A neural network based on the encoder-decoder architecture the modeling power of modern sequence models, Transformers with a set of promising experimental features from various papers. ... @misc ...

Transformer Architecture: Implemented various Transformer components, including multi-head attention, feed-forward layers, layer normalization, encoder, and decoder blocks, following the Attention is ...

The transformer’s encoder doesn’t just send a final step of encoding to the decoder; it transmits all hidden states and encodings. This rich information allows the decoder to apply attention ...

In recent works on semantic segmentation, there has been a significant focus on designing and integrating transformer-based encoders. However, less attention has been given to transformer-based ...

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 self-attention layers to encode the input ...

The 330 million parameter model was trained using Azure’s A100 GPUs and fine-tuned through a multi-phase process.