#!/bin/bash
#
# This is an example script to show how to made customized task
#
#
SCRIPT=$(readlink -f "$0")
SCRIPT_DIR=$(dirname "$SCRIPT")
cd $SCRIPT_DIR

cache_dir=/tmp/DeBERTa/

Task=MyRACE
# Download the data from http://www.cs.cmu.edu/~glai1/data/race/

init=$1
tag=$init
case ${init,,} in
	base-mnli)
	parameters=" --num_train_epochs 6 \
	--warmup 100 \
	--learning_rate 2e-5 \
	--train_batch_size 32 \
	--cls_drop_out 0.2 \
	--max_seq_len 320"
		;;
	large-mnli)
	parameters=" --num_train_epochs 6 \
	--warmup 100 \
	--learning_rate 8e-6 \
	--train_batch_size 32 \
	--cls_drop_out 0.2 \
	--max_seq_len 320"
		;;
	xlarge-mnli)
	parameters=" --num_train_epochs 6 \
	--warmup 100 \
	--learning_rate 7e-6 \
	--train_batch_size 48 \
	--cls_drop_out 0.2\
	--fp16 True \
	--max_seq_len 320"
		;;
	xlarge-v2-mnli)
	parameters=" --num_train_epochs 6 \
	--warmup 100 \
	--learning_rate 4e-6 \
	--train_batch_size 48 \
	--cls_drop_out 0.2 \
	--fp16 True \
	--max_seq_len 320"
		;;
	xxlarge-v2-mnli)
	parameters=" --num_train_epochs 6 \
	--warmup 100 \
	--learning_rate 3e-6 \
	--train_batch_size 48 \
	--cls_drop_out 0.2 \
	--fp16 True \
	--max_seq_len 320"
		;;
	*)
		echo "usage $0 <Pretrained model configuration>"
		echo "Supported configurations"
		echo "base-mnli - Pretrained DeBERTa v1 model with 140M parameters (12 layers, 768 hidden size)"
		echo "large-mnli - Pretrained DeBERta v1 model with 380M parameters (24 layers, 1024 hidden size)"
		echo "xlarge-mnli - Pretrained DeBERTa v1 model with 750M parameters (48 layers, 1024 hidden size)"
		echo "xlarge-v2-mnli - Pretrained DeBERTa v2 model with 900M parameters (24 layers, 1536 hidden size)"
		echo "xxlarge-v2-mnli - Pretrained DeBERTa v2 model with 1.5B parameters (48 layers, 1536 hidden size)"
		exit 0
		;;
esac

python -m DeBERTa.apps.run --model_config config.json  \
	--tag $tag \
	--do_train \
	--task_dir . \
	--task_name $Task \
	--data_dir <Your race data directory> \
	--init_model $init \
	--output_dir /tmp/ttonly/$tag/$task  $parameters