| # 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](https://rdcu.be/cMAp5), 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](https://www.sciencedirect.com/) using the [Elsevier API](https://dev.elsevier.com/). The detailed methodology is given in the paper. | |
| The codes for pretraining and finetuning on downstream tasks are shared on [GitHub](https://github.com/m3rg-repo/MatSciBERT). | |
| If you find this useful in your research, please consider citing: | |
| ``` | |
| @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" | |
| } | |
| ``` |