sentence-transformers How to use tomaarsen/static-embedding-miriad with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("tomaarsen/static-embedding-miriad")
sentences = [
"How is snapping pes anserinus syndrome typically managed, and what are the available treatment options?\n",
"Initially, the management of snapping pes anserinus syndrome is conservative, focusing on symptom management through medical interventions and physical therapy. Surgical options, such as pes anserine tendon resection (open or arthroscopic), are available if conservative measures fail to provide relief. The choice of treatment depends on the individual patient's response to conservative measures and the severity of their symptoms.",
"Research on patients with various chronic diseases indicates that coexisting chronic diseases, adverse health risk behaviors, depressive symptoms, insomnia, and cognitive impairment are associated with impaired HRQoL. Therefore, early treatment of conditions like diabetes, obesity, left ventricular dysfunction, and interventions for reducing psychological morbidity can lead to improved HRQoL.",
"Mass media, such as television, radio, newspapers, magazines, films, banners, posters, wall writings, and health exhibitions, can play an important role in promoting appropriate breastfeeding practices. These media can be used for Information, Education, and Communication (IEC) campaigns to raise awareness about the benefits of breastfeeding, dispel misconceptions, and provide information on how to support and promote breastfeeding. By utilizing mass media, the general public can be reached and educated about the importance of breastfeeding for the health and well-being of both mothers and babies."
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] 
