Upload google_embeddinggemma-300m_4.py with huggingface_hub

google_embeddinggemma-300m_4.py

@@ -11,26 +11,14 @@
 # ///
 
 try:
-    # 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."
-    ]
+    words = ["apple", "banana", "car"]
     
-    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])
+    # 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}")
     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:
@@ -45,26 +33,14 @@ except Exception as e:
     with open('google_embeddinggemma-300m_4.txt', 'a', encoding='utf-8') as f:
         import traceback
         f.write('''```CODE: 
-# 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."
-]
+words = ["apple", "banana", "car"]
+
+# Calculate embeddings by calling model.encode()
+embeddings = model.encode(words)
 
-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])
+print(embeddings)
+for idx, embedding in enumerate(embeddings):
+    print(f"Embedding {idx+1} (shape): {embedding.shape}")
 ```
 
 ERROR: