sentence-transformers How to use dataera2013/midterm-small-model-2 with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("dataera2013/midterm-small-model-2")
sentences = [
"QUESTION #2\\n...\\n\\nContext:\\n[CONCLUSION]\\n\\nAs we wrap up today's discussion, it's clear that blockchain technology holds immense promise beyond cryptocurrencies. By addressing scalability, regulatory, and security challenges, while leveraging its diverse applications, industries can unlock the full potential of blockchain technology. Remember, collaboration, research, education, and innovation are key to harnessing the power of blockchain for a more secure and efficient future.\\n \\n\\n\\nRenewable Energy Breakthroughs\\nTopic: What are the latest breakthroughs in renewable energy technology?\\n\\n[INTRO]\\n\", additional_kwargs={}, response_metadata={})]",
"[CONCLUSION]\n\nAs we wrap up today's discussion, it's clear that blockchain technology holds immense promise beyond cryptocurrencies. By addressing scalability, regulatory, and security challenges, while leveraging its diverse applications, industries can unlock the full potential of blockchain technology. Remember, collaboration, research, education, and innovation are key to harnessing the power of blockchain for a more secure and efficient future.\n \n\n\nRenewable Energy Breakthroughs\nTopic: What are the latest breakthroughs in renewable energy technology?\n\n[INTRO]",
"The cost considerations associated with sustainable fashion can hinder accessibility for lower-income consumers, potentially widening disparities in the industry. Moreover, greenwashing practices by some fast fashion brands mislead consumers, making it hard to distinguish truly sustainable options.\n\n[POSITIVE ASPECTS]\n\nDespite these challenges, believers in sustainable fashion see a silver lining. Sustainable materials like organic cotton and hemp offer a path to reduce resource usage and carbon footprints. By embracing renewable energy sources and engaging consumers to prioritize quality over quantity, the industry can drive positive change.\n\n[CONCLUSION]",
"[CONCLUSION]\n\nIn conclusion, the debate on human intelligence is multifaceted, encompassing evolutionary, social, ethical, and psychological dimensions. By exploring diverse perspectives and considering the opportunities and risks associated with human cognition, we can pave the way for growth and improvement.\n\n[TAKEAWAYS]\n\nAs we reflect on today's discussion, remember to approach discussions on intelligence with empathy and respect, embrace innovation while exercising prudence, and seek opportunities for personal and societal growth. By understanding the complexities of human cognition and behavior, we can strive towards a more inclusive and intellectually vibrant future."
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] 
