jina-embeddings-v5-text: Task-Targeted Embedding Distillation
Text embedding models are widely used for semantic similarity tasks, including information retrieval, clustering, and classification. General-purpose models are typically trained with single- or multi-stage processes using contrastive loss functions. We introduce a novel training regimen that combines model distillation techniques with task-specific contrastive loss to produce compact, high-performance embedding models. Our findings suggest that this approach is more effective for training small models than purely contrastive or distillation-based training paradigms alone. Benchmark scores for the resulting models, jina-embeddings-v5-text-small and jina-embeddings-v5-text-nano, exceed or match the state-of-the-art for models of similar size. jina-embeddings-v5-text models additionally support long texts (up to 32k tokens) in many languages, and generate embeddings that remain robust under truncation and binary quantization. Model weights are publicly available, hopefully inspiring further advances in embedding model development.
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Mohammad Kalim Akram
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2026-02-18T13:06:40.906Z
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Mohammad Kalim Akram
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Saba Sturua
admin_assigned
2026-02-18T13:06:49.861Z
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Saba Sturua
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Nastia Havriushenko
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2026-02-18T13:06:58.693Z
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Nastia Havriushenko
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Quentin Herreros
admin_assigned
2026-02-18T13:07:05.539Z
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Quentin Herreros
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Michael Günther
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2026-02-19T09:52:24.830Z
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Michael Günther
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michael-guenther
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Maximilian Werk
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2026-02-18T13:07:16.360Z
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Maximilian Werk
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Han Xiao
claimed_verified
2026-02-18T12:34:37.784Z
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Han Xiao
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hanxiao
jina-embeddings-v5-text: Task-Targeted Embedding Distillation
Text embedding models are widely used for semantic similarity tasks, including information retrieval, clustering, and classification. General-purpose models are typically trained with single- or multi-stage processes using contrastive loss functions. We introduce a novel training regimen that combines model distillation techniques with task-specific contrastive loss to produce compact, high-performance embedding models. Our findings suggest that this approach is more effective for training small models than purely contrastive or distillation-based training paradigms alone. Benchmark scores for the resulting models, jina-embeddings-v5-text-small and jina-embeddings-v5-text-nano, exceed or match the state-of-the-art for models of similar size. jina-embeddings-v5-text models additionally support long texts (up to 32k tokens) in many languages, and generate embeddings that remain robust under truncation and binary quantization. Model weights are publicly available, hopefully inspiring further advances in embedding model development.
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.
text embedding models contrastive loss model distillation semantic similarity information retrieval clustering classification embedding models long texts binary quantization