export CUDA_VISIBLE_DEVICES=4,5,6,7
python -m torch.distributed.launch --nproc_per_node=4 hugnlp_runner.py \
  --model_name_or_path=/wjn/pre-trained-lm/chinese_pretrain_mrc_macbert_large \
  --data_dir=./datasets/cpic \
  --output_dir=./outputs/cpic \
  --seed=42 \
  --exp_name=cpic-large-wjn \
  --max_seq_length=512 \
  --max_eval_seq_length=512 \
  --do_train \
  --per_device_train_batch_size=8 \
  --per_device_eval_batch_size=8 \
  --gradient_accumulation_steps=1 \
  --evaluation_strategy=epoch \
  --save_strategy=epoch \
  --learning_rate=2e-05 \
  --num_train_epochs=20 \
  --logging_steps=20 \
  --save_total_limit=1 \
  --warmup_steps=100 \
  --report_to=none \
  --task_name=cpic \
  --task_type=global_pointer \
  --model_type=bert \
  --metric_for_best_model=macro_f1 \
  --pad_to_max_length=True \
  --remove_unused_columns=False \
  --overwrite_output_dir \
  --load_best_model_at_end \
  --fp16 \
  --label_names=short_labels \
  --early_stopping_patience=10 \
  --sharded_ddp=zero_dp_2 \
  --keep_predict_labels
