Token embedding vectors are a way to represent words or sub words as numerical vectors in a high-dimensional space.
These vectors capture the semantic meaning of the tokens, allowing AI models to understand the relationships between words and their context within a sentence or document.
- Tokenization: The input text is first broken down into individual units called tokens. These can be words, sub words, or even characters, depending on the specific model and task.
- Embedding: Each token is then mapped to a corresponding vector in a high-dimensional space. This mapping is learned by the model during training, where it analyzes vast amounts of text data to understand the relationships between words.
- Vector Representation: The resulting vectors are dense and continuous, meaning that each element in the vector is a real number. The position of a token in this vector space reflects its semantic meaning, with similar words having vectors that are closer together.
As a simple given example, the embeddings for our tokens consist of vectors with three elements.
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