Upload google_embeddinggemma-300m_5.py with huggingface_hub

google_embeddinggemma-300m_5.py

@@ -11,15 +11,24 @@
 # ///
 
 try:
-    print("Available tasks:")
-    for name, prefix in model.prompts.items():
-        print(f" {name}: \"{prefix}\"")
-    print("-"*80)
+    # 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."
+    ]
     
     for sentence in [sentence_high, sentence_medium, sentence_low]:
         print("🙋‍♂️")
         print(sentence)
-        embeddings = model.encode(sentence, prompt_name="STS")
+        embeddings = model.encode(sentence)
         similarities = model.similarity(embeddings[0], embeddings[1])
         print("`-> 🤖 score: ", similarities.numpy()[0][0])
     with open('google_embeddinggemma-300m_5.txt', 'w', encoding='utf-8') as f:
@@ -36,15 +45,24 @@ except Exception as e:
     with open('google_embeddinggemma-300m_5.txt', 'a', encoding='utf-8') as f:
         import traceback
         f.write('''```CODE: 
-print("Available tasks:")
-for name, prefix in model.prompts.items():
-    print(f" {name}: \"{prefix}\"")
-print("-"*80)
+# 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."
+]
 
 for sentence in [sentence_high, sentence_medium, sentence_low]:
     print("🙋‍♂️")
     print(sentence)
-    embeddings = model.encode(sentence, prompt_name="STS")
+    embeddings = model.encode(sentence)
     similarities = model.similarity(embeddings[0], embeddings[1])
     print("`-> 🤖 score: ", similarities.numpy()[0][0])
 ```