language:
- en
dataset_info:
features:
- name: image
dtype: image
- name: question
dtype: string
- name: choices
dtype: string
- name: label
dtype: int64
- name: description
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 705885259.25
num_examples: 66166
- name: valid
num_bytes: 100589192.25
num_examples: 9486
- name: test
num_bytes: 100021131
num_examples: 9480
download_size: 866619578
dataset_size: 906495582.5
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
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
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
@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}},
}