What is BERT language model and how it works

BERT (Bidirectional Encoder Representations from Transformers) is a deep learning model introduced by Google in 2018 that processes text bidirectionally to understand context. It uses two training techniques: Masked Language Model (MLM), which predicts randomly masked words, and Next Sentence Prediction (NSP), which determines sentence relationships. BERT excels at tasks like sentiment analysis, question answering, text classification, and named entity recognition. The model can be fine-tuned for specific applications and has spawned numerous specialized variations including BioBERT for biomedical texts, SciBERT for scientific content, and DistilBERT for faster performance. Unlike GPT which generates text unidirectionally, BERT focuses on deep contextual understanding by analyzing text from both directions simultaneously.