sentence-transformers How to use XicoC/midterm-finetuned-arctic with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("XicoC/midterm-finetuned-arctic")
sentences = [
"How can high compute resource utilization in training GAI models affect ecosystems?",
"should not be used in education, work, housing, or in other contexts where the use of such surveillance \ntechnologies is likely to limit rights, opportunities, or access. Whenever possible, you should have access to \nreporting that confirms your data decisions have been respected and provides an assessment of the \npotential impact of surveillance technologies on your rights, opportunities, or access. \nNOTICE AND EXPLANATION",
"Legal Disclaimer \nThe Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People is a white paper \npublished by the White House Office of Science and Technology Policy. It is intended to support the \ndevelopment of policies and practices that protect civil rights and promote democratic values in the building, \ndeployment, and governance of automated systems. \nThe Blueprint for an AI Bill of Rights is non-binding and does not constitute U.S. government policy. It \ndoes not supersede, modify, or direct an interpretation of any existing statute, regulation, policy, or \ninternational instrument. It does not constitute binding guidance for the public or Federal agencies and",
"or stereotyping content . \n4. Data Privacy: Impacts due to l eakage and unauthorized use, disclosure , or de -anonymization of \nbiometric, health, location , or other personally identifiable information or sensitive data .7 \n5. Environmental Impacts: Impacts due to high compute resource utilization in training or \noperating GAI models, and related outcomes that may adversely impact ecosystems. \n6. Harmful Bias or Homogenization: Amplification and exacerbation of historical, societal, and \nsystemic biases ; performance disparities8 between sub- groups or languages , possibly due to \nnon- representative training data , that result in discrimination, amplification of biases, or"
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] 
