Tagged “embeddings”
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Vector Normalisation
Rescaling a vector to unit length while keeping its direction, which makes dot product and cosine similarity produce the same ranking.
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Embedding
A fixed-length list of numbers representing a piece of text, produced so that texts with similar meanings land near one another in the space.
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Cross-Encoder
A model that takes a query and a document as one joined input and outputs a relevance score, rather than encoding each into a comparable vector.
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Quantisation
Storing each value of a vector in fewer bits than the model produced, cutting memory and comparison cost while keeping the number of values the same.
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Bi-Encoder
A model arrangement that encodes query and document separately into vectors that are compared afterwards, so document vectors can be computed in advance.
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Dense Retrieval
Retrieval that compares learned vectors in which every value is used, matching query to text by meaning rather than by shared terms.
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Cosine Similarity
A measure of how closely two vectors point in the same direction, ignoring their length — the usual way embedding similarity is scored.
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Dimensionality
The number of values in an embedding vector — fixed by the model that produced it, and the same for every input that model ever sees.