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.

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 break...

Jan 9, 2024 · Transformers are a current state-of-the-art NLP model and are considered the evolution of the encoder-decoder architecture. However, while the encoder-decoder architecture relies mainly on Recurrent Neural Networks (RNNs) to extract sequential information, Transformers completely lack this recurrency. So, how do they do it?

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 ...

Feb 25, 2025 · In this guide, we’ll walk through how to implement a Transformer model from scratch using TensorFlow. We will be implementing transformers model in python. 1. Importing Required Libraries. tensorflow: TensorFlow is used to build and train machine learning models.

Nov 12, 2024 · Decoder: The decoder takes the encoder’s output as input, allowing it to reference the entire encoded sequence and extract relevant context for generating coherent outputs. With its own...

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.

Sep 28, 2024 · Since its inception, the transformer model has been a game-changer in natural language processing (NLP). It appears in three variants: encoder-decoder, encoder-only, and decoder-only. The original model was the encoder-decoder form, which provides a comprehensive view of its foundational design.

To use self-attention in decoders, we need to ensure we can’t peek at the future. At every timestep, we could change the set of keys and queries to include only past words. (Inefficient!) To enable parallelization, we mask out attention to future words by setting attention scores to −∞.

Explore the full architecture of the Transformer, including encoder/decoder stacks, positional encoding, and residual connections.