MatSciBERT

A Materials Domain Language Model for Text Mining and Information Extraction

This is the pretrained model presented in MatSciBERT: A materials domain language model for text mining and information extraction, which is a BERT model trained on material science research papers.

The training corpus comprises papers related to the broad category of materials: alloys, glasses, metallic glasses, cement and concrete. We have utilised the abstracts and full text of papers(when available). All the research papers have been downloaded from ScienceDirect using the Elsevier API. The detailed methodology is given in the paper.

The codes for pretraining and finetuning on downstream tasks are shared on GitHub.

@article{gupta_matscibert_2022,
title   = "{MatSciBERT}: A Materials Domain Language Model for Text Mining and Information Extraction",
author  = "Gupta, Tanishq and
Zaki, Mohd and
Krishnan, N. M. Anoop and
Mausam",
year    = "2022",
month   = may,
journal = "npj Computational Materials",
volume  = "8",
number  = "1",
pages   = "102",
issn    = "2057-3960",
url     = "https://www.nature.com/articles/s41524-022-00784-w",
doi     = "10.1038/s41524-022-00784-w"
}

Mask token: [MASK]