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Methods for Decoding Transformers

Python in Plain English

During text generation tasks, the crucial step of decoding bridges the gap between a model’s internal vector representation and the final human-readable text output. The selection of decoding strategi...

Methods for Decoding Transformers

Level Up Coding

During text generation tasks, the crucial step of decoding bridges the gap between a model’s internal vector representation and the final human-readable text output. The selection of decoding strategi...

LLMs and Transformers from Scratch: the Decoder

Towards Data Science

As always, the code is available on our GitHub . One Big While Loop After describing the inner workings of the encoder in transformer architecture in our previous article , we shall see the next segme...

TransformerDecoder

PyTorch documentation

TransformerDecoder is a stack of N decoder layers decoder_layer – an instance of the TransformerDecoderLayer() class (required). num_layers – the number of sub-decoder-layers in the decoder (required)...

TransformerDecoderLayer

PyTorch documentation

TransformerDecoderLayer is made up of self-attn, multi-head-attn and feedforward network. This standard decoder layer is based on the paper “Attention Is All You Need”. Ashish Vaswani, Noam Shazeer, N...

Encoding data with Transformers

Towards Data Science

Data encoding has been one of the most recent technological advancements in the domain of Artificial Intelligence. By using encoder models, we can convert categorical data into numerical data, and…

Joining the Transformer Encoder and Decoder Plus Masking

MachineLearningMastery.com

Last Updated on January 6, 2023 We have arrived at a point where we have implemented and tested the Transformer encoder and decoder separately, and we may now join the two together into a complete mod...

Transformer

PyTorch documentation

A transformer model. User is able to modify the attributes as needed. The architecture is based on the paper “Attention Is All You Need”. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Ll...

De-coded: Transformers explained in plain English

Towards Data Science

No code, maths, or mention of Keys, Queries and Values Since their introduction in 2017, transformers have emerged as a prominent force in the field of Machine Learning, revolutionizing the capabilit...

Using Transformers for Computer Vision

Towards Data Science

Are Vision Transformers actually useful? Continue reading on Towards Data Science

Understanding Transformers

Towards Data Science

A straightforward breakdown of “Attention is All You Need”¹ The transformer came out in 2017. There have been many, many articles explaining how it works, but I often find them either going too deep ...

The Map Of Transformers

Towards Data Science

Transformers A broad overview of Transformers research Fig. 1. Isometric map. Designed by vectorpocket / Freepik. 1\. Introduction The pace of research in deep learning has accelerated significantly ...

Transformers (Attention Is All You Need) In Depth

Python in Plain English

Transformers, in the context of machine learning and artificial intelligence, refer to a type of deep learning model architecture designed primarily for natural language processing (NLP) tasks. They h...

The A-Z of Transformers: Everything You Need to Know

Towards Data Science

Everything you need to know about Transformers, and how to implement them Image by author Why another tutorial on Transformers? You have probably already heard of Transformers, and everyone talks abo...

A Journey into the Fabulous Applications of Transformers — Part 1

Towards AI

A Journey Into the Fabulous Applications of Transformers — Part 1 Demo with Emphasis on NLP using Python, Hugging Face. Photo by Arseny Togulev on Unsplash The introduction of transformers has made a...

Implementing the Transformer Decoder from Scratch in TensorFlow and Keras

MachineLearningMastery.com

Last Updated on January 6, 2023 There are many similarities between the Transformer encoder and decoder, such as their implementation of multi-head attention, layer normalization, and a fully connecte...

Simplifying Transformers: State of the Art NLP Using Words You Understand — part 5— Decoder and…

Towards Data Science

Simplifying Transformers: State of the Art NLP Using Words You Understand , Part 5: Decoder and Final Output The final part of the Transformer series Image from the original paper. This 4th part of t...

👾🤖 Simpler, More Efficient Transformers

TheSequence

📝 Editorial It is hard to argue that transformers have become the most relevant architectures in modern machine learning (ML). Since the publication of the now-iconic Attention is All You Need paper,...

A Deep Dive into Transformers

Analytics Vidhya

If you have not heard about Transformers in recent times in the field of NLP(Natural Language Processing) or Artificial Intelligence, then you are probably living under a rock. There has been an…

Using Transformers

Analytics Vidhya

This article works best when you can try out the different methods yourself — run my notebook on deepnote.com to try it! I love the transformers library. It is by far the easiest way to get started…

Transformers: How Do They Transform Your Data?

Towards Data Science

Diving into the Transformers architecture and what makes them unbeatable at language tasks Image by the author In the rapidly evolving landscape of artificial intelligence and machine learning, one i...

Hierarchical Transformers — part 2

Towards Data Science

Hierarchical attention is faster Continue reading on Towards Data Science

The Concept of Transformers and Training A Transformers Model

Towards Data Science

Step by step guide on how transformer networks work Source What is Natural Language Processing (NLP) Natural Language Processing is the branch of artificial intelligence that deals with giving machin...

Implementing a Transformer Encoder from Scratch with JAX and Haiku

Towards Data Science

Understanding the fundamental building blocks of Transformers. Transformers, in the style of Edward Hopper (generated by Dall.E 3) Introduced in 2017 in the seminal paper “Attention is all you need”[...