readme: # This is just a Test Model. Do NOT use for anything! 
  
  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.
    
   
  