BERT (Bidirectional Encoder Representations from Transformers) is a natural language processing model developed by Google in 2018. It is based on the transformer architecture and is pre-trained using large amounts of text data in an unsupervised manner, which allows it to learn general language representations that can be fine-tuned for specific tasks.
One of the key innovations of BERT is its ability to learn bidirectional representations of language, meaning that it can take into account both the context before and after a word or phrase when generating its representation. This enables it to better understand the nuances and complexities of language, particularly in cases where the meaning of a word or phrase depends on the surrounding context.
BERT has achieved state-of-the-art results on a wide range of natural language processing tasks, including question-answering, sentiment analysis, and named entity recognition, among others. Its ability to perform well on a wide range of tasks with minimal fine-tuning has made it a popular choice for natural language processing applications.
