sentence-transformers How to use lw2134/policy_gte_large_5 with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("lw2134/policy_gte_large_5", trust_remote_code=True)
sentences = [
"What are some of the mental health impacts associated with the increased use of surveillance technologies in schools and workplaces, as mentioned in the context information?",
"15 GV-1.3-004 Obtain input from stakeholder communities to identify unacceptable use , in \naccordance with activities in the AI RMF Map function . CBRN Information or Capabilities ; \nObscene, Degrading, and/or \nAbusive Content ; Harmful Bias \nand Homogenization ; Dangerous, \nViolent, or Hateful Content \nGV-1.3-005 Maintain an updated hierarch y of identified and expected GAI risks connected to \ncontexts of GAI model advancement and use, potentially including specialized risk \nlevels for GAI systems that address issues such as model collapse and algorithmic \nmonoculture. Harmful Bias and Homogenization \nGV-1.3-006 Reevaluate organizational risk tolerances to account for unacceptable negative risk \n(such as where significant negative impacts are imminent, severe harms are actually occurring, or large -scale risks could occur); and broad GAI negative risks, \nincluding: Immature safety or risk cultures related to AI and GAI design, development and deployment, public information integrity risks, including impacts on democratic processes, unknown long -term performance characteristics of GAI. Information Integrity ; Dangerous , \nViolent, or Hateful Content ; CBRN \nInformation or Capabilities \nGV-1.3-007 Devise a plan to halt development or deployment of a GAI system that poses unacceptable negative risk. CBRN Information and Capability ; \nInformation Security ; Information \nIntegrity \nAI Actor Tasks: Governance and Oversight \n \nGOVERN 1.4: The risk management process and its outcomes are established through transparent policies, procedures, and other \ncontrols based on organizational risk priorities. \nAction ID Suggested Action GAI Risks \nGV-1.4-001 Establish policies and mechanisms to prevent GAI systems from generating \nCSAM, NCII or content that violates the law. Obscene, Degrading, and/or \nAbusive Content ; Harmful Bias \nand Homogenization ; \nDangerous, Violent, or Hateful Content\n \nGV-1.4-002 Establish transparent acceptable use policies for GAI that address illegal use or \napplications of GAI. CBRN Information or \nCapabilities ; Obscene, \nDegrading, and/or Abusive Content ; Data Privacy ; Civil \nRights violations\n \nAI Actor Tasks: AI Development, AI Deployment, Governance and Oversight",
"DATA PRIVACY \nWHY THIS PRINCIPLE IS IMPORTANT\nThis section provides a brief summary of the problems which the principle seeks to address and protect \nagainst, including illustrative examples. \nData privacy is a foundational and cross-cutting principle required for achieving all others in this framework. Surveil -\nlance and data collection, sharing, use, and reuse now sit at the foundation of business models across many industries, \nwith more and more companies tracking the behavior of the American public, building individual profiles based on this data, and using this granular-level information as input into automated systems that further track, profile, and impact the American public. Government agencies, particularly law enforcement agencies, also use and help develop a variety of technologies that enhance and expand surveillance capabilities, which similarly collect data used as input into other automated systems that directly impact people’s lives. Federal law has not grown to address the expanding scale of private data collection, or of the ability of governments at all levels to access that data and leverage the means of private collection. \nMeanwhile, members of the American public are often unable to access their personal data or make critical decisions about its collection and use. Data brokers frequently collect consumer data from numerous sources without consumers’ permission or \nknowledge.60 Moreover, there is a risk that inaccurate and faulty data can be used to \nmake decisions about their lives, such as whether they will qualify for a loan or get a job. Use of surveillance \ntechnologies has increased in schools and workplaces, and, when coupled with consequential management and \nevaluation decisions, it is leading to mental health harms such as lowered self-confidence, anxiet y, depression, and \na reduced ability to use analytical reasoning.61 Documented patterns show that personal data is being aggregated by \ndata brokers to profile communities in harmful ways.62 The impact of all this data harvesting is corrosive, \nbreeding distrust, anxiety, and other mental health problems; chilling speech, protest, and worker organizing; and \nthreatening our democratic process.63 The American public should be protected from these growing risks. \nIncreasingl y, some companies are taking these concerns seriously and integrating mechanisms to protect consumer \nprivacy into their products by design and by default, including by minimizing the data they collect, communicating collection and use clearl y, and improving security practices. Federal government surveillance and other collection and \nuse of data is governed by legal protections that help to protect civil liberties and provide for limits on data retention in some cases. Many states have also enacted consumer data privacy protection regimes to address some of these harms. \nHoweve r, these are not yet standard practices, and the United States lacks a comprehensive statutory or regulatory \nframework governing the rights of the public when it comes to personal data. While a patchwork of laws exists to guide the collection and use of personal data in specific contexts, including health, employment, education, and credit, it can be unclear how these laws apply in other contexts and in an increasingly automated societ y. Additional protec\n-\ntions would assure the American public that the automated systems they use are not monitoring their activities, collecting information on their lives, or otherwise surveilling them without context-specific consent or legal authori\n-\nty. \n31",
"Applying The Blueprint for an AI Bill of Rights \nSENSITIVE DATA: Data and metadata are sensitive if they pertain to an individual in a sensitive domain \n(defined below); are generated by technologies used in a sensitive domain; can be used to infer data from a \nsensitive domain or sensitive data about an individual (such as disability-related data, genomic data, biometric data, behavioral data, geolocation data, data related to interaction with the criminal justice system, relationship history and legal status such as custody and divorce information, and home, work, or school environmental data); or have the reasonable potential to be used in ways that are likely to expose individuals to meaningful harm, such as a loss of privacy or financial harm due to identity theft. Data and metadata generated by or about those who are not yet legal adults is also sensitive, even if not related to a sensitive domain. Such data includes, but is not limited to, numerical, text, image, audio, or video data. \nSENSITIVE DOMAINS: “Sensitive domains” are those in which activities being conducted can cause material \nharms, including significant adverse effects on human rights such as autonomy and dignit y, as well as civil liber-\nties and civil rights. Domains that have historically been singled out as deserving of enhanced data protections \nor where such enhanced protections are reasonably expected by the public include, but are not limited to, health, family planning and care, employment, education, criminal justice, and personal finance. In the context of this framework, such domains are considered sensitive whether or not the specifics of a system context would necessitate coverage under existing la w, and domains and data that are considered sensitive are under-\nstood to change over time based on societal norms and context. \nSURVEILLANCE TECHNOLOGY : “Surveillance technology” refers to products or services marketed for \nor that can be lawfully used to detect, monitor, intercept, collect, exploit, preserve, protect, transmit, and/or \nretain data, identifying information, or communications concerning individuals or groups. This framework \nlimits its focus to both government and commercial use of surveillance technologies when juxtaposed with \nreal-time or subsequent automated analysis and when such systems have a potential for meaningful impact \non individuals’ or communities’ rights, opportunities, or access. UNDERSERVED COMMUNITIES: The term “underserved communities” refers to communities that have \nbeen systematically denied a full opportunity to participate in aspects of economic, social, and civic life, as \nexemplified by the list in the preceding definition of “equit y.” \n11"
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] 
