Pretraining

1. Pretraining roberta through running the script below. Detailed parameter descriptions can be found in the arguments.py. data_path_prefix is absolute path specifies output of preprocessing. You have to modify the hostfile according to your cluster.

bash run_pretrain.sh

• --hostfile: servers' host name from /etc/hosts
• --include: servers which will be used
• --nproc_per_node: number of process(GPU) from each server
• --data_path_prefix: absolute location of train data, e.g., /h5/0.h5
• --eval_data_path_prefix: absolute location of eval data
• --tokenizer_path: tokenizer path contains huggingface tokenizer.json, e.g./tokenizer/tokenizer.json
• --bert_config: config.json which represent model
• --mlm: model type of backbone, bert or deberta_v2

2. if resume training from earlier checkpoint, run the script below.

bash run_pretrain_resume.sh

• --resume_train: whether to resume training
• --load_pretrain_model: absolute path which contains model checkpoint
• --load_optimizer_lr: absolute path which contains optimizer checkpoint