How to use acpotts/finetuned_arctic with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("acpotts/finetuned_arctic") sentences = [ "What is the importance of having a human fallback system in automated systems, especially for the American public?", "ing a system from use. Automated systems should not be designed \nwith an intent or reasonably foreseeable possibility of endangering \nyour safety or the safety of your community. They should be designed \nto proactively protect you from harms stemming from unintended, \nyet foreseeable, uses or impacts of automated systems. You should be \nprotected from inappropriate or irrelevant data use in the design, de\nvelopment, and deployment of automated systems, and from the \ncompounded harm of its reuse. Independent evaluation and report\ning that confirms that the system is safe and effective, including re\nporting of steps taken to mitigate potential harms, should be per\nformed and the results made public whenever possible. \n15", "with disabilities. \nIn addition to being able to opt out and use a human alternative, the American public deserves a human fallback \nsystem in the event that an automated system fails or causes harm. No matter how rigorously an automated system is \ntested, there will always be situations for which the system fails. The American public deserves protection via human \nreview against these outlying or unexpected scenarios. In the case of time-critical systems, the public should not have \nto wait—immediate human consideration and fallback should be available. In many time-critical systems, such a \nremedy is already immediately available, such as a building manager who can open a door in the case an automated \ncard access system fails.", "information used to build or validate the risk assessment shall be open to public inspection,\" and that assertions \nof trade secrets cannot be used \"to quash discovery in a criminal matter by a party to a criminal case.\" \n22" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
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