path=/wjn/pre-trained-lm/chinese-macbert-large # 89
#path=/wjn/pre-trained-lm/chinese_pretrain_mrc_macbert_large
#path=/wjn/pre-trained-lm/chinese-pert-large
#path=/wjn/pre-trained-lm/chinese-pert-large-mrc
# path=/wjn/pre-trained-lm/chinese-roberta-wwm-ext-large # 78
#path=/wjn/pre-trained-lm/chinese_pretrain_mrc_roberta_wwm_ext_large
#path=/wjn/pre-trained-lm/chinesebert-large
#path=/wjn/pre-trained-lm/structbert-large-zh
#path=/wjn/pre-trained-lm/Erlangshen-MegatronBert-1.3B

# data_path=/wjn/nlp_task_datasets/zh_instruction
data_path=/wjn/nlp_task_datasets/information_extraction/extractive_unified_ie/

export CUDA_VISIBLE_DEVICES=0,1
python3 -m torch.distributed.launch --nproc_per_node=2 --master_port=6019 hugnlp_runner.py \
--model_name_or_path=$path \
--data_dir=$data_path \
--output_dir=./outputs/information_extraction/extractive_unified_ie_test/ \
--seed=42 \
--exp_name=unified-ie-wjn \
--max_seq_length=512 \
--max_eval_seq_length=512 \
--do_train \
--do_eval \
--per_device_train_batch_size=32 \
--per_device_eval_batch_size=64 \
--gradient_accumulation_steps=1 \
--evaluation_strategy=steps \
--learning_rate=2e-05 \
--num_train_epochs=3 \
--logging_steps=100000000 \
--eval_steps=500 \
--save_steps=500 \
--save_total_limit=1 \
--warmup_steps=200 \
--load_best_model_at_end \
--report_to=none \
--task_name=zh_mrc_instruction \
--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 \
--fp16 \
--label_names=short_labels \
--keep_predict_labels \
--cache_dir=/wjn/.cache
# --do_adv
