BERT

BERT (Bidirectional Encoder Representations from Transformers) is a language model introduced by Google in 2018 that fundamentally changed how machines understand text. Unlike previous models that read text left-to-right or right-to-left, BERT reads entire sequences at once, allowing it to understand context from both directions simultaneously.

BERT is pre-trained on two tasks: masked language modeling (MLM), where random words in a sentence are hidden and the model predicts them using surrounding context, and next sentence prediction (NSP), where the model learns whether two sentences logically follow each other. This pre-training on massive unlabeled text corpora gives BERT a deep understanding of language structure and semantics.

What made BERT revolutionary was the fine-tuning paradigm it popularized. After pre-training, BERT can be adapted to specific downstream tasks -- sentiment analysis, question answering, named entity recognition, text classification -- by adding a simple output layer and training on a relatively small labeled dataset. This transfer learning approach dramatically reduced the amount of task-specific data needed to achieve strong performance.

BERT and its variants (RoBERTa, ALBERT, DistilBERT, DeBERTa) remain widely used in production systems, particularly for tasks that require deep text understanding rather than text generation. While large language models like GPT have captured more attention for generation tasks, BERT-family models are often preferred for classification and extraction tasks due to their efficiency and strong bidirectional understanding.