In machine learning, particularly in natural language processing (NLP), tokenizers and embeddings serve different but complementary purposes:
Tokenizer
Function: A tokenizer is responsible for breaking down (or “tokenizing”) text into smaller parts, usually words or subwords. This process is crucial for converting raw text into a format that can be processed by machine learning algorithms. Tokenizers can vary in complexity, from simple space-based splitting to more advanced methods that understand linguistic structures like word stems or subword units.
Purpose: The primary goal of a tokenizer is to convert unstructured text into a structured form. It prepares text for further processing, like creating embeddings.
Embedding
Function: An embedding is a technique for representing text (or other types of data) as vectors of real numbers. These vectors capture semantic meaning and relationships between words or phrases. For example, words with similar meanings tend to have similar embeddings. There are various methods to create embeddings, such as Word2Vec, GloVe, and more recent transformer-based approaches like BERT.
Purpose: Embeddings transform the structured text (post-tokenization) into a format that can be effectively used for machine learning tasks. These vector representations allow models to understand text semantically and perform operations like classification, clustering, or similarity analysis.
In summary, the tokenizer is the first step that processes raw text, breaking it down into manageable units, while embeddings are a subsequent step that converts these units into numerical vectors that encapsulate semantic information, making them usable for various machine learning tasks.