sentence-transformers How to use sahithkumar7/final-mpnet-base-peft with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("sahithkumar7/final-mpnet-base-peft")
sentences = [
"What is the department of medicine located at?",
"Publisher’s Note: MDPI stays neutral\nwith regard to jurisdictional claims in\npublished maps and institutional afil-\n\niations.\n\nonon)\n\nCopyright: © 2021 by the author.\nLicensee MDPI, Basel, Switzerland.\nThis article is an open access article\ndistributed under the terms and\nconditions of the Creative Commons\nAttribution (CC BY) license (https://\ncreativecommons.org/licenses/by/\n4.0/).\n\nJoan and Sanford I. Weill Department of Medicine, Weill Cornell Medical College, 525 East 68th Street,\nRoom M-522, Box 130, New York, NY 10065, USA; str2020@med.cornell.edu or Stefan.Ryter@proterris.com",
"Results At the parameters used, the ultrasound did not directly affect bCSC proliferation, with no evident changes in\nmorphology. In contrast, the ultrasound protocol affected the migration and invasion ability of bCSCs, limiting their\ncapacity to advance while a major affection was detected on their angiogenic properties. LIPUS-treated bCSCs were\nunable to transform into aggressive metastatic cancer cells, by decreasing their migration and invasion capacity as\nwell as vessel formation. Finally, RNA-seq analysis revealed major changes in gene expression, with 676 differentially",
"Tesfaye, M. & Savoldo, B. Adoptive cell therapy in\ntreating pediatric solid tumors. Curr. Oncol. Rep. 20,\n73 (2018).\n\nMarofi, F. et al. CAR T cells in solid tumors: challenges\nand opportunities. Stem Cell Res. Ther. 12, 81 (2021).\nDeng, Q. et al. Characteristics of anti-CD19 CAR T cell\ninfusion products associated with efficacy and toxicity\n\nin patients with large B cell lymphomas. Nat. Med. 26,\n\n1878-1887 (2020).\nBoulch, M. A cross-talk between CAR T cell subsets\nand the tumor microenvironment is essential for\nsustained cytotoxic activity. Sci. Immunol. 6,\neabd4344 (2021)."
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] 
