jinaai/jina-embeddings-v5-text-nano
feature-extraction model by jinaai
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Model card & documentation
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jina-embeddings-v5-text-nano is the fifth generation of Jina AI's multilingual embedding models, released on February 18, 2026. For higher performance at a larger size, see jina-embeddings-v5-text-small. Elastic Inference Service | ArXiv | Release Note | Blog jina-embeddings-v5-text-nano scores 71.0 average on MTEB English v2 and 65.5 on MMTEB with only 239M parameters, matching or exceeding all other sub-500M embedding models including KaLM-mini-v2.5 (494M) and Gemma-300M (308M). Built on EuroBERT-210M and trained by combining embedding distillation from Qwen3-Embedding-4B with task-specific contrastive losses, it supports multilingual text up to 32K tokens and produces embeddings robust under truncation and binary quantization. For training details and evaluation results, see our technical report. Requirements The following Python packages are required: via Elastic Inference Service The fastest way to use v5-text in production. Elastic Inference Service (EIS) provides managed embedding inference with built-in scaling, so you can generate embeddings directly within your Elastic deployment. See the Elastic…
Read the full model card ↗ · Preview checked 2026-11-01T13:45:11.294Z
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- jina-embeddings-v5-text-nano
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Model overview
jinaai/jina-embeddings-v5-text-nano is a feature-extraction model repository published by jinaai on Hugging Face. The source reports the transformers library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- feature-extraction
- Library
- transformers
- Recent downloads (30 days)
- 91,868
- Cumulative likes
- 100
- Hugging Face trending score
- 0
- Reported safetensors parameters
- 211,766,016
- Architecture
- JinaEmbeddingsV5Model
- License
- cc-by-nc-4.0
- Access
- Not gated by Hugging Face
- Created
- 2026-01-22T09:42:12.000Z
- Last modified
- 2026-04-15T09:15:08.000Z
Compatibility and lineage
Reported languages: multilingual
Reported base models: EuroBERT/EuroBERT-210m
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-11-01T06:54:29.511Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.