training.sh

dataset_name=ChEMBL
python  train.py -data data/${dataset_name}/${dataset_name} \
                 -save_model checkpoints/${dataset_name}/${dataset_name}_model -world_size 1 \
		             -seed 42 -gpu_ranks 0 -save_checkpoint_steps 10000 -keep_checkpoint 20 \
                 -train_steps 500000 -param_init 0  -param_init_glorot -max_generator_batches 32 \
                 -batch_size 4096 -batch_type tokens -normalization tokens -max_grad_norm 0  -accum_count 4 \
                 -optim adam -adam_beta1 0.9 -adam_beta2 0.998 -decay_method noam -warmup_steps 8000  \
                 -learning_rate 2 -label_smoothing 0.0 -report_every 1000 \
                 -layers 4 -rnn_size 256 -word_vec_size 256 -encoder_type transformer -decoder_type transformer \
                 -dropout 0.1 -position_encoding -share_embeddings \
                 -global_attention general -global_attention_function softmax -self_attn_type scaled-dot \
                 -heads 8 -transformer_ff 2048