Sentence Embeddings

Sentence Embeddings are a key technology in natural language processing, designed to convert sentences into fixed-length vector representations to capture their semantic and contextual information. Through deep learning models, Sentence Embeddings can map textual data into high-dimensional space, enabling efficient computation and comparison of sentence similarities. This technology is of significant value in applications such as sentiment analysis, text classification, and information retrieval, notably enhancing the performance and generalization capabilities of models.