| # 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://arxiv.org/abs/2109.15290), 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 length 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_2021, | |
| title = {{{MatSciBERT}}: A {{Materials Domain Language Model}} for {{Text Mining}} and {{Information Extraction}}}, | |
| shorttitle = {{{MatSciBERT}}}, | |
| author = {Gupta, Tanishq and Zaki, Mohd and Krishnan, N. M. Anoop and Mausam}, | |
| year = {2021}, | |
| month = sep, | |
| journal = {arXiv:2109.15290 [cond-mat]}, | |
| eprint = {2109.15290}, | |
| eprinttype = {arxiv}, | |
| primaryclass = {cond-mat}, | |
| archiveprefix = {arXiv}, | |
| keywords = {Computer Science - Computation and Language,Condensed Matter - Materials Science}} | |
| } | |
| ``` |