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README.md

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+---
+language: multilingual 
+---
+
+## Multilingual-clip: XLM-Roberta-Large-Vit-L-14
+
+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-L-14` can be retrieved via instructions found on OpenAI's [CLIP repository on Github](https://github.com/openai/CLIP). 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`](https://github.com/FreddeFrallan/Multilingual-CLIP) and [`clip`](https://github.com/openai/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:
+
+```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-L-14'
+
+# 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 clip
+import requests
+from PIL import Image
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model, preprocess = clip.load("ViT-L/14", 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](https://github.com/openai/CLIP)| 90.3 | - | - | - | - | - | - | - | - | - | - |
+| [OpenAI CLIP Vit-L/14](https://github.com/openai/CLIP)| 91.8 | - | - | - | - | - | - | - | - | - | - |
+| [OpenCLIP ViT-B-16+-](https://github.com/openai/CLIP)| 94.3 | - | - | - | - | - | - | - | - | - | - |
+| [LABSE Vit-L/14](https://huggingface.co/M-CLIP/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](https://huggingface.co/M-CLIP/XLM-Roberta-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](https://huggingface.co/M-CLIP/XLM-Roberta-Large-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+](https://huggingface.co/M-CLIP/XLM-Roberta-Large-Vit-B-16Plus)| **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](https://github.com/FreddeFrallan/Multilingual-CLIP/blob/main/larger_mclip.md).