navteca/ms-marco-MiniLM-L-6-v2

Cross-Encoder for MS Marco

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

Training Data

This model was trained on the MS Marco Passage Ranking task.

Usage

The usage becomes easier when you have SentenceTransformers installed. Then, you can use the pre-trained models like this:

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

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-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
sebastian-hofstaetter/distilbert-cat-margin_mse-T2-msmarco	72.82	37.88	720

Note: Runtime was computed on a V100 GPU.