| This model repository presents "TinySapBERT", tiny-sized biomedical entity representations (language model) trained using [official SapBERT code and instructions (Liu et al., NAACL 2021)](https://github.com/cambridgeltl/sapbert). | |
| We used our [TinyPubMedBERT](https://huggingface.co/dmis-lab/TinyPubMedBERT-v1.0), a tiny-sized LM, as an initial starting point to train using the SapBERT scheme. | |
| <br> | |
| cf) TinyPubMedBERT is a distillated [PubMedBERT (Gu et al., 2021)](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract), open-sourced along with the release of the KAZU (Korea University and AstraZeneca) framework. | |
| * For details, please visit [KAZU framework](https://github.com/AstraZeneca/KAZU) or see our paper entitled **Biomedical NER for the Enterprise with Distillated BERN2 and the Kazu Framework**, (EMNLP 2022 industry track). | |
| * For the demo of KAZU framework, please visit http://kazu.korea.ac.kr | |
| ### Citation info | |
| Joint-first authorship of **Richard Jackson** (AstraZeneca) and **WonJin Yoon** (Korea University). | |
| <br>Please cite the simplified version using the following section, or find the [full citation information here](https://aclanthology.org/2022.emnlp-industry.63.bib) | |
| ``` | |
| @inproceedings{YoonAndJackson2022BiomedicalNER, | |
| title="Biomedical {NER} for the Enterprise with Distillated {BERN}2 and the Kazu Framework", | |
| author="Yoon, Wonjin and Jackson, Richard and Ford, Elliot and Poroshin, Vladimir and Kang, Jaewoo", | |
| booktitle="Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track", | |
| month = dec, | |
| year = "2022", | |
| address = "Abu Dhabi, UAE", | |
| publisher = "Association for Computational Linguistics", | |
| url = "https://aclanthology.org/2022.emnlp-industry.63", | |
| pages = "619--626", | |
| } | |
| ``` | |
| The model used resources of [SapBERT paper](https://aclanthology.org/2021.naacl-main.334.pdf). We appreciate the authors for making the resources publicly available! | |
| ``` | |
| Liu, Fangyu, et al. "Self-Alignment Pretraining for Biomedical Entity Representations." | |
| Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021. | |
| ``` | |
| ### Contact Information | |
| For help or issues using the codes or model (NER module of KAZU) in this repository, please contact WonJin Yoon (wonjin.info (at) gmail.com) or submit a GitHub issue. | |