# 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.
  
 

{}
transformers pytorch jax tensorboard roberta fill-mask autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us 

# 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)[URL

## Train for 180k steps for 128 sequences:

## Train for 20k steps for 512 sequences:




Approximate additional training time: 1 week.
  
 

# 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)[URL ## Train for 180k steps for 128 sequences: ## Train for 20k steps for 512 sequences:




Approximate additional training time: 1 week.
TAGS
#transformers #pytorch #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us 
 # 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)[URL ## Train for 180k steps for 128 sequences: ## Train for 20k steps for 512 sequences:




Approximate additional training time: 1 week.
passage: TAGS
#transformers #pytorch #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us 
# 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)[URL## Train for 180k steps for 128 sequences:## Train for 20k steps for 512 sequences:




Approximate additional training time: 1 week.