Encoder-only models like BERT are AI models designed to understand text by focusing solely on the input context. They encode entire sentences or documents into rich, contextual embeddings without generating new text, making them ideal for tasks like classification, sentiment analysis, and information retrieval.
BERT uses multiple layers of transformers’ encoders to process input text bidirectionally, capturing context from both left and right sides simultaneously. This deep contextual understanding allows it to represent nuances in language, improving accuracy in understanding intent and meaning.
For product managers, BERT enables improved user experience in search, recommendation, and moderation systems through better text understanding. It offers efficient inference with lower latency compared to generative models and scales well for classification tasks, making it cost-effective and practical for many AI-driven features.