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Token IDs → Embedding Vectors - Coggle Diagram
Token IDs → Embedding Vectors
Tokenization Process
Break sentence into tokens
Types:
Word Tokenization
Subword Tokenization
Example:
["AI", "is", "powerful"]
Vocabulary Creation
Build dictionary of tokens
Assign index to each token
Example:
AI → 1
is → 2
powerful → 3
Token ID Sequence
Convert tokens → integers
Example:
[1, 2, 3]
Machine-readable format
Embedding Layer Function
Core component in neural networks
Maps IDs → vectors
Trainable parameters
Applications
NLP Tasks:
Text Classification
Machine Translation
Chatbots
Sentiment Analysis
Conversion of Token IDs into Embedding Vectors
Input Sentence
Example: "AI is powerful"
Natural Language Text
Preprocessing
Lowercasing
Removing special characters (optional)
Generated Embeddings
Dense vectors
Example:
[0.12, -0.45, 0.67, ...]
Encodes semantic meaning
Output Sequence
List of embedding vectors
Example:
[[v1], [v2], [v3]]
Input to deep learning models
Key Benefit
Captures context & meaning
Reduces dimensionality
Improves model performance