roberta_jan_128_ncc / run_128_recover_5e.sh
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Saving weights and logs of step 1000
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python run_mlm_flax.py \
--output_dir="./" \
--model_type="roberta" \
--model_name_or_path="./" \
--config_name="roberta-base" \
--tokenizer_name="NbAiLab/nb-roberta-base" \
--dataset_name="NbAiLab/NCC" \
--cache_dir="/mnt/disks/flaxdisk/cache/" \
--max_seq_length="128" \
--weight_decay="0.01" \
--per_device_train_batch_size="232" \
--per_device_eval_batch_size="232" \
--pad_to_max_length \
--learning_rate="0.000042248" \
--warmup_steps="0" \
--overwrite_output_dir \
--num_train_epochs="5" \
--adam_beta1="0.9" \
--adam_beta2="0.98" \
--adam_epsilon="1e-6" \
--logging_steps="1000" \
--save_steps="1000" \
--eval_steps="1000" \
--auth_token="True" \
--do_train \
--do_eval \
--dtype="bfloat16" \
--push_to_hub