README.md

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 the paper using the simplified citation format provided in 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",
}
```

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.