Data for "Flexible, Model-Agnostic Method for Materials Data Extraction from Text Using General Purpose Language Models"

Datasets for the paper entitled "Flexible, Model-Agnostic Method for Materials Data Extraction from Text Using General Purpose Language Models" by Maciej P. Polak, Shrey Modi, Anna Latosinska, Jinming Zhang, Ching-Wen Wang, Shanonan Wang, Ayan Deep Hazra, and Dane MorganMPPolak_BulkModulus_ValidationData.xlsx - a dataset of bulk modulus sentences, positive - containing bulk modulus data, and negative - not contaning data, used for model assessment.MPPolak_BulkModulus_AllTrainData.xlsx - a dataset of bulk modulus sentences, positive - containing bulk modulus data, and negative - not contaning data, used for fine tuning of the model and model assessment.MPPolak_CritCoolRate_Dataset.xlsx - a dataset of critical cooling rates for metallic glasses developed in this paper with the ,ethod presented in the paper, consisting of names of materials, values of critical cooling rates, their units, and DOIs of the source documents.MPPolak_DataExtraction_codes.zip - simple example codes necessary to reproduce the results. The provided 'positive' and 'negative' files are a shortened versions of the training data allowing for quick execution and testing. The 'pos' and 'neg' files contain full testing sets. The 'plotting' directory contains data and scripts which allow to reproduce the figures.