sentence-transformers How to use afalaudn/gemma-embedding-ft3 with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("afalaudn/gemma-embedding-ft3")
sentences = [
"How many 'Laboratory' sub-type admissions from the last year have not yet generated an invoice, and what is the average duration (in days) from their admission date to today?",
"Primary reference table listing the most granular level of administrative geography, typically representing villages or 'kelurahan'. It provides standardized names and codes for these local areas, linking each sub-district to its parent `DistrictId` to complete the geographic hierarchy. **Use this table to** perform highly localized demographic analysis, standardize patient addresses to the village level, or define precise service areas. **Note: This is a static lookup list of geographic areas and does not contain any patient-specific addresses, postal codes, or household information.**",
"Primary reference table defining the lifecycle stages of a patient admission event. It categorizes visits into states such as 'Active' (currently in hospital), 'Discharged' (left hospital), 'Invoiced' (bill generated), or 'Cancelled'. Use this table to interpret AdmissionStatusId in transaction tables to filter visits by their current operational state (e.g., calculating current census vs. historical discharges). Note: This is a static lookup table for status definitions, NOT a transaction log of patient movements.",
"Operational transaction table (Financial Log) recording the header-level details of patient invoices and billing events. This table captures the financial breakdown of a visit, distinguishing between Patient responsibility (Out-of-pocket) and Payer responsibility (Insurance/Corporate Coverage), including Gross Amounts, Discounts, Taxes, and Net Payable values. Use this table to analyze hospital revenue streams, track Accounts Receivable (AR), monitor billing cancellations, or calculate the financial yield per admission. Note: This is the Invoice HEADER table containing total values; it does not typically list the specific individual line items (drugs, labs, services) charged within the bill. For any financial analysis related to hospital revenue, Payments, Accounts Receivable (AR), billing breakdowns, or insurance claims, this invoice header table is the definitive starting point."
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] 
