Datasets:

rafmacalaba
/

datause-agents-v3

task_categories:
  - token-classification
tags:
  - ner
  - span-extraction
  - data-mentions
license: cc-by-4.0
configs:
  - config_name: gliner
    data_files:
      - split: train
        path: gliner_train.jsonl
      - split: val
        path: gliner_val.jsonl
      - split: holdout
        path: gliner_holdout.jsonl
  - config_name: gliner2
    data_files:
      - split: train
        path: gliner2_train.jsonl
      - split: val
        path: gliner2_val.jsonl
      - split: holdout
        path: gliner2_holdout.jsonl

datause-agents-v3 — data-use mentions

Annotation workflow

Labels are agent-produced under the project data-use doctrine; they are not owner rulings. The training and holdout splits are unchanged.

Validation review refresh

The val labels in both gliner and gliner2 were refreshed on 2026-09-29T17:54:34+00:00 using the gliner_val_review passage-review lane and shards 01–04. The review covered 1,260 passages and 2,171 candidate mentions drawn from published validation labels plus GLiNER predictions scoring at least 0.6. All decisions remain annotated_by: agent, owner_reviewed: false. The other 862 candidate-free validation passages retain their existing empty labels. Because review scope is candidate-generated and does not assess mentions missed by the candidate policy, this split is an agent-reviewed audit set, not independent full-recall gold.

The review-specific source metadata and span counts are in split_stats.json.

Labels

NAMED_DATA — a proper name, title, or acronym of a specific data source
DESCRIPTIVE_DATA — a source described in words but not named
VAGUE_DATA — generic data wording with no identifiable source

Provenance

bundle: agent_annotations_v3 (agent-annotated; owner_reviewed: false)
lanes: origin_strat_1, origin_strat_2, origin_strat_3, umar_pads, umar_pads_500
carve: agents-v3 (origin-stratified) — document-disjoint, so no two passages of one document straddle train and eval, and stratified by origin, so each split holds each corpus family's exact share
guard: 77 eval-text and 253 duplicate-text passages excluded; 128 eval documents checked, 0 leaks
not evaluable: jdc_operational have no documents in val/holdout (too few to stratify), so they are train-only and no split metric says anything about them
labels: labels.json, per-split counts and per-origin document counts: split_stats.json

Configs

gliner — {"tokenized_text": [...], "ner": [[start, end, LABEL], ...]} (ner is inclusive on both ends)
gliner2 — {"input": "...", "output": {"entities": {grade_key: [span strings]}}}

Splits

21,073 passages · 16,630 spans · 12,831 documents, document-disjoint.

split	rows	spans	documents	NAMED	DESCRIPTIVE	VAGUE
train	16,843	12,891	10,265	9,723	3,142	26
val	2,122	1,821	1,283	1,275	538	8
holdout	2,108	1,918	1,283	1,360	548	10

Usage

uv run python training/finetune_gliner.py \
    --dataset rafmacalaba/datause-agents-v3 --config gliner \
    --epochs 5 --batch-size 16 --output-dir models/gliner_agents_v3

from datasets import load_dataset
ds = load_dataset("rafmacalaba/datause-agents-v3", "gliner")   # ds['train'] / ds['val'] / ds['holdout']