sentence-transformers How to use dboyker-code/all-MiniLM-L6-v2-nfcorpus with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("dboyker-code/all-MiniLM-L6-v2-nfcorpus")
sentences = [
"colon health because of the fiber content , plant-based diets may produce healthier stools and lead to larger bowel movements , which is important for the prevention of a number of medical conditions . interestingly , antioxidant-rich fruits and vegetables appear to increase stool size independent of fiber . furthermore",
"Abstract BACKGROUND: The aging process can lead to a decline in cellular immunity. Therefore, the elderly could benefit from safe and effective interventions that restore cellular immune functions. OBJECTIVE: We determined whether dietary supplementation",
"Abstract OBJECTIVES: Standard therapies for antibiotic-associated diarrhea (AAD) and Clostridium difficile-associated diarrhea (CDAD) have limited efficacy. Probiotic prophylaxis is a promising alternative for reduction of AAD and CDAD incidence.",
"Abstract BACKGROUND: Heterocyclic amines, mutagens formed in meats cooked at high temperatures, have been demonstrated as mammary carcinogens in animals. We conducted a nested, case-control study among 41836 cohort members of the Iowa"
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] 
