--- dataset_info: - config_name: ScienceQA-FULL features: - name: image dtype: image - name: question dtype: string - name: choices sequence: string - name: answer dtype: int8 - name: hint dtype: string - name: task dtype: string - name: grade dtype: string - name: subject dtype: string - name: topic dtype: string - name: category dtype: string - name: skill dtype: string - name: lecture dtype: string - name: solution dtype: string splits: # - name: train # num_bytes: 422199906.182 # num_examples: 12726 - name: validation num_bytes: 140142913.699 num_examples: 4241 - name: test num_bytes: 138277282.051 num_examples: 4241 download_size: 679275875 dataset_size: 700620101.932 - config_name: ScienceQA-IMG features: - name: image dtype: image - name: question dtype: string - name: choices sequence: string - name: answer dtype: int8 - name: hint dtype: string - name: task dtype: string - name: grade dtype: string - name: subject dtype: string - name: topic dtype: string - name: category dtype: string - name: skill dtype: string - name: lecture dtype: string - name: solution dtype: string splits: # - name: train # num_bytes: 413310651.0 # num_examples: 6218 - name: validation num_bytes: 137253441.0 num_examples: 2097 - name: test num_bytes: 135188432.0 num_examples: 2017 download_size: 663306124 dataset_size: 685752524.0 configs: - config_name: ScienceQA-FULL data_files: # - split: train # path: ScienceQA-FULL/train-* - split: validation path: ScienceQA-FULL/validation-* - split: test path: ScienceQA-FULL/test-* - config_name: ScienceQA-IMG data_files: # - split: train # path: ScienceQA-IMG/train-* - split: validation path: ScienceQA-IMG/validation-* - split: test path: ScienceQA-IMG/test-* ---

# Large-scale Multi-modality Models Evaluation Suite > Accelerating the development of large-scale multi-modality models (LMMs) with `lmms-eval` 🏠 [Homepage](https://lmms-lab.github.io/) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab) # This Dataset This is a formatted version of [derek-thomas/ScienceQA](https://huggingface.co/datasets/derek-thomas/ScienceQA). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models. ``` @inproceedings{lu2022learn, title={Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering}, author={Lu, Pan and Mishra, Swaroop and Xia, Tony and Qiu, Liang and Chang, Kai-Wei and Zhu, Song-Chun and Tafjord, Oyvind and Clark, Peter and Ashwin Kalyan}, booktitle={The 36th Conference on Neural Information Processing Systems (NeurIPS)}, year={2022} } ```