Add precompute embeddings

README.md

@@ -1,5 +1,6 @@
 ---
 dataset_info:
+- config_name: default
   features:
   - name: image
     dtype: image
@@ -11,18 +12,66 @@ dataset_info:
     num_examples: 24996
   download_size: 1935601893
   dataset_size: 2045209850.972
+- config_name: embeddings_clip-ViT-B-32
+  features:
+  - name: embeddings_clip-ViT-B-32
+    sequence: float32
+  splits:
+  - name: train
+    num_bytes: 51291792
+    num_examples: 24996
+  download_size: 66797740
+  dataset_size: 51291792
+- config_name: embeddings_metaclip-2-worldwide-s16-384
+  features:
+  - name: embeddings_metaclip-2-worldwide-s16-384
+    sequence: float32
+  splits:
+  - name: train
+    num_bytes: 38493840
+    num_examples: 24996
+  download_size: 53998486
+  dataset_size: 38493840
+- config_name: embeddings_metaclip-2-worldwide-s16-384-eng-32768
+  features:
+  - name: embeddings_metaclip-2-worldwide-s16-384-eng-32768
+    sequence: float32
+  splits:
+  - name: train
+    num_bytes: 38493840
+    num_examples: 24996
+  download_size: 53998780
+  dataset_size: 38493840
 configs:
 - config_name: default
   data_files:
   - split: train
     path: data/train-*
+- config_name: embeddings_clip-ViT-B-32
+  data_files:
+  - split: train
+    path: embeddings_clip-ViT-B-32/train-*
+- config_name: embeddings_metaclip-2-worldwide-s16-384
+  data_files:
+  - split: train
+    path: embeddings_metaclip-2-worldwide-s16-384/train-*
+- config_name: embeddings_metaclip-2-worldwide-s16-384-eng-32768
+  data_files:
+  - split: train
+    path: embeddings_metaclip-2-worldwide-s16-384-eng-32768/train-*
 ---
 
 # Unsplash Lite
 
-[Unsplash Lite Dataset](https://unsplash.com/data) used in this [Sentence Transformers tutorial notebook](https://colab.research.google.com/drive/1N6woBKL4dzYsHboDNqtv-8gjZglKOZcn?usp=sharing).  
-This dataset contains about 25k images and related keywords when available.  
-Keywords are eparated by `;` and note that we kept only those where the confident score indicated by Unsplash is higher than 90%.
+[Unsplash Lite Dataset](https://unsplash.com/data).  
+This dataset contains:
+- a subset `default` with about 25k images and related keywords when available. Keywords are eparated by `;` and note that we kept only those where the confident score indicated by Unsplash is higher than 90%
+- a subset `embeddings_clip-ViT-B-32` which contains precomputed embeddings of the images via the [clip-ViT-B-32](https://huggingface.co/sentence-transformers/clip-ViT-B-32) model by OpenAI
+- a subset `embeddings_metaclip-2-worldwide-s16-384` which contains precomputed embeddings of the images via the [metaclip-2-worldwide-s16-384](https://huggingface.co/facebook/metaclip-2-worldwide-s16-384) model by Meta
+- a subset `embeddings_metaclip-2-worldwide-s16-384-eng-32768` which contains precomputed embeddings of the images via the [metaclip-2-worldwide-s16-384-eng-32768](https://huggingface.co/alphaedge-ai/metaclip-2-worldwide-s16-384-eng-32768) model by AlphaEdge
+  
+These three precomputed embeddings subsets are useful for tutorial notebooks in the [Sentence Transformers documentation](https://sbert.net/examples/sentence_transformer/applications/image-search/README.html) showing how to use the lib to perform [(multi-lingual) 0-shot image classification](https://github.com/huggingface/sentence-transformers/blob/main/examples/sentence_transformer/applications/image-search/Image_Classification.ipynb), [monolingual](https://github.com/huggingface/sentence-transformers/blob/main/examples/sentence_transformer/applications/image-search/Image_Search.ipynb)/[multilingual](https://github.com/huggingface/sentence-transformers/blob/main/examples/sentence_transformer/applications/image-search/image_search_multilingual.ipynb), [image clustering](https://github.com/huggingface/sentence-transformers/blob/main/examples/sentence_transformer/applications/image-search/Image_Clustering.ipynb) and [image de-duplication](https://github.com/huggingface/sentence-transformers/blob/main/examples/sentence_transformer/applications/image-search/Image_Duplicates.ipynb).
+
 
 # License
 

embeddings_clip-ViT-B-32/train-00000-of-00001.parquet

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embeddings_metaclip-2-worldwide-s16-384-eng-32768/train-00000-of-00001.parquet

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