How to use checkthisout/finetuned_arctic with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("checkthisout/finetuned_arctic") sentences = [ "How have algorithms in hiring and credit decisions been shown to impact existing inequities, according to the context?", "Shoshana Zuboff. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of\nPower. Public Affairs. 2019.\n64. Angela Chen. Why the Future of Life Insurance May Depend on Your Online Presence. The Verge. Feb.\n7, 2019.\nhttps://www.theverge.com/2019/2/7/18211890/social-media-life-insurance-new-york-algorithms-big\ndata-discrimination-online-records\n68", "SECTION TITLE\nFOREWORD\nAmong the great challenges posed to democracy today is the use of technology, data, and automated systems in \nways that threaten the rights of the American public. Too often, these tools are used to limit our opportunities and \nprevent our access to critical resources or services. These problems are well documented. In America and around \nthe world, systems supposed to help with patient care have proven unsafe, ineffective, or biased. Algorithms used \nin hiring and credit decisions have been found to reflect and reproduce existing unwanted inequities or embed \nnew harmful bias and discrimination. Unchecked social media data collection has been used to threaten people’s", "ways and to the greatest extent possible; where not possible, alternative privacy by design safeguards should be \nused. Systems should not employ user experience and design decisions that obfuscate user choice or burden \nusers with defaults that are privacy invasive. Consent should only be used to justify collection of data in cases \nwhere it can be appropriately and meaningfully given. Any consent requests should be brief, be understandable \nin plain language, and give you agency over data collection and the specific context of use; current hard-to\nunderstand notice-and-choice practices for broad uses of data should be changed. Enhanced protections and" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
