What is a transformer model? Neural network architectures that transform input sequences by learning contextual relationships.
Transformer models are a type of neural network architecture characterized by their use of self-attention mechanisms, allowing them to weigh the importance of different parts of the input data differently. This enables more effective handling of sequential data by capturing long-range dependencies and contextual relationships without the limitations of sequence-based processing. Transformer models are foundational to the development of large language models (LLMs) like GPT and BERT, significantly enhancing tasks such as translation, text generation, and semantic analysis.
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