![]() ![]() In this implementation, we'll be using the skip-gram architecture because it performs better than CBOW. There are two architectures for implementing word2vec, CBOW (Continuous Bag-Of-Words) and Skip-gram. Words that show up in similar contexts, such as "black", "white", and "red" will have vectors near each other. These vectors also contain semantic information about the words. The word2vec algorithm finds much more efficient representations by finding vectors that represent the words. Trying to one-hot encode these words is massively inefficient, you'll have one element set to 1 and the other 50,000 set to 0. When you're dealing with language and words, you end up with tens of thousands of classes to predict, one for each word. ![]()
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