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README.md

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----
-license: apache-2.0
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-> # Cloned from https://github.com/amazon-science/mm-cot
-
-# Multimodal Chain-of-Thought Reasoning in Language Models
-
-<h5 align="center"><i>"Imagine learning a textbook without figures or tables."</i></h5>
-
-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.
-
-![](vision_features/mm-cot.png)
-
-
-## Requirements
-
-Install all required python dependencies:
-
-```
-pip install -r requirements.txt
-```
-
-## Datasets
-
-Download the dataset from the following repository:
-
-```
-https://github.com/lupantech/ScienceQA/tree/main/data
-```
-
-Download the extracted vision features from [vision_features](https://drive.google.com/file/d/13B0hc_F_45-UlqPLKSgRz-ALtFQ8kIJr/view?usp=share_link) and unzip the files under `vision_features`
-
-## Instructions
-
-### Training 
-
-```
-# 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
-```
-
-### Inference 
-
-Our trained models are available at [models](https://drive.google.com/file/d/1FtTYOJPHnWnFfCxNC6M3gar4RAX5E21b/view?usp=share_link). 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
-```
-
-## Citing MM-CoT
-
-```
-@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}
-}
-```
-
-## License
-
-This project is licensed under the Apache-2.0 License.
-
-## Acknowledgement
-
-Part of our codes are adapted from [ScienceQA](https://github.com/lupantech/ScienceQA) and [Transformers](https://github.com/huggingface/transformers).
-
-We thank Pan Lu for providing parameter size for ScienceQA baselines.