metadata
license: apache-2.0
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 Transformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained('model_name')
tokenizer = AutoTokenizer.from_pretrained('model_name')
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)
Usage with SentenceTransformers
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') , ('Query', 'Paragraph3')])
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.