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Teacher train files containing query and passage IDs from the MSMARCO-Passage collection, created for the paper 'Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation'. Sebastian Hofstätter and colleagues at TU Wien developed this resource to support research into distilling ranking knowledge between different neural architectures. The associated documentation and code are hosted on GitHub.
The raw description indicates the files contain 'ids only'; users must obtain the full MSMARCO-Passage data separately to use these training IDs.