Day 2: Tokenization Demystified

1. 1Apply tokenization techniques to text data using libraries like NLTK or spaCy
2. 2Configure tokenization parameters to balance model performance and cost
3. 3Test tokenization strategies on multilingual datasets to ensure fairness
4. 4Build custom tokenization models for specific languages or domains
5. 5Run experiments to measure the impact of tokenization on model latency and cost

NLP engineers and data scientists on a team benefit from understanding tokenization to optimize model performance and reduce costs, while product managers can use this knowledge to inform product decisions

💡 Tokenization is a critical component of NLP pipelines, affecting model performance, cost, and fairness

🤖 Tokenization controls AI model cost, latency & fairness! 📊