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#!/bin/bash
export HF_PROJECT="t5-base-dutch"
# Variables for training the tokenizer and creating the config
export VOCAB_SIZE="32000"
export N_INPUT_SENTENCES="1000000" # Num of sentences to train the tokenizer
export DATASET="yhavinga/mc4_nl_cleaned" # Name of the dataset in the Huggingface Hub
export DATASET_CONFIG="full" # Config of the dataset in the Huggingface Hub
export DATASET_SPLIT="train" # Split to use for training tokenizer and model
export TEXT_FIELD="text" # Field containing the text to be used for training
export CONFIG_TYPE="t5-base" # Config that our model will use
export MODEL_PATH="${HOME}/data/${HF_PROJECT}" # Path to the model, e.g. here inside the mount
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
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