nephrology-gemma-300m-emb

pip install -U sentence-transformers

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("yasserrmd/nephrology-gemma-300m-emb")
# Run inference
queries = [
    "How do some participants believe that reimbursement or compensation for living kidney donors can help minimize disadvantage?",
]
documents = [
    'Some participants believe that reimbursement or compensation can effectively help donors and recipients who are socioeconomically disadvantaged by removing financial barriers to donation. They advocate for government subsidies or special paid leave to support potential donors who may not be able to take leave or afford donation-related expenses. The goal is to ensure that financial constraints do not penalize individuals who are willing to donate.',
    'The time in therapeutic range (TTR) of INR (International Normalized Ratio) is an important factor in determining the risk of hemorrhagic and ischemic events in hemodialysis patients. If the INR is below 1.5, there is an increased risk of hemorrhagic events, while an INR above 5 increases the risk of ischemic events. Maintaining the INR within the therapeutic range is challenging but crucial in minimizing these risks.',
    'Urinary L-PGDS excretions have been found to be superior to other markers, including urinary excretions of type-IV collagen, beta-2 microglobulin, and NAG, as well as serum creatinine levels, in predicting renal injury in type-2 diabetes. Studies have shown that urinary L-PGDS excretions better predict ≥30 mg/gCr albuminuria in type-2 diabetes. The use of urinary L-PGDS excretions as a marker for renal injury in type-2 diabetes is supported by its ability to reflect a slight change in glomerular permeability and its positive correlation with albuminuria.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 768] [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.6341, 0.0019, 0.0465]])