Introduction

• A type of deep learning model
• Trained on massive text datasets to understand, generate, or summarize human language.
• Transformer-based models that can handle text generation, translation, summarization, Q&A, and reasoning.
  • Use self-attention instead of RNNs to handle sequences.
  • Parallel processing → faster training
  • Captures long-range dependencies better than RNN/LSTM

Tokenization

• Converts text into tokens (words, subwords, or characters)
• Example: “I love AI” → [“I”, “love”, “AI”]
• Uses subword tokenization (BPE, WordPiece) for rare words

Workflow:

1. Collect large text corpus
2. Tokenize text → tokens
3. Pretrain transformer on next-word or masked-token prediction
4. Fine-tune on task-specific datasets
5. Deploy for inference → text generation / Q&A / summarization