rafmacalaba/gliner-datause-displacement
gliner-datause-displacement
Fine-tune of urchade/gliner_large-v2.1 for data-use mention extraction with a single `DATA_MENTION` class, trained on `rafmacalaba/data-use-mentions-tiered` — the tiered copy of rafmacalaba/data-use-mentions where Luna/classifier-judged T3 (non-mention) and junk spans are untagged hard negatives (text stays, span removed). The extractor owns the mention boundary only (T1 evidential ∪ T2 declaration vs T3/junk); specificity detail is recovered downstream by the multitask SFT model.
Labels
DATA_MENTION— a real data mention that carries an analytic or declarative use (T1 evidential ∪ T2 declaration)
Training
- base model:
urchade/gliner_large-v2.1 - dataset:
rafmacalaba/datause-displacement-reviewed(gliner_reviewed config) - epochs: 8
- learning rate: 5e-06
- batch size: 16
- precision: bf16
- checkpoint selection: val span-F0.5 (post-hoc sweep of epoch checkpoints; eval_loss was explicitly not used)
Evaluation (tiered holdout)
Gold = T1∪T2 spans; a true-FP cluster matching a dropped T3/junk span counts as a T3 leak (lower is better). Label-agnostic Hungarian matching, jaccard >= 0.5 — identical to prior data-use-mentions evals.
Best F0.5: 0.7047 (thr=0.7) Best F1: 0.6976 (thr=0.6)
Full per-doc predictions (raw scores, gold spans with tier decisions): holdout_predictions.jsonl on this repo.