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jina-embeddings-v5-text: Task-Targeted Embedding Distillation research paper by Jina AI, 2026

Jina AI · Feb 17, 2026 · Retrieval and data · 22 citations · 28 upvotes · unverified

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What it shows

Compact text embedding models are developed through a combined training approach using distillation and contrastive loss, achieving state-of-the-art performance while supporting long-context sequences and efficient quantization.

UnverifiedHugging Face's summary; not yet checked by hand.

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About this paper
Authors
Mohammad Kalim Akram, Saba Sturua, Nastia Havriushenko and 4 more
arXiv
2602.15547 · PDF
Venue
arXiv.org
Citations
22, 1 influential · Semantic Scholar
Upvotes
28 · Hugging Face
Lab
Jina AI · on Companies · on Acquisitions

Changes

What changed
Influential citationsfirst count: 1Sep 25, 2026
Citationsfirst count: 22Sep 25, 2026
New paperFound by the weekly scan, unverifiedSep 25, 2026

Sources: each lab's own papers and arXiv, with citation and upvote counts from Semantic Scholar and Hugging Face. One-line summaries are for orientation, not a substitute for the paper. Logos via logo.dev; trademarks belong to their owners.

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