How to use MinhPhuc0804/me5-docling-checkthat-task1-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("MinhPhuc0804/me5-docling-checkthat-task1-v1") sentences = [ "query: Our work is not trauma-informed unless it is rooted in anti-racist, anti-oppressive, culturally sustaining, liberating practices. Stolen Breaths | NEJM", "passage: title: Long COVID symptoms and duration in SARS-CoV-2 positive children — a nationwide cohort study\nabstract: Most children have a mild course of acute COVID-19. Only few mainly non-controlled studies with small sample size have evaluated long-term recovery from SARS-CoV-2 infection in children. The aim of this study was to evaluate symptoms and duration of 'long COVID' in children. A nationwide cohort study of 37,522 children aged 0-17 years with RT-PCR verified SARS-CoV-2 infection (response rate 44.9%) and a control group of 78,037 children (response rate 21.3%). An electronic questionnaire was sent to all children from March 24th until May 9th, 2021. Symptoms lasting > 4 weeks were common among both SARS-CoV-2 children and controls. However, SARS-CoV-2 children aged 6-17 years reported symptoms more frequently than the control group (percent difference 0.8%).", "passage: ke-specific antibodies to mediate antibody-dependent cellular phagocytosis and complement deposition.\n\ntitle: Class switch toward noninflammatory, spike-specific IgG4 antibodies after repeated SARS-CoV-2 mRNA vaccination\nBecause Fc-mediated effector functions are critical for antiviral immunity, these findings may have consequences for the choice and timing of vaccination regimens using mRNA vaccines, including future booster immunizations against SARS-CoV-2.", "passage: title: Stolen Breaths\nabstract: In the wake of George Floyd’s public execution, uprisings have ignited in cities throughout the United States The words “I can’t breathe” hang heavy in the air Black people cannot breathe because we are currently battling at least two public health emergencies" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
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