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 ...

But not all transformer applications require both the encoder and decoder module. For example, the GPT family of large language models uses stacks of decoder modules to generate text.

The positional encodings and the word vector embeddings are summed together then passed into both the encoder and decoder networks. While transformer neural networks use encoder/decoder schemas just ...

Microsoft Corp. (MSFT) has introduced a compact, on-device language model named Mu, designed for fast and private AI ...

Table structure recognition (TSR) aims to convert tabular images into a machine-readable format, where a visual encoder extracts image features and a textual decoder generates table-representing ...

Table structure recognition (TSR), the task of inferring the layout of tables, including the row, column, and cell structure, is a surprisingly complex task. With the growing amount and importance of ...