| language: | |
| - en | |
| tags: | |
| - retrieval | |
| - document-rewriting | |
| datasets: | |
| - irds:msmarco-passage | |
| library_name: transformers | |
| A DeepCT model based on `bert-base-uncased` and trained on MS MARCO. This is a version of [the checkpoint released by the original authors](http://boston.lti.cs.cmu.edu/appendices/arXiv2019-DeepCT-Zhuyun-Dai/outputs/marco.zip), converted to pytorch format and ready for use in PyTerrier. | |
| ## References | |
| - [Dai19]: Zhuyun Dai, Jamie Callan. Context-Aware Sentence/Passage Term Importance Estimation For First Stage Retrieval. https://arxiv.org/abs/1910.10687 | |
| - [Macdonald20]: Craig Macdonald, Nicola Tonellotto. Declarative Experimentation in Information Retrieval using PyTerrier. Craig Macdonald and Nicola Tonellotto. In Proceedings of ICTIR 2020. https://arxiv.org/abs/2007.14271 | |