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Journey Through Embeddings: CBOW with negative sampling

In previous post, we explored the fundamentals of Word2Vec and implemented the Continuous Bag of Words (CBOW) model, which efficiently predicts target words based on their surrounding context. While CBOW is powerful, scaling it to large vocabularies can be computationally challenging due to the softmax function. This is where negative sampling comes in—a clever optimization […]

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Journey Through Embeddings: Word2vec CBOW

Word embeddings have revolutionized natural language processing by transforming text into meaningful numerical representations that capture the relationships between words. In this blog, we focus on Word2Vec, a foundational technique in embedding methods, with a particular emphasis on the Continuous Bag of Words (CBOW) model. CBOW learns embeddings by predicting a target word from its