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Devendran M
Devendran M

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Building an AI Tokenization Demo: From Workshop to App

#webdev #ai #huggingface #javascript

How I turned learning from Steve Kinney's AI workshop into a hands-on web application to demystify tokenization

The Spark of Inspiration

Last week, I attended Steve Kinney's AI Fundamentals with Python Workshop on Frontend Masters. One concept that particularly fascinated me was tokenization – the process of converting human-readable text into tokens that AI models can understand, and vice versa.

I wanted to create something interactive that would help others (and future me) visualize this process. That's how the AI Tokenizer Demo was born.

What is Tokenization?

Before diving into the implementation, let's quickly understand what tokenization means in the AI context:

Tokenization is the process of breaking down text into smaller units called tokens. These tokens are then converted into numerical IDs that machine learning models can process. Different models use different tokenization strategies:

  • BERT: Uses WordPiece tokenization
  • GPT-2: Uses Byte Pair Encoding (BPE)
  • RoBERTa: Uses similar BPE approach as GPT-2
  • T5: Uses SentencePiece tokenization

The Project Vision

I wanted to create a simple, browser-based application that would:

  1. Demonstrate tokenization visually - Show how text gets broken down into tokens
  2. Support multiple models - Compare how different models tokenize the same text
  3. Be educational - Help developers understand this fundamental AI concept
  4. Require zero setup - Run entirely in the browser without API keys or backends

Technical Implementation

Choosing the Right Tools

Hugging Face's Transformers.js library.

This JavaScript library brings the power of transformer models directly to the browser without requiring a backend.

The Architecture

The app follows a simple, educational architecture:

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   User Input    │───▶│   Tokenization   │───▶│   Display       │
│   (Text/IDs)    │    │   Processing     │    │   Results       │
└─────────────────┘    └──────────────────┘    └─────────────────┘
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Key Features Implemented

Here are the core features:

Model Selection: The app supports four different tokenization models:

  • GPT-2 (max length: 1024 tokens)
  • BERT Base Uncased (max length: 512 tokens)
  • RoBERTa Base (max length: 512 tokens)
  • T5 Small (max length: 512 tokens)

Bidirectional Processing: Users can:

  • Tokenize & Encode: Enter text to see how it breaks down into tokens and numerical IDs
  • Decode: Enter token IDs to see the reconstructed text

Educational Display: The interface shows:

  • The selected model name
  • Maximum text length for the model
  • Tokenized representation
  • Encoded numerical IDs
  • Decoded text output

Development Insights

Learning from Real Implementation

Working with Transformers.js taught me several important lessons:

1. Browser-First AI is Powerful: The ability to run AI models directly in the browser without servers or API keys is game-changing. Users can experiment freely without worrying about rate limits or costs.

2. Model Loading is Asynchronous: When users select a different model, the app needs to download and initialize it. This taught me the importance of:

  • Loading indicators
  • Error handling
  • User feedback during async operations

3. Different Models, Different Behaviors: Each model tokenizes text differently. For example, the same input text might produce:

  • Different numbers of tokens
  • Different token boundaries
  • Different numerical representations

How to Run Locally

Want to explore the code and run it yourself? Here's how:

Quick Setup 

# Clone the repository
git clone https://github.com/devendran-m/ai-tokenizer.git
cd ai-tokenizer
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Running the Application

Since this is a pure HTML/CSS/JavaScript application that uses CDN resources, you just need to serve it over HTTP:

Option 1: Using Live Server (VS Code)

Option 2: Using Node.js serve

npx serve .
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Navigate to http://localhost:5501 (or your server's port) and open index.html.

Project Structure 

ai-tokenizer/
├── index.html          # Main HTML file with UI structure
├── script.js           # Core tokenization logic
├── styles.css          # Styling and layout
└── README.md          # Setup and usage instructions
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Key Code Concepts

The application demonstrates several important concepts for developers:

Using Transformers.js via CDN:

<script type="module">
  import { AutoTokenizer } from 'https://cdn.jsdelivr.net/npm/@huggingface/transformers@latest/dist/transformers.min.js';
</script>
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Model Loading Pattern:
The app loads different tokenizer models on demand, showing how to handle:

  • Asynchronous model loading
  • User feedback during loading states
  • Error handling for network requests

Tokenization Workflow:

  1. User selects a model (GPT-2, BERT, RoBERTa, T5)
  2. App loads the corresponding tokenizer
  3. User inputs text to tokenize
  4. App displays tokens and numerical IDs
  5. User can decode IDs back to text

What You'll Learn

By exploring the code, you'll understand:

  • How different AI models tokenize text differently
  • Browser-based AI without backend requirements
  • Async/await patterns for model loading
  • DOM manipulation for dynamic UI updates
  • Error handling in AI applications

Example Usage

Step 1: Choose Your Model
The dropdown allows you to select from four different tokenization models. Each model will tokenize text differently, giving you insight into how various AI systems process language.

Step 2: Tokenize Text
Enter text and click "Tokenize & Encode". You'll see:

  • Tokenized: How the text breaks into individual tokens
  • Encoded: The numerical IDs representing each token
  • Model Info: Current model and its maximum text length

Tokenize and Encode

Step 3: Decode Tokens
Take the encoded IDs and paste them into the decode section to see how they convert back to text.

Decode

The Final Result

The completed application demonstrates tokenization with these key capabilities:

🔧 Model Flexibility: Choose between GPT-2, BERT, RoBERTa, and T5 tokenizers

📊 Visual Learning: See exactly how your text gets broken down into tokens

🔄 Bidirectional Processing: Convert text to tokens and tokens back to text

📋 Copy-Friendly Output: Easy to copy token IDs and decoded text for further experimentation

📱 No Installation Required: Runs entirely in the browser via CDN

🔗 Live Demo: https://ai-tokenizer.netlify.app/
🔗 Source Code: https://github.com/devendran-m/ai-tokenizer

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