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#!/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
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