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Add dataset creation script
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#!/bin/bash
export LC_ALL=C.UTF-8
export LANG=C.UTF-8
export MODEL_NAME_OR_PATH=/home/m3hrdadfi/code/gpt2-medium-persian
export OUTPUT_DIR=/home/m3hrdadfi/code/gpt2-medium-persian
# export MODEL_TYPE=gpt2
# export CONFIG_NAME=/home/m3hrdadfi/code/gpt2-medium-persian
# export TOKENIZER_NAME=/home/m3hrdadfi/code/gpt2-medium-persian
export TRAIN_FILE=/home/m3hrdadfi/data/train.csv
export VALIDATION_FILE=/home/m3hrdadfi/data/test.csv
#export TEST_FILE=/home/m3hrdadfi/code/data/...csv
# export DATASET_NAME=oscar
# export DATASET_CONFIG_NAME=unshuffled_deduplicated_fa
export MAX_SEQUENCE_LENGTH=512
#export MAX_TRAIN_SAMPLE=5000
#export MAX_EVAL_SAMPLES=5000
export PER_DEVICE_TRAIN_BATCH_SIZE=16
export PER_DEVICE_EVAL_BATCH_SIZE=16
export NUM_TRAIN_EPOCHS=9.0
export LEARNING_RATE=8e-4
export WARMUP_STEPS=5000
export LOGGING_STEPS=500
export EVAL_STEPS=2500
export SAVE_STEPS=2500
python src/run_clm_flax.py \
--output_dir="$OUTPUT_DIR" \
--model_name_or_path="$MODEL_NAME_OR_PATH" \
--train_file="$TRAIN_FILE" \
--validation_file="$VALIDATION_FILE" \
--block_size=$MAX_SEQUENCE_LENGTH \
--per_device_train_batch_size=$PER_DEVICE_TRAIN_BATCH_SIZE \
--per_device_eval_batch_size=$PER_DEVICE_EVAL_BATCH_SIZE \
--num_train_epochs=$NUM_TRAIN_EPOCHS \
--learning_rate=$LEARNING_RATE \
--warmup_steps=$WARMUP_STEPS \
--logging_step=$LOGGING_STEPS \
--eval_steps=$EVAL_STEPS \
--save_steps=$SAVE_STEPS \
--do_train \
--do_eval \
--overwrite_output_dir \
--push_to_hub
# python src/run_clm_flax.py \
# --output_dir="$OUTPUT_DIR" \
# --model_type="$MODEL_TYPE" \
# --config_name="$CONFIG_NAME" \
# --tokenizer_name="$TOKENIZER_NAME" \
# --dataset_name="$DATASET_NAME" \
# --dataset_config_name="$DATASET_CONFIG_NAME" \
# --block_size=$MAX_SEQUENCE_LENGTH \
# --per_device_train_batch_size=$PER_DEVICE_TRAIN_BATCH_SIZE \
# --per_device_eval_batch_size=$PER_DEVICE_EVAL_BATCH_SIZE \
# --num_train_epochs=$NUM_TRAIN_EPOCHS \
# --learning_rate=$LEARNING_RATE \
# --warmup_steps=$WARMUP_STEPS \
# --logging_step=$LOGGING_STEPS \
# --eval_steps=$EVAL_STEPS \
# --save_steps=$SAVE_STEPS \
# --do_train \
# --do_eval \
# --overwrite_output_dir \
# --push_to_hub