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This is a cross-encoder model with dot-product based scoring mechanism trained on MS-MARCO dataset. The parameters of the cross-encoder are initialized using bert-base-uncased. This model is used as a teacher model for training a MiniLM-based cross-encoder model which is used in experiments of our EMNLP 2023 and ICLR 2024 papers.
See our EMNLP 2022 paper titled "Efficient Nearest Neighbor Search for Cross-Encoder Models using Matrix Factorization" for more details on the dot-product based scoring mechanism.
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