Embedding

Embedding is a technique used in machine learning and natural language processing to represent words or phrases in a numerical format. It is a way of transforming high-dimensional data into a lower-dimensional space while preserving the important relationships between the data points. In natural language processing, embedding is used to represent words or phrases as vectors of real numbers, where each dimension of the vector represents a different feature of the word or phrase. These embeddings can be learned from large amounts of text data using techniques such as Word2Vec, GloVe, or FastText. Embeddings are used in a variety of natural language processing tasks such as language translation, sentiment analysis, and text classification.
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