Feb 27, 2025 · Transformers have transformed deep learning by using self-attention mechanisms to efficiently process and generate sequences capturing long-range dependencies and contextual relationships. Their encoder-decoder architecture combined with multi-head attention and feed-forward networks enables highly effective handling of sequential data.

Like earlier seq2seq models, the original transformer model used an encoder-decoder architecture. The encoder consists of encoding layers that process all the input tokens together one layer after another, while the decoder consists of decoding layers that iteratively process the encoder's output and the decoder's output tokens so far.

Jan 9, 2024 · STEP 3.2 - Encoder-Decoder Multi-Head Attention or Cross Attention. In the second multi-headed attention layer of the decoder, we see a unique interplay between the encoder and decoder's components.

Apr 7, 2025 · The transformer architecture has revolutionized the field of deep learning, ... The transformer model consists of an encoder and decoder, ... Transformers use multi-head attention to capture different aspects of word relationships. A single attention mechanism may focus too much on one pattern, but multiple heads allow the model to learn ...

The original introduction of the transformer [Vaswani et al. 2017] had an encoder-decoder architecture (T5 is an example). It was only later that the standard paradigm for causal language model was defined by using only the decoder part of this architecture.

Implementing a Transformer model from scratch using PyTorch, based on the "Attention Is All You Need" paper. It covers the full model architecture, including multi-head attention, positional encoding, and encoder-decoder layers, with a focus on deep learning concepts.

Mar 29, 2025 · The Transformer Architecture. 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 3, 2024 · At its core, the Transformer architecture consists of a stack of encoder layers and decoder layers. To avoid confusion, we will refer to individual layers as Encoder or Decoder and use...

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 ...

Jan 6, 2023 · The Transformer Architecture. The Transformer architecture follows an encoder-decoder structure but does not rely on recurrence and convolutions in order to generate an output.