endocrinology-gemma-300m-emb

pip install -U sentence-transformers

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("yasserrmd/endocrinology-gemma-300m-emb")
# Run inference
queries = [
    "How does in utero exposure to excess anti-M\u00fcllerian hormone (AMH) affect the GnRH neuronal morphology and electrical activity in offspring?\n",
]
documents = [
    'In utero exposure to excess AMH leads to protracted changes in GnRH neuronal morphology and electrical activity in offspring. PAMH female mice exhibit increased spine density on the soma and along the primary dendrite of GnRH neurons compared to controls during diestrus. This increased spine density is accompanied by a significant increase in the number of vesicular GABA transporter (vGaT) appositions onto GnRH cells. While there are no significant differences in the number of vesicular glutamate transporter 2 (vGluT2) appositions, it is important to note that GABA, although primarily recognized as an inhibitory neurotransmitter in the adult brain, is excitatory in adult GnRH neurons. This elevated hypothalamic excitatory apposition onto GnRH neurons in PAMH animals translates into increased neuronal activity.',
    'Prophylactic thyroidectomy is recommended as early as the age of five years in confirmed RET mutation carriers in MEN2A or FMTC families with normal (stimulated) plasma calcitonin levels. However, some clinicians may prefer to wait until the pentagastrin test results are abnormal before performing thyroidectomy. This is because the test for calcitonin levels may give false negative results, and medullary thyroid carcinoma has been encountered in children with normal calcitonin levels who underwent thyroidectomy after DNA diagnosis.',
    'The most common co-morbidities reported by patients with GHD are hypertension, arthritis, and diabetes mellitus. Additionally, 26% of patients had a history of fractures.',
]
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([[0.7737, 0.0678, 0.0061]])