readme: # 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"
  }
  ```