embeddingsare
Embeddingsare is a term used to describe the concept that embeddings are dense, real-valued vector representations of discrete items that reside in a continuous space. In this space, geometric relationships among vectors reflect semantic or relational similarities between the items. Embeddingsare are central to many machine learning and information retrieval tasks because they convert symbolic data into a form that models can readily process.
Embeddingsare are typically learned from data by optimizing objectives that bring related items closer together in
There are several families of embeddingsare. Word embeddings (static) map words to fixed vectors, while contextual
Limitations of embeddingsare include bias inherited from training data, interpretability challenges, and dependence on the domain
See also: embeddings, vector representations, representation learning, dimensionality reduction.