Reducing Catastrophic Forgetting for Domain Adaptation in Multi-choice Q&A
The exams-qa dataset can be found here. The arXiv-10 dataset can be found here.
conda install transformers pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
pip install adapters tqdmpython ./scripts/experiments/run_multiple_choice.py \
--model_type $MODEL_TYPE \
--task_name $TASK_NAME \
--tb_log_dir runs/${TRAIN_OUTPUT_SUBDIR}/$RUN_SETTING_NAME \
--model_name_or_path $TRAINED_MODEL_DIR \
--do_train \
--do_eval \
--warmup_proportion ${WARM_UP} \
--evaluate_during_training \
--logging_steps ${LOGGING_STEPS} \
--save_steps ${LOGGING_STEPS} \
--data_dir $TRAIN_DATA_DIR \
--learning_rate $LEARNING_RATE \
--num_train_epochs $MAX_EPOCHS \
--max_seq_length $MAX_SEQ_LENGTH \
--output_dir $TRAIN_OUTPUT \
--weight_decay $WEIGHT_DECAY \
--overwrite_cache \
--per_gpu_eval_batch_size=$EVAL_BATCH_SIZE \
--per_gpu_train_batch_size=$BATCH_SIZE \
--gradient_accumulation_steps $GRADIENT_ACC_STEPS \
--overwrite_output python evaluate_exams.py \
--predictions_path predictions.json \
--dataset_path dev.jsonl \
--granularity all \
--output_path results.jsonpython ./scripts/experiments/run_multiple_choice.py \
--model_type $MODEL_TYPE \
--task_name exams \
--do_test \
--para_type per_choice \
--model_name_or_path $TRAINED_MODEL_DIR \
--data_dir $INPUT_DATA_DIR \
--max_seq_length $MAX_SEQ_LENGTH \
--output_dir $OUTPUT_DIR \
--per_gpu_eval_batch_size=$EVAL_BATCH_SIZE \
--overwrite_cache \
--overwrite_output