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---
license: mit
---
# Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-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 our paper [R2ANKER](https://arxiv.org/pdf/2206.08063.pdf) for more details.
## Usage with Transformers
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
tokenizer = AutoTokenizer.from_pretrained("YCZhou/R2ANKER")
model = AutoModelForSequenceClassification.from_pretrained("YCZhou/R2ANKER")
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)
```
## Citation
```
@inproceedings{DBLP:conf/acl/Zhou0GTXLJJ23,
author = {Yucheng Zhou and
Tao Shen and
Xiubo Geng and
Chongyang Tao and
Can Xu and
Guodong Long and
Binxing Jiao and
Daxin Jiang},
title = {Towards Robust Ranker for Text Retrieval},
booktitle = {Findings of the Association for Computational Linguistics: {ACL} 2023,
Toronto, Canada, July 9-14, 2023},
pages = {5387--5401},
publisher = {Association for Computational Linguistics},
year = {2023},
url = {https://doi.org/10.18653/v1/2023.findings-acl.332},
doi = {10.18653/V1/2023.FINDINGS-ACL.332},
timestamp = {Sat, 30 Sep 2023 09:33:34 +0200},
biburl = {https://dblp.org/rec/conf/acl/Zhou0GTXLJJ23.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` |