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license: apache-2.0
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This is a cross-encoder model with dot-product based scoring mechanism trained on MS-MARCO dataset.
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The parameters of the cross-encoder are initialized using a 6-layer [minilm model](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) and is trained via distillation using scores from three different teacher models.
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This model is used in experiments of our [EMNLP 2023](https://aclanthology.org/2023.findings-emnlp.544/) and [ICLR 2024](https://openreview.net/forum?id=1CPta0bfN2) papers.
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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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license: apache-2.0
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