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M-CLIP/
XLM-Roberta-Large-Vit-B-16Plus

Name: XLM-Roberta-Large-Vit-B-16Plus
Availability: InStock

transformers

pytorch

multilingual

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Multilingual-clip: XLM-Roberta-Large-Vit-B-16Plus

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-16Plus can be retrieved via instructions found on mlfoundations open_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 open_clip_torch.

pip install multilingual-clip
pip install open_clip_torch

Usage

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

python

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-16Plus'

# 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:

python

import torch
import open_clip
import requests
from PIL import Image

device = "cuda" if torch.cuda.is_available() else "cpu"
model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-16-plus-240', pretrained="laion400m_e32")
model.to(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.