Bidirectional Transformers for Language Understanding
Explore how bidirectional transformers, especially BERT, advance language understanding by processing text in both directions simultaneously. Understand its architecture, training methods like masked language modeling and next sentence prediction, and how it is fine-tuned for tasks such as question answering and sentiment analysis. This lesson provides the foundation to grasp the impact of BERT on modern NLP and generative AI models.
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In the last lesson, we learned how transformers enable every word to attend to every other, but they still struggle to capture context from both directions. Early advances tried to fix this: ELMo (2018) used LSTMs for context-aware embeddings, while GPT applied unidirectional transformers for fluent text generation.
The real breakthrough came with BERT (Bidirectional Encoder Representations from Transformers) in 2018. Unlike models that read only left-to-right or right-to-left, BERT processes text in both directions at once. For example, in the sentence “The bat flew out of the cave,” BERT considers both “flew” and “cave” to decide that “bat” means an animal, not a baseball bat. This bidirectional view allows it to grasp nuanced meaning with remarkable accuracy, making BERT one of the first true large language models.
What is BERT?
So, what exactly is BERT? ...