What are tokenization techniques used in AI?

What are tokenization techniques used in AI?

Tokenization techniques are methods used to break down text into smaller units, or tokens, which can be words, phrases, or even characters. This process is crucial for natural language processing tasks.

Can you explain the difference between word and subword tokenization?

Sure! Word tokenization splits text into whole words, while subword tokenization breaks words into smaller parts, allowing for better handling of rare words and improving model efficiency.

What are some popular tokenization libraries?

Some popular tokenization libraries include Hugging Face's Tokenizers, NLTK, and SpaCy. Each has its own strengths depending on the use case.

How does tokenization affect the performance of AI models?

Tokenization can significantly impact model performance by influencing how well the model understands context and semantics. Proper tokenization can lead to better accuracy and efficiency.