sentence-transformers How to use afalaudn/gemma-embedding-ft2 with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("afalaudn/gemma-embedding-ft2")
sentences = [
"organization id",
"Primary reference table for classifying Payers into broader financial or business categories. This table groups Payers into segments such as 'Insurance Private', 'Insurance Government (BPJS)', 'Corporate', and 'Related Parties'. Use this table to aggregate revenue reporting by payer channel, analyze market segmentation (e.g., Private Insurance vs. Government Scheme), or apply high-level billing policies to groups of payers. Note: This serves as a categorization layer above the individual 'Payer' table.",
"Operational transaction table recording unstructured free-text medical notes and preliminary clinical remarks associated with a patient admission. It captures initial diagnosis impressions, symptoms, or observation notes (e.g., 'Asthma', 'Observation Febris') entered during the admission process. Use this table to retrieve qualitative clinical context for a visit or search for specific medical conditions mentioned in preliminary notes. Note: This table contains raw free-text descriptions, NOT structured ICD-10 diagnosis codes used for billing.",
"Operational transaction table recording every patient registration and visit event at the hospital. This table consolidates patient demographics, visit types (Inpatient, Outpatient, Emergency), primary and referral doctors, payer/insurance eligibility, and critical timelines (Admission and Discharge dates). Use this table to calculate patient census, Average Length of Stay (ALOS), track patient flow, or analyze admission volume by doctor or department. Note: This table focuses on administrative registration and billing initiation; it does not contain detailed clinical notes, specific lab results, or medication prescriptions. When analyzing patient administrative inflow and outflow data, this table is the primary and essential source for all patient visit metrics."
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] 
