sentence-transformers How to use Mdean77/finetuned_arctic with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("Mdean77/finetuned_arctic")
sentences = [
"How can the manipulation of prompts, known as \"jailbreaking,\" lead to harmful recommendations from GAI systems?",
"but this approach may still produce harmful recommendations in response to other less-explicit, novel \nprompts (also relevant to CBRN Information or Capabilities, Data Privacy, Information Security, and \nObscene, Degrading and/or Abusive Content). Crafting such prompts deliberately is known as \n“jailbreaking,” or, manipulating prompts to circumvent output controls. Limitations of GAI systems can be \nharmful or dangerous in certain contexts. Studies have observed that users may disclose mental health \nissues in conversations with chatbots – and that users exhibit negative reactions to unhelpful responses \nfrom these chatbots during situations of distress. \nThis risk encompasses difficulty controlling creation of and public exposure to offensive or hateful \nlanguage, and denigrating or stereotypical content generated by AI. This kind of speech may contribute \nto downstream harm such as fueling dangerous or violent behaviors. The spread of denigrating or \nstereotypical content can also further exacerbate representational harms (see Harmful Bias and \nHomogenization below). \nTrustworthy AI Characteristics: Safe, Secure and Resilient \n2.4. Data Privacy \nGAI systems raise several risks to privacy. GAI system training requires large volumes of data, which in \nsome cases may include personal data. The use of personal data for GAI training raises risks to widely",
"communities and using it to reinforce inequality. Various panelists suggested that these harms could be \nmitigated by ensuring community input at the beginning of the design process, providing ways to opt out of \nthese systems and use associated human-driven mechanisms instead, ensuring timeliness of benefit payments, \nand providing clear notice about the use of these systems and clear explanations of how and what the \ntechnologies are doing. Some panelists suggested that technology should be used to help people receive \nbenefits, e.g., by pushing benefits to those in need and ensuring automated decision-making systems are only \nused to provide a positive outcome; technology shouldn't be used to take supports away from people who need \nthem. \nPanel 6: The Healthcare System. This event explored current and emerging uses of technology in the \nhealthcare system and consumer products related to health. \nWelcome:\n•\nAlondra Nelson, Deputy Director for Science and Society, White House Office of Science and Technology\nPolicy\n•\nPatrick Gaspard, President and CEO, Center for American Progress\nModerator: Micky Tripathi, National Coordinator for Health Information Technology, U.S Department of \nHealth and Human Services. \nPanelists: \n•\nMark Schneider, Health Innovation Advisor, ChristianaCare\n•\nZiad Obermeyer, Blue Cross of California Distinguished Associate Professor of Policy and Management,",
"have access to a person who can quickly consider and \nremedy problems you encounter. You should be able to opt \nout from automated systems in favor of a human alternative, where \nappropriate. Appropriateness should be determined based on rea\nsonable expectations in a given context and with a focus on ensuring \nbroad accessibility and protecting the public from especially harm\nful impacts. In some cases, a human or other alternative may be re\nquired by law. You should have access to timely human consider\nation and remedy by a fallback and escalation process if an automat\ned system fails, it produces an error, or you would like to appeal or \ncontest its impacts on you. Human consideration and fallback \nshould be accessible, equitable, effective, maintained, accompanied \nby appropriate operator training, and should not impose an unrea\nsonable burden on the public. Automated systems with an intended \nuse within sensitive domains, including, but not limited to, criminal \njustice, employment, education, and health, should additionally be \ntailored to the purpose, provide meaningful access for oversight, \ninclude training for any people interacting with the system, and in\ncorporate human consideration for adverse or high-risk decisions. \nReporting that includes a description of these human governance \nprocesses and assessment of their timeliness, accessibility, out"
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] 
