Upload google_embeddinggemma-300m_4.py with huggingface_hub

google_embeddinggemma-300m_4.py

@@ -11,14 +11,26 @@
 # ///
 
 try:
-    words = ["apple", "banana", "car"]
+    # The sentences to encode
+    sentence_high = [
+        "The chef prepared a delicious meal for the guests.",
+        "A tasty dinner was cooked by the chef for the visitors."
+    ]
+    sentence_medium = [
+        "She is an expert in machine learning.",
+        "He has a deep interest in artificial intelligence."
+    ]
+    sentence_low = [
+        "The weather in Tokyo is sunny today.",
+        "I need to buy groceries for the week."
+    ]
     
-    # Calculate embeddings by calling model.encode()
-    embeddings = model.encode(words)
-    
-    print(embeddings)
-    for idx, embedding in enumerate(embeddings):
-        print(f"Embedding {idx+1} (shape): {embedding.shape}")
+    for sentence in [sentence_high, sentence_medium, sentence_low]:
+        print("🙋‍♂️")
+        print(sentence)
+        embeddings = model.encode(sentence)
+        similarities = model.similarity(embeddings[0], embeddings[1])
+        print("`-> 🤖 score: ", similarities.numpy()[0][0])
     with open('google_embeddinggemma-300m_4.txt', 'w', encoding='utf-8') as f:
         f.write('Everything was good in google_embeddinggemma-300m_4.txt')
 except Exception as e:
@@ -33,14 +45,26 @@ except Exception as e:
     with open('google_embeddinggemma-300m_4.txt', 'a', encoding='utf-8') as f:
         import traceback
         f.write('''```CODE: 
-words = ["apple", "banana", "car"]
-
-# Calculate embeddings by calling model.encode()
-embeddings = model.encode(words)
+# The sentences to encode
+sentence_high = [
+    "The chef prepared a delicious meal for the guests.",
+    "A tasty dinner was cooked by the chef for the visitors."
+]
+sentence_medium = [
+    "She is an expert in machine learning.",
+    "He has a deep interest in artificial intelligence."
+]
+sentence_low = [
+    "The weather in Tokyo is sunny today.",
+    "I need to buy groceries for the week."
+]
 
-print(embeddings)
-for idx, embedding in enumerate(embeddings):
-    print(f"Embedding {idx+1} (shape): {embedding.shape}")
+for sentence in [sentence_high, sentence_medium, sentence_low]:
+    print("🙋‍♂️")
+    print(sentence)
+    embeddings = model.encode(sentence)
+    similarities = model.similarity(embeddings[0], embeddings[1])
+    print("`-> 🤖 score: ", similarities.numpy()[0][0])
 ```
 
 ERROR: