A Turkish search query and a collection of candidate passages.
Turkish embedding models
Models & Datasets
Evaluate Turkish retrieval models on your task.
Compact Turkish embedding models derived from BGE-M3 turn text into vectors for semantic search. Choosing between distilled and quantized variants involves evaluating retrieval quality on the target collection alongside model size and runtime requirements. Model cards explain the evaluation conditions behind published results.
01 / INPUT TO OUTPUT
Encode the query and passages with the selected embedding model, then rank them by vector similarity.
Ranked candidate passages to evaluate in a search or retrieval workflow.
02 / WHAT IT DOES
Represent Turkish text as vectors
Encode Turkish sentences or passages as vectors for similarity comparison and semantic search.
Compare model options
Review model cards for architecture, size, intended use, and published evaluation conditions.
Measure task fit
Evaluate candidate embedding models on queries and documents that represent the target collection.
03 / WORK WITH ALTAI
From a component
to your application.
ALTAI builds the model layer of search applications by comparing these models with alternatives on your organization's data. Selection is based on measured retrieval quality and the resource requirements of the target environment.
Explore custom developmentWORK WITH ALTAI
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