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