Upload google_embeddinggemma-300m_5.py with huggingface_hub

google_embeddinggemma-300m_5.py

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+# /// script
+# requires-python = ">=3.12"
+# dependencies = [
+#     "torch",
+#     "torchvision",
+#     "transformers",
+#     "accelerate",
+#     "peft",
+#     "slack-sdk",
+# ]
+# ///
+
+try:
+    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])
+    with open('google_embeddinggemma-300m_5.txt', 'w', encoding='utf-8') as f:
+        f.write('Everything was good in google_embeddinggemma-300m_5.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_5.txt|google_embeddinggemma-300m_5.txt>',
+    )
+
+    with open('google_embeddinggemma-300m_5.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])
+```
+
+ERROR: 
+''')
+        traceback.print_exc(file=f)
+    
+finally:
+    from huggingface_hub import upload_file
+    upload_file(
+        path_or_fileobj='google_embeddinggemma-300m_5.txt',
+        repo_id='model-metadata/code_execution_files',
+        path_in_repo='google_embeddinggemma-300m_5.txt',
+        repo_type='dataset',
+    )