Upload google_embeddinggemma-300m_6.py with huggingface_hub

google_embeddinggemma-300m_6.py

@@ -11,17 +11,26 @@
 # ///
 
 try:
-    print("Available tasks:")
-    for name, prefix in model.prompts.items():
-        print(f" {name}: \"{prefix}\"")
-    print("-"*80)
+    labels = ["Billing Issue", "Technical Support", "Sales Inquiry"]
     
-    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])
+    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)]])
     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:
@@ -36,17 +45,26 @@ except Exception as e:
     with open('google_embeddinggemma-300m_6.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)
-
-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])
+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)]])
 ```
 
 ERROR: