Tokenization & BPE

Introduction

Tokenization is the process of converting a sequence of characters into a sequence of tokens, which can be words, subwords, or characters. It is a crucial step in Natural Language Processing (NLP) as it prepares the data for machine learning models, particularly Large Language Models (LLMs).

This lesson focuses on two types of tokenization techniques, with a specific emphasis on Byte Pair Encoding (BPE).

Key Concepts

Byte Pair Encoding (BPE)

BPE is a simple and effective algorithm used for tokenization, particularly for text processing in machine learning. It begins with a base vocabulary and progressively merges the most frequent pairs of tokens.

Step-by-Step Process

1. Initialize: Start with a list of all characters in the dataset.
2. Count Pairs: Count the frequency of all pairs of tokens in the dataset.
3. Merge: Identify the most frequent pair and merge it into a new token.
4. Update: Repeat the counting and merging process until a predetermined vocabulary size is achieved.

Example Implementation

from collections import Counter

def get_stats(vocab):
    """Count frequency of token pairs in the vocabulary."""
    pairs = Counter()
    for word, freq in vocab.items():
        symbols = word.split()
        for i in range(len(symbols) - 1):
            pairs[(symbols[i], symbols[i + 1])] += freq
    return pairs

def merge_vocab(pair, v_in):
    """Merge the most frequent pair in the vocabulary."""
    v_out = {}
    bigram = ' '.join(pair)
    replacement = ''.join(pair)
    for word in v_in:
        new_word = word.replace(bigram, replacement)
        v_out[new_word] = v_in[word]
    return v_out

# Example vocabulary
vocab = {'l o w': 5, 'l o w e r': 2, 'n e w e s t': 6, 'w e s t': 3}
# Get the most frequent pairs
pairs = get_stats(vocab)
most_frequent_pair = pairs.most_common(1)[0][0]
# Merge the most frequent pair
new_vocab = merge_vocab(most_frequent_pair, vocab)
print(new_vocab)
            

Best Practices

To ensure effective tokenization for LLMs, consider the following best practices:

• Choose an appropriate vocabulary size based on the dataset.
• Use BPE for languages with rich morphology to handle inflections.
• Regularly update the tokenization model with new data to adapt to changing language use.

FAQ