#!/bin/bash

R_DIR=`dirname $0`; MYDIR=`cd $R_DIR;pwd`
export FLAGS_sync_nccl_allreduce=1
export FLAGS_eager_delete_tensor_gb=0.0
export PYTHONPATH=./ernie:${PYTHONPATH:-}

if [[ -f ./model_conf ]];then
    source ./model_conf
else
    export CUDA_VISIBLE_DEVICES=0
fi


mkdir -p log/

timestamp=`date "+%Y-%m-%d-%H-%M-%S"`

lr=3e-5
batch_size=64
epoch=3

for i in {1..5};do
python -u ernie/run_classifier.py                                                          \
       --use_cuda true                                                               \
       --for_cn  False                                                               \
       --use_fast_executor ${e_executor:-"true"}                                     \
       --tokenizer ${TOKENIZER:-"FullTokenizer"}                                     \
       --use_fp16 ${USE_FP16:-"false"}                                               \
       --do_train true                                                               \
       --do_val true                                                                 \
       --do_test true                                                                \
       --batch_size $batch_size                                                      \
       --init_pretraining_params ${MODEL_PATH}/params                                \
       --verbose true                                                                \
       --train_set ${TASK_DATA_PATH}/CoLA/train.tsv                                  \
       --dev_set   ${TASK_DATA_PATH}/CoLA/dev.tsv                                    \
       --test_set  ${TASK_DATA_PATH}/CoLA/test.tsv                                   \
       --vocab_path script/en_glue/ernie_base/vocab.txt                              \
       --checkpoints ./checkpoints                                                   \
       --save_steps 1000                                                             \
       --weight_decay  0.0                                                           \
       --warmup_proportion 0.1                                                       \
       --validation_steps 1000000000                                                 \
       --epoch $epoch                                                                \
       --max_seq_len 128                                                             \
       --ernie_config_path script/en_glue/ernie_base/ernie_config.json               \
       --learning_rate $lr                                                           \
       --skip_steps 10                                                               \
       --num_iteration_per_drop_scope 1                                              \
       --num_labels 2                                                                \
       --metric 'matthews_corrcoef'                                                  \
       --test_save output/test_out.$i.$lr.$batch_size.$epoch.$timestamp.tsv          \
       --random_seed 1 2>&1 | tee  log/job.$i.$lr.$batch_size.$epoch.$timestamp.log  \


done