transformers pytorch safetensors electra token-classification autotrain_compatible endpoints_compatible region:us
---
widget:
- text: Those in the aspirin group experienced reduced duration of headache compared
    to those in the placebo arm (P<0.05)
---

BioELECTRA-PICO

Cite our paper using below citation 
```
@inproceedings{kanakarajan-etal-2021-bioelectra,
    title = "{B}io{ELECTRA}:Pretrained Biomedical text Encoder using Discriminators",
    author = "Kanakarajan, Kamal raj  and
      Kundumani, Bhuvana  and
      Sankarasubbu, Malaikannan",
    booktitle = "Proceedings of the 20th Workshop on Biomedical Language Processing",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.bionlp-1.16",
    doi = "10.18653/v1/2021.bionlp-1.16",
    pages = "143--154",
    abstract = "Recent advancements in pretraining strategies in NLP have shown a significant improvement in the performance of models on various text mining tasks. We apply {`}replaced token detection{'} pretraining technique proposed by ELECTRA and pretrain a biomedical language model from scratch using biomedical text and vocabulary. We introduce BioELECTRA, a biomedical domain-specific language encoder model that adapts ELECTRA for the Biomedical domain. WE evaluate our model on the BLURB and BLUE biomedical NLP benchmarks. BioELECTRA outperforms the previous models and achieves state of the art (SOTA) on all the 13 datasets in BLURB benchmark and on all the 4 Clinical datasets from BLUE Benchmark across 7 different NLP tasks. BioELECTRA pretrained on PubMed and PMC full text articles performs very well on Clinical datasets as well. BioELECTRA achieves new SOTA 86.34{\%}(1.39{\%} accuracy improvement) on MedNLI and 64{\%} (2.98{\%} accuracy improvement) on PubMedQA dataset.",
}
```