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 SentenceTransformers The usage is easy when you have SentenceTransformers installed. Then you can use the pre trained models like this: Usage with Transformers 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 L 2 v2 69.84 32.56 9000 cross encoder/ms marco MiniLM L 2 v2 71.01 34.85 4100 cross encoder/ms marco MiniLM L 4 v2 73.04 37.70 2500 cross encoder/ms marco MiniLM L 6 v2 74.30 39.01 1800 cross encoder/ms marco MiniLM L 12 v2 74.31 39.02 960 Version 1 models cross encoder/ms marco TinyBERT L 2 67.43 30.15 9000 cross encoder/ms ma…
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