What is a Tokenizer?

A tokenizer is a tool that breaks text into smaller pieces called tokens.

Tokens can be words, subwords, or characters
AI models cannot understand entire sentences directly
Tokenizer assigns each token a unique ID so the model can process it

How Tokens Work in AI Models (Example)

Sentence: "MY NAME IS MANOJ"

Character-based: Every character (including spaces) is a token
Word-based: Only words are tokens; spaces may or may not count

Why this matters:

Affects vocabulary size
Affects performance
Affects processing speed

Custom Tokenizer API – JavaScript (Node.js + Express)

Features Implemented:

Char-Level Tokenization: Treats each character as a token
Special Tokens: <PAD>, <UNK>, <START>, <END>

APIs Provided:

/encode → Convert text into token IDs
/decode → Convert token IDs back to text
/vocab → Show vocabulary info and token mappings

Other features:

vocab.json generated from sample data containing all unique tokens
Clear README.md with setup, usage, and Postman testing examples
Concept diagram explaining input tokens, input sequences, and tokenizer roles

Why Tokenization Matters in NLP

Breaks language into manageable pieces for AI models
Handles unknown words and sentence structure
Prepares clean, consistent input for accurate predictions

Final Takeaway 💡

A tokenizer is like a language translator for AI:

Takes human-readable text and breaks it into small, structured pieces (tokens) that machines can understand
Without tokenization, AI models like GPT or BERT wouldn’t know where one word ends and another begins

Benefits of building your own Custom Tokenizer API:

Learn how text becomes data for AI
Understand special tokens that control processing
See how encoding and decoding keep language intact

Conclusion:

Mastering tokenization is one of the first and most important steps in NLP.
Once you understand it, you’re no longer just a user of AI, you can shape how AI understands language.

Explain Tokenization to Fresher

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