Tokenization vs Embedding - How are they Different?

As AI technology advances rapidly, you will find exciting business opportunities. If you are a beginner, diving into AI can open doors to creating cutting-edge models and technologies. To effectively develop and work with these AI systems, you must learn and grasp the key steps like tokenization and embedding. Both are the building blocks in handling and interpreting data for AI models but have differences in their functions. This article will help you master tokenization and token embeddings and how they differ. With this knowledge, you’ll be well-equipped to build AI-driven applications such as chatbots, generative AI assistants, language translators, and recommender systems.

What Is Tokenization?

Tokenization is the process of taking the input text and partitioning it into small, secure, manageable units called tokens. These units can be words, phrases, sub-words, punctuation marks, or characters. According to OpenAI, one token is about four characters and ¾ words in English. This indicates that 100 tokens are approximately equal to 75 words.

Tokenization is the crucial step in Natural Language Processing (NLP). During this process, you are preparing your input text in a format that makes more sense to AI models without losing its context. Once tokenized, your AI systems can analyze and interpret human language efficiently.

Let’s take a look at the key steps to perform tokenization:

Step 1: Normalization

An initial step in which you need to convert the input text to lowercase using NLP tools to ensure uniformity. You can then strip out unnecessary punctuation marks and replace or remove special characters like emojis or hashtags.

Step 2: Splitting

You can break down your text into tokens using any one of the following approaches:

Word Tokenization

The word tokenization method is suitable for traditional language models like n-gram. It allows you to split the input text into individual words.

Consider the sentence: “The chatbots are beneficial.”

In the word-tokenization approach, this sentence would be tokenized as:

[“The”, “chatbots”, “are”, “beneficial”]

Sub-word Tokenization

Modern language models like GPT-3.5, GPT-4, and BERT use a sub-word tokenization approach. This approach breaks down text into smaller units than words, which helps handle a broader range of vocabulary and complex paragraphs.

Consider the sentence: “Generative AI Assistants are Beneficial”

In the sub-word tokenization approach, the sentences can be split as:

[“Gener”, “ative”, “AI”, “Assist”, “ants”, “are”, “Benef”, “icial”]

Here, you see eight individual tokens. If you were using word tokenization, there would be only five tokens.

You can also explore the sub-word tokenization using the OpenAI tokenizer tool.

Character Tokenization

Character tokenization is commonly used for systems like spell checkers that require fine-grained analysis. It enables you to partition the whole text into an array of single characters.

Consider the sentence: “I like Cats.”

The character-based tokenization would split the sentence into the following tokens:

[“I”, “ “, “l”, “i”, “k”, “e”, “ “, “C”, “a”, “t”, “s”]

Step 3: Mapping

In this step, you must assign each token a unique identifier and add it to a pre-defined vocabulary.

Step 4: Adding Special Tokens

You can add the following special tokens during tokenization to help the model understand the structure and context of the input data.

CLS

CLS is a classification token added at the beginning of every input sequence. After the text passes through the model, the output vector corresponding to this token can be used to predict the entire input. 

SEP

A separator token that helps you distinguish different segments of text within the same input. It is useful in tasks like question-answering or sentence-pair classification.

An illustration of the tokenization process is given below:

What are Embeddings? 

Embedding is a process of representing the tokens as continuous vectors in a high-dimensional space where similar tokens have similar vector representations. These vectors, also known as embeddings, help AI/ML models capture the semantic meaning of the tokens and their relationships in the input text.

To create these embeddings, you can use machine learning algorithms such as Word2Vec or GloVe. The resulting embeddings are organized in a matrix, where each row corresponds to the vector representation of a specific token from a pre-defined vocabulary.

For instance, if a vocabulary consists of 10,000 tokens and each embedding has 300 dimensions, the embedding matrix will be a 10,000 x 300 matrix.

Here are the steps to perform the embedding process with an example:

Step 1: Tokenization

In the above example, there are two input texts:

• Text 1: “The mouse ran up the clock”
• Text 2: “The mouse ran down”

In the tokenization process, you can split up each input text into individual tokens. Then, add each unique token to a vocabulary list with an index.

Step 2: Generating Output Vectors

Once tokenized, you will get one-dimensional output vectors as follows:

• For Text 1, the token indices are represented as the one-dimensional vector: [1,2,3,4,1,5].
• For Text 2, the indices are [1,2,3,6].

These indices are used to identify the corresponding embeddings in the embedding matrix.

Step 3: Creating an Embedding Matrix

The embedding matrix is the fundamental component in representing tokens as vectors. Each row of the matrix corresponds to the vector representation of a specific token.

In the given example, the dimension of the vectors is four. So, the embeddings for the output vector [1,2,3,4,1,5] are as follows:

• For index 1: [0.236, -0.141, 0.000, 0.045].
• For index 2: [0.006, 0.652, 0.270, -0.556].

Similarly, the embedding matrix will contain vector representations for all other four indices in the output vector.

The same process applies in the second output vector [1,2,3,6], which differs from the first vector only in the last index.

Step 4: Applying Embeddings

When an AI or ML system processes the text, it retrieves the embeddings from the matrix. This allows the model to understand the context and meaning of the tokens based on their vector representations.

Tokenization vs. Embeddings

Let’s take a look at the key differences between tokenization and embeddings:

Leveraging Airbyte for Efficient Tokenization and Embedding in AI Modeling

AI models like LLMs (Large Language Models) are trained on vast amounts of data, enabling them to answer a wide range of questions across various topics. However, when it comes to extracting information from proprietary data, LLMs can be limited and lead to inaccuracies. In such cases, consider leveraging Airbyte, a no-code data integration and replication platform.

Airbyte allows you to integrate data from all sources into a target system using its 350+ pre-built connectors. If no existing connector meets your needs, you can build a new one with basic coding knowledge through its CDK feature.

In addition, Airbyte supports RAG-based transformations, like LangChain-powered chunkings and OpenAI-enabled embeddings. This will simplify the data integration process in a single step and enable LLMs to produce more accurate, relevant, and up-to-date text.

Here are some key features of Airbyte:

• Modern Generative AI workflows: Airbyte helps you streamline AI processes by loading unstructured data into vector database destinations, such as Pinecone, Milvus, and Weaviate. Vector destinations offer efficient similarity searches and relevance-based retrieval, which is helpful for data analysis.
• ‍Efficient Data Transformation: With dbt integration, you can create and implement custom transformations within your data pipelines.
• ‍Developer-Friendly Pipelines: Airbyte provides an open-source, developer-friendly Python library, PyAirbyte. It allows you to programmatically connect with Airbyte connectors to extract data from varied sources in your Python workflows.
• ‍Data Synchronization: Airbyte offers a CDC feature to track the latest updates in the source systems and replicate them in your target system. This ensures that data in the destination remains aligned with the source database.
• ‍Open-Source: Airbyte provides an open-source version that helps you deploy your Airbyte instance locally using Docker or on a virtual machine. This edition allows you to leverage all the built-in connectors, schema propagation, and low-code/no-code connector builder features.
• ‍Data Security: Airbyte offers high security during integration with TLS and HTTPS encryption, SSH tunneling, role-based access controls, and credential management. It also adheres to ISO 27001 and SOC 2 Type II regulatory compliance for a secure process.

Summary

Tokenization and embeddings are essential steps in processing text for machine learning workflows. While tokenization provides structure, embedding offers a way to represent these structures numerically. This enables the AI/ML models to understand the context and nuances. In this article, you explored a few key differences between tokenization and token embeddings. However, together, these processes help enhance the performance of models in interpreting and generating human language.

FAQs

What is the difference between tokens and embeddings?

Tokens are individual units of text (words, subwords, or characters). Embeddings, on the other hand, are numerical representations of tokens that help AI models capture semantic meaning in a vector space.

Should you tokenize before embedding?

Yes, tokenization is usually performed before embedding, as it requires text to be split into tokens.

Is tokenization the same as word embedding?

No, if tokenization breaks the text into words and transforms these words into numerical vectors, then it is word embedding.

What is the difference between vectorization and tokenization?

Vectorization is a method that helps models to count the frequency of words in a document. In contrast, tokenization is a method of partitioning the input text into small, secure units called tokens.

What is the difference between token and tokenization?

A token is an individual unit of text, while tokenization is the way to split an entire text into these tokens.

What is tokenization in NLP?

Tokenization in NLP is an essential step for text preprocessing. It helps you divide large paragraphs or sentences into smaller, meaningful chunks, such as words, subwords, or characters, for further analysis.