Feb 27, 2025 · The transformer model is built on an encoder-decoder architecture where both the encoder and decoder are composed of a series of layers that utilize self-attention mechanisms and feed-forward neural networks.

Apr 29, 2023 · Real-world examples of the transformer encoder-decoder architecture include Google Translate, which uses the T5 model to translate text between languages, and Facebook’s M2M-100, a massive...

Jul 29, 2024 · In the realm of Transformers, two key components stand out: the encoder and the decoder. Understanding the roles and differences between these components is essential for students and...

Jan 6, 2023 · In a nutshell, the task of the encoder, on the left half of the Transformer architecture, is to map an input sequence to a sequence of continuous representations, which is then fed into a decoder.

Feb 20, 2025 · In Transformer models, the encoder and decoder are two key components used primarily in sequence-to-sequence tasks, such as machine translation. Let’s break them down: 1. Encoder. The encoder...

Apr 7, 2025 · 6. Decoder. Receives encoder output along with target sequence. Uses masked self-attention to prevent looking ahead. Combines encoder-decoder attention to refine output predictions. Example of Transformer in Action. Let’s consider an example of English-to-French translation using a Transformer model.

Mar 29, 2025 · The original transformer architecture is composed of an encoder and a decoder. Its layout is shown in the figure below. Recall that the transformer model was developed for translation tasks, replacing the seq2seq architecture that was commonly used with RNNs. Therefore, it borrowed the encoder-decoder architecture.

Dec 26, 2024 · In this article, we’ll explore the core components of the transformer architecture: encoders, decoders, and encoder-decoder models. Don’t worry if you’re new to these concepts — we’ll...

Mar 19, 2025 · Encoder and Decoder Structure. Encoder: Processes the input sequence and generates contextual representations. Decoder: Uses the encoder’s output and generates the final sequence step by step. Each consists of multiple layers, and within each layer, there are core components that enable the Transformer’s high performance. Self-Attention ...

Each encoder layer consists of two main components: the self-attention mechanism and the feedforward neural network. Understanding these two parts is crucial, as both contribute significantly to the encoders' ability to generate meaningful embeddings from text data.