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Abstract
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.
AI-generated summary
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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our 5th gen of jina embeddings
This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
The following papers were recommended by the Semantic Scholar API
- Diffusion-Pretrained Dense and Contextual Embeddings (2026)
- Bagging-Based Model Merging for Robust General Text Embeddings (2026)
- Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and Ranking (2026)
- VIRTUE: Versatile Video Retrieval Through Unified Embeddings (2026)
- Nemotron ColEmbed V2: Top-Performing Late Interaction embedding models for Visual Document Retrieval (2026)
- VidVec: Unlocking Video MLLM Embeddings for Video-Text Retrieval (2026)
- Compass-Embedding v4: Robust Contrastive Learning for Multilingual E-commerce Embeddings (2025)
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