Continued pretrained from the nb-roberta-base. The domain specific pretraining is done on the 102GB (Scandinavian corpus)[https://huggingface.co/datasets/NbAiLab/scandinavian]. ## Train for 180k steps for 128 sequences: ```bash ./run_mlm_flax_stream.py \ --output_dir="./" \ --model_type="roberta" \ --config_name="./" \ --tokenizer_name="./" \ --model_name_or_path="./" \ --dataset_name="NbAiLab/scandinavian" \ --max_seq_length="128" \ --weight_decay="0.01" \ --per_device_train_batch_size="128" \ --per_device_eval_batch_size="128" \ --learning_rate="6e-5" \ --warmup_steps="5000" \ --overwrite_output_dir \ --cache_dir /mnt/disks/flaxdisk/cache/ \ --num_train_steps="180000" \ --adam_beta1="0.9" \ --adam_beta2="0.98" \ --logging_steps="10000" \ --save_steps="10000" \ --eval_steps="10000" \ --preprocessing_num_workers 96 \ --auth_token True \ --adafactor \ --push_to_hub ``` ## Train for 20k steps for 512 sequences: ```bash ./run_mlm_flax_stream.py \ --output_dir="./" \ --model_type="roberta" \ --config_name="./" \ --tokenizer_name="./" \ --model_name_or_path="./" \ --dataset_name="NbAiLab/scandinavian" \ --max_seq_length="512" \ --weight_decay="0.01" \ --per_device_train_batch_size="48" \ --per_device_eval_batch_size="48" \ --learning_rate="3e-5" \ --warmup_steps="5000" \ --overwrite_output_dir \ --cache_dir /mnt/disks/flaxdisk/cache/ \ --num_train_steps="20000" \ --adam_beta1="0.9" \ --adam_beta2="0.98" \ --logging_steps="20000" \ --save_steps="10000" \ --eval_steps="10000" \ --preprocessing_num_workers 96 \ --auth_token True \ --adafactor \ --push_to_hub ``` Approximate additional training time: 1 week.