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.

Next, we load the WordPiece tokenizer used in BERT. The AutoTokenizer.from_pretrained() method allows us to load a pre-trained tokenizer by specifying the model name.

Now, let's tokenize a sample sentence using the WordPiece tokenizer. The tokenize() method breaks the sentence into subword units.

Output:

In this example, the sentence is tokenized into subwords, demonstrating how WordPiece handles various elements like numbers, symbols, and proper nouns. This sentence includes proper nouns, apostrophes, numbers, symbols, hyphenated words, and a comparison phrase, making it useful for testing different tokenization techniques.

SentencePiece is a versatile tokenization technique used in models like T5. Unlike BPE and WordPiece, SentencePiece treats the input text as a raw byte sequence, allowing it to handle any language without relying on language-specific preprocessing. It uses a unigram language model or BPE to learn the subword units, making it effective for multilingual tasks.

To use SentencePiece, we need to import the AutoTokenizer from the transformers library.

We load the SentencePiece-based tokenizer used in T5. The AutoTokenizer.from_pretrained() method allows us to load a pre-trained tokenizer by specifying the model name.

Now, let's tokenize a sample sentence using the SentencePiece tokenizer. The tokenize() method breaks the sentence into subword units.

Output:

In this example, SentencePiece uses a special character (▁) to indicate the start of a new word, showcasing its unique approach to tokenization. This method is particularly useful for handling languages with complex scripts and for tasks requiring language-agnostic tokenization.

Now that we've explored WordPiece and SentencePiece, let's summarize their key differences and similarities in a table:

Each technique has its strengths and is suited for different NLP tasks. Choosing the right tokenization method depends on the specific requirements of your application.

In this lesson, we compared three popular tokenization techniques: BPE, WordPiece, and SentencePiece. We explored how each technique works and provided code examples to demonstrate their application using a complex sentence that includes proper nouns, apostrophes, numbers, symbols, hyphenated words, and a comparison phrase. Understanding these techniques is crucial for effectively processing text data in NLP tasks.

As you move on to the practice exercises, you'll have the opportunity to apply what you've learned and reinforce your understanding of these tokenization methods. Keep up the great work, and remember that mastering tokenization is a key step in becoming proficient in NLP!