About

As opposed to previous models that read sentences in a left-to-right manner, BERT is bi-directional, enabling it to understand the context of words by considering the words that come before and after them. This allows BERT to capture nuances and dependencies in language more effectively. BERT has been trained on a large corpus of textual data and has achieved state-of-the-art results on various NLP benchmarks. Its application spans a range of tasks, including sentence classification, named entity recognition, question answering, and sentiment analysis. With the release of BERT, the field of NLP witnessed significant advancements and many subsequent models have been built upon its success.