What are Embeddings?
Dense numerical representations of data, such as text or images, used in tasks like semantic search and retrieval.
More about Embeddings:
Embeddings are dense vectors generated by neural network models to represent data, such as text, sentences, or images, in a high-dimensional space. They are designed to capture semantic relationships and similarities between data points.
Embeddings are foundational to technologies like dense retrieval, semantic search, and contextual embeddings, enabling AI systems to perform tasks such as knowledge retrieval and document similarity.
Frequently Asked Questions
How are embeddings used in AI applications?
They enable models to understand semantic relationships, making tasks like retrieval fusion and question answering more effective.
What models are commonly used to generate embeddings?
Models like BERT, GPT, and sentence transformers are widely used for generating embeddings.
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