BERT Learning Path: Architecture, Training, Variants

BERT overview and purpose

BERT (Bidirectional Encoder Representations from Transformers) is a language model introduced by Google in October 2018. Its purpose is to learn contextual representations of text by converting input tokens into vectors using a Transformer encoder-only architecture. BERT is widely used as a baseline in NLP because it can be adapted to many tasks with relatively small amounts of task-specific data. BERT is trained using self-supervised objectives, primarily masked language modeling (predicting masked tokens from their surrounding context) and next sentence prediction (predicting whether one sentence follows another). This training enables BERT to capture bidirectional context—understanding how words relate to both the left and right sides of their usage—making it effective for natural language understanding tasks such as classification, question answering, and other text interpretation problems.

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Encoder-only transformer architecture

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