amberoad/bert-multilingual-passage-reranking-msmarco

This module takes a search query [1] and a passage [2] and calculates if the passage matches the query . It can be used as an improvement for Elasticsearch Results and boosts the relevancy by up to 100% . The model is trained using the Microsoft MS Marco Dataset . This training dataset contains approximately 400M tuples of a query, relevant and non-relevant passages . The training procedure is performed the same way as stated in this README. The evaluation was made on Top 1000 Dev. Dev. There are 6,900 queries in total in the development dataset, where each query is mapped to top 1,000 passage retrieved using BM25 from MS MARCO corpus,