Understanding Tokenization in Deep Learning
Have you ever wondered how human language is understood by machines? Tokenization, a critical stage in Natural Language Processing (NLP), provides the solution by dividing text into smaller units known as tokens.
Why is Tokenization Important?
a. Creates structured data from raw text for machine learning models.
b. Makes words, phrases, or subwords easier for models to understand.
c. Vital for jobs like text generation, translation, and chatbots
Types of Tokenization:
Word Tokenization: This process divides the text into individual words (for example, "Deep Learning" → ["Deep", "Learning")
Character Tokenization: This process separates text into distinct characters (for example, "AI" → ["A", "I"]).
Subword Tokenization (BPE, WordPiece): Effectively handles uncommon words (e.g., "unhappiness" → ["un", "happiness").
Sentence Tokenization: This technique divides text into sentences to improve comprehension of context.
Tokenization in Action:
a. Tokenization is necessary for transformer-based models (such as BERT and GPT) to process input effectively.
b. WordPiece and Byte-Pair Encoding (BPE) make sure models can handle invisible words efficiently.
The performance of the model improves with better tokenization!
2March 18, 2025 1.1K 4
