The Transformer model, introduced in the paper “Attention is All You Need” represents a significant advancement in natural language processing and sequence modeling.
Unlike previous architectures, such as recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, which processed input data sequentially, the Transformer employs a self-attention mechanism that allows it to weigh the significance of different words in a sequence simultaneously. This enables the model to capture long-range dependencies and contextual relationships more effectively.
The Transformer consists of an encoder-decoder structure, where the encoder processes the input data and the decoder generates the output sequence. Both components are built using stacked layers of multi-head attention and feedforward neural networks. The architecture’s parallelization capabilities make it highly efficient for training on large datasets, leading to its widespread adoption in various NLP tasks, including translation, summarization, and text generation. Transformer’s innovations have also paved the way for subsequent models, such as BERT and GPT, further advancing the field of artificial intelligence.