Fine-tuning a Sentence-BERT model with triplet loss on entity records yields embeddings that improve entity matching F1 by 3-19% over non-fine-tuned SBERT and TF-IDF on the tested datasets.
WDC LSPM Dataset
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Leveraging large language models for efficient representation learning for entity resolution
Fine-tuning a Sentence-BERT model with triplet loss on entity records yields embeddings that improve entity matching F1 by 3-19% over non-fine-tuned SBERT and TF-IDF on the tested datasets.