Semantic ranking
Embedding models can rank content along virtually any dimension. This capability provides significant value by enabling users to explore and analyze the embeddings to create a spectrum of any features.
When searching for information, I want to rank content semantically so I can quickly access the most relevant information and make insightful comparisons.
- Enhanced Information Retrieval: By ranking content based on semantic relevance, users can quickly access the most pertinent information, fostering creative ways of searching.
- Insightful Comparisons: Displaying results along a ranked spectrum facilitates comparison of relevant attributes, providing valuable insights into the relationships and similarities between different pieces of content.
- Relevancy Thresholds: A semantic ranker can incorporate a relevancy threshold to exclude results that are highly irrelevant, ensuring higher quality and more useful outputs.
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