#!/bin/bash #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