What are Contextual Embeddings?
Embeddings that capture the meaning of words or phrases based on the surrounding context.
More about Contextual Embeddings:
Contextual Embeddings are vector representations of words, phrases, or sentences that capture their meaning within a specific context. Unlike static embeddings, contextual embeddings adjust their representation based on the input sequence, making them ideal for tasks like semantic search and dense retrieval.
These embeddings are crucial for systems like retrieval-augmented generation (RAG), where understanding context improves the relevance and accuracy of results.
Frequently Asked Questions
How do contextual embeddings differ from static embeddings?
Contextual embeddings adjust their representation based on surrounding context, while static embeddings remain fixed.
What models are commonly used for generating contextual embeddings?
Models like BERT, RoBERTa, and GPT are widely used for generating contextual embeddings.
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