siddharthl1293/engineering_design_facts
Dataset Copyright - L. Siddharth, Singapore University of Technology and Design, Singapore. The dataset includes 375,084 example sentences (187200 positive, 187884 negative), each including a pair of entities and the engineering design relation between these. The dataset was manually constructed using sentences in 4,205 patents granted by USPTO, stratified according to 130 classes. The dataset is used to train token classification and Seq2Seq transformer models to populate explicit engineering… See the full description on the dataset page: https://huggingface.co/datasets/siddharthl1293/engineering_design_facts.
Dataset Copyright - L. Siddharth, Singapore University of Technology and Design, Singapore.
The dataset includes 375,084 example sentences (187200 positive, 187884 negative), each including a pair of entities and the engineering design relation between these. The dataset was manually constructed using sentences in 4,205 patents granted by USPTO, stratified according to 130 classes.
The dataset is used to train token classification and Seq2Seq transformer models to populate explicit engineering design facts from artefact descriptions.
More details in the following paper. Siddharth, L., Luo, J., 2024. Retrieval-Augmented Generation using Engineering Design Knowledge. arXiv (cs.CL) https://arxiv.org/abs/2307.06985.
In each example, The input is a pair of entities is marked in a sentence using {HEAD ~ ...} and {TAIL ~ ...} notations. The output is the relation between the pair of entities as identified using actual tokens in the sentence. If there is no relation, the output in None.
The dataset could be used to train Seq2Seq models, i.e., marked sentence --> relation. The dataset could also be used to train token classification models, i.e., tokenized marked sentence --> token tags.
For more information, please write to siddharthl.iitrpr.sutd@gmail.com