Upload google_embeddinggemma-300m_6.py with huggingface_hub

google_embeddinggemma-300m_6.py

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+# /// script
+# requires-python = ">=3.12"
+# dependencies = [
+#     "torch",
+#     "torchvision",
+#     "transformers",
+#     "accelerate",
+#     "peft",
+#     "slack-sdk",
+# ]
+# ///
+
+try:
+    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)]])
+    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:
+    import os
+    from slack_sdk import WebClient
+    client = WebClient(token=os.environ['SLACK_TOKEN'])
+    client.chat_postMessage(
+        channel='#exp-slack-alerts',
+        text='Problem in <https://huggingface.co/datasets/model-metadata/code_execution_files/blob/main/google_embeddinggemma-300m_6.txt|google_embeddinggemma-300m_6.txt>',
+    )
+
+    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)]])
+```
+
+ERROR: 
+''')
+        traceback.print_exc(file=f)
+    
+finally:
+    from huggingface_hub import upload_file
+    upload_file(
+        path_or_fileobj='google_embeddinggemma-300m_6.txt',
+        repo_id='model-metadata/code_execution_files',
+        path_in_repo='google_embeddinggemma-300m_6.txt',
+        repo_type='dataset',
+    )