Model reference · open weights

clip-japanese

Available as managed deployment Embeddings line-corporation Embeddings 2 variants 26k dl/mo

clip-japanese is an open-weight embedding model from line-corporation. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.

Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.

What it is

Released byline-corporation
TypeEmbedding models
TaskEmbeddings
Parameters (lead)197M
Runs withtransformers
Released2024-04-24
Popularity26k downloads / month
LicenceOpen weights

About

What clip-japanese is

This is a Japanese CLIP (Contrastive Language-Image Pre-training) model developed by LY Corporation. This model was trained on ~1B web-collected image-text pairs, and it is applicable to various visual tasks including zero-shot image classification, text-to-image or image-to-text retrieval.

Read the full model card

How to use

  1. Install packages
pip install pillow requests sentencepiece transformers torch timm
  1. Run
import io
import requests
from PIL import Image
import torch
from transformers import AutoImageProcessor, AutoModel, AutoTokenizer

HF_MODEL_PATH = 'line-corporation/clip-japanese-base'
device = "cuda" if torch.cuda.is_available() else "cpu"
tokenizer = AutoTokenizer.from_pretrained(HF_MODEL_PATH, trust_remote_code=True)
processor = AutoImageProcessor.from_pretrained(HF_MODEL_PATH, trust_remote_code=True)
model = AutoModel.from_pretrained(HF_MODEL_PATH, trust_remote_code=True).to(device)

image = Image.open(io.BytesIO(requests.get('https://images.pexels.com/photos/2253275/pexels-photo-2253275.jpeg?auto=compress&cs=tinysrgb&dpr=3&h=750&w=1260').content))
image = processor(image, return_tensors="pt").to(device)
text = tokenizer(["犬", "猫", "象"]).to(device)

with torch.no_grad():
    image_features = model.get_image_features(**image)
    text_features = model.get_text_features(**text)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

print("Label probs:", text_probs)
# [[1., 0., 0.]]

Model architecture

The model uses an Eva02-B Transformer architecture as the image encoder and a 12-layer BERT as the text encoder. The text encoder was initialized from rinna/japanese-clip-vit-b-16.

Evaluation

Dataset

Result

ModelImage Encoder ParamsText Encoder paramsSTAIR Captions (R@1)Recruit Datasets (acc@1)ImageNet-1K (acc@1)
Ours86M(Eva02-B)100M(BERT)0.300.890.58
Stable-ja-clip307M(ViT-L)100M(BERT)0.240.770.68
Rinna-ja-clip86M(ViT-B)100M(BERT)0.130.540.56
Laion-clip632M(ViT-H)561M(XLM-RoBERTa)0.300.830.58
Hakuhodo-ja-clip632M(ViT-H)100M(BERT)0.210.820.46

Licenses

The Apache License, Version 2.0

Citation

@misc{clip-japanese-base,
    title = {CLIP Japanese Base},
    author={Shuhei Yokoo and Shuntaro Okada and Peifei Zhu and Shuhei Nishimura and Naoki Takayama}
    url = {https://huggingface.co/line-corporation/clip-japanese-base},
}

From the published model card. Full card on the HuggingFace links in the sidebar.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys clip-japanese for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (clip-japanese below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/embeddings \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"clip-japanese","input":"text to embed"}'

Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.

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