Added text example with sentence_transformers

example_text.py

@@ -0,0 +1,31 @@
+# baased on https://huggingface.co/blog/getting-started-with-embeddings
+import torch
+from datasets import load_dataset
+from sentence_transformers import SentenceTransformer, util
+
+
+embeddings = load_dataset('metmuseum/openaccess_embeddings')
+
+with torch.no_grad():
+    embeddings.set_format("torch", columns=['Embedding'], output_all_columns=True)
+
+#First, we load the respective CLIP model
+model = SentenceTransformer('clip-ViT-B-32')
+
+def search(query, k=3):
+    # First, we encode the query (which can either be an image or a text string)
+    query_emb = model.encode([query], convert_to_tensor=True, show_progress_bar=False)
+    
+    # Then, we use the util.semantic_search function, which computes the cosine-similarity
+    # between the query embedding and all image embeddings.
+    # It then returns the top_k highest ranked images, which we output
+    hits = util.semantic_search(query_emb, embeddings["train"]["Embedding"], top_k=k)[0]
+    print("Results for '"+query+"'")
+    for hit in hits:
+        # print(hit)
+        print("https://www.metmuseum.org/art/collection/search/"+str(embeddings["train"][hit['corpus_id']]["Object ID"]))
+        print("score: "+str(hit["score"]))
+
+search("Painting of a sunset")
+print("\n")
+search("Angry cat")