How to use vab46/nomic-embed-text-v1.5_Clinical-Trials_Matryoshka_final with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("vab46/nomic-embed-text-v1.5_Clinical-Trials_Matryoshka_final", trust_remote_code=True) sentences = [ "TITLE: The Effect of Mindfulness-Based Self-Compassion Program on Self-Compassion, Secondary Traumatic Stress, and Professional Quality of Life in Midwives and Nurses Working in Obstetrics: A Randomized Controlled Trial\nSUMMARY: This study aims to examine the effect of a Mindfulness-Based Self-Compassion Program on self-compassion levels, secondary traumatic stress, and professional quality of life in midwives and nurses working in obstetrics. Healthcare professionals in this field are frequently exposed to traumatic experiences such as complicated deliveries, perinatal loss, and obstetric emergencies, which may lead to secondary traumatic stress and reduced professional quality of life over time. This study is designed as a pre-test/post-test randomized controlled trial. A total of 80 participants (40 intervention, 40 control) will be recruited from Gaziantep Cengiz Gökçek Obstetrics and Pediatrics Hospital. The intervention group will receive an 8-week Mindfulness-Based Self-Compassion Program (one session per week, 60-90 minutes each). The control group will receive no intervention. Outcomes will be measured before and after the program using validated scales for self-compassion, secondary traumatic stress, and professional quality of life.\nINCLUSION_CRITERIA:\nWorking as a midwife or nurse in obstetrics\nActively employed in the relevant unit of the study institution\n18 years of age or older\nAble to read and understand Turkish\nWilling to participate voluntarily in the study", "I'm pregnant and living with HIV, will I be able to get the RSV vaccine to protect my baby?", "What is the primary goal of this clinical trial?", "What is the primary intervention being tested in this trial?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
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