🔥 How I Built My Own Tokenizer (Like ChatGPT)
If you've ever wondered how ChatGPT understands what you type, the secret sauce is something called a tokenizer. It breaks down your text into smaller pieces (tokens) that an AI model can understand. But here’s the twist — I decided to build one from scratch that supports Hindi and English, just like OpenAI's GPT tokenizer.
🧠 Step 1: What Even Is a Tokenizer?
Imagine you're talking to your AI buddy, and you say:
"Main school ja raha hoon"
Your model doesn't "see" it like we do. It breaks it down into tokens:
["Main", "school", "ja", "raha", "hoon"]
But in OpenAI’s world, it can go even deeper:
["M", "ai", "n", " school", " ja", " ra", "ha", " ho", "on"]
They use Byte Pair Encoding (BPE) – the Sholay of tokenization algorithms. Solid, reliable, and classic.
“Itna sannata kyun hai bhai?” – because BPE doesn’t just split by space... it finds patterns in your data!
🔨 Step 2: Training Your Own Tokenizer
First, you gather some desi-style sentences (in Hindi + English):
corpus = ["Main school ja raha hoon", "Tum kon ho?", "I love Golgappa", "Dosti ho gayi yaar"]
Then, like Babu Bhaiya sorting pickles by size, you break each word into characters:
["M", "a", "i", "n", "</w>"], ["s", "c", "h", "o", "o", "l", "</w>"] ...
The </w> is like a The End in movies — to mark word endings.
Now, you look for character pairs that occur most frequently (like "ra" in "raha", "rahul", "raj"), and merge them again and again.
After 50 merges, you've created your own tokenizer vocabulary, like:
["ra", "ho", "on", "ja", "rah", "main", "school"]
Congrats! Your tokenizer is now trained — "Beta, tumse na ho payega" ka zamana gaya.
🧪 Step 3: Tokenizing New Sentences
Now when someone types:
"Tumhara naam kya hai?"
Your tokenizer goes:["T", "um", "ha", "ra", " naam", " kya", " hai", "?"]
If it doesn’t find something in its vocab, it breaks it down into characters — like Krrish losing powers but still fighting 😄
🤖 Bonus: Byte-Level Tokenization
Just like Shah Rukh Khan has fans in every country, GPT uses bytes instead of just text — so even rare characters (like emoji or Urdu or Tamil) can be tokenized.
text = "मैं स्कूल जा रहा हूँ"
bytes_text = list(text.encode("utf-8"))
This means your tokenizer can handle:
💬 Hindi-English mixed text (a.k.a. Hinglish)
🤯 Slangs
👨💻 Emojis & symbols
📦 Packaging the Tokenizer
You can save the tokenizer rules in a .json file. Use it later in your app or even build your own desi ChatGPT clone!
tokenizer.save("my_tokenizer.json")
“Tumse yeh na ho payega?”
Bhai, ho gaya! 💪
🎬 Final Thoughts (feat. Memes)
Building a tokenizer isn’t rocket science. It’s more like assembling a DDLJ-style love story — small steps, pattern recognition, and some tears (of joy 😅). And now you know:
How GPT-style tokenization works
How to make your own tokenizer for Hindi + English
And how to make learning fun — Desi style!
If you liked this, share it with your coding friends. Or as Gabbar would say:
“Jo tokenizer banaye hain... unka kuch toh karo!” 😆
Happy Coding!!!
😊