sentence-transformers How to use pattonma/AIE4_midterm_tuned_embeddings_2 with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("pattonma/AIE4_midterm_tuned_embeddings_2")
sentences = [
"What is meant by \"mission creep\" in the context of data collection, and how can it be avoided?",
"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",
"HUMAN ALTERNATIVES, \nCONSIDERATION, AND \nFALLBACK \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. \n•\nAn unemployment benefits system in Colorado required, as a condition of accessing benefits, that applicants\nhave a smartphone in order to verify their identity. No alternative human option was readily available,\nwhich denied many people access to benefits.101\n•\nA fraud detection system for unemployment insurance distribution incorrectly flagged entries as fraudulent,\nleading to people with slight discrepancies or complexities in their files having their wages withheld and tax",
"collection should be minimized and clearly communicated to the people whose data is collected. Data should \nonly be collected or used for the purposes of training or testing machine learning models if such collection and \nuse is legal and consistent with the expectations of the people whose data is collected. User experience \nresearch should be conducted to confirm that people understand what data is being collected about them and \nhow it will be used, and that this collection matches their expectations and desires. \nData collection and use-case scope limits. Data collection should be limited in scope, with specific, \nnarrow identified goals, to avoid \"mission creep.\" Anticipated data collection should be determined to be"
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] 
