How to use achapman/finetuned_arctic_ai_risk with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("achapman/finetuned_arctic_ai_risk") sentences = [ "What considerations should be taken into account regarding the specific set or types of users for the AI system?", "46 \nMG-4.3-003 \nReport GAI incidents in compliance with legal and regulatory requirements (e.g., \nHIPAA breach reporting, e.g., OCR (2023) or NHTSA (2022) autonomous vehicle \ncrash reporting requirements. \nInformation Security; Data Privacy \nAI Actor Tasks: AI Deployment, Affected Individuals and Communities, Domain Experts, End-Users, Human Factors, Operation and \nMonitoring", "reporting, data protection, data privacy, or other laws. \nData Privacy; Human-AI \nConfiguration; Information \nSecurity; Value Chain and \nComponent Integration; Harmful \nBias and Homogenization \nGV-6.2-004 \nEstablish policies and procedures for continuous monitoring of third-party GAI \nsystems in deployment. \nValue Chain and Component \nIntegration \nGV-6.2-005 \nEstablish policies and procedures that address GAI data redundancy, including \nmodel weights and other system artifacts.", "times, and availability of critical support. \nHuman-AI Configuration; \nInformation Security; Value Chain \nand Component Integration \nAI Actor Tasks: AI Deployment, Operation and Monitoring, TEVV, Third-party entities \n \nMAP 1.1: Intended purposes, potentially beneficial uses, context specific laws, norms and expectations, and prospective settings in \nwhich the AI system will be deployed are understood and documented. Considerations include: the specific set or types of users" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
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