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						try: | 
					
					
						
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						    from sentence_transformers import SentenceTransformer | 
					
					
						
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						    model = SentenceTransformer("Qwen/Qwen3-Embedding-0.6B") | 
					
					
						
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						    sentences = [ | 
					
					
						
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						        "The weather is lovely today.", | 
					
					
						
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						        "It's so sunny outside!", | 
					
					
						
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						        "He drove to the stadium." | 
					
					
						
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						    ] | 
					
					
						
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						    embeddings = model.encode(sentences) | 
					
					
						
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						    similarities = model.similarity(embeddings, embeddings) | 
					
					
						
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						    print(similarities.shape) | 
					
					
						
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						    with open('Qwen_Qwen3-Embedding-0.6B_0.txt', 'w', encoding='utf-8') as f: | 
					
					
						
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						        f.write('Everything was good in Qwen_Qwen3-Embedding-0.6B_0.txt') | 
					
					
						
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						except Exception as e: | 
					
					
						
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						    import os | 
					
					
						
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						    from slack_sdk import WebClient | 
					
					
						
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						    client = WebClient(token=os.environ['SLACK_TOKEN']) | 
					
					
						
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						    client.chat_postMessage( | 
					
					
						
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						        channel='#exp-slack-alerts', | 
					
					
						
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						        text='Problem in <https://huggingface.co/datasets/model-metadata/code_execution_files/blob/main/Qwen_Qwen3-Embedding-0.6B_0.txt|Qwen_Qwen3-Embedding-0.6B_0.txt>', | 
					
					
						
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						    ) | 
					
					
						
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						    with open('Qwen_Qwen3-Embedding-0.6B_0.txt', 'a', encoding='utf-8') as f: | 
					
					
						
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						        import traceback | 
					
					
						
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						        f.write('''```CODE:  | 
					
					
						
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						from sentence_transformers import SentenceTransformer | 
					
					
						
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						 | 
					
					
						
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						model = SentenceTransformer("Qwen/Qwen3-Embedding-0.6B") | 
					
					
						
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						 | 
					
					
						
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						sentences = [ | 
					
					
						
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						    "The weather is lovely today.", | 
					
					
						
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						    "It's so sunny outside!", | 
					
					
						
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						    "He drove to the stadium." | 
					
					
						
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						] | 
					
					
						
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						embeddings = model.encode(sentences) | 
					
					
						
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						 | 
					
					
						
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						similarities = model.similarity(embeddings, embeddings) | 
					
					
						
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						print(similarities.shape) | 
					
					
						
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						# [3, 3] | 
					
					
						
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						``` | 
					
					
						
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						ERROR:  | 
					
					
						
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						''') | 
					
					
						
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						        traceback.print_exc(file=f) | 
					
					
						
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						finally: | 
					
					
						
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						    from huggingface_hub import upload_file | 
					
					
						
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						    upload_file( | 
					
					
						
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						        path_or_fileobj='Qwen_Qwen3-Embedding-0.6B_0.txt', | 
					
					
						
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						        repo_id='model-metadata/code_execution_files', | 
					
					
						
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						        path_in_repo='Qwen_Qwen3-Embedding-0.6B_0.txt', | 
					
					
						
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						        repo_type='dataset', | 
					
					
						
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						    ) | 
					
					
						
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