long-t5-local-base-dutch-english / run_longt5-local-base-mc4.sh

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	export CORES=`grep -c ^processor /proc/cpuinfo`
	export XLA_PYTHON_CLIENT_PREALLOCATE=false

	export HF_PROJECT="long-t5-local-base-dutch-english"
	export DATASET="yhavinga/mc4_nl_cleaned"
	export DATASET_CONFIG="tiny_en_nl" # Config of the dataset in the Huggingface Hub
	export DATASET_SPLIT="train" # Split to use for training tokenizer and model
	export CONFIG_NAME="google/long-t5-local-base"
	export TOKENIZER_NAME="yhavinga/t5-small-24L-ccmatrix-multi"
	export MODEL_PATH="${HOME}/data/${HF_PROJECT}" # Path to the model

	mkdir -p "${MODEL_PATH}"

	# from paper:
	# batch size 128
	# input length 4096 output length 910 output - pegasus style
	# for span corruption set to seq length 1024

	python ../train/run_t5_mlm_flax_pmap.py \
	--output_dir="${MODEL_PATH}" \
	--resume_from_checkpoint="${MODEL_PATH}" \
	--model_type="longt5" \
	--config_name="${CONFIG_NAME}" \
	--tokenizer_name="${TOKENIZER_NAME}" \
	--preprocessing_num_workers="${CORES}" \
	--do_train --do_eval \
	--dataset_name="${DATASET}" \
	--dataset_config_name="${DATASET_CONFIG}" \
	--max_seq_length="1024" \
	--per_device_train_batch_size="8" \
	--per_device_eval_batch_size="8" \
	--gradient_accumulation_steps="16" \
	--mean_noise_span_length="3" \
	--dtype="float32" \
	--optim="adafactor" \
	--learning_rate="0.005" \
	--lr_decay="linear" \
	--overwrite_output_dir \
	--num_train_epochs="8" \
	--logging_steps="20" \
	--save_steps="1000" \
	--eval_steps="2000" \
	--warmup_steps="300" \
	--validation_split_count="15000" \
	--wandb_project="long-t5-local-base" \
	--wandb_job_type="pmap"


	# --max_train_samples="160000" \
	# --max_eval_samples="1000"

	# --model_name_or_path="${MODEL_PATH}" \

	# \

	# --lr_decay="exponential" \
	# --lr_transition_steps="400000" \
	# --lr_decay_rate="0.7" \
	# --lr_staircase="false" \
	# --auth_token="$(cat ~/.huggingface/token)" \