Update README.md

README.md

@@ -4,6 +4,25 @@ Recent advancements in pretraining strategies in NLP have shown a significant im
 
 For a detailed description and experimental results, please refer to our paper [BioELECTRA:Pretrained Biomedical text Encoder using Discriminators](https://www.aclweb.org/anthology/2021.bionlp-1.16/).
 
+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.",
+}
+```
+
 
 ## How to use the discriminator in `transformers`