Three quick usage examples for these scripts:
python run_pre_train.py \
--pregenerated_data /path/to/pregenerated_data \
--bert_model /path/to/bert_model \
--do_lower_case \
--output_dir /path/to/output_dir \
--epochs 20 \
--train_batch_size 128 \
--fp16 \
--scratch \
--save_name ZEN_pretrain_base_
python run_sequence_level_ft_task.py \
--task_name TASKNAME \
--do_train \
--do_eval \
--do_lower_case \
--data_dir /path/to/dataset \
--init_checkpoint /path/to/zen_model \
--max_seq_length 512 \
--train_batch_size 32 \
--learning_rate 2e-5 \
--num_train_epochs 30.0
where TASKNAME can be one of DC, SA, SPM, NLI and QA
script of fine-tuning thucnews
python run_sequence_level_ft_task.py \
--task_name thucnews \
--do_train \
--do_eval \
--do_lower_case \
--data_dir /path/to/dataset/thucnews \
--init_checkpoint /path/to/zen_model \
--max_seq_length 512 \
--train_batch_size 32 \
--learning_rate 2e-5 \
--num_train_epochs 30.0
script of fine-tuning chnsenticorp
python run_sequence_level_ft_task.py \
--task_name ChnSentiCorp \
--do_train \
--do_eval \
--do_lower_case \
--data_dir /path/to/dataset/ChnSentiCorp \
--init_checkpoint /path/to/zen_model \
--max_seq_length 512 \
--train_batch_size 32 \
--learning_rate 2e-5 \
--num_train_epochs 30.0
script of fine-tuning LCQMC
python run_sequence_level_ft_task.py \
--task_name lcqmc \
--do_train \
--do_eval \
--do_lower_case \
--data_dir /path/to/dataset/lcqmc \
--init_checkpoint /path/to/zen_model \
--max_seq_length 128 \
--train_batch_size 128 \
--learning_rate 5e-5 \
--num_train_epochs 30.0
script of fine-tuning BQ-Corpus
python run_sequence_level_ft_task.py \
--task_name bq \
--do_train \
--do_eval \
--do_lower_case \
--data_dir /path/to/dataset/lcqmc \
--init_checkpoint /path/to/zen_model \
--max_seq_length 128 \
--train_batch_size 128 \
--learning_rate 5e-5 \
--num_train_epochs 30.0
script of fine-tuning XNLI
python run_sequence_level_ft_task.py \
--task_name xnli \
--do_train \
--do_eval \
--do_lower_case \
--data_dir /path/to/dataset/xnli \
--init_checkpoint /path/to/zen_model \
--max_seq_length 128 \
--train_batch_size 128 \
--learning_rate 5e-5 \
--num_train_epochs 30.0
python run_token_level_ft_task.py \
--task_name TASKNAME \
--do_train \
--do_eval \
--do_lower_case \
--data_dir /path/to/dataset \
--init_checkpoint /path/to/zen_model \
--max_seq_length 128 \
--do_train \
--do_eval \
--train_batch_size 128 \
--num_train_epochs 30 \
--warmup_proportion 0.1
where TASKNAME can be one of CWS, POS and NER
script of fine-tuning msra
python run_token_level_ft_task.py \
--task_name cwsmsra \
--do_train \
--do_eval \
--do_lower_case \
--data_dir /path/to/dataset \
--init_checkpoint /path/to/zen_model \
--max_seq_length 256 \
--do_train \
--do_eval \
--train_batch_size 96 \
--num_train_epochs 30 \
--warmup_proportion 0.1
script of fine-tuning CTB5
python run_token_level_ft_task.py \
--task_name pos \
--do_train \
--do_eval \
--do_lower_case \
--data_dir /path/to/dataset \
--init_checkpoint /path/to/zen_model \
--max_seq_length 256 \
--do_train \
--do_eval \
--train_batch_size 96 \
--num_train_epochs 30 \
--warmup_proportion 0.1
script of fine-tuning msra_ner
python run_token_level_ft_task.py \
--task_name msra \
--do_train \
--do_eval \
--do_lower_case \
--data_dir /path/to/dataset \
--init_checkpoint /path/to/zen_model \
--max_seq_length 128 \
--do_train \
--do_eval \
--train_batch_size 128 \
--num_train_epochs 30 \
--warmup_proportion 0.1
python run_mrc_task.py \
--task_name TASKNAME \
--do_train \
--do_eval \
--do_lower_case \
--train_file /path/to/train_file \
--test_file /path/to/test_file \
--predict_file /path/to/predict_file \
--init_checkpoint /path/to/zen_model \
--max_seq_length 512 \
--doc_stride=128 \
--do_train \
--do_eval \
--train_batch_size 128 \
--num_train_epochs 30 \
--warmup_proportion 0.1
where TASKNAME can be one of MRC
script of fine-tuning cmrc2018
python run_mrc_task.py \
--task_name cmrc2018 \
--do_train \
--do_eval \
--do_lower_case \
--data_dir /path/to/dataset \
--init_checkpoint /path/to/zen_model \
--max_seq_length 512 \
--doc_stride=128 \
--do_train \
--do_eval \
--train_batch_size 96 \
--num_train_epochs 30 \
--warmup_proportion 0.1