BERT

BERT (Bidirectional Encoder Representations from Transformers) is a groundbreaking transformer-based language model developed by Google that revolutionized natural language processing by introducing bidirectional context understanding. Unlike previous models that processed text unidirectionally, BERT simultaneously considers both left and right context in all layers, enabling deeper semantic understanding. The model uses two pre-training objectives: Masked Language Modeling (MLM), where random tokens are masked and predicted, and Next Sentence Prediction (NSP) for understanding sentence relationships. BERT’s architecture consists of multiple transformer encoder layers with self-attention mechanisms that capture long-range dependencies and contextual relationships. The model can be fine-tuned for various downstream tasks including sentiment analysis, question answering, named entity recognition, and text classification. BERT’s bidirectional approach achieved state-of-the-art results across multiple NLP benchmarks and spawned numerous variants like RoBERTa, ALBERT, and DistilBERT, establishing transformers as the dominant architecture for language understanding tasks.