#### pre-trained lm path
path=gpt2
MODEL_TYPE=gpt2

#### task data path (user should change this path)
data_path=./datasets/data_example/incontext_cls

export CUDA_VISIBLE_DEVICES=-1
python3 hugnlp_runner.py \
  --model_name_or_path=$path \
  --data_dir=$data_path\
  --output_dir=./outputs/instruction/incontext_learning \
  --seed=42 \
  --exp_name=gpt2-incontext-cls \
  --max_seq_length=512 \
  --max_eval_seq_length=512 \
  --do_eval \
  --do_predict \
  --per_device_train_batch_size=1 \
  --per_device_eval_batch_size=1 \
  --gradient_accumulation_steps=1 \
  --evaluation_strategy=steps \
  --logging_steps=100000000 \
  --eval_steps=1 \
  --save_steps=1 \
  --save_total_limit=1 \
  --load_best_model_at_end \
  --report_to=none \
  --task_name=causal_incontext_cls \
  --task_type=causal_prompt_cls \
  --model_type=$MODEL_TYPE \
  --metric_for_best_model=macro_f1 \
  --pad_to_max_length=True \
  --remove_unused_columns=False \
  --overwrite_output_dir \
  --label_names=short_labels \
  --keep_predict_labels \
  --user_defined="num_incontext_example=4 l=1 use_calibrate=True" \
  --use_prompt_for_cls
