Transformer is a groundbreaking architecture in natural language processing (NLP) that revolutionizes sequence modeling by leveraging self-attention mechanisms. Unlike traditional RNNs or CNNs, it processes input in parallel, making it highly efficient for tasks like machine translation and text generation. Here's a breakdown:
Key Components 🛠️
Self-Attention Mechanism
⚡ Enables the model to weigh the importance of different words in a sentence dynamically.Self_attention_mechanismPositional Encoding
🧩 Adds information about the position of tokens to the input embeddings.Transformer_architectureMulti-Layer Perceptron (MLP)
🔄 Used in feed-forward layers between attention blocks for non-linear transformations.Feed_forward_layer
Applications 🚀
- Machine translation (e.g., Google Translate)
- Text summarization
- Question answering systems
- Generative AI models like GPT and BERT
For deeper insights, explore our guide on Attention Mechanism or Deep Learning Tutorials.