mm-cot / run_training.sh
Antonio Cheong
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# rationale generation
CUDA_VISIBLE_DEVICES=0,1 python main.py \
--model allenai/unifiedqa-t5-base \
--user_msg rationale --img_type detr \
--bs 8 --eval_bs 4 --eval_acc 10 --output_len 512 \
--final_eval --prompt_format QCM-LE
# answer inference
CUDA_VISIBLE_DEVICES=0,1 python main.py \
--model allenai/unifiedqa-t5-base \
--user_msg answer --img_type detr \
--bs 8 --eval_bs 4 --eval_acc 10 --output_len 64 \
--final_eval --prompt_format QCMG-A \
--eval_le experiments/rationale_allenai-unifiedqa-t5-base_detr_QCM-LE_lr5e-05_bs16_op512_ep20/predictions_ans_eval.json \
--test_le experiments/rationale_allenai-unifiedqa-t5-base_detr_QCM-LE_lr5e-05_bs16_op512_ep20/predictions_ans_test.json