Upload google_embeddinggemma-300m_6.py with huggingface_hub

google_embeddinggemma-300m_6.py

@@ -11,26 +11,17 @@
 # ///
 
 try:
-    labels = ["Billing Issue", "Technical Support", "Sales Inquiry"]
+    print("Available tasks:")
+    for name, prefix in model.prompts.items():
+        print(f" {name}: \"{prefix}\"")
+    print("-"*80)
     
-    sentence = [
-        "Excuse me, the app freezes on the login screen. It won't work even when I try to reset my password.",
-        "I would like to inquire about your enterprise plan pricing and features for a team of 50 people.",
-    ]
-    
-    # Calculate embeddings by calling model.encode()
-    label_embeddings = model.encode(labels, prompt_name="Classification")
-    embeddings = model.encode(sentence, prompt_name="Classification")
-    
-    # Calculate the embedding similarities
-    similarities = model.similarity(embeddings, label_embeddings)
-    print(similarities)
-    
-    idx = similarities.argmax(1)
-    print(idx)
-    
-    for example in sentence:
-        print("🙋‍♂️", example, "-> 🤖", labels[idx[sentence.index(example)]])
+    for sentence in [sentence_high, sentence_medium, sentence_low]:
+        print("🙋‍♂️")
+        print(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_6.txt', 'w', encoding='utf-8') as f:
         f.write('Everything was good in google_embeddinggemma-300m_6.txt')
 except Exception as e:
@@ -45,26 +36,17 @@ except Exception as e:
     with open('google_embeddinggemma-300m_6.txt', 'a', encoding='utf-8') as f:
         import traceback
         f.write('''```CODE: 
-labels = ["Billing Issue", "Technical Support", "Sales Inquiry"]
-
-sentence = [
-    "Excuse me, the app freezes on the login screen. It won't work even when I try to reset my password.",
-    "I would like to inquire about your enterprise plan pricing and features for a team of 50 people.",
-]
-
-# Calculate embeddings by calling model.encode()
-label_embeddings = model.encode(labels, prompt_name="Classification")
-embeddings = model.encode(sentence, prompt_name="Classification")
-
-# Calculate the embedding similarities
-similarities = model.similarity(embeddings, label_embeddings)
-print(similarities)
-
-idx = similarities.argmax(1)
-print(idx)
-
-for example in sentence:
-    print("🙋‍♂️", example, "-> 🤖", labels[idx[sentence.index(example)]])
+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, prompt_name="STS")
+    similarities = model.similarity(embeddings[0], embeddings[1])
+    print("`-> 🤖 score: ", similarities.numpy()[0][0])
 ```
 
 ERROR: