数据集 / cross-encoder/ms-marco-MiniLM-L6-v2

cross-encoder/ms-marco-MiniLM-L6-v2 已完整同步

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:

from sentence_transformers import CrossEncoder

model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L6-v2')
scores = model.predict([
    ("How many people live in Berlin?", "Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers."),
    ("How many people live in Berlin?", "Berlin is well known for its museums."),
])
print(scores)
# [ 8.607138 -4.320078]

Usage with Transformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/ms-marco-MiniLM-L6-v2')
tokenizer = AutoTokenizer.from_pretrained('cross-encoder/ms-marco-MiniLM-L6-v2')

features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'],  padding=True, truncation=True, return_tensors="pt")

model.eval()
with torch.no_grad():
    scores = model(**features).logits
    print(scores)

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-marco-TinyBERT-L4 68.09 34.50 2900
cross-encoder/ms-marco-TinyBERT-L6 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.

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