How to use yasserrmd/pharma-gemma-300m-emb with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yasserrmd/pharma-gemma-300m-emb") sentences = [ "How can training influence nurses' awareness of medication errors?\n", "Treatment with obeticholic acid resulted in significant reductions in serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) concentrations over the first 36 weeks of treatment, and these reductions were sustained for the duration of treatment. However, serum alkaline phosphatase concentrations increased with obeticholic acid treatment, although γ-glutamyl transpeptidase concentrations (another indicator of cholestasis) decreased. These changes in liver enzyme concentrations reversed after obeticholic acid was stopped, and at 24 weeks after treatment discontinuation, there were no significant differences between the obeticholic acid group and the placebo group.", "Training can increase nurses' awareness of medication errors by providing them with the knowledge and skills necessary to identify and prevent errors. Through training, nurses can learn about medication safety protocols, proper medication administration techniques, and the importance of error reporting. This increased awareness can help nurses recognize potential errors and take appropriate actions to prevent harm to patients.", "ML171, also known as 2-acetylphenothiazine, has been identified as a specific NOX1 oxidase inhibitor at nanomolar concentrations. It shows minimal activity on other cellular ROS-producing sources, including xanthine oxidase and other NADPH oxidases. ML171 targets the NOX1 catalytic subunit without affecting its cytosolic regulators, such as the NOXO1, NOXA1, or RAC1 subunits. It effectively blocks NOX1 oxidase-dependent ROS-mediated formation of extracellular matrix-degrading invadopodia in colon cancer cells." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
