| # SciBERT | |
| This is the pretrained model presented in [SciBERT: A Pretrained Language Model for Scientific Text](https://www.aclweb.org/anthology/D19-1371/), which is a BERT model trained on scientific text. | |
| The training corpus was papers taken from [Semantic Scholar](https://www.semanticscholar.org). Corpus size is 1.14M papers, 3.1B tokens. We use the full text of the papers in training, not just abstracts. | |
| SciBERT has its own wordpiece vocabulary (scivocab) that's built to best match the training corpus. We trained cased and uncased versions. | |
| Available models include: | |
| * `scibert_scivocab_cased` | |
| * `scibert_scivocab_uncased` | |
| The original repo can be found [here](https://github.com/allenai/scibert). | |
| If using these models, please cite the following paper: | |
| ``` | |
| @inproceedings{beltagy-etal-2019-scibert, | |
| title = "SciBERT: A Pretrained Language Model for Scientific Text", | |
| author = "Beltagy, Iz and Lo, Kyle and Cohan, Arman", | |
| booktitle = "EMNLP", | |
| year = "2019", | |
| publisher = "Association for Computational Linguistics", | |
| url = "https://www.aclweb.org/anthology/D19-1371" | |
| } | |
| ``` | |