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BERT is a Transformer encoder model introduced by Google researchers in 2018. It demonstrated that deeply bidirectional pre-training could produce strong language representations that transfer effectively to many natural-language understanding tasks.
How it works
BERT is trained to build contextual token representations using surrounding text rather than generating text one token at a time like a typical autoregressive decoder. Fine-tuned BERT variants became widely used for classification, question answering, retrieval, and information extraction.
Why it matters
BERT helped establish pre-training plus task adaptation as a dominant NLP pattern and remains a reference point for understanding encoder models, embeddings, and transfer learning.
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