If you still want to add a decoder model, you could extend the architecture with a BART or T5 model, both of which are encoder-decoder models. Here’s how you would modify it for a sequence generation ...

Natural language processing (NLP) is a branch of artificial intelligence (AI) that enables machines to understand, generate, and interact with human language. NLP is used for various applications ...

This project implements a GPT-like transformer model consisting of an encoder and a decoder for various natural language processing tasks, including text classification and language modeling. The ...

While effective in various NLP tasks, few LLMs, such as Flan-T5 ... over prefix tokens and unidirectional attention on generated tokens. Like the encoder-decoder architecture, prefix decoders can ...

Natural Language Processing (NLP) has experienced some of the most ... “Attention is All You Need” by Vaswani et al., the architecture is divided into two main parts: the encoder and the decoder. Both ...

The RNN encoder–decoder neural network architecture, introduced by Cho et al ... Attention mechanisms have been a game-changer in many NLP tasks such as text summarization, question answering, ...

Additionally, the training process lacks labelled video data. To enhance the precision of the performance we have used encoder decoder architecture for semantic segmentation where VGGNet (VGG16 and ...