sentence-transformers How to use pattonma/AIE4_midterm_tuned_embeddings with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("pattonma/AIE4_midterm_tuned_embeddings")
sentences = [
"What is the purpose of the Blueprint for an AI Bill of Rights in relation to government and private sector practices?",
"Proportionate. The availability of human consideration and fallback, along with associated training and \nsafeguards against human bias, should be proportionate to the potential of the automated system to meaning\nfully impact rights, opportunities, or access. Automated systems that have greater control over outcomes, \nprovide input to high-stakes decisions, relate to sensitive domains, or otherwise have a greater potential to \nmeaningfully impact rights, opportunities, or access should have greater availability (e.g., staffing) and over\nsight of human consideration and fallback mechanisms. \nAccessible. Mechanisms for human consideration and fallback, whether in-person, on paper, by phone, or",
"Moderator: Kathy Pham Evans, Deputy Chief Technology Officer for Product and Engineering, U.S \nFederal Trade Commission. \nPanelists: \n•\nLiz O’Sullivan, CEO, Parity AI\n•\nTimnit Gebru, Independent Scholar\n•\nJennifer Wortman Vaughan, Senior Principal Researcher, Microsoft Research, New York City\n•\nPamela Wisniewski, Associate Professor of Computer Science, University of Central Florida; Director,\nSocio-technical Interaction Research (STIR) Lab\n•\nSeny Kamara, Associate Professor of Computer Science, Brown University\nEach panelist individually emphasized the risks of using AI in high-stakes settings, including the potential for \nbiased data and discriminatory outcomes, opaque decision-making processes, and lack of public trust and",
"enforcement, and other regulatory contexts may require government actors to protect civil rights, civil liberties, \nand privacy in a manner consistent with, but using alternate mechanisms to, the specific principles discussed in \nthis framework. The Blueprint for an AI Bill of Rights is meant to assist governments and the private sector in \nmoving principles into practice. \nThe expectations given in the Technical Companion are meant to serve as a blueprint for the development of \nadditional technical standards and practices that should be tailored for particular sectors and contexts. While \nexisting laws informed the development of the Blueprint for an AI Bill of Rights, this framework does not detail"
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] 
