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MODEL="t5-base-dutch"
MODEL_DIR="${HOME}/${MODEL}"
mkdir -p "${MODEL_DIR}/runs"
# T5 paper lr 0.01 with batch size 128
# We have a batch size of 8 devices * 32 = 256, so lr = 0.01/2
#SEED=9200
#
#./run_t5_mlm_flax_custom_dataset.py \
# --output_dir="${MODEL_DIR}" \
# --model_type="t5" \
# --config_name="flax-community/${MODEL}" \
# --tokenizer_name="${MODEL_DIR}" \
# --seed="${SEED}" \
# --preprocessing_num_workers="96" \
# --do_train --do_eval \
# --adafactor \
# --max_seq_length="512" \
# --per_device_train_batch_size="32" \
# --per_device_eval_batch_size="32" \
# --dtype="bfloat16" \
# --learning_rate="5e-3" \
# --overwrite_output_dir \
# --num_train_epochs="3" \
# --logging_steps="50" \
# --save_steps="100" \
# --eval_steps="5000" \
# --warmup_steps="3413"
#exit
while true; do
# Set the seed to random before each run, so date shuffling per epoch is different each run.
# This kills reproducibility, but is required as long as during training ValueError can be raised.
# SEED=$RANDOM
SEED=22384
./run_t5_mlm_flax_custom_dataset.py \
--output_dir="${MODEL_DIR}" \
--model_type="t5" \
--config_name="flax-community/${MODEL}" \
--tokenizer_name="${MODEL_DIR}" \
--seed="${SEED}" \
--preprocessing_num_workers="96" \
--do_train --do_eval \
--adafactor \
--max_seq_length="512" \
--per_device_train_batch_size="16" \
--per_device_eval_batch_size="16" \
--dtype="bfloat16" \
--learning_rate="1e-3" \
--overwrite_output_dir \
--num_train_epochs="1" \
--logging_steps="50" \
--save_steps="500" \
--eval_steps="5000" \
--resume_from_checkpoint="${MODEL_DIR}" \
--warmup_steps="6519"
# \
# --push_to_hub
echo "RESTARTING"
sleep 20
done
#
# \
#git add pytorch_model.bin
#git commit -m "Update pytorch model after training"
#git push origin main
# --gradient_accumulation_steps="2" \
# --resume_from_checkpoint="${MODEL_DIR}/ckpt-18000" \
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