BioELECTRA:Pretrained Biomedical text Encoder using Discriminators

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.

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Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Natural Language Inference MedNLI BioELECTRA-Base Accuracy 86.34 # 2
Question Answering PubMedQA BioELECTRA uncased Accuracy 64.2 # 3
Medical Named Entity Recognition ShARe/CLEF eHealth corpus BioELECTRA F1 0.8371 # 1

Methods