The Continuous Bag of Words (CBOW) model is a neural network architecture used in natural language processing to predict a target word based on its surrounding context words. It takes a set of context words as input and aims to predict the center word in a given window of text. This approach helps capture the semantic meaning of words by considering their relationships with nearby words. CBOW is part of the Word2Vec framework, developed by Google researchers. It contrasts with the Skip-gram model, which predicts context words from a target word. Both models are effective for generating word embeddings, which represent words in a continuous vector space.