datause-agents-v3 — data-use mentions

Annotation workflow

Labels were produced with a locally run LLM through the passage-annotation harness, with human-in-the-loop passage review and adjudication under the project's data-use doctrine. The same passage-level annotation workflow was used for train, validation, and holdout.

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 (local LLM plus passage-annotation harness with human-in-the-loop review)
• 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 · 16,474 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	1,665	1,283	1,212	451	2
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']