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# export TRAIN_FILE=../data/datasets/penn/train.txt
# export TEST_FILE=../data/datasets/penn/test.txt
# export GPU_ID=0,1
# CUDA_VISIBLE_DEVICES=$GPU_ID python examples/big_ae/run_lm_finetuning_baseline.py \
# --output_dir=../output/local_lm_gpt_penn \
# --dataset Yahoo \
# --model_type=gpt2 \
# --model_name_or_path=gpt2 \
# --train_data_file=$TRAIN_FILE \
# --do_train \
# --do_eval \
# --eval_data_file=$TEST_FILE \
# --num_train_epochs 2.0 \
# --save_steps 1000 \
# --logging_steps 100 \
# --overwrite_output_dir \
# --per_gpu_train_batch_size=2 \
# --gloabl_step_eval 600
export PYTHONPATH="${PYTHONPATH}:/workspace/code"
export TRAIN_FILE=../data/datasets/debug_data/train.txt
export TEST_FILE=../data/datasets/debug_data/test.txt
export GPU_ID=0,1
CUDA_VISIBLE_DEVICES=$GPU_ID python examples/big_ae/run_lm_gpt2_training.py \
--dataset Debug \
--output_dir=../output/local_gpt2_debug \
--encoder_model_type=bert \
--encoder_model_name_or_path=bert-base-cased \
--decoder_model_type=gpt2 \
--decoder_model_name_or_path=gpt2 \
--train_data_file=$TRAIN_FILE \
--do_train \
--do_eval \
--beta 1.0 \
--ratio_zero .5 \
--ratio_increase 0.25 \
--eval_data_file=$TEST_FILE \
--num_train_epochs 20.0 \
--save_steps 20 \
--logging_steps 4 \
--overwrite_output_dir \
--per_gpu_train_batch_size 1 \
--gloabl_step_eval 4 \
--block_size 50 \
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