MS MARCO 跨编码器

MS MARCO 是一个大规模的信息检索语料库，它基于使用Bing搜索引擎的真实用户搜索查询创建。提供的模型可用于语义搜索，即，给定关键词/搜索短语/问题，模型将找到与搜索查询相关的段落。

训练数据包含超过50万个示例，而完整的语料库包含超过880万个段落。

使用 SentenceTransformers

预训练模型可以这样使用：

from sentence_transformers import CrossEncoder

model = CrossEncoder("model_name", max_length=512)
scores = model.predict(
    [("Query", "Paragraph1"), ("Query", "Paragraph2"), ("Query", "Paragraph3")]
)

与 Transformers 一起使用

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model = AutoModelForSequenceClassification.from_pretrained("model_name")
tokenizer = AutoTokenizer.from_pretrained("model_name")

features = tokenizer(["Query", "Query"], ["Paragraph1", "Paragraph2"], padding=True, truncation=True, return_tensors="pt")

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

模型与性能

在下表中，我们提供了各种预训练的跨编码器及其在TREC深度学习2019和MS Marco Passage Reranking数据集上的表现。

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 with Hugging Face Transformers v4.