This repository contains an implementation of the Transformer Encoder-Decoder model from scratch in C++. The objective is to build a sequence-to-sequence model that leverages pre-trained word ...

A Transformer model built from scratch to perform basic arithmetic operations, implementing multi-head attention, feed-forward layers, and layer normalization from the Attention is All You Need paper.

We present competitive results using a Transformer encoder-decoder-attention model for end-to-end speech recognition needing less training time compared to a similarly performing LSTM model. We ...

Accurate traffic flow forecasting is crucial for managing and planning urban transportation systems. Despite the widespread use of sequence modelling models like Long Short-Term Memory (LSTM) for this ...

The proposed model explores the effectiveness of encoder-decoder transformer models for six software engineering tasks, including thirteen sub-tasks. CodeTrans. CodeTrans adapts the encoder-decoder ...

This paper studies a novel pre-training technique with unpaired speech data, Speech2C, for encoder-decoder based automatic speech recognition (ASR). Within a multi-task learning framework, we ...