Upload google_embeddinggemma-300m_5.py with huggingface_hub

google_embeddinggemma-300m_5.py

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