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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 ...