Update README.md (#3)

- Update README.md (f5c730c3315e32e7748b5da541674441c7492706)

README.md

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   - split: test
     path: data/test-*
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+
+# Dataset Card for ChemQA
+
+Introducing ChemQA: a Multimodal Question-and-Answering Dataset on Chemistry Reasoning. This work is inspired by IsoBench[1] and ChemLLMBench[2].
+
+
+## Content
+
+There are 5 QA Tasks in total: 
+* 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. 
+* Calculating Molecular Weights in Organic Molecules: adapted from the 600 PubChem molecules created from [2], evenly divided into validation and evaluation datasets.
+* Name Conversion: From SMILES to IUPAC: adapted from the 600 PubChem molecules created from [2], evenly divided into validation and evaluation datasets.
+* Molecule Captioning and Editing: inspired by [2], adapted from dataset provided in [3], following the same training, validation and evaluation splits.
+* Retro-synthesis Planning:  inspired by [2], adapted from dataset provided in [4], following the same training, validation and evaluation splits.
+
+## Load the Dataset
+
+```python
+from datasets import load_dataset
+dataset_train = load_dataset('shangzhu/ChemQA', split='train')
+dataset_valid = load_dataset('shangzhu/ChemQA', split='valid')
+dataset_test = load_dataset('shangzhu/ChemQA', split='test')
+```
+
+## Reference
+
+[1] Fu, D., Khalighinejad, G., Liu, O., Dhingra, B., Yogatama, D., Jia, R., & Neiswanger, W. (2024). IsoBench: Benchmarking Multimodal Foundation Models on Isomorphic Representations.
+
+[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).
+
+[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.
+
+[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.
+
+
+
+## Citation
+
+```BibTeX
+@misc{chemQA2024,
+      title={ChemQA: a Multimodal Question-and-Answering Dataset on Chemistry Reasoning}, 
+      author={Shang Zhu and Xuefeng Liu and Ghazal Khalighinejad},
+      year={2024},
+      publisher={Hugging Face},
+      howpublished={\url{https://huggingface.co/datasets/shangzhu/ChemQA}},
+}
+```
+
+
+## Contact
+
+shangzhu@umich.edu