 however, only English speakers benefit due to the significant scarcity of high-quality medical documents, such as PubMed, in each language. Therefore, we propose a method to train a high-performance BERT model using a small corpus . We introduce the method to train a BERT model on a small medical corpus both in English and in Japanese , and we present the evaluation of each model in terms of the biomedical language understanding evaluation (BLUE) benchmark and the medical document classification task in Japanese, respectively.
<meaning-changed>  however, only English speakers benefit due to the significant scarcity of high-quality medical documents, such as PubMed, in each language. Therefore, we propose a method to train a high-performance BERT model using a small corpus . We introduce the method to train a BERT model on a small medical corpus both in English and in Japanese , and we present the evaluation of each model in terms of the biomedical language understanding evaluation (BLUE) benchmark and the medical document classification task in Japanese, respectively.
 however, it is difficult to train specific BERT models that perform well for domains in which there are few publicly available databases of high quality and large size. We hypothesized that this problem can be addressed by up-sampling a domain-specific corpus and using it for pre-training with a larger corpus in a balanced manner. Our proposed method consists of a single intervention with one option: simultaneous pre-training after up-sampling and amplified vocabulary. We conducted three experiments and evaluated the resulting products. We confirmed that our Japanese medical BERT outperformed conventional baselines and the other BERT models in terms of the medical document classification task and that our English BERT pre-trained using both the general and medical-domain corpora performed sufficiently well for practical use in terms of the biomedical language understanding evaluation (BLUE) benchmark and the medical document classification task in Japanese, respectively.