• download datasets and models

bash ./download.sh

• run inference

bash ./run.sh context.json test.json submission.csv

• install requirements

pip install -r requirements.txt

• download datasets

bash download.sh

• start training multiple choice model

python train_mc.py --dataset_name weitung8/ntuadlhw1 --model_name_or_path hfl/chinese-roberta-wwm-ext --push_to_hub --hub_model_id weitung8/ntuadlhw1-multiple-choice --with_tracking --report_to wandb --max_seq_length 512 --per_device_train_batch_size 1 --per_device_eval_batch_size 1 --gradient_accumulation_steps 4 --output_dir weitung8/ntuadlhw1-multiple-choice --num_train_epochs 1 --learning_rate 2e-5 --weight_decay 0.001 --checkpointing_steps epoch

• start training question answering model

python train_qa.py --dataset_name weitung8/ntuadlhw1 --model_name_or_path hfl/chinese-roberta-wwm-ext --push_to_hub --hub_model_id weitung8/ntuadlhw1-question-answering --with_tracking --report_to wandb --max_seq_length 512 --per_device_train_batch_size 4 --per_device_eval_batch_size 4 --gradient_accumulation_steps 4 --output_dir weitung8/ntuadlhw1-question-answering --num_train_epochs 3 --learning_rate 2e-5 --checkpointing_steps epoch --lr_scheduler_type cosine

• get the test results

bash run.sh context.json test.json submission.csv

• submit results to kaggle

kaggle competitions submit -c ntuadl2023hw1 -f submission.csv -m "some message"

• multi-gpu training

TOKENIZERS_PARALLELISM=false accelerate launch train_qa.py --dataset_name weitung8/ntuadlhw1 --model_name_or_path hfl/chinese-roberta-wwm-ext-large --push_to_hub --hub_model_id weitung8/ntuadlhw1-question-answering --with_tracking --report_to wandb --max_seq_length 512 --per_device_train_batch_size 2 --per_device_eval_batch_size 2 --gradient_accumulation_steps 8 --output_dir weitung8/ntuadlhw1-question-answering --num_train_epochs 3 --learning_rate 8e-5 --checkpointing_steps epoch --lr_scheduler_type cosine --weight_decay 0.0001 --max_answer_length 60