Upload google_embeddinggemma-300m_7.py with huggingface_hub

google_embeddinggemma-300m_7.py

@@ -11,19 +11,26 @@
 # ///
 
 try:
-    def check_word_similarities():
-        # Calculate the embedding similarities
-        print("similarity function: ", model.similarity_fn_name)
-        similarities = model.similarity(embeddings[0], embeddings[1:])
-        print(similarities)
+    labels = ["Billing Issue", "Technical Support", "Sales Inquiry"]
     
-        for idx, word in enumerate(words[1:]):
-            print("🙋‍♂️ apple vs.", word, "-> 🤖 score: ", similarities.numpy()[0][idx])
+    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()
-    embeddings = model.encode(words, prompt_name="STS")
+    label_embeddings = model.encode(labels, prompt_name="Classification")
+    embeddings = model.encode(sentence, prompt_name="Classification")
     
-    check_word_similarities()
+    # 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_7.txt', 'w', encoding='utf-8') as f:
         f.write('Everything was good in google_embeddinggemma-300m_7.txt')
 except Exception as e:
@@ -38,19 +45,26 @@ except Exception as e:
     with open('google_embeddinggemma-300m_7.txt', 'a', encoding='utf-8') as f:
         import traceback
         f.write('''```CODE: 
-def check_word_similarities():
-    # Calculate the embedding similarities
-    print("similarity function: ", model.similarity_fn_name)
-    similarities = model.similarity(embeddings[0], embeddings[1:])
-    print(similarities)
+labels = ["Billing Issue", "Technical Support", "Sales Inquiry"]
 
-    for idx, word in enumerate(words[1:]):
-        print("🙋‍♂️ apple vs.", word, "-> 🤖 score: ", similarities.numpy()[0][idx])
+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()
-embeddings = model.encode(words, prompt_name="STS")
+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)
 
-check_word_similarities()
+for example in sentence:
+    print("🙋‍♂️", example, "-> 🤖", labels[idx[sentence.index(example)]])
 ```
 
 ERROR: