| This model repository presents "TinyPubMedBERT", a distillated PubMedBERT (Gu et al., 2021) model. | |
| TinyPubMedBERT is used as the initial weights for the training of the [dmis-lab/KAZU-NER-module-distil-v1.0](https://huggingface.co/dmis-lab/KAZU-NER-module-distil-v1.0) which is used in the initial release of the KAZU (Korea University and AstraZeneca) framework. | |
| The model is composed of 4-layers and distillated following methods introduced in TinyBERT paper (Jiao et al., 2020). | |
| * For the framework, please visit https://github.com/AstraZeneca/KAZU | |
| * For details about the model, please see our paper entitled **Biomedical NER for the Enterprise with Distillated BERN2 and the Kazu Framework**, (EMNLP 2022 industry track). | |
| More details to be announced soon. | |
| ### Citation info | |
| Joint-first authorship of **Richard Jackson** (AstraZeneca) and **WonJin Yoon** (Korea University). | |
| <br>Please cite: (Full citation info will be announced soon) | |
| ``` | |
| @inproceedings{YoonAndJackson2022BiomedicalNER, | |
| title={Biomedical NER for the Enterprise with Distillated BERN2 and the Kazu Framework}, | |
| author={Wonjin Yoon, Richard Jackson, Elliot Ford, Vladimir Poroshin, Jaewoo Kang}, | |
| booktitle={Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, | |
| year={2022} | |
| } | |
| ``` | |
| The model used resources of PubMedBERT paper and TinyBERT paper. | |
| Gu, Yu, et al. "Domain-specific language model pretraining for biomedical natural language processing." ACM Transactions on Computing for Healthcare (HEALTH) 3.1 (2021): 1-23. | |
| Jiao, Xiaoqi, et al. "TinyBERT: Distilling BERT for Natural Language Understanding." Findings of the Association for Computational Linguistics: EMNLP 2020. 2020. | |
| ### 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. | |