Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation
<p>Teacher train files (ids only), based on the MSMARCO-Passage collection, for the paper: </p> <p><em>Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation</em> Sebastian Hofstätter, Sophia Althammer, Michael Schröder, Mete Sertkan and Allan Hanbury (<a href="https://arxiv.org/abs/2010.02666">https://arxiv.org/abs/2010.02666</a>)</p> <p>for the documentation please see: https://github.com/sebastian-hofstaetter/neural-ranking-kd</p>
ShareScore
28/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 8
- Harmonization
- 4
- Access
- 16
- Reuse readiness
- 0
- Engagement
- 0
Topics
Dataset details
- Repository
- zenodo
- Published
- October 6, 2020
- License
- cc-by-4.0
- DOI
- 10.5281/zenodo.4068215
Scored with release 0.9.0
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