Just as we used static embeddings like word2vec to represent the meaning of words, we can use contextual embeddings as representations of word meanings in context for any task that might require a model of word meaning. Where static embeddings represent the meaning of word types (vocabulary entries), contextual embeddings represent the meaning of word tokens: instances of a particular word type in a particular context. Contextual embeddings can thus by used for tasks like measuring the semantic similarity of two words in context, and are useful in linguistic tasks that require models of word meaning.