Passage Reranking Multilingual BERT 🔃 🌍 Model description Input: Supports over 100 Languages. See List of supported languages for all available. Purpose: 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%. Architecture: On top of BERT there is a Densly Connected NN which takes the 768 Dimensional [CLS] Token as input and provides the output (Arxiv). Output: Just a single value between between 10 and 10. Better matching query,passage pairs tend to have a higher a score. Intended uses & limitations Both query[1] and passage[2] have to fit in 512 Tokens. As you normally want to rerank the first dozens of search results keep in mind the inference time of approximately 300 ms/query. How to use This Model can be used as a drop in replacement in the Nboost Library Through this you can directly improve your Elasticsearch Results without any coding. Training data This model is trained using the Microsoft MS Marco Dataset . This training dataset contains approximately 400M tuples of a query, relevant and non relevant passages. All datasets u…
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