Token Embeddings
Dense vector representations that encode the semantic meaning of words or tokens in a continuous numerical space
Overview
Token embeddings are numerical representations that capture the meaning of words or tokens in a way that computers can understand and process. Think of them as translating words into a special number format where similar words have similar numbers.
What Are Token Embeddings?
When you read the words "cat" and "kitten", you understand they're related. Token embeddings help AI models make similar connections by:
- Converting words into lists of numbers (vectors)
- Placing similar words close together in this number space
- Capturing relationships between words
- Enabling mathematical operations on language
How They Work
Token embeddings create meaningful number patterns:
- Each word becomes a list of numbers
- Similar words get similar number patterns
- Relationships are preserved mathematically
- Models can learn from these patterns
Common Uses
- Language understanding
- Search systems
- Translation
- Text classification
- Semantic analysis
Benefits
- Makes text understandable to AI
- Captures word relationships
- Enables similarity comparisons
- Supports advanced language tasks
- Improves model performance