#SBATCH --job-name=slurm-test # create a short name for your job | |
#SBATCH --nodes=1 # node count | |
#SBATCH --ntasks=1 # total number of tasks across all nodes | |
#SBATCH --cpus-per-task=30 # cpu-cores per task (>1 if multi-threaded tasks) | |
#SBATCH --mem-per-cpu=4G # memory per cpu-core (4G is default) | |
#SBATCH --gres=gpu:1 # number of gpus per node | |
#SBATCH --mail-type=ALL # send email when job begins, ends or failed etc. | |
#SBATCH --requeue | |
#SBATCH --qos=preemptive | |
TASK=tnews #clue 上的任务 ,可选afqmc、tnews、iflytek、wsc、ocnli、csl、chid、c3 | |
DATA_ROOT_PATH=./data #数据集路径 | |
DATA_DIR=$DATA_ROOT_PATH/$TASK | |
PRETRAINED_MODEL_PATH=IDEA-CCNL/Erlangshen-UniMC-RoBERTa-110M-Chinese #预训练模型的路径 | |
CHECKPOINT_PATH=./checkpoint #训练模型保存的路径 | |
LOAD_CHECKPOINT_PATH=./checkpoints/last.ckpt #加载预训练好的模型 | |
OUTPUT_PATH=./predict/${TASK}_predict.json | |
DEFAULT_ROOT_DIR=./log # 模型日志输出路径 | |
DATA_ARGS="\ | |
--data_dir $DATA_DIR \ | |
--train_data train.json \ | |
--valid_data dev.json \ | |
--test_data test1.1.json \ | |
--batchsize 1 \ | |
--max_length 512 \ | |
" | |
# 如果使用的是 UniMC-DeBERTa-1.4B模型,学习率要设置1e-6 | |
MODEL_ARGS="\ | |
--learning_rate 0.000002 \ | |
--weight_decay 0.1 \ | |
--warmup 0.06 \ | |
" | |
MODEL_CHECKPOINT_ARGS="\ | |
--monitor val_acc \ | |
--save_top_k 3 \ | |
--mode max \ | |
--every_n_train_steps 100 \ | |
--save_ckpt_path $CHECKPOINT_PATH \ | |
--filename model-{epoch:02d}-{val_acc:.4f} \ | |
" | |
TRAINER_ARGS="\ | |
--max_epochs 17 \ | |
--gpus 1 \ | |
--check_val_every_n_epoch 1 \ | |
--val_check_interval 100 \ | |
--gradient_clip_val 0.25 \ | |
--default_root_dir $DEFAULT_ROOT_DIR \ | |
" | |
#--load_checkpoints_path $LOAD_CHECKPOINT_PATH \ 如果想加载预训练好的ckpt模型,可以使用这个参数加载 | |
options=" \ | |
--pretrained_model_path $PRETRAINED_MODEL_PATH \ | |
--output_path $OUTPUT_PATH \ | |
--train \ | |
$DATA_ARGS \ | |
$MODEL_ARGS \ | |
$MODEL_CHECKPOINT_ARGS \ | |
$TRAINER_ARGS \ | |
" | |
SCRIPT_PATH=./solution/clue_unimc.py | |
python3 $SCRIPT_PATH $options | |