chungimungi/GLINT-data

GLINT training data The central artifact is a seven-source hard-negative mixture mined with a multi-vector late-interaction model, rather than a dense bi-encoder or BM25. Candidate documents are retrieved under MaxSim: each query token is matched to its best document token, and those best similarities are summed. This produces negatives that are hard under the same token-aware scoring geometry GLINT uses at retrieval time. Multi-vector mining model:… See the full description on the dataset page: https://huggingface.co/datasets/chungimungi/GLINT-data.

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