| --- |
| pretty_name: GLINT training data |
| task_categories: |
| - text-retrieval |
| - text-ranking |
| 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](https://huggingface.co/lightonai/LateOn-unsupervised) |
| - **Mining backend:** [PyLate](https://github.com/lightonai/pylate) + |
| [WARP](https://github.com/jlscheerer/xtr-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 |
|
|
| ```python |
| 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") |
| ``` |
