What are text embeddings and how are they used?
easyAnswer
- Embeddings map text into a dense vector space where semantic similarity corresponds to geometric proximity (cosine or dot product).
- They are produced by encoders trained with contrastive or masked objectives.
- Uses: semantic search, RAG retrieval, clustering, deduplication, classification, and recommendation.
- Choose a model that matches your language, domain, and length; measure quality on your own retrieval eval set.
Check yourself — multiple choice
- Embeddings map text into a fixed-length dense vector
- Embeddings only work for English
- Embeddings preserve exact wording
- Higher dimension always means better retrieval
Embeddings are dense vectors capturing semantic similarity.
#embeddings#retrieval
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