Token Embeddings Trend 2026

Token embeddings are dense vector representations of words or subwords, mapping discrete tokens into a continuous semantic space. Used in natural language processing models like BERT and GPT, they capture contextual meanings for tasks such as translation and sentiment analysis. Data scientists and machine learning engineers benefit by improving model accuracy and efficiency in understanding language patterns.