How does the Transformer model process text?

The Transformer model processes text using a neural network architecture that relies solely on attention mechanisms, bypassing recurrent connections for efficient parallel computation.

Core principles involve self-attention to capture contextual relationships between words and positional encoding to maintain sequence order. Necessary conditions include large-scale data and significant computational resources for training. The model applies broadly to NLP tasks like translation and summarization, but precautions are needed for handling long sequences due to quadratic attention complexity.

Implementation starts with tokenization and embedding, converting text into vectors. These inputs are processed through stacked encoder and decoder layers with multi-head self-attention and feed-forward networks, generating contextual representations. Attention weights dynamically focus on relevant words for output generation. Typical scenarios include real-time machine translation and chatbots, offering business value through scalable automation of language tasks and enhanced AI capabilities.