Upload google_embeddinggemma-300m_7.py with huggingface_hub

google_embeddinggemma-300m_7.py

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