Token embeddings are a foundational concept in natural language processing (NLP) and are essential for the functioning of modern language models. In simple terms, token embeddings are numerical representations of words, subwords, or characters (collectively called "tokens") that capture their semantic meaning. Instead of processing raw text, models use embeddings to understand language in mathematical form.

When a sentence is input into a model, each word or token is mapped to a high-dimensional vector using an embedding matrix. These vectors are designed so that tokens with similar meanings have similar vector representations. For example, the words "king" and "queen" would be embedded in such a way that they are closer in vector space than unrelated words like "apple" and "run."

Token embeddings allow language models to generalize, compare meanings, and detect context. This becomes particularly powerful in large language models like GPT, BERT, and T5, where embeddings serve as the initial step before further layers process the information.

There are different types of embeddings:

Static embeddings like Word2Vec or GloVe assign a fixed vector to each word.

Contextual embeddings (used in transformers like BERT or GPT) change depending on the word's usage in a sentence, making them much more effective in capturing meaning.

Embeddings are not learned manually—they are trained during the model's learning phase using large text corpora. Over time, the model fine-tunes these embeddings to better understand language, tone, and nuance.

Understanding token embeddings is crucial for anyone working with NLP, AI development, or model fine-tuning. If you're aiming to master these concepts in a practical, hands-on environment, enrolling in a Generative AI certification course is a valuable step forward.