readme: This model repository presents "TinyPubMedBERT", a distillated [PubMedBERT (Gu et al.,  2021)](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract) model. 
  
  The model is composed of 4-layers and distillated following methods introduced in the [TinyBERT paper](https://aclanthology.org/2020.findings-emnlp.372/) (Jiao et al., 2020).
  
  * For the framework, please visit https://github.com/AstraZeneca/KAZU
  * For the demo, please visit http://kazu.korea.ac.kr
  * 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).
  
  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) for the KAZU (Korea University and AstraZeneca) framework.
  
  
  ### 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={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 (EMNLP)},
    year={2022}
  }
  ```
  
  This model used resources from [PubMedBERT paper](https://dl.acm.org/doi/10.1145/3458754) and [TinyBERT paper](https://aclanthology.org/2020.findings-emnlp.372/).
  ```
  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.
  