Annotated Transformers
- [University of Toronto]
- Overview
In the field of natural language processing (NLP), Transformer has become a breakthrough architecture that revolutionizes the way machines understand and generate human language.
Transformers are a type of neural network architecture that transforms an input sequence into an output sequence. They are different from traditional models that process words one after another, as they can look at an entire sentence at once. This makes them very efficient at picking up the nuances of language.
Annotated transformers refer to transformer models that come with detailed explanations and annotations, making them more accessible and understandable for researchers, developers, and enthusiasts.
These annotations typically include comments on the architecture, layer functionalities, and the underlying mathematics. Annotated transformers serve as educational tools, providing insights into the inner workings of complex models.
Annotated Transformers play a crucial role in demystifying complex NLP models and making them more understandable and accessible. By providing detailed explanations and annotations, these models facilitate learning, development, and innovation in natural language processing. Annotated transformers provide valuable insights into the fascinating world of transformer architecture.
