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@@ -97,4 +97,15 @@ computed by summing, over query tokens, the maximum similarity to a document tok
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  The corresponding private training artifacts are in
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  [GLINT-data](https://huggingface.co/datasets/chungimungi/GLINT-data). It contains the complete
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  prepared SFT data, the 1,046,009-row seven-source KD mixture, and Jina teacher-score parquet
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- shards. The repository contains no BEIR evaluation corpus or evaluation labels.
 
 
 
 
 
 
 
 
 
 
 
 
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  The corresponding private training artifacts are in
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  [GLINT-data](https://huggingface.co/datasets/chungimungi/GLINT-data). It contains the complete
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  prepared SFT data, the 1,046,009-row seven-source KD mixture, and Jina teacher-score parquet
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+ shards. The repository contains no BEIR evaluation corpus or evaluation labels.
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+
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+ ## Citation
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+
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+ ```
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+ @misc{aarush2026glint,
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+ title={GLInt: Geometry-Matched Hard Negatives for Late-Interaction Retrieval},
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+ author={Aarush},
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+ year={2026},
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+ howpublished={\url{https://huggingface.co/blog/chungimungi/glint}},
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+ }
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+ ```