Byte Pair Encoding(BPE)

Author

Abhishek Kumar Pandey
March 17, 2024

Byte Pair Encoding(BPE)

Easy:

Imagine you have a giant box of Legos with all sorts of shapes and sizes. You want to build a cool spaceship, but you might not have all the specific pieces you need. Byte Pair Encoding (BPE) is like a trick to help you build awesome things anyway!

Here’s how it works:

  • BPE takes the regular Legos (letters in text) and finds the two that are used together the most, like “th” or “in”.

  • Then, it smashes those two Legos together to make a new, bigger Lego. This new Lego represents those two letters together.

  • BPE keeps doing this, finding the most common pairs of Legos (letters) and sticking them together.

  • Now, even if you don’t have a special Lego for a rare word (like “spaceship”), BPE can build it using the smaller, more common Legos you do have (like “space” and “ship”).

By doing this, BPE creates a special set of Legos (vocabulary) that can be used to build almost anything, even with some missing pieces! This is super helpful for computers that are trying to understand and use language.

Another easy example:

Imagine you have a bunch of different words written on pieces of paper. Now, let’s say you want to make a secret code to write these words in a shorter way.

Byte Pair Encoding (BPE) is a bit like making a secret code for words. Here’s how it works:

  1. Starting with Letters: First, we start by looking at each letter in all the words. Each letter is like its own piece of our secret code.

  2. Finding Popular Pairs: Next, we look at pairs of letters that appear next to each other a lot. Like ‘th’ in ‘the’, or ‘ing’ in ‘running’. We write these pairs down as part of our secret code too.

  3. Combining: Then, we make a special rule. Whenever we see one of these pairs, we can write it as a single code instead of two separate letters. So, ‘th’ could become just one symbol, and ‘ing’ could also become one symbol.

  4. Repeat: We keep looking for the most common pairs and combining them until we’re happy with our secret code. This way, we can write words using fewer symbols.

So, with BPE, we make a special way to write words using smaller parts that appear a lot. It’s like having a cool secret code for words, and it helps make things shorter and easier to work with, especially in computers when dealing with lots of text!

Moderate:

Byte Pair Encoding (BPE) is a data compression algorithm that can also be used as a method for subword tokenization in natural language processing. The basic idea behind BPE is to iteratively replace the most frequent pair of bytes in a corpus with a single, unused byte. This process creates a vocabulary of increasingly complex character combinations and allows for efficient encoding of text by representing recurring patterns with a single symbol.

In NLP, BPE has been used as an alternative to traditional word-level tokenization methods such as whitespace tokenization or byte-level byte-pair encoding (BBPE). By using subword units instead of individual characters or whole words, BPE can handle out-of-vocabulary words more gracefully and improve model performance on rare or misspelled words.

The BPE algorithm works as follows:

  1. Start with a vocabulary containing all possible byte sequences in the training data.

  2. Count the frequency of each byte pair in the training data.

  3. Replace the most frequent byte pair with a new, previously unused byte.

  4. Add the newly created byte pair to the vocabulary.

  5. Update the count of byte pairs in the training data based on the updated vocabulary.

  6. Repeat steps 2–5 until reaching the desired number of merge operations or vocabulary size.

After applying BPE to a dataset, the resulting tokens are typically represented as sequences of integers corresponding to their position in the final vocabulary. These integer sequences can then be fed into machine learning models for various NLP tasks such as translation, summarization, or text classification.

Hard:

Byte Pair Encoding (BPE) is a subword tokenization algorithm used in Natural Language Processing (NLP) to represent a large vocabulary with a small set of subword units. It was initially developed for text compression and later adopted for tokenization in models like GPT, GPT-2, RoBERTa, BART, and DeBERTa.

The BPE algorithm starts by computing the unique set of words used in a corpus and building a vocabulary with all the symbols used to write those words. Initially, the vocabulary includes all the ASCII characters, and possibly some Unicode characters. The algorithm then adds new tokens until the desired vocabulary size is reached by learning merges, which are rules to merge two elements of the existing vocabulary together into a new one. These merges start with two-character tokens and progress to longer subwords as training progresses. At each step, the BPE algorithm searches for the most frequent pair of existing tokens (two consecutive tokens in a word) and merges them.

The process of BPE involves several steps:

  1. Initialize the vocabulary with all the bytes or characters in the text corpus.

  2. Calculate the frequency of each byte or character in the text corpus.

  3. Repeat the following steps until the desired vocabulary size is reached:

  4. Find the most frequent pair of consecutive bytes or characters in the text corpus.

- Merge the pair to create a new subword unit.

- Update the frequency counts of all the bytes or characters that contain the merged pair.

- Add the new subword unit to the vocabulary.

BPE is particularly effective for tokenization because it has low computational overhead and remains consistent and reliable. It encodes rare and unknown words as sequences of subword units, allowing for the representation of various word classes via smaller units than words, such as names, compounds, and cognates [2][3].

In practice, BPE can be implemented by first pre-tokenizing a corpus into words, computing the frequencies of each word, and then computing the base vocabulary formed by all the characters used in the corpus. The algorithm then splits each word into individual characters to start training, computing the frequency of each pair of consecutive characters and merging the most frequent pairs to create new subword units.

A few notable books on deep learning: