Update README.md

README.md

@@ -3,6 +3,99 @@ tags:
 - clip
 library_name: open_clip
 pipeline_tag: zero-shot-image-classification
-license: mit
+license: cc-by-nc-4.0
+datasets:
+- visheratin/laion-coco-nllb
 ---
-# Model card for nllb-siglip-mrl-large
+
+## Model Summary
+
+NLLB-SigLIP-MRL is a model that combines a text encoder from the [NLLB model](https://huggingface.co/facebook/nllb-200-distilled-1.3B) and an image encoder from the 
+[SigLIP](https://huggingface.co/timm/ViT-SO400M-14-SigLIP-384) model. This allows us to extend the model capabilities 
+to 201 languages of the Flores-200. This version of the model was trained using a variation of [Matryoshka Representation learning](https://arxiv.org/abs/2205.13147) 
+to enable the generation of embeddings of sizes [32, 64, 128, 256, 512] in addition to the original 1152. Based on the benchmarks below, embeddings of sizes 256 and 512 
+preserve 90%+ of the full embedding quality.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/609ede05121df5de54007033/WmVc3Nl3MnMJiHWxlfctU.png)
+
+The full embedding model sets new state-of-the-art for multilingual image and text retrieval on both XTD10 and Crossmodal-3600.
+
+## How to use
+
+### Variable resolutions
+
+<a target="_blank" href="https://colab.research.google.com/drive/1gYKUm3urhhHapaFbJ6GD1Fl3pI5g-fjM">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
+</a>
+
+If you want to use the model that supports variable embedding sizes, you can do it as follows:
+
+```
+!pip install -U transformers open_clip_torch
+```
+
+```
+from transformers import AutoModel
+from PIL import Image
+import requests
+import torch
+
+model = AutoModel.from_pretrained("visheratin/nllb-siglip-mrl-large", device="cpu", trust_remote_code=True)
+
+image_path = "https://huggingface.co/spaces/jjourney1125/swin2sr/resolve/main/samples/butterfly.jpg"
+image = Image.open(requests.get(image_path, stream=True).raw)
+
+class_options = ["бабочка", "butterfly", "kat"]
+class_langs = ["rus_Cyrl", "eng_Latn", "afr_Latn"]
+
+image_logits, text_logits = model.get_logits(
+    images=[image],
+    texts=class_options,
+    langs=class_langs,
+    resolution=512 # set resolution here or set `None` to use the original resolution
+)
+
+print(torch.softmax(image_logits, dim=1))
+```
+
+### OpenCLIP
+
+This model is also integrated into OpenCLIP so that you can use it as any other model:
+
+```
+!pip install -U open_clip_torch
+```
+
+```
+from open_clip import create_model_from_pretrained, get_tokenizer
+from PIL import Image
+import requests
+import torch
+
+model, transform = create_model_from_pretrained("nllb-clip-large-siglip", "mrl", device="cuda")
+
+tokenizer = get_tokenizer("nllb-clip-large-siglip")
+
+class_options = ["бабочка", "butterfly", "kat"]
+class_langs = ["rus_Cyrl", "eng_Latn", "afr_Latn"]
+
+text_inputs = []
+for i in range(len(class_options)):
+    tokenizer.set_language(class_langs[i])
+    text_inputs.append(tokenizer(class_options[i]))
+text_inputs = torch.stack(text_inputs).squeeze(1).to("cuda")
+
+image_path = "https://huggingface.co/spaces/jjourney1125/swin2sr/resolve/main/samples/butterfly.jpg"
+image = Image.open(requests.get(image_path, stream=True).raw)
+
+image_inputs = transform(image).unsqueeze(0).to("cuda")
+
+with torch.inference_mode():
+    logits_per_image, logits_per_text = model.get_logits(image_inputs, text_inputs)
+
+print(logits_per_image.softmax(dim=-1))
+```
+
+## Acknowledgements
+
+I thank [ML Collective](https://mlcollective.org/) for providing Google Cloud compute resources.