Recent advancements in pretraining strategies in NLP have shown a significant improvement in the performance of models on various text mining tasks. In this paper, we introduce BioELECTRA, a biomedical domain-specific language encoder model that adapts ELECTRA (Clark et al., 2020) for the Biomedical domain. 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 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.
For a detailed description and experimental results, please refer to our paper
BioELECTRA:Pretrained Biomedical text Encoder using Discriminators.
1from transformers import ElectraForPreTraining, ElectraTokenizerFast
2import torch
3
4discriminator = ElectraForPreTraining.from_pretrained("kamalkraj/bioelectra-base-discriminator-pubmed")
5tokenizer = ElectraTokenizerFast.from_pretrained("kamalkraj/bioelectra-base-discriminator-pubmed")
6
7sentence = "The quick brown fox jumps over the lazy dog"
8fake_sentence = "The quick brown fox fake over the lazy dog"
9
10fake_tokens = tokenizer.tokenize(fake_sentence)
11fake_inputs = tokenizer.encode(fake_sentence, return_tensors="pt")
12discriminator_outputs = discriminator(fake_inputs)
13predictions = torch.round((torch.sign(discriminator_outputs[0]) + 1) / 2)
14
15[print("%7s" % token, end="") for token in fake_tokens]
16
17[print("%7s" % int(prediction), end="") for prediction in predictions[0].tolist()]
