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.
