BERT (Bidirectional Encoder Representations from Transformers) is a machine learning model designed for natural language processing (NLP). Introduced by Google in 2018, it uses a neural network architecture called the Transformer to analyze text by considering the context of words in both directions (left and right). Unlike earlier models that processed text sequentially (e.g., left-to-right or right-to-left), BERT’s bidirectional approach allows it to capture relationships between words more effectively. For example, in the sentence “The bank account is by the river,” BERT can distinguish that “bank” refers to a financial institution, not a riverbank, by examining surrounding words in both directions. This ability to understand context makes BERT highly effective for tasks like text classification, question answering, and named entity recognition.
BERT’s popularity stems from its versatility and performance. Before BERT, many NLP models required extensive task-specific modifications or large labeled datasets. BERT simplified this by introducing a two-step process: pre-training on a massive corpus of text (e.g., Wikipedia and books) followed by fine-tuning on smaller, task-specific datasets. Pre-training teaches BERT general language patterns, such as grammar and word relationships, using objectives like masked language modeling (predicting hidden words in a sentence) and next-sentence prediction. Developers can then fine-tune the pre-trained model with minimal task-specific data. For instance, adding a single classification layer on top of BERT allows it to perform sentiment analysis with high accuracy. This approach reduced the need for custom architectures and made advanced NLP accessible to more developers.
Another reason for BERT’s popularity is its open-source availability and impact on benchmarks. When released, BERT achieved state-of-the-art results on 11 NLP tasks, including the GLUE benchmark (a collection of language understanding tasks). For example, in question answering, BERT outperformed previous models by understanding complex queries like, “What ingredient neutralizes acidity?” and correctly identifying “baking soda” from a passage about recipes. Its success encouraged widespread adoption in industry and research, with developers integrating BERT into chatbots, search engines, and content moderation systems. While newer models have since emerged, BERT’s balance of performance, accessibility, and adaptability keeps it relevant. Its design also influenced later models like RoBERTa and ALBERT, which build on its bidirectional Transformer foundation.