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

Jina AI · Retrieval and data · Feb 17, 2026 · 28 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.

By Mohammad Kalim Akram, Saba Sturua, Nastia Havriushenko and 4 more · arXiv 2602.15547 · PDF

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

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About this paper
Venue
arXiv.org
Citations
28, 1 influential · Semantic Scholar
Upvotes
28 · Hugging Face
Lab
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Changes

CitationsNov 2, 2026Citations rose from 25 to 28.
CitationsOct 12, 2026Citations rose from 23 to 25.
CitationsOct 5, 2026Citations rose from 22 to 23.
CitationsSep 25, 2026A first count of 1 influential citations.
CitationsSep 25, 2026A first count of 22 citations.
New paperSep 25, 2026Found by the weekly Hugging Face scan; not yet checked by hand.