Tokenizer MicroSim

To use this MicroSim in your course, just add the following HTML line to your website:

<iframe src="https://dmccreary.github.io/tracking-ai-course/sims/tokenizer/main.html"  height="470px" scrolling="no"
  style="overflow: hidden;"></iframe>

Run the Tokenizer MicroSim

Edit the Tokenizer MicroSim

References

• OpenAI Tokenizer Demo - uses the text background color to show individual tokens.

• HuggingFace Xenova Tokenizer Playground - Allows you to compare 14 different tokenizers!. This one also color codes each word.

Self-Assessment Quiz

Test your understanding of tokenizers and how they work.

Question 1: What is the main purpose of a tokenizer?

1. To check grammar and spelling
2. To convert text into numerical representations that AI models can process
3. To translate text between languages
4. To encrypt sensitive data

Answer

B) To convert text into numerical representations that AI models can process - Tokenizers break text into tokens and assign each a unique numerical ID from the model's vocabulary.

Question 2: Why do different AI models use different tokenizers?

1. For marketing differentiation only
2. Each model's tokenizer is optimized for its training data and architecture
3. Tokenizers are all identical
4. Government regulations require different tokenizers

Answer

B) Each model's tokenizer is optimized for its training data and architecture - Different tokenizers have different vocabulary sizes, subword algorithms, and handling of special characters optimized for their specific model.

Question 3: What does color-coding tokens in a visualization help users understand?

1. Which tokens are most expensive
2. How text is segmented into individual tokens, showing word boundaries
3. The age of each token
4. Which tokens are errors

Answer

B) How text is segmented into individual tokens, showing word boundaries - Color-coding makes it visually clear where the tokenizer splits text, revealing whether words become one or multiple tokens.

Question 4: What happens when a word is not in a tokenizer's vocabulary?

1. The model crashes
2. The word is split into smaller subword pieces that are in the vocabulary
3. The word is ignored completely
4. A new vocabulary is created

Answer

B) The word is split into smaller subword pieces that are in the vocabulary - Modern tokenizers use subword algorithms to handle unknown words by breaking them into recognized smaller pieces.

Question 5: Why is comparing multiple tokenizers useful?

1. To find the most colorful one
2. To understand how different models handle the same text and estimate relative token counts
3. Comparison serves no purpose
4. To find spelling errors

Answer

B) To understand how different models handle the same text and estimate relative token counts - Comparing tokenizers reveals differences in how models process text, which affects context window usage, costs, and model behavior.