tokenrepresentationer
Token Representationer is a conceptual framework and set of techniques used in natural language processing (NLP) and machine learning to encode discrete units of text, known as tokens, into a numerical format that can be processed by algorithms. These tokens can be words, sub-word units (like prefixes or suffixes), or even characters, depending on the chosen tokenization strategy. The core idea is to transform linguistic data, which is inherently symbolic, into a vector space where mathematical operations can be performed.
Different methods exist for token representation. One common approach is one-hot encoding, where each unique token