Comparing BPE, WordPiece, and SentencePiece in NLP

Welcome to this lesson on comparing tokenization techniques used in modern Natural Language Processing (NLP) models. Tokenization is a crucial step in NLP that involves breaking down text into smaller units called tokens. This process is essential for AI and Large Language Models (LLMs) to understand and process text data effectively. In previous lessons, we explored rule-based tokenization and Byte-Pair Encoding (BPE). Today, we will build on that knowledge by comparing BPE with two other popular tokenization techniques: WordPiece and SentencePiece.

Before diving into WordPiece and SentencePiece, let's briefly recall Byte Pair Encoding (BPE). BPE is a subword tokenization technique that reduces vocabulary size and handles rare words by encoding text into subword units. It merges the most frequent pairs of characters or subwords iteratively to form a compact vocabulary. This technique is particularly useful for languages with rich morphology and has been widely adopted in NLP tasks.

WordPiece tokenization is an extension of BPE and is used in models like BERT. It builds on BPE by introducing additional rules for handling subword units, which helps in better capturing the semantics of words. WordPiece uses a probabilistic model to determine the likelihood of subword sequences, allowing it to choose the most semantically meaningful tokenization.

Example of WordPiece Tokenization:

Consider the word "unbelievable". WordPiece might break it down into subwords like "un", "##believ", and "##able". The "##" prefix indicates that the subword is a continuation of the previous token. This allows the model to understand the semantic components of the word, such as the prefix "un-" and the root "believe".

Let's explore how WordPiece tokenization works using the transformers library.

First, we need to import the AutoTokenizer from the transformers library. This library provides pre-trained tokenizers for various models, including BERT.

from transformers import AutoTokenizer