BPE Tokenizer Tutorial: Build a Byte-Pair Encoding Tokenizer from Scratch
Welcome, fellow AI enthusiasts!
Have you ever wondered how language models like GPT-3 or GPT-4 understand and process text? The answer lies in a key component called the tokenizer.
In this BPE tokenizer tutorial, we’ll demystify this process by building a Byte-Pair Encoding (BPE) tokenizer from scratch — step by step and in clear, actionable terms. Understanding tokenization is essential for any NLP engineer, data scientist, or AI researcher. By the end, you’ll have your own functional tokenizer — the foundation on which modern language models operate.
🧩 Step 1: From Text to Bytes
Every modern tokenizer begins by converting raw text into UTF-8 bytes — the lowest level representation of text. Each byte is a value from 0 to 255, giving us an initial vocabulary of 256 byte tokens.
In a BPE tokenizer, the core idea is to merge frequently co-occurring adjacent byte pairs into new tokens. So our first task is to scan through a training corpus and count how often each pair of bytes appears. These statistics let us pinpoint the most “valuable” merge — the one that compresses the data the most.
⚙️ Step 2: Train Your BPE Tokenizer (Iterative Merging)
The training process in this BPE tokenizer tutorial is straightforward but powerful:
Count frequencies of adjacent token pairs.
Merge the most frequent pair into a new token.
Update your token vocabulary and replacement rules.
Repeat until your vocabulary reaches a target size (for example, 50,000 tokens).
As you iterate, simple subwords and entire words emerge from the byte-level foundation. When encoding new text, you apply the same merge sequence greedily; decoding is just reversing — concatenating byte sequences and converting back to UTF-8. A round-trip test (encode → decode) ensures correctness.
🧱 Step 3: Bringing It into Practice (Extensions)
A raw BPE tokenizer is useful, but production usage requires a few enhancements:
Pre-tokenization (regex chunking): Before merging, split input on spaces, punctuation, or numbers to avoid awkward tokens like “dog.” merging poorly.
Special tokens: Models often require tokens like
<|endoftext|>or<|pad|>. These must be added after training and should not be merged.Persistence: Save your vocabulary map (token ID ↔ byte sequence) and ordered merges so you can reload the tokenizer later.
Performance: Python is fine for prototyping. For large-scale or high-throughput use, optimized implementations (e.g. in Rust or C++) are preferred.
🚀 Step 4: Experiment & Iterate
You now have the knowledge and structure of a proper BPE tokenizer tutorial. You understand how raw text becomes tokens that AI models can read, and how those tokens compress and expand meaningfully.
Try these next steps:
Train on domain-specific corpora (e.g. legal text, code, tweets) and observe how your learned vocabulary changes.
Vary vocabulary size and measure token count per sentence vs. model efficiency.
Explore alternatives like WordPiece or SentencePiece, which build on BPE ideas but add their own mechanisms.
Debug real models by looking at token boundaries and how merges affect model behavior.