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@@ -37,3 +37,55 @@ configs:
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  - split: test
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  path: data/test-*
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  - split: test
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  path: data/test-*
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  ---
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+
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+ # Dataset Card for ChemQA
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+
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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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+
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+
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+ ## Content
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+
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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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+
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+ ## Load the Dataset
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+
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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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+
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+ ## Reference
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+
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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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+
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+ ## Citation
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+
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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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+
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+
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+ ## Contact
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+
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+ shangzhu@umich.edu