**Sequence to Sequence (often abbreviated to seq2seq) models is a special class of Recurrent Neural Network architectures that we typically use (but not restricted) to solve complex Language problems ...

Basic seq2seq model including simplest encoder & decoder and attention-based ones. This repository implements the "most" basic seq2seq learning as one small step of my own project. Basically, it ...

ENCODER-DECODER in Seq2Seq models is a game-changer for sequence-based tasks: 1. ENCODER: - Processes and compresses input sequence. - Uses RNN, LSTM, or GRU for handling long-term dependencies.

The basic Seq2Seq model consists of an encoder and a decoder, both typically implemented using RNNs, LSTM or GRU. ... ConvSeq2Seq models use CNNs in both the encoder and decoder.

It has major applications in question-answering systems and language translation systems. Sequence-to-Sequence (Seq2Seq) modelling is about training the models that can convert sequences from one ...

Large language models (LLMs) have changed the game for machine translation (MT). LLMs vary in architecture, ranging from decoder-only designs to encoder-decoder frameworks. Encoder-decoder models, ...

Seq2Seq models are a type of neural network architecture specifically designed to process and generate sequences. They consist of two main components: an encoder and a decoder.