Update README.md

README.md

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----
-license: cc-by-sa-4.0
----
+---
+license: cc-by-sa-4.0
+language:
+- en
+---
+# Dataset Card
+
+## Dataset Details
+This dataset contains a set of candidate documents for second-stage re-ranking on msmarco 
+(dev, test split in [BEIR](https://huggingface.co/BeIR)). Those candidate documents are composed of hard negatives mined from 
+[gtr-t5-xl](https://huggingface.co/sentence-transformers/gtr-t5-xl) as Stage 1 ranker
+and ground-truth documents that are known to be relevant to the query. This is a release from our paper 
+[Policy-Gradient Training of Language Models for Ranking](https://gao-g.github.io/), so 
+please cite it if using this dataset.
+
+## Direct Use
+You can load the dataset by:
+```python
+from datasets import load_dataset
+dataset = load_dataset("NeuralPGRank/msmarco-hard-negatives")
+```
+Each example is an dictionary:
+```python
+>>> python dataset['test'][0]
+{
+    "qid" : ...,  # query ID
+    "topk" : {
+        doc ID: ..., # document ID as the key; None or a score as the value
+        doc ID: ...,
+        ...
+    },
+}
+```
+
+
+## Citation 
+```
+@inproceedings{Gao2023PolicyGradientTO,
+  title={Policy-Gradient Training of Language Models for Ranking},
+  author={Ge Gao and Jonathan D. Chang and Claire Cardie and Kiant{\'e} Brantley and Thorsten Joachims},
+  booktitle={Conference on Neural Information Processing Systems (Foundation Models for Decising Making Workshop)},
+  year={2023},
+  url={https://arxiv.org/pdf/2310.04407}
+}
+```
+
+
+## Dataset Card Author and Contact
+[Ge Gao](https://gao-g.github.io/)