Learn the key features of an effective NLP architecture for AI applications, such as data preprocessing, embedding layer, encoder-decoder structure, pre-trained models, and evaluation metrics.

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 ...

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 ...

Encoder-Decoder Architecture. Based on the vanilla Transformer model, the encoder-decoder architecture consists of two stacks: an encoder and a decoder. The encoder uses stacked multi-head ...

In NLP, this two-step process of converting the prompt into a context, and then converting the context into a response, is realized using an encoder and decoder, respectively. An encoder-decoder ...

Images are essential for communicating ideas, feelings, and narratives in the era of digital media and content consumption. Computers to produce textual data for an image that replaces humans. Image ...

The Transformer Architecture. The landscape of NLP underwent a dramatic transformation with the introduction of the transformer model in the landmark paper “Attention is All You Need” by Vaswani et al ...

Transformers are neural network architecture that has become the foundation for most recent advancements in natural language processing (NLP). It was introduced in the paper “Attention is All You Need ...