty LENGTH_PENALTY]

optional arguments:
  -h, --help            show this help message and exit
  --model MODEL         Model file name to use
  --gpu_id GPU_ID       GPU ID to use. -1 for CPU. Default=-1
  --batch_size BATCH_SIZE
                        Mini batch size for parallel inference. Default=128
  --max_length MAX_LENGTH
                        Maximum sequence length for inference. Default=255
  --n_best N_BEST       Number of best inference result per sample. Default=1
  --beam_size BEAM_SIZE
                        Beam size for beam search. Default=5
  --lang LANG           Source language and target language. Example: enko
  --length_penalty LENGTH_PENALTY
                        Length penalty parameter that higher value produce
                        shorter results. Default=1.2

$ python translate.py --model ./model/enko.12.1.18-3.24.1.37-3.92.pth --gpu_id -1 --batch_size 128 --beam_size 8 < test.txt > test.result.txt