Transformer encoder vs transformer decoder. Sep 8, 2023 · The Transformer architectu...
Transformer encoder vs transformer decoder. Sep 8, 2023 · The Transformer architecture comprises an encoder and a decoder, which can be used separately or in combination as an encoder-decoder model. Dec 13, 2021 · The transformer is an encoder-decoder network at a high level, which is very easy to understand. Encoder — The encoder part of the model is explained in detail. In this article, we will explore the different types of transformer models and their applications. Model As an instance of the encoder–decoder architecture, the overall architecture of the Transformer is presented in Fig. Master attention mechanisms, model components, and implementation strategies. 1 day ago · Channel Name Switch 2 AI Hashtags #GPT #BERT #Transformer #NLP #LLM #DeepLearning #MachineLearning #MaskedLanguageModel #NextSentencePrediction #Switch2AI SEO Tags gpt vs bert explained bert model About GPT vs BERT Explained Transformer Based Models Decoder vs Encoder Simple Readme Activity 0 stars The Full Transformer Architecture An encoder processes the entire input sequence to build a rich contextual understanding, which the decoder then uses via cross-attention to generate an output sequence one token at a time. While both share similarities in their use of self-attention mechanisms, their roles and internal structures differ significantly. This blog discusses the Transformer model, starting with its original encoder-decoder configuration, and provides a foundational understanding of its mechanisms and capabilities. The encoder processes the input sequence into a vector, while the decoder converts this vector back into a sequence. Understanding these components is crucial… A general high-level introduction to the Encoder-Decoder, or sequence-to-sequence models using the Transformer architecture. 2 days ago · 3 Concepts in Transformer • Encoder-Decoder Framework • Attention Mechanism • Transfer learning • Model is split as Body and Head • Domain A knowledge -> Body A -> Head A -> Predictions A • For predictions of new task B, you can directly copy the Body A from Domain A • Domain B knowledge -> Body A -> Head B -> Predictions B We evaluate 19 publicly available transformer checkpoints spanning encoder-only and decoder-only families and a broad range of parameter scales (approximately 4M to 355M). 7. The decoder takes this representation and produces the output sequence, attending to both: Itself, Encoder's output. In this video, we’ll explore the three main types of transformer architectures used in deep learning and NLP, Encoder-Only, Decoder-Only, and Encoder-Decoder transformers. Mar 23, 2021 · I'm not sure I understand the difference between "encoder only" and "decoder only". What is it, when should you use it? Dec 17, 2024 · What Are Encoder-Only Models? Encoder-only Transformers consist of a stack of Transformer encoders without a decoder component. Then I understood Encoder-Decoder architecture. In the following section, we will delve into the fundamental methodology underlying the Transformer model and most sequence-to-sequence modeling approaches: the encoder and the decoder. Jul 13, 2024 · This chapter will dive deeper into the transformer architecture: the encoder and decoder models. Expand 4 [PDF] Learn about encoders, cross attention and masking for LLMs as SuperDataScience Founder Kirill Eremenko returns to the SuperDataScience podcast, to speak with Mar 17, 2023 · In contrast to BERT, which employs the encoder, the GPT models (GPT-1 to GPT-4) have mostly remained the same architecture utilizing Transformer decoder stacks. Decodent, Decode, Decoding And More Nov 3, 2020 · A transformer decoder then takes as input a small fixed number of learned positional embeddings, which we call object queries, and additionally attends to the encoder output. Learn what tokens are, how embeddings represent text, and how encoders and decoders handle tasks like translation, text generation, and semantic search. It uses the context provided by the encoder to make sure the output is a coherent and accurate translation or response. switch2ai / GPT-vs-BERT-Explained-Transformer-Based-Models-Decoder-vs-Encoder-Simple Public Notifications You must be signed in to change notification settings Fork 0 Star 0 Projects Security Insights Actions switch2ai / GPT-vs-BERT-Explained-Transformer-Based-Models-Decoder-vs-Encoder-Simple Public Notifications You must be signed in to change notification settings Fork 0 Star 0 Projects Security Insights Watch short videos about transformer architecture diagram labeled encoder decoder attention from people around the world. Now, let's code up a short example of the encoder part of our MarianMT encoder-decoder models to verify that the explained theory holds in practice. The Transformer was initially designed for machine translation, and since then, it has become the default architecture for solving all AI tasks. Oct 19, 2021 · Encoder-Decoder — The transformer-based encoder-decoder model is presented and it is explained how the model is used for inference. Encoder-Decoder We’re on a journey to advance and democratize artificial intelligence through open source and open science. (2017), consists of two primary components: the encoder and the decoder. Isn't a "decoder only" transformer the same as an encoder, since it doesn't receive any external attention, but relies entirely on self-attention? (At least if you add a Linear + Softmax to the top of the encoder. Jan 6, 2023 · How the Transformer architecture implements an encoder-decoder structure without recurrence and convolutions How the Transformer encoder and decoder work How the Transformer self-attention compares to the use of recurrent and convolutional layers Kick-start your project with my book Building Transformer Models with Attention. Labelled Diagram, Decodent, Decode And More 4 days ago · All tokens are then processed with a transformer encoder, and the output weight tokens are used to modify the weights of an implicit neural representation (INR) that reconstructs the structure V i. 11. Still, other tasks make use of both the Transformer’s encoder-decoder structure. These networks can be different types such as Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), Gated Recurrent Units (GRUs), Convolutional Neural Networks (CNNs) or even more advanced models like Transformers. Encoder-decoder models offer the benefits of sequence-to-sequence mapping, contextual understanding, transfer learning, and multi-modal capabilities. Jul 27, 2023 · The original transformer paper presents the transformer as a model consisting of both encoder and decoder. We then construct an encoder-decoder-based framework by using a diffusion model and Swin Transformer for dynamic bounding box generation. Dual-decoder Transformer is an architecture that integrates two decoders (left-to-right and right-to-left) to capture comprehensive contextual signals from both past and future tokens. This will serve as a springboard for dissecting the Transformer model architecture and gaining an in-depth understanding of its inner workings. Apr 30, 2023 · The transformer architecture is composed of an encoder and a decoder, each of which is made up of multiple layers of self-attention and feedforward neural networks. Day 73 | 100 Days of AI/ML Learning Challenge Today I explored the architectural differences between encoder-based and decoder-based transformer models, two foundational approaches used in modern Aug 16, 2023 · Navigating Transformers: A Comprehensive Exploration of Encoder-Only and Decoder-Only Models, Right Shift, and Beyond Introduction Before we start, if you want to learn more about Transformers Jun 17, 2023 · The original transformer The original transformer architecture (Attention Is All You Need, 2017), which was developed for English-to-French and English-to-German language translation, utilized both an encoder and a decoder, as illustrated in the figure below. As we can see, the Transformer is composed of an encoder and a decoder. While the original transformer paper introduced a full encoder-decoder model, variations of this architecture have emerged to serve different purposes. Working Principle Architecture and Working of Decoders in Transformers Input Embeddings are passed into the decoder with positional encodings. Feb 13, 2023 · Here we will explore the different types of transformer architectures that exist, the applications that they can be applied to and list some example models using the different architectures. The encoder is an autoencoder (AE) model that encodes input sequences into latent representations. The encoder processes the input sequence and generates hidden states that capture the contextual information. Are you building with Encoder-based models for analysis, or Decoder-based models for creation? Let’s talk in the comments! 👇 #MachineLearning #Transformers #DeepLearning #GenAI #LLMs # Multimodal Systems Part 2 of 5 ENCODER-DECODER: THE BACKBONE I spent weeks confused by CLIP, BLIP, and DALL-E. ) May 3, 2023 · The transformer encoder-decoder architecture is a popular NLP model that uses self-attention and feed-forward layers to process input and generate output sequences. The encoder is a critical component of the transformer, responsible for processing the input sequence and producing representations that the decoder or downstream tasks can utilize Sep 25, 2024 · The encoder-decoder transformer is one of the most influential architectures in natural language processing (NLP) and various machine learning applications. Each layer consists of two sub-layers: 11. Their encoder-decoder architecture combined with multi-head attention and feed-forward networks enables highly effective handling of sequential data. Mar 23, 2025 · For example, while the original Transformer used 6 encoder and 6 decoder layers, modern models like GPT-3 scale up to 96 layers—each layer contributing to a progressively richer understanding of May 7, 2021 · What is the difference between Transformer encoder vs Transformer decoder vs Transformer encoder-decoder? Asked 4 years, 10 months ago Modified 1 year, 1 month ago Viewed 4k times Apr 2, 2025 · Conclusion: A Diverse Toolkit for Language AI The Transformer architecture revolutionized NLP, but its genius lies also in its flexibility. Dec 26, 2024 · The transformer architecture, with its encoders and decoders, has transformed NLP. Apr 7, 2025 · Today, we’re unpacking the core dual structure of the Transformer: the Encoder and the Decoder — two complementary halves that power everything from machine translation to generative text models. It allows the model to understand relationships between words regardless of their position in a sentence. First, we reformulate an SOD task as a Noise-to-Box process. In contrast to Bahdanau attention for sequence-to-sequence learning in Fig. Each plays a distinct Sep 15, 2023 · The Transformer architecture consists of an encoder and a decoder, each of which is composed of several layers. Because it uses self-attention, every word in the input can look at every other word to understand its context. Sep 22, 2024 · Encoder-only and decoder-only architectures play vital roles in natural language processing tasks. By leveraging self-attention, MLPs, and positional encoding, they provide LLMs with the ability to understand and generate text with a level Transformer Model — Encoder and Decoder In Transformer models, the encoder and decoder are two key components used primarily in sequence-to-sequence tasks, such as machine translation. The standard Transformer architecture consists of an encoder stack on the left and a decoder stack on the right. Each type has its own characteristics and is suitable for different natural language processing tasks. The Transformer replaces recurrence with self-attention mechanisms. Each part is a stack of identical layers that perform specific jobs. A transformer architecture is an encoder-decoder network that uses self-attention on the encoder side and attention on the decoder side. Conclusion In this article, we discussed the three main types of Transformer architectures: encoder only models, encoder-decoder models, and decoder only models. So, this article starts with a bird-view of the architecture and aims to introduce essential components and give an overview of the entire model architecture. It revolutionized tasks such as machine The encoder is a critical component of the transformer, responsible for processing the input sequence and producing rep-resentations that the decoder or downstream tasks can utilize. Decoder-Only What is the difference between an auto-regressive transformer and a sequence-to-sequence transformer? The straightforward answer is that the auto-regressive one Explore the full architecture of the Transformer, including encoder/decoder stacks, positional encoding, and residual connections. Whether you’re working on machine translation, text generation, or sequence classification, understanding these Apr 22, 2025 · At the heart of the Transformer lies two major components — the Encoder and the Decoder — working together to process input data and generate meaningful outputs. Much machine learning research focuses on encoder-decoder models for natural language processing (NLP) tasks . The Encoder's job is to read the input and build a rich, bidirectional representation. io The battle of transformer architectures: Encoder-only vs Encoder-decoder vs Decoder-only models. May 19, 2024 · Encoder-Decoder vs. Some tasks lend themselves to the Transformer’s encoder structure, while others are better suited for the decoder. Encoder-decoder models provide a versatile architecture that can handle a wide range of tasks, from machine translation and text summarization to complex question answering and document generation. This vector of source of attention (decoder hidden state, French) vector of target of attention (encoder hidden state, English) Query : what source need (J’adore : “I want subject pronoun & verb”) Key : what the target provide (I : “Here is the subject”) Value : the information to be retrieved (information related to Je or J’ ) Multi-Encoder Transformers extend the standard Transformer by processing multiple input streams in parallel for robust performance in multilingual, multimodal, and structured tasks. Jun 24, 2025 · Role of Decoders The encoder transforms the input sequence into a vector representation. The encoder's job is to read and understand the input text. Sep 12, 2025 · Transformer models have revolutionized natural language processing (NLP) with their powerful architecture. Since the first transformer architecture emerged, hundreds of encoder-only, decoder-only, and encoder-decoder hybrids have been developed. Here, we introduce CryoHype, a transformer-based hypernetwork architecture designed to resolve extreme compositional heterogeneity. Let’s … Oct 13, 2025 · In an encoder-decoder model both the encoder and decoder are separate networks each one has its own specific task. We explored various applications, advantages, and disadvantages of these models. Watch short videos about transformer encoder decoder attention from people around the world. 1 {}^1 1 An in-detail explanation of the role the feed-forward layers play in transformer-based models is out-of-scope for this notebook. Full Transformer Architecture: An encoder-decoder structure uses self-attention, cross-attention, and feed-forward layers with residual connections to transform input sequences into output sequences. Watch short videos about transformer encoder decoder cross attention diagram from people around the world. A from-scratch PyTorch encoder-decoder Transformer for English → Hindi machine translation, trained on a raw Tatoeba EN-HI export (13 186 sentence pairs, including multiple Hindi translations per English sentence). Note: it uses the pre-LN convention, which is different from the post-LN convention used in the original 2017 transformer. Decodent, Decode, Decoding And More The decoder's job is to take this rich representation from the encoder and generate the output sentence, one word at a time. Sep 28, 2025 · Understanding Whisper’s Encoder–Decoder Transformer Architecture Whisper is a state-of-the-art encoder–decoder Transformer for speech recognition and translation. Oct 18, 2025 · Transformers have transformed deep learning by using self-attention mechanisms to efficiently process and generate sequences capturing long-range dependencies and contextual relationships. Sep 11, 2025 · BERT Architecture The architecture of BERT is a multilayer bidirectional transformer encoder which is quite similar to the transformer model. Apr 4, 2023 · This mechanism is applicable in both autoencoder-like and encoder-decoder configurations, enabling Transformers to capture long-range dependencies and relationships between elements in the input data. Decoder models are excellent for generating text, making them ideal for creative tasks such as story generation, chatbot responses, and text completion. Feb 27, 2026 · Understand Transformer architecture, including self-attention, encoder–decoder design, and multi-head attention, and how it powers models like OpenAI's GPT models. Encoder-only models excel in predictive tasks, while decoder-only models shine in generative applications. Try Voice Writer - speak your thoughts and let AI handle the grammar: https://voicewriter. 58 KB main Breadcrumbs GPT-vs-BERT-Explained-Transformer-Based-Models-Decoder-vs-Encoder-Simple / switch2ai / GPT-vs-BERT-Explained-Transformer-Based-Models-Decoder-vs-Encoder-Simple Public Notifications You must be signed in to change notification settings Fork 0 Star 0 Insights: switch2ai/GPT-vs-BERT-Explained-Transformer-Based-Models-Decoder-vs-Encoder-Simple Dec 10, 2025 · The encoder-decoder structure is key to transformer models. Dec 14, 2023 · The encoder in the transformer converts a given set of tokens from a sentence into its equivalent vectors, also called hidden state or context. May 22, 2025 · The transformer architecture has revolutionized natural language processing by leveraging self-attention mechanisms to capture dependencies in sequential data without relying on recurrent or convolutional layers. Diagnosing diseases from medical images and reporting them at the paragraph level is a significant challenge for deep learning-based autonomous systems Dec 16, 2025 · AI-powered analysis of 'Context Representation via Action-Free Transformer encoder-decoder for Meta Reinforcement Learning'. Jul 23, 2025 · 4) Conclusion Understanding the differences between encoder-only and decoder-only transformer architectures is crucial for making informed decisions in AI applications. It leverages dual-attention mechanisms and reinforcement learning fine-tuning to strengthen encoder training and achieve ensemble-like inference in tasks such as math problem solving and speech recognition. In simple terms, it has two … Aug 26, 2025 · A simple breakdown of how transformer models work. Let’s get started. These models leverage the transformer architecture to process input sequences and generate corresponding output sequences effectively. Transformer (deep learning) A standard transformer architecture, showing on the left an encoder, and on the right a decoder. They focus on processing input sequences and creating meaningful Aug 31, 2024 · Decoder-only transformers are remarkable architectures. Overview This Mar 10, 2026 · Learn transformer encoder vs decoder differences with practical examples. Here are the one-sentence summaries for the core topics in this study guide. Jun 28, 2023 · The combination of encoder and decoder models in the Transformer architecture has significantly advanced the field of natural language processing. Encoder-decoder models are pivotal in handling sequence-to-sequence tasks, particularly in applications like translation and summarization. Sep 29, 2024 · Transformers are powerful neural network architectures primarily used for natural language processing (NLP), and they consist of two key components: encoders and decoders. And everything clicked in 10 minutes The Encoder-Decoder Structure The original Transformer architecture has two main parts: an encoder and a decoder. ipynb Latest commit History History 286 lines (286 loc) · 6. The encoder processes encode the input data, and the decoder generates the output data based on the encoded representation, which serves as the "context" for the decoder. The Encoder-only, Decoder-only, and Encoder-Decoder variants represent powerful specializations, each optimized for different facets of the complex challenge of understanding and generating human language. Jul 29, 2024 · This blog post delves into the concepts, functions, and applications of encoders and decoders in Transformers, providing a clear and comprehensive comparison. In deep learning, the encoder-decoder architecture is a type of neural network most widely associated with the transformer architecture and used in sequence-to-sequence learning. 2, the input (source) and output (target) sequence embeddings are added with positional encoding Encoder-decoder models (also called sequence-to-sequence models) use both parts of the Transformer architecture. A high-level view of the Transformer's encoder-decoder architecture. Sep 28, 2024 · These include the original encoder-decoder structure, and encoder-only and decoder-only variations, catering to different facets of NLP challenges. It does not merely compress input into vector space but attempts to encode inter-token dependencies via operations that are both parallel and non-local. switch2ai / GPT-vs-BERT-Explained-Transformer-Based-Models-Decoder-vs-Encoder-Simple Public Notifications You must be signed in to change notification settings Fork 0 Star 0 BERT. Encoder and Decoder: Architectural Distinctions The transformer architecture, introduced by Vaswani et al. This paper proposes a model that leverages a visual transformer encoder with a parallel twin decoder, consisting of a visual transformer decoder and a CNN decoder with multi-resolution connections working in parallel, which achieves state-of-the-art performance on the Cityscapes and ADE20K datasets. Mar 11, 2026 · The proposed architecture, called FAST-MRG, is a low computational cost and high-performance hybrid encoder-decoder architecture capable of producing autonomous medical reports that can support doctors in diagnosis and treatment processes. 4. 1. TL;DR Transformers are neural network architectures that use self-attention mechanisms to process sequential data in parallel, replacing the need for recurrence To tackle such issues, this work proposes a new encoder-decoder-based SOD framework with a Diffusion Model and Swin Transformer given aerial images. At each stage, the attention layers of the encoder can access all the words in the initial sentence, whereas the attention layers of the decoder can only access the words positioned before a given word in the input. Literature thus refers to encoder-decoders at times as a form of sequence-to-sequence model (seq2seq model). Transformer encoder architecture For the Tomotwin-100 experiment, our transformer encoder consists of six transformer blocks, with each block consist-ing of a multi-head self-attention layer followed by an MLP consisting of two linear layers with GeLU activations [21]. Jun 27, 2025 · Encoder-Decoder Architecture in Transformers Encoders in Transformers The encoder functions as the first half of the transformer model, facilitating the internal representation of input elements. These vectors capture the semantics and May 3, 2023 · The transformer decoder is a crucial component in the transformer architecture for generating the final output sequence. However, many times you will see (or hear) people describing their model as a "transformer model", but it actually consists only of an encoder or only of a decoder. Reinforcement learning (RL) enables robots to operate in uncertain environments, but standard approaches often struggle with poor generalization to un Explore with advanced AI tools for machine learning research. afgjdrlf ezzzm pkavo ooagw gnmcusk hwwyq afitj ynhzmi kbqkw qqfpl
