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Published online by Cambridge University Press: 01 February 2024
In the previous chapter, we introduced word embeddings, which are real-valued vectors that encode semantic representation of words. We discussed how to learn them and how they capture semantic information that makes them useful for downstream tasks. In this chapter, we show how to use word embeddings that have been pretrained using a variant of the algorithm discussed in the previous chapter. We show how to load them, explore some of their characteristics, and show their application for a text classification task.
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