---
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
```
@article{zhou2022towards,
  title={Towards robust ranker for text retrieval},
  author={Zhou, Yucheng and Shen, Tao and Geng, Xiubo and Tao, Chongyang and Xu, Can and Long, Guodong and Jiao, Binxing and Jiang, Daxin},
  journal={arXiv preprint arXiv:2206.08063},
  year={2022}
}
```