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What is Transformer Architecture?
The Transformer architecture provides an innovative way to process and generate sequences of data, such as natural language, by leveraging self-attention mechanisms. Traditionally, recurrent neural networks (RNNs) were extensively used for sequence modeling tasks. However, RNNs suffer from inherent limitations like sequential computation, making it difficult to parallelize and capturing long-range dependencies effectively. The Transformer architecture, on the other hand, eliminates the sequential computation bottleneck and achieves parallelization by introducing self-attention.
Self-attention is the key mechanism that sets the Transformer apart from other models. It allows the model to focus on different parts of the input sequence to determine the importance and relationships among its elements. The input sequence is divided into queries, keys, and values, and attention weights are computed between them. These attention weights represent the relevance of each element in the sequence to every other element.The attention mechanism enables the model to capture both local and global dependencies efficiently, making it particularly well-suited for tasks involving long-range dependencies, such as language translation or text generation. The self-attention mechanism also allows the model to consider the context of each word within the entire sequence, facilitating better understanding and generation of coherent and contextually relevant output.
The Transformer architecture consists of multiple layers of self-attention and feed-forward neural networks, known as encoder and decoder layers. The encoder processes the input sequence, while the decoder generates the output sequence based on the encoder's representations. Both the encoder and decoder are composed of identical layers, each with its own set of parameters. The layers are connected through residual connections and layer normalization, enabling effective information flow and alleviating the vanishing gradient problem.
During training, the Transformer architecture employs a technique called masked self-attention in the decoder. This ensures that the model attends only to the previous positions in the sequence, preventing it from cheating by looking ahead during generation. The model is trained using a variant of the sequence-to-sequence framework called the transformer model, which uses teacher forcing and cross-entropy loss to optimize the parameters.
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