export CUDA_VISIBLE_DEVICES=0

python data_aug.py

#
#for (( i = 0; i < 5; i++ ));
#
#do
#python train.py \
#--model_type xlm-roberta \
#--do_train \
#--do_eval_during_train \
#--model_name_or_path ../xlm-roberta-base/ \
#--data_dir ../data_kannada/data_StratifiedKFold_42/data_origin_$i \
#--output_dir ../checkpoints/bert_kannada/$i \
#--max_seq_length 80 \
#--learning_rate 2e-5 \
#--per_gpu_train_batch_size 32 \
#--gradient_accumulation_steps 8 \
#--num_train_epochs 6
#
#done



python predict.py \
--model_type xlm-roberta \
--vote_model_paths ../checkpoints/bert_kannada/ \
--predict_file ../data_kannada/test.csv \
--predict_result_file ../prediction_result/result_kannada.csv


