Bidirectional Encoder Representations from Transformers — Google's landmark language model that reads text bidirectionally, capturing richer contextual understanding than left-to-right models, and became the foundation for NLP fine-tuning.
In Depth
BERT, introduced by Google in 2018, revolutionized NLP by demonstrating that bidirectional pre-training of Transformers produces dramatically better language representations than previous unidirectional approaches. While GPT reads text left-to-right, BERT reads in both directions simultaneously — understanding each word in the context of all words that come before and after it. This gives BERT a richer, more accurate representation of meaning, particularly for tasks like question answering where the relationship between words across a sentence is critical.
BERT is pre-trained using two objectives. Masked Language Modeling (MLM) randomly masks 15% of input tokens and trains the model to predict the masked tokens from surrounding context — a task that requires deep bidirectional understanding. Next Sentence Prediction (NSP) trains the model to determine whether two sentences appear consecutively in text — capturing discourse-level relationships. Together, these objectives create representations that encode syntax, semantics, and pragmatics from vast text corpora.
BERT's impact on NLP was immediate and profound. Fine-tuning BERT on downstream tasks (question answering, sentiment analysis, named entity recognition, textual entailment) produced state-of-the-art results on virtually every benchmark of the time, often surpassing prior specialized architectures. It established fine-tuning of pre-trained Transformers as the dominant NLP paradigm. Variants like RoBERTa (which improved pre-training), DistilBERT (smaller and faster), and ALBERT (parameter-efficient) extended BERT's influence further.
BERT showed that reading a sentence in both directions simultaneously provides fundamentally richer understanding than left-to-right reading — a simple insight that set new records across every NLP benchmark in 2018.
Real-World Applications
Frequently Asked Questions
What is the difference between BERT and GPT?
BERT is an encoder model that reads text bidirectionally — considering both left and right context simultaneously — making it excellent for understanding tasks (classification, question answering, NER). GPT is a decoder model that reads left-to-right, making it ideal for text generation. BERT understands context better; GPT generates text better. Modern models increasingly combine both capabilities.
How does BERT's masked language model work?
During pre-training, BERT randomly masks 15% of tokens in the input and learns to predict them from surrounding context. For example, given 'The cat sat on the [MASK],' BERT learns to predict 'mat' by attending to all surrounding words simultaneously. This bidirectional context (seeing both left and right) gives BERT a deeper understanding than left-to-right models.
Is BERT still relevant in 2025?
Yes, though in different ways than at its peak. BERT and its successors (RoBERTa, DeBERTa, ALBERT) remain widely deployed for classification, search ranking, NER, and sentence embedding tasks where efficiency matters. They're smaller and faster to deploy than GPT-scale models. Google Search still uses BERT-based models for query understanding. For new projects, the choice depends on whether you need understanding (BERT-family) or generation (GPT-family).