| | --- |
| | 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 |
| |
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