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--- |
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license: mit |
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language: |
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- en |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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library_name: transformers |
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--- |
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# MatSciBERT |
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## A Materials Domain Language Model for Text Mining and Information Extraction |
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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. |
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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. |
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The codes for pretraining and finetuning on downstream tasks are shared on [GitHub](https://github.com/m3rg-repo/MatSciBERT). |
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If you find this useful in your research, please consider citing: |
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``` |
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@article{gupta_matscibert_2022, |
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title = "{MatSciBERT}: A Materials Domain Language Model for Text Mining and Information Extraction", |
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author = "Gupta, Tanishq and |
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Zaki, Mohd and |
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Krishnan, N. M. Anoop and |
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Mausam", |
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year = "2022", |
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month = may, |
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journal = "npj Computational Materials", |
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volume = "8", |
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number = "1", |
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pages = "102", |
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issn = "2057-3960", |
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url = "https://www.nature.com/articles/s41524-022-00784-w", |
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doi = "10.1038/s41524-022-00784-w" |
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} |
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``` |
