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#!/bin/bash
export LC_ALL=C.UTF-8
export LANG=C.UTF-8
export OUTPUT_DIR=/to/our/path
export MODEL_NAME_OR_PATH=t5-base
export NUM_BEAMS=3
export TRAIN_FILE=/to/../train.csv
export VALIDATION_FILE=/to/../dev.csv
export TEST_FILE=/to/../test.csv
export TEXT_COLUMN=inputs
export TARGET_COLUMN=targets
export MAX_SOURCE_LENGTH=128
export MAX_TARGET_LENGTH=1024
export SOURCE_PREFIX=ingredients
export PER_DEVICE_TRAIN_BATCH_SIZE=8
export PER_DEVICE_EVAL_BATCH_SIZE=8
export GRADIENT_ACCUMULATION_STEPS=2
export NUM_TRAIN_EPOCHS=3.0
export LEARNING_RATE=5e-4
export WARMUP_STEPS=5000
python run_ed_recipe_nlg.py \
--output_dir="$OUTPUT_DIR" \
--train_file="$TRAIN_FILE" \
--validation_file="$VALIDATION_FILE" \
--test_file="$TEST_FILE" \
--text_column="$TEXT_COLUMN" \
--target_column="$TARGET_COLUMN" \
--source_prefix="$SOURCE_PREFIX: " \
--max_source_length="$MAX_SOURCE_LENGTH" \
--max_target_length="$MAX_TARGET_LENGTH" \
--model_name_or_path="$MODEL_NAME_OR_PATH" \
--extra_tokens="" \
--special_tokens="<sep>,<section>" \
--per_device_train_batch_size=$PER_DEVICE_TRAIN_BATCH_SIZE \
--per_device_eval_batch_size=$PER_DEVICE_EVAL_BATCH_SIZE \
--gradient_accumulation_steps=$GRADIENT_ACCUMULATION_STEPS \
--num_train_epochs=$NUM_TRAIN_EPOCHS \
--learning_rate=$LEARNING_RATE \
--warmup_steps=$WARMUP_STEPS \
--preprocessing_num_workers=4 \
--prediction_debug \
--do_train \
--do_eval \
--do_predict \
--overwrite_output_dir \
--predict_with_generate |