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export PYTHONPATH="${PYTHONPATH}:/workspace/code"
export TRAIN_FILE=../data/datasets/penn/train.txt
export TEST_FILE=../data/datasets/penn/test.txt
export GPU_ID=0,1
CUDA_VISIBLE_DEVICES=$GPU_ID python examples/big_ae/run_lm_vae_training.py \
--dataset Penn \
--checkpoint_dir=../output/local_bert_gpt_init \
--output_dir=../output/local_bert_gpt_init \
--encoder_model_type=bert \
--encoder_model_name_or_path=bert-base-cased \
--decoder_model_type=gpt2 \
--decoder_model_name_or_path=gpt2 \
--train_data_file=$TRAIN_FILE \
--do_train \
--do_eval \
--beta 1.0 \
--ratio_zero .5 \
--ratio_increase 0.25 \
--eval_data_file=$TEST_FILE \
--num_train_epochs 2.0 \
--save_steps 20 \
--logging_steps 4 \
--overwrite_output_dir \
--per_gpu_train_batch_size 1 \
--gloabl_step_eval 60000 \
--block_size 128 \
--latent_as_gpt_emb 1 \
--latent_as_gpt_memory 1 \
--save_bert_gpt_init \
--latent_size 768
# --use_pretrained_model
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