How to use vinay-pepakayala/embedding_model_custom with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("vinay-pepakayala/embedding_model_custom") sentences = [ "SD-V3_01_RD00.0 FORM_SD_INVOICE_EN <Banking data> VF02/ VF03 *** Only printed on the last page ***", "FSCM01 AR Accounting Create four debit dispute cases, for four approval levels in EUR/PLN/GBP for 4 different countries\nAttach backup, mark X for attachment, Create case title, Add information in Text field AR Accounting FBL5N Validate that the dispute case is created against the open line item on FBL5N.\nNote the Case ID# to find the dispute case in Dispute Management.\nCheck if automatic posting was done on a proper account linked to Special GL Indicator", "SD-V3_01_RD00.0 FORM_SD_INVOICE_EN <Company Code data on the footer> VF02/ VF03 *** Printed on all pages *** Name and address data of the issuing Company Code\n\n", "FA03_Depreciation run Recalculate Depreciation - it might be nessesary to recalculate deprec. if depreciation key has been changed (executed after depreciation run) Asset Accounting AFAR all proceed assets shouldn't have errors and changes" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
