Multimodal-CoT incorporates vision features in a decoupled training framework. The framework consists of two training stages: (i) rationale generation and (ii) answer inference. Both stages share the same model architecture but differ in the input and output.
Install all required python dependencies:
pip install -r requirements.txt
Download the dataset from the following repository:
https://github.com/lupantech/ScienceQA/tree/main/data
Download the extracted vision features from vision_features and unzip the files under vision_features
# 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
Our trained models are available at models. To use our trained models, please put the them under the models folder.
# 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 \
--evaluate_dir models/MM-CoT-UnifiedQA-base-Rationale
# 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 models/rationale/predictions_ans_eval.json \
--test_le models/rationale/predictions_ans_test.json \
--evaluate_dir models/MM-CoT-UnifiedQA-base-Answer
@article{zhang2023multicot,
title={Multimodal Chain-of-Thought Reasoning in Language Models},
author={Zhang, Zhuosheng and Zhang, Aston and Li, Mu and Zhao, Hai and Karypis, George and Smola, Alex},
journal={arXiv preprint arXiv:2302.00923},
year={2023}
}
This project is licensed under the Apache-2.0 License.
Part of our codes are adapted from ScienceQA and Transformers.
We thank Pan Lu for providing parameter size for ScienceQA baselines.
![dependabot[bot] avatar](https://avatars.githubusercontent.com/in/29110?v=4 "dependabot[bot]")
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent