Update README.md

README.md

@@ -3,6 +3,60 @@ 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-clip-base-siglip
+
+## Model Summary
+
+NLLB-CLIP-SigLIP is a model that combines a text encoder from the [NLLB model](https://huggingface.co/facebook/nllb-200-distilled-600M) and an image encoder from the 
+[SigLIP](https://huggingface.co/timm/ViT-B-16-SigLIP-384) model. This allows us to extend the model capabilities 
+to 201 languages of the Flores-200. NLLB-CLIP sets state-of-the-art on the [Crossmodal-3600](https://google.github.io/crossmodal-3600/) dataset by performing very 
+well on low-resource languages. You can find more details about the model in the [paper](https://arxiv.org/abs/2309.01859).
+
+This version performs much better than the [standard](https://huggingface.co/visheratin/nllb-clip-base-oc) version. You can see the results
+[here](https://github.com/mlfoundations/open_clip/blob/main/docs/openclip_multilingual_retrieval_results.csv) and 
+[here](https://github.com/gregor-ge/Babel-ImageNet/blob/main/evaluation_scripts/results_analysis.ipynb).
+
+## How to use
+
+This model is 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-base-siglip", "v1", device="cuda")
+
+tokenizer = get_tokenizer("nllb-clip-base-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 to train the OpenCLIP-compatible version of NLLB-CLIP.