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README.md

@@ -1,3 +1,32 @@
 ---
 license: mit
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+# 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}
+}
+```