Overview

BERT (Bidirectional Encoder Representations from Transformers) is a deep learning model developed by Google in 2018 that revolutionized natural language processing tasks.

It uses the transformer architecture to pre-train deep bidirectional representations from text, enabling it to understand context and nuances in human language more effectively than previous models.

Key aspects

By 2026, while newer models like LLaMA and GPT will have become mainstream for many tasks, BERT's influence on model architecture and training methods remains significant.

Practitioners still refer to BERT for fine-tuning specific NLP applications such as sentiment analysis or question answering due to its robustness and interpretability.