Training & evaluation library for text-based neural re-ranking and dense retrieval models built with PyTorch
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Updated
Jan 27, 2023 - Python
Training & evaluation library for text-based neural re-ranking and dense retrieval models built with PyTorch
WSDM'22 Best Paper: Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrieval
code and data to faciliate BERT/ELECTRA for document ranking. Details refer to the paper - PARADE: Passage Representation Aggregation for Document Reranking.
An easy-to-use python toolkit for flexibly adapting various neural ranking models to any target domain.
Code repository of the NAACL'21 paper "CoRT: Complementary Rankings from Transformers"
Efficient interpolation-based ranking on CPUs
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