README.md

metadata

pretty_name: GLINT training data
task_categories:
  - text-retrieval
language:
  - en
tags:
  - hard-negatives
  - late-interaction
  - multi-vector
  - colbert
  - retrieval
  - maxsim
size_categories:
  - 1M<n<10M
license: apache-2.0

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: lightonai/LateOn-unsupervised
• Mining backend: PyLate + WARP
• KD mixture: 1,046,009 query rows × 32 candidate documents
• Teacher targets: listwise scores from jinaai/jina-reranker-v3.5

How the hard negatives were mined

1. Encode. Queries and source corpora were encoded with LateOn-unsupervised into 128-dimensional token embeddings. This is the multi-vector model used for mining; it is the same late-interaction family as the final GLINT retriever.
2. Retrieve by MaxSim. Corpus shards were indexed with WARP and searched with the token-level MaxSim operator. WARP makes deep late-interaction retrieval tractable at mining scale; it is not required to run the released model.
3. Merge shard rankings. Per-shard candidate lists were merged by their MaxSim scores to form a global candidate pool for each query.
4. Remove obvious positives. The annotated positive was excluded. The very top miner ranks were skipped before candidate selection to reduce unlabelled-positive leakage.
5. Construct KD lists. The retained MaxSim candidates and gold anchors were deduplicated and assembled into fixed 32-document candidate sets. A frozen Jina listwise reranker then scored each complete set jointly; those scores are aligned position-for-position with document_ids.

The repository packages the prepared artifacts used by GLINT, not a re-ranker or an inference index. WARP is therefore a data-construction dependency only; GLINT-base can be retrieved with a standard late-interaction backend such as PyLate/PLAID.

Contents

• sft_bica/: prepared SFT rows and metadata. The sources are MS MARCO, Natural Questions, HotpotQA, FEVER, FiQA, SQuAD v2, TriviaQA, and the BiCA citation-traversal augmentation.
• kd_7src/: the seven-source KD mixture in PyLate parquet layout (train, queries, and documents).
• kd_7src_jina_scores/: raw Jina listwise teacher-score shards keyed by query ID.

Format

The KD mixture follows the PyLate layout:

path	fields	notes
train/train.parquet	query_id, document_ids, scores	one row per query; document_ids has exactly 32 entries
queries/train.parquet	query_id, text	query text keyed by query ID
documents/train.parquet	document_id, text	candidate document text keyed by document ID
kd_7src_jina_scores/*.parquet	query_id, scores	32 Jina scores in exactly the document_ids order

Composition

subset	queries
msmarco	502,939
nq	152,132
squadv2	130,217
fever	109,810
hotpotqa	85,000
trivia	60,413
fiqa	5,498
total	1,046,009

Usage

from datasets import load_dataset

# Load the prepared KD rows and their query/document tables.
train = load_dataset("chungimungi/GLINT-data", "kd_7src", split="train")