Description: A Transformer neural network is a deep learning architecture designed to efficiently process sequential data, especially text. It relies on a self-attention mechanism that allows the model to determine which parts of a sequence are most relevant to each other, without needing to process the input strictly in order.
History: The Transformer architecture was introduced by Google researchers in 2017 in the paper “Attention Is All You Need.” It marked a major shift from earlier models like RNNs and LSTMs by removing the need for sequential processing, enabling faster and more parallelized training. Since then, it has become the foundation for many advanced language models such as BERT, GPT, T5, and others.
Uses:
- Natural Language Processing (NLP)
- Machine Translation
- Text Generation (chatbots, virtual assistants)
- Sentiment Analysis and Text Classification
- Code Generation
- Audio and Vision Processing (adaptations like Vision Transformers)
Examples:
GPT-4 (OpenAI): Generates coherent text, answers questions, writes essays, etc.
- BERT (Google): Enhances search engines by better understanding queries.
- T5 (Text-to-Text Transfer Transformer): Converts all NLP tasks into text-to-text format.
- Codex: Generates programming code from natural language.
- Vision Transformer (ViT): Transformer adaptation for image classification tasks.
