Feature	Value
Parameters	239M
Supported Task	`clustering`
Max Sequence Length	8192
Embedding Dimension	768
Pooling Strategy	Last-token pooling
Base Model	EuroBERT/EuroBERT-210m
Format	MLX float16 safetensors

For training details and evaluation results, see our technical report.

Requirements

pip install mlx tokenizers

Usage

bash

git clone https://huggingface.co/jinaai/jina-embeddings-v5-text-nano-clustering-mlx
cd jina-embeddings-v5-text-nano-clustering-mlx

python

import mlx.core as mx
from tokenizers import Tokenizer
from model import JinaEmbeddingModel
import json

with open("config.json") as f:
    config = json.load(f)

model = JinaEmbeddingModel(config)
weights = mx.load("model.safetensors")
model.load_weights(list(weights.items()))

tokenizer = Tokenizer.from_file("tokenizer.json")

texts = [
    "We propose a novel neural network architecture for image segmentation.",
    "This paper analyzes the effects of monetary policy on inflation.",
    "Our method achieves state-of-the-art results on object detection benchmarks.",
    "We study the relationship between interest rates and housing prices.",
]
embeddings = model.encode(texts, tokenizer, task_type="clustering")

License

jina-embeddings-v5-text-nano is licensed under CC BY-NC 4.0. For commercial use, please contact sales@jina.ai.

Citation

If you find jina-embeddings-v5-text-nano useful in your research, please cite the following paper:

bibtex

@article{akram2026jina,
  title={jina-embeddings-v5-text: Task-Targeted Embedding Distillation},
  author={Mohammad Kalim Akram and Saba Sturua and Nastia Havriushenko and Quentin Herreros and Michael G{\"u}nther and Maximilian Werk and Han Xiao},
  journal={arXiv preprint arXiv:2602.15547},
  year={2026}
}