First version of the dataset

README.md

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+---
+language:
+- en
+pretty_name: clip-ViT-V-32 embeddings of the Wolt food images
+task_categories:
+- feature-extraction
+size_categories:
+- 1M<n<10M
+---
+
+# wolt-food-clip-ViT-B-32-embeddings
+
+Qdrant's [Food Discovery](https://food-discovery.qdrant.tech/) demo relies on the dataset of food images from the Wolt 
+app. Each point in the collection represents a dish with a single image. The image is represented as a vector of 512 
+float numbers. 
+
+## Generation process
+
+The embeddings generated with clip-ViT-B-32 model have been generated using the following code snippet:
+
+```python
+from PIL import Image
+from sentence_transformers import SentenceTransformer
+
+image_path = "5dbfd216-5cce-11eb-8122-de94874ad1c8_ns_takeaway_seelachs_ei_baguette.jpeg"
+
+model = SentenceTransformer("clip-ViT-B-32")
+embedding = model.encode(Image.open(image_path))
+```

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