🚀 Transformers Tutorial: Building Blocks of Modern NLP

Transformers have revolutionized natural language processing (NLP) with their self-attention mechanisms. Below is a beginner-friendly guide to understanding this architecture!

🧠 Core Concepts

1. Self-Attention

   • Enables models to weigh the importance of different words in a sentence
   • Visualize how attention flows between tokens:

2. Positional Encoding

   • Adds location information to token embeddings
   • Example:

3. Encoder-Decoder Architecture

   • Used in tasks like machine translation
   • Key component:

🌍 Applications

• Machine Translation: Try our demo
• Text Summarization: Explore advanced techniques
• Question Answering: See implementation examples

📚 Next Steps

1. Learn about BERT training
2. Compare Transformers with RNNs
3. Experiment with pre-trained models

Tip: Use 📌 for key takeaways and 💡 for practical insights!