Jan 9, 2024 · The Encoder WorkFlow. The encoder is a fundamental component of the Transformer architecture. The primary function of the encoder is to transform the input tokens into contextualized representations. Unlike earlier models that processed tokens independently, the Transformer encoder captures the context of each token with respect to the entire ...

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.

Oct 20, 2024 · In this article, we’ll explore the core components of the decoder: input embeddings, positional encoding, masked self-attention, encoder-decoder attention, layer normalization, residual...

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.

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 26, 2025 · 7. Transformer Model. This block defines the main Transformer class which combines the encoder and decoder layers. It also includes the embedding layers and the final output layer. self.encoder_embedding = nn.Embedding(src_vocab_size, d_model): Initializes the embedding layer for the source sequence, mapping tokens to continuous vectors of size ...

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.

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...

4 days ago · Transformers use encoder-decoder architecture, which means the model contains two AI agents performing separate tasks. An encoder’s job is to compress the data down to the barest elements, while a decoder’s job is to rebuild the data using similar data from training materials. In a transformer, the encoder uses positional encoding, which ...

Sep 28, 2024 · This blog discusses the Transformer model, starting with its original encoder-decoder configuration, and provides a foundational understanding of its mechanisms and capabilities. By exploring the intricate design and the operational dynamics of the model, we aim to shed light on why the Transformer has become a cornerstone in modern NLP ...