README.md

---
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-*
---
<p align="center" width="100%">
<img src="https://i.postimg.cc/g0QRgMVv/WX20240228-113337-2x.png"  width="100%" height="80%">
</p>

# 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}
}
```