Transformer models revolutionized machine learning by processing sequential data with self-attention mechanisms, enabling parallel computation over long-range dependencies. They power language tasks like translation, summarization, and chatbots (e.g., GPT, BERT). Developers, researchers, and enterprises benefit from their efficiency, scalability, and accuracy in natural language understanding and generation.
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