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jinaai/jina-embeddings-v5-text-nano-classification-mlx

sourceHugging Facecc-by-nc-4.0updated 7mo agoView on Hugging Face
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jina-embeddings-v5-text-nano-classification-mlx

MLX port of `jina-embeddings-v5-text-nano-classification` for efficient inference on Apple Silicon. For the full-size model, see jina-embeddings-v5-text-small-classification-mlx.

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Model Overview

FeatureValue
Parameters239M
Supported Taskclassification
Max Sequence Length8192
Embedding Dimension768
Pooling StrategyLast-token pooling
Base ModelEuroBERT/EuroBERT-210m
FormatMLX float16 safetensors

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

Requirements

bash
pip install mlx tokenizers

Usage

bash
git clone https://huggingface.co/jinaai/jina-embeddings-v5-text-nano-classification-mlx
cd jina-embeddings-v5-text-nano-classification-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 = [
    "My order hasn't arrived yet and it's been two weeks.",
    "How do I reset my password?",
    "I'd like a refund for my recent purchase.",
    "Your product exceeded my expectations. Great job!",
]
embeddings = model.encode(texts, tokenizer, task_type="classification")

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}
}