Transformers have revolutionized the field of natural language processing (NLP) and have become a cornerstone of modern AI systems. This guide will provide an overview of transformer models, their architecture, and their applications.
Architecture
The core of a transformer model is the self-attention mechanism, which allows the model to weigh the importance of different words in a sentence when predicting the next word.
- Self-Attention: This mechanism allows the model to focus on different parts of the input sequence when predicting the output.
- Encoder-Decoder Structure: Transformers typically consist of an encoder and a decoder. The encoder processes the input sequence and the decoder generates the output sequence.
Applications
Transformers have been applied to a wide range of tasks in NLP, including:
- Machine Translation: Transformers have significantly improved the accuracy of machine translation models.
- Text Generation: Transformers can be used to generate text, such as poetry or news articles.
- Summarization: Transformers can be used to generate summaries of long texts.
Resources
For more information on transformer models, we recommend the following resources:
Transformer Architecture