AI가 움직이고 미래가 업데이트됩니다.ClinicalBERT
This model card describes the ClinicalBERT model, which was trained on a large multicenter dataset with a large corpus of 1.2B words of diverse diseases we constructed. We then utilized a large-scale corpus of EHRs from over 3 million patient records to fine tune the base language model. The ClinicalBERT model was trained on a large multicenter dataset with a large corpus of 1.2B words of diverse diseases we constructed. The ClinicalBERT was initialized from BERT. Then the training followed the principle of masked language model, in which given a piece of text, we randomly replace some tokens by MASKs, special tokens for masking, and then require the model to predict the original tokens via contextual text.
번역 준비 중입니다. 원문을 표시합니다.
This model card describes the ClinicalBERT model, which was trained on a large multicenter dataset with a large corpus of 1.2B words of diverse diseases we constructed. We then utilized a large-scale corpus of EHRs from over 3 million patient records to fine tune the base language model. The ClinicalBERT model was trained on a large multicenter dataset with a large corpus of 1.2B words of diverse diseases we constructed. The ClinicalBERT was initialized from BERT. Then the training followed the principle of masked language model, in which given a piece of text, we randomly replace some tokens by MASKs, special tokens for masking, and then require the model to predict the original tokens via contextual text.