sentence-transformers How to use mancer146/embeddinggemma-300m-haystack-contrastive-thin-fixed with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("mancer146/embeddinggemma-300m-haystack-contrastive-thin-fixed")
sentences = [
"TaxYear: 2025 PRELIMINARY\nLandJustValue: $571,965\nImprovementsJustValue: $444,893\nTotalJustValue: $1,016,858\nSchoolAssessedValue: $657,902\nCountyTaxableValue: $607,180\nTotalTaxes: $5,881.02\n\nTaxYear: 2024\nLandJustValue: $529,037\nImprovementsJustValue: $522,202\nTotalJustValue: $1,051,239\nSchoolAssessedValue: $639,361\nCountyTaxableValue: $589,361\nTotalTaxes: $6,003.53\n\nTaxYear: 2023\nLandJustValue: $500,470\nImprovementsJustValue: $572,889\nTotalJustValue: $1,073,359\nSchoolAssessedValue: $620,739\nCountyTaxableValue: $570,739\nTotalTaxes: $5,956.52\n\nTaxYear: 2022\nLandJustValue: $230,519\nImprovementsJustValue: $610,503\nTotalJustValue: $841,022\nSchoolAssessedValue: $602,659\nCountyTaxableValue: $552,659\nTotalTaxes: $6,124.86\n\nTaxYear: 2021\nLandJustValue: $112,658\nImprovementsJustValue: $472,448\nTotalJustValue: $585,106\nSchoolAssessedValue: $585,106\nCountyTaxableValue: $535,106\nTotalTaxes: $6,190.98section: Tax,\ncounty: Collier,\nparcel_id: 82660002628",
"area_under_air: 2111\nlivable_floor_area: 2111\nparcel_identifier: 51978031927\nproperty_structure_built_year: 2004\nproperty_type: SingleFamily\nsubdivision: INDIGO LAKES UNIT\ntotal_area: 2551",
"monthly_tax_amount: 490.09\nperiod_end_date: 2025-12-31\nperiod_start_date: 2025-01-01\nproperty_assessed_value_amount: 657902\nproperty_building_amount: 444893\nproperty_land_amount: 571965\nproperty_market_value_amount: 1016858\nproperty_taxable_value_amount: 607180\ntax_year: 2025\nyearly_tax_amount: 5881.02\n\nmonthly_tax_amount: 510.41\nperiod_end_date: 2022-12-31\nperiod_start_date: 2022-01-01\nproperty_assessed_value_amount: 602659\nproperty_building_amount: 610503\nproperty_land_amount: 230519\nproperty_market_value_amount: 841022\nproperty_taxable_value_amount: 552659\ntax_year: 2022\nyearly_tax_amount: 6124.86",
"ownership_transfer_date: 2013-07-09\npurchase_price_amount: 830000\n\nownership_transfer_date: 2011-10-03\npurchase_price_amount: 685000\n\nownership_transfer_date: 2009-07-01\npurchase_price_amount: 432500\n\nownership_transfer_date: 1999-02-22\npurchase_price_amount: 0\n\nownership_transfer_date: 2001-01-25\npurchase_price_amount: 360000"
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] 
