How to use Gonalb/flucold-ft-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Gonalb/flucold-ft-v1") sentences = [ "QUESTION #2: What percentage of patients in the study reported experiencing \"chills\" and \"feverish discomfort\"?", "been proven superior. Annual influenza vaccination is recommended for all people six months and older who do not have \ncontraindications. ( Am Fam Physician. 2019; 100(12):751-758. Copyright © 2019 American Academy of Family Physicians.)\nBEST PRACTICES IN INFECTIOUS DISEASE \nRecommendations from the Choosing \nWisely Campaign\nRecommendation Sponsoring organization\nDo not routinely avoid \ninfluenza vaccination in \negg-allergic patients.\nAmerican Academy of Allergy, \nAsthma, and Immunology\nSource: For more information on the Choosing Wisely Campaign,", "Review\n722 Vol 5 November 2005\naccompanied by fever and some subjects have a transient\nfall in body temperature during the early stages of\ncommon cold. In a study of 272 patients with sore throat\nassociated with URTIs, the mean aural temperature was\n36·8ºC and around 35% of these patients said they were\nsuffering from “chills” and “feverish discomfort”.49 The\nsensation of chilliness may be unrelated to any change in\nskin or body temperature. In a study of human\nvolunteers, a sensation of chill still develops on\nadministration of exogenous pyrogen even though the", "ered when the results will modify management or when a \npatient with signs or symptoms of influenza is hospitalized.19 \nTABLE 2\nComplications of Influenza\nCardiovascular 26\nCerebrovascular accidents\nIschemic heart disease\nMyocarditis\nHematologic 26\nHemolytic uremic syndrome\nHemophagocytic syndrome\nThrombotic thrombocytope -\nnic purpura\nMusculoskeletal 19,26\nMyositis\nRhabdomyolysis\nNeurologic 26\nAcute disseminated \nencephalomyelitis\nEncephalitis\nGuillain-Barré syndrome\nPostinfluenza encephalopathy \n(neurologic symptoms occur -\nring after resolution but within" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
