In the realm of Natural Language Processing (NLP) and data compression, Byte Pair Encoding (BPE) stands out as a fascinating technique. Originally used for data compression, BPE has found its way into the world of NLP, particularly in tokenizing text for language models. Understanding BPE can be quite enlightening, especially for those interested in how language models process and understand text. In this post, we’ll dive into creating a simple BPE algorithm in Python to illustrate the concept.

Step 1: Define the Text and Initialize the Vocabulary

We begin with a sample text and initialize our vocabulary with unique characters. For simplicity, we’ll remove spaces from our text. Here, we’re breaking down our text into a set of characters, each treated as a separate token.   

Step 2: Define the BPE Function

      The core of the BPE algorithm lies in its ability to iteratively merge frequent pairs of characters. Let’s define our BPE function, which includes two parts: `get_stats` and `merge_vocab`.           –`get_stats` calculates the frequency of each adjacent symbol pair in the vocabulary. –`merge_vocab` merges the most frequent pair throughout the vocabulary.

Step 3: Apply BPE Iteratively

We apply BPE iteratively to merge the most frequent pairs, updating our vocabulary with new merged tokens each time. In each iteration, we:
— Calculate the most frequent pairs of symbols.
— Merge them to form new tokens.
— Update our vocabulary accordingly.          

Understanding the Mechanics


Iterative Merging: The algorithm repeatedly merges the most frequent pairs of symbols, thereby reducing the overall size of the vocabulary.

The Role of `</w>`: We append `</w>` to the end of each word in our vocabulary. This marker helps distinguish between pairs occurring within a word and at word boundaries.


This simple implementation of BPE in Python serves as an educational tool to understand the basics of this technique. In real-world applications, especially in advanced NLP models like GPT, BPE is implemented on a much larger scale and with more sophisticated nuances.

By running this script, you can observe how BPE incrementally builds a subword vocabulary, merging characters and sequences based on their frequency. Such insights are invaluable in understanding the preprocessing steps in modern language models.

The above script is a very rudimentary implementation of BPE, there are various packages and libraries out there that will help with creating much more intricate and robust embeddings suitable for use in larger models. Below is a quick example using Hugging Face Tokenizers to implement a much larger BPE.