Sentence embeddings are numerical representations of sentences that capture their meanings in a way that computers can understand. By converting sentences into fixed-size vectors, these embeddings allow for easier comparison and analysis of text. They are often generated using machine learning models, such as BERT or Word2Vec, which learn to represent words and sentences based on their context in large datasets. These embeddings enable various natural language processing tasks, such as text classification, semantic similarity, and information retrieval. By measuring the distance between sentence embeddings, we can determine how similar or different two sentences are, facilitating better understanding and processing of human language by machines.