Cross Encoder for MS Marco This model was trained on the MS Marco Passage Ranking task. The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See SBERT.net Retrieve & Re rank for more details. The training code is available here: SBERT.net Training MS Marco Usage with Transformers Usage with SentenceTransformers The usage becomes easier when you have SentenceTransformers installed. Then, you can use the pre trained models like this: Performance In the following table, we provide various pre trained Cross Encoders together with their performance on the TREC Deep Learning 2019 and the MS Marco Passage Reranking dataset. Model Name NDCG@10 (TREC DL 19) MRR@10 (MS Marco Dev) Docs / Sec : Version 2 models cross encoder/ms marco TinyBERT L2 v2 69.84 32.56 9000 cross encoder/ms marco MiniLM L2 v2 71.01 34.85 4100 cross encoder/ms marco MiniLM L4 v2 73.04 37.70 2500 cross encoder/ms marco MiniLM L6 v2 74.30 39.01 1800 cross encoder/ms marco MiniLM L12 v2 74.31 39.02 960 Version 1 models cross encoder/ms marco TinyBERT L2 67.43 30.15 9000 cross encoder/ms…
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