jinaai/jina-embeddings-v5-text-nano-classification-mlx
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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
For training details and evaluation results, see our technical report.
Requirements
pip install mlx tokenizersUsage
git clone https://huggingface.co/jinaai/jina-embeddings-v5-text-nano-classification-mlx
cd jina-embeddings-v5-text-nano-classification-mlximport 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:
@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}
}