Token Embedding 🔢
Token embedding is the foundational step in any Natural Language Processing (NLP) pipeline. It involves converting discrete tokens (like words or subwords) into continuous vector representations that capture semantic meaning.
🌍
References & Disclaimer
This content is adapted from A deep understanding of AI language model mechanisms. It has been curated and organized for educational purposes on this portfolio. No copyright infringement is intended.
📖 Lessons
🔢1. Text to Numbers📖2. Preparing Text for Tokens🛠️3. Coding Challenge: Make a Tokenizer🕰️4. Tokenizing 'The Time Machine'🧬5. Byte Pair Encoding (BPE) Concepts➰6. Coding Challenge: BPE Loop🤖7. Exploring GPT-4's Tokenizer
Module Overview
In this module, we explore the journey from raw text to numerical vectors:
- Basic Tokenization: Splitting text into words and characters.
- Vocabulary Creation: Building a unique lexicon from a corpus.
- Encoding & Decoding: Implementing the mapping between text and integers.
- Vectorization: Moving beyond integers to high-dimensional embeddings.