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In this video, the tutorial covers the use of sentence transformers and sentence embeddings in natural language processing (NLP) for semantic similarity applications. It begins with a brief recap of the development of transformers, including recurring neural networks and the attention mechanism. The presenter defines the differences between traditional transformers and sentence transformers, highlighting the effectiveness of the embeddings produced by the latter. Finally, the video guides viewers through the implementation of their own sentence transformers in Python. The tutorial aims to provide a comprehensive understanding of the significance and functionality of sentence transformers in NLP.
