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README.md

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-This model is a T5-large reranker fine-tuned on the MS MARCO passage dataset for 10k steps (or 1 epochs).
+This model is a T5-large reranker fine-tuned on the MS MARCO passage dataset for 10k steps (or 1 epoch).
 
-For more details on how to use it, check [pygaggle.ai](pygaggle.ai)
+This model usually has a better zero-shot performance than `monot5-large-msmarco`, i.e., it performs better on datasets different from MS MARCO.
+
+For more details on how to use it, check the following links:
+- [A simple reranking example](https://github.com/castorini/pygaggle#a-simple-reranking-example)
+- [Rerank MS MARCO passages](https://github.com/castorini/pygaggle/blob/master/docs/experiments-msmarco-passage-subset.md)
+- [Rerank Robust04 documents](https://github.com/castorini/pygaggle/blob/master/docs/experiments-robust04-monot5-gpu.md)
 
 Paper describing the model: [Document Ranking with a Pretrained Sequence-to-Sequence Model](https://www.aclweb.org/anthology/2020.findings-emnlp.63/)