What are Transformers?

Transformers are neural network architectures introduced in 2017 that revolutionized NLP. They use "attention mechanisms" to weigh the importance of different words when processing language, understanding context much better than previous approaches.

BERT: Understanding Language Bidirectionally

BERT (Bidirectional Encoder Representations from Transformers) reads text in both directions simultaneously. When processing "bank," it looks at words before AND after to determine if we're talking about a financial institution or a river bank. BERT powers Google Search, understanding query intent better than keyword matching ever could.

GPT: Generating Human-Like Text

GPT (Generative Pre-trained Transformer) models like ChatGPT excel at generating coherent, contextual text. They're trained on massive text datasets to predict the next word in a sequence, developing an impressive understanding of language patterns, facts, and reasoning.

Why Transformers Changed Everything

  •  They can process entire sentences in parallel, making training much faster
  •  Attention mechanisms capture long-range dependencies in text
  •  Pre-training on massive datasets creates general language understanding
  •  Fine-tuning for specific tasks requires relatively little data