import open_clip

model, preprocess = open_clip.create_model_from_pretrained('hf-hub:llm-jp/llm-jp-clip-vit-large-patch14')
tokenizer = open_clip.get_tokenizer('hf-hub:llm-jp/llm-jp-clip-vit-large-patch14')

import torch
from PIL import Image
import requests

url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
image = preprocess(image).unsqueeze(0)
text = tokenizer(["猫", "犬", "鳥"])

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image)
    text_features = model.encode_text(text)
    image_features /= image_features.norm(dim=-1, keepdim=True)
    text_features /= text_features.norm(dim=-1, keepdim=True)

    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

print("Label probs:", text_probs)
# Label probs: tensor([[9.9425e-01, 5.2273e-03, 5.2600e-04]])

Reference:

Using OpenCLIP at Hugging Face, HuggingFace Docs
OpenCLIP repository

Training Details

Model Architecture

Text Encoder: RoBERTa base with llm-jp-tokenizer
Image Encoder: ViT-L/14

Training Data

This model is trained on relaion2B-en-research-safe-japanese-translation. Due to a 70% success rate in image downloads, the dataset size was 1.45 billion samples, and we processed it over 9 epochs (13 billion samples in total).

Evaluation

Evaluation Code: https://github.com/llm-jp/clip-eval

Table: Performance of each model in zero-shot image classification and image-text retrieval tasks. Bold indicates first place, and underline indicates second place.

Model	Params (M)	ImageNet	Recruit	CIFAR10	CIFAR100	Food101	Caltech101	XM3600 I → T	XM3600 T → I	Avg.
Japanese CLIP
Rinna ViT-B/16	196	50.6	39.9	90.7	64.0	53.2	84.6	53.8	54.0	61.4
Rinna ViT-B/16 cloob	196	54.6	41.6	88.2	60.3	57.2	80.2	53.4	53.4	61.1
LY ViT-B/16	196	52.0	83.8	96.3	76.7	73.9	88.4	76.9	78.0	78.3
llm-jp-ViT-B/16	248	54.2	59.4	91.8	69.2	82.2	85.6	73.6	72.7	73.6
StabilityAI ViT-L/16	414	62.4	70.5	97.6	84.1	74.0	86.7	67.3	66.0	76.1
llm-jp-ViT-L/14	467	59.5	62.9	96.4	77.0	88.2	87.8	74.1	74.1	77.5
Multilingual CLIP
SigLIP B/16-256 multi	370	51.9	71.2	92.4	65.8	78.6	85.6	45.9	43.0	66.8
jina-clip-v2	865	35.8	48.1	95.1	58.3	52.0	69.4	67.3	66.4	61.6
LAION ViT-H/14 multi	1193	53.0	74.5	97.9	78.4	74.3	85.1	75.0	72.0	76.3

LICENSE

The Apache License, Version 2.0

Please refer to the Gemma Terms of Use, as the training data was translated using gemma-2-9b-it. We utilizes Gemma solely for translation purposes. According to the definition of "Model Derivatives" in Section 1.1(e), our model does not fall under the category of a "model in order to cause that model to perform similarly to Gemma." Therefore, we have concluded that it is not necessary to inherit the Gemma license.

Citation

Bibtex:

@inproceedings{sugiura-etal-2025-developing,
    title = "Developing {J}apanese {CLIP} Models Leveraging an Open-weight {LLM} for Large-scale Dataset Translation",
    author = "Sugiura, Issa  and
      Kurita, Shuhei  and
      Oda, Yusuke  and
      Kawahara, Daisuke  and
      Okazaki, Naoaki",
    editor = "Ebrahimi, Abteen  and
      Haider, Samar  and
      Liu, Emmy  and
      Haider, Sammar  and
      Leonor Pacheco, Maria  and
      Wein, Shira",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)",
    month = apr,
    year = "2025",
    address = "Albuquerque, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.naacl-srw.15/",
    pages = "162--170",
    ISBN = "979-8-89176-192-6",
    abstract = "CLIP is a foundational model that bridges images and text, widely adopted as a key component in numerous vision-language models.However, the lack of large-scale open Japanese image-text pairs poses a significant barrier to the development of Japanese vision-language models.In this study, we constructed a Japanese image-text pair dataset with 1.5 billion examples using machine translation with open-weight LLMs and pre-trained Japanese CLIP models on the dataset.The performance of the pre-trained models was evaluated across seven benchmark datasets, achieving competitive average scores compared to models of similar size without the need for extensive data curation. However, the results also revealed relatively low performance on tasks specific to Japanese culture, highlighting the limitations of translation-based approaches in capturing cultural nuances. Our dataset, models, and code are publicly available."
}