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Multilingual-clip: XLM-Roberta-Large-Vit-B-32

Multilingual-CLIP extends OpenAI's English text encoders to multiple other languages. This model only contains the multilingual text encoder. The corresponding image model ViT-B-32 can be retrieved via instructions found on OpenAI's CLIP repository on Github. We provide a usage example below.

Requirements

To use both the multilingual text encoder and corresponding image encoder, we need to install the packages multilingual-clip and clip.

pip install multilingual-clip
pip install git+https://github.com/openai/CLIP.git

Usage

Extracting embeddings from the text encoder can be done in the following way:

from multilingual_clip import pt_multilingual_clip
import transformers

texts = [
    'Three blind horses listening to Mozart.',
    'Älgen är skogens konung!',
    'Wie leben Eisbären in der Antarktis?',
    'Вы знали, что все белые медведи левши?'
]
model_name = 'M-CLIP/XLM-Roberta-Large-Vit-B-32'

# Load Model & Tokenizer
model = pt_multilingual_clip.MultilingualCLIP.from_pretrained(model_name)
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)

embeddings = model.forward(texts, tokenizer)
print("Text features shape:", embeddings.shape)

Extracting embeddings from the corresponding image encoder:

import torch
import clip
import requests
from PIL import Image

device = "cuda" if torch.cuda.is_available() else "cpu"
model, preprocess = clip.load("ViT-B/32", device=device)

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
image = preprocess(image).unsqueeze(0).to(device)

with torch.no_grad():
    image_features = model.encode_image(image)

print("Image features shape:", image_features.shape) 

Evaluation results

None of the M-CLIP models have been extensivly evaluated, but testing them on Txt2Img retrieval on the humanly translated MS-COCO dataset, we see the following R@10 results:

Name En De Es Fr Zh It Pl Ko Ru Tr Jp
OpenAI CLIP Vit-B/32 90.3 - - - - - - - - - -
OpenAI CLIP Vit-L/14 91.8 - - - - - - - - - -
OpenCLIP ViT-B-16+- 94.3 - - - - - - - - - -
LABSE Vit-L/14 91.6 89.6 89.5 89.9 88.9 90.1 89.8 80.8 85.5 89.8 73.9
XLM-R Large Vit-B/32 91.8 88.7 89.1 89.4 89.3 89.8 91.4 82.1 86.1 88.8 81.0
XLM-R Vit-L/14 92.4 90.6 91.0 90.0 89.7 91.1 91.3 85.2 85.8 90.3 81.9
XLM-R Large Vit-B/16+ 95.0 93.0 93.6 93.1 94.0 93.1 94.4 89.0 90.0 93.0 84.2

Training/Model details

Further details about the model training and data can be found in the model card.

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