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--- |
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language: |
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- en |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: question |
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dtype: string |
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- name: choices |
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dtype: string |
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- name: label |
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dtype: int64 |
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- name: description |
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dtype: string |
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- name: id |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 705885259.25 |
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num_examples: 66166 |
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- name: valid |
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num_bytes: 100589192.25 |
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num_examples: 9486 |
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- name: test |
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num_bytes: 100021131.0 |
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num_examples: 9480 |
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download_size: 866619578 |
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dataset_size: 906495582.5 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: valid |
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path: data/valid-* |
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- split: test |
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path: data/test-* |
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--- |
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# Dataset Card for ChemQA |
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Introducing ChemQA: a Multimodal Question-and-Answering Dataset on Chemistry Reasoning. This work is inspired by IsoBench[1] and ChemLLMBench[2]. |
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## Content |
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There are 5 QA Tasks in total: |
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* Counting Numbers of Carbons and Hydrogens in Organic Molecules: adapted from the 600 PubChem molecules created from [2], evenly divided into validation and evaluation datasets. |
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* Calculating Molecular Weights in Organic Molecules: adapted from the 600 PubChem molecules created from [2], evenly divided into validation and evaluation datasets. |
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* Name Conversion: From SMILES to IUPAC: adapted from the 600 PubChem molecules created from [2], evenly divided into validation and evaluation datasets. |
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* Molecule Captioning and Editing: inspired by [2], adapted from dataset provided in [3], following the same training, validation and evaluation splits. |
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* Retro-synthesis Planning: inspired by [2], adapted from dataset provided in [4], following the same training, validation and evaluation splits. |
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## Load the Dataset |
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```python |
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from datasets import load_dataset |
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dataset_train = load_dataset('shangzhu/ChemQA', split='train') |
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dataset_valid = load_dataset('shangzhu/ChemQA', split='valid') |
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dataset_test = load_dataset('shangzhu/ChemQA', split='test') |
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``` |
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## Reference |
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[1] Fu, D., Khalighinejad, G., Liu, O., Dhingra, B., Yogatama, D., Jia, R., & Neiswanger, W. (2024). IsoBench: Benchmarking Multimodal Foundation Models on Isomorphic Representations. |
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[2] Guo, T., Guo, kehan, Nan, B., Liang, Z., Guo, Z., Chawla, N., Wiest, O., & Zhang, X. (2023). What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks. Advances in Neural Information Processing Systems (Vol. 36, pp. 59662–59688). |
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[3] Edwards, C., Lai, T., Ros, K., Honke, G., Cho, K., & Ji, H. (2022). Translation between Molecules and Natural Language. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 375–413. |
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[4] Irwin, R., Dimitriadis, S., He, J., & Bjerrum, E. J. (2022). Chemformer: a pre-trained transformer for computational chemistry. Machine Learning: Science and Technology, 3(1), 15022. |
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## Citation |
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```BibTeX |
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@misc{chemQA2024, |
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title={ChemQA: a Multimodal Question-and-Answering Dataset on Chemistry Reasoning}, |
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author={Shang Zhu and Xuefeng Liu and Ghazal Khalighinejad}, |
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year={2024}, |
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publisher={Hugging Face}, |
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howpublished={\url{https://huggingface.co/datasets/shangzhu/ChemQA}}, |
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} |
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``` |
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## Contact |
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shangzhu@umich.edu |
