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#!/bin/bash
python run_t5_mlm_flax.py \
--output_dir="${MODEL_PATH}" \
--model_type="t5" \
--config_name="${MODEL_PATH}" \
--tokenizer_name="${MODEL_PATH}" \
--preprocessing_num_workers="96" \
--do_train --do_eval \
--dataset_name="${DATASET}" \
--dataset_config_name="${DATASET_CONFIG}" \
--max_seq_length="512" \
--per_device_train_batch_size="16" \
--per_device_eval_batch_size="16" \
--adafactor \
--learning_rate="0.005" \
--overwrite_output_dir \
--num_train_epochs="1" \
--logging_steps="500" \
--save_steps="80000" \
--eval_steps="2500" \
--weight_decay="0.01" \
--warmup_steps="10000" \
--validation_split_count="15000" \
--push_to_hub \
# --adam_beta1="0.9" \
# --adam_beta2="0.98" \
# --resume_from_checkpoint="${MODEL_DIR}" \ # Uncomment to resume from ckpt
# --max_train_samples 100000 \
# --max_eval_samples 1000 \
# --adafactor \
# --save_steps="80000" \
# Instead of adafactor: adamw
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