task.sh

#!/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

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

mkdir -p log/

lr=3e-5
batch_size=32
epoch=5

for i in {1..5};do

    timestamp=`date "+%Y-%m-%d-%H-%M-%S"`
    python -u 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_large/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_large/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.$epoch.tsv         \
           --random_seed 1 2>&1 | tee  log/job.$i.$lr.$batch_size.$epoch.log \

done