MS MARCO Cross-Encoders

MS MARCO is a large scale information retrieval corpus that was created based on real user search queries using Bing search engine. The provided models can be used for semantic search, i.e., given keywords / a search phrase / a question, the model will find passages that are relevant for the search query.

The training data constist of over 500k examples, while the complete corpus consist of over 8.8 Million passages.


Pre-trained models can be used like this:

from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name', max_length=512)
scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])

Models & 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
cross-encoder/ms-marco-TinyBERT-L-2 67.43 30.15 9000
cross-encoder/ms-marco-TinyBERT-L-4 68.09 34.50 2900
cross-encoder/ms-marco-TinyBERT-L-6 69.57 36.13 680
cross-encoder/ms-marco-electra-base 71.99 36.41 340
Other models
nboost/pt-tinybert-msmarco 63.63 28.80 2900
nboost/pt-bert-base-uncased-msmarco 70.94 34.75 340
nboost/pt-bert-large-msmarco 73.36 36.48 100
Capreolus/electra-base-msmarco 71.23 36.89 340
amberoad/bert-multilingual-passage-reranking-msmarco 68.40 35.54 330

Note: Runtime was computed on a V100 GPU with Huggingface Transformers v4.