#!/bin/bash R_DIR=`dirname $0`; MYDIR=`cd $R_DIR;pwd` export FLAGS_eager_delete_tensor_gb=0.0 export FLAGS_sync_nccl_allreduce=1 if [[ -f ./model_conf ]];then source ./model_conf else export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 fi mkdir -p log/ lr=3e-5 batch_size=64 epoch=3 for i in {1..5};do timestamp=`date "+%Y-%m-%d-%H-%M-%S"` python -u run_classifier.py \ --use_cuda true \ --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}/MNLI/train.tsv \ --dev_set ${TASK_DATA_PATH}/MNLI/m/dev.tsv,${TASK_DATA_PATH}/MNLI/mm/dev.tsv \ --test_set ${TASK_DATA_PATH}/MNLI/m/test.tsv,${TASK_DATA_PATH}/MNLI/mm/test.tsv \ --vocab_path script/en_glue/ernie_base/vocab.txt \ --checkpoints ./checkpoints \ --save_steps 25000 \ --weight_decay 0.0 \ --warmup_proportion 0.1 \ --validation_steps 1000000000000 \ --epoch $epoch \ --max_seq_len 128 \ --ernie_config_path script/en_glue/ernie_base/ernie_config.json \ --learning_rate $lr \ --skip_steps 10 \ --num_iteration_per_drop_scope 1 \ --num_labels 3 \ --for_cn False \ --test_save output/test_out.$i.m.tsv,output/test_out.$i.mm.tsv \ --diagnostic ${TASK_DATA_PATH}/diagnostic.tsv \ --diagnostic_save output/test_out.$i.$lr.$batch_size.$epoch.$timestamp.m.diagnostic.tsv \ --random_seed 1 2>&1 | tee log/job.$i.$lr.$batch_size.$epoch.$timestamp.log \ done