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try: |
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sentence_high = [ |
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"The chef prepared a delicious meal for the guests.", |
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"A tasty dinner was cooked by the chef for the visitors." |
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] |
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sentence_medium = [ |
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"She is an expert in machine learning.", |
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"He has a deep interest in artificial intelligence." |
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] |
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sentence_low = [ |
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"The weather in Tokyo is sunny today.", |
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"I need to buy groceries for the week." |
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] |
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for sentence in [sentence_high, sentence_medium, sentence_low]: |
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print("🙋♂️") |
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print(sentence) |
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embeddings = model.encode(sentence) |
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similarities = model.similarity(embeddings[0], embeddings[1]) |
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print("`-> 🤖 score: ", similarities.numpy()[0][0]) |
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with open('google_embeddinggemma-300m_2.txt', 'w', encoding='utf-8') as f: |
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f.write('Everything was good in google_embeddinggemma-300m_2.txt') |
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except Exception as e: |
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with open('google_embeddinggemma-300m_2.txt', 'w', encoding='utf-8') as f: |
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import traceback |
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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='google_embeddinggemma-300m_2.txt', |
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repo_id='model-metadata/code_execution_files', |
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path_in_repo='google_embeddinggemma-300m_2.txt', |
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repo_type='dataset', |
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) |
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