@taskmaster4450le's thread

To give you a better idea, here are some examples of token sizes in popular AI models:

• BERT (Bidirectional Encoder Representations from Transformers): BERT uses subword-level tokens, with an average token size of around 2-3 words.
• RoBERTa (Robustly Optimized BERT Pretraining Approach): RoBERTa also uses subword-level tokens, with an average token size of around 2-3 words.
• Word2Vec: Word2Vec uses word-level tokens, with each token being a single word.
• Character-level language models: These models use character-level tokens, with each token being a single character.

Keep in mind that the size of a token can vary depending on the specific model and application. If you're working with a specific AI model, it's best to consult the documentation or research papers to understand the token size and structure used in that model.