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 self-supervised learning, enabling strong performance across many natural language processing (NLP) tasks. BERT is widely used as a baseline model in NLP experiments and is especially effective for understanding relationships between words and sentences in context. BERT’s core training objective is designed to produce bidirectional context-aware token embeddings. During pre-training, it learns from two tasks: masked language modeling (predicting masked words from surrounding context) and next sentence prediction (predicting whether one sentence follows another). Architecturally, BERT uses an encoder-only Transformer (no causal masking), and after pre-training it can be fine-tuned with fewer resources for specific downstream tasks such as sentiment classification, question answering, and other language understanding problems.

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Encoder-only Transformer architecture

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