gliner_datause_extended

Fine-tune of urchade/gliner_large-v2.1 for data-use mention extraction (dataset / survey / census / registry mentions in economics research papers).

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

Training

• base model: urchade/gliner_large-v2.1
• dataset: rafmacalaba/data-use-mentions-extended (gliner config)
• epochs: 5
• learning rate: 5e-06
• batch size: 16
• precision: bf16

Evaluation (holdout)

thr	tp	fp	fn	precision	recall	f0.5	f1
0.10	12283	8280	281	0.5973	0.9776	0.6477	0.7416
0.20	12192	6065	372	0.6678	0.9704	0.7122	0.7911
0.30	12062	4883	502	0.7118	0.9600	0.7506	0.8175
0.40	11845	3857	719	0.7544	0.9428	0.7858	0.8381
0.50	11498	2837	1066	0.8021	0.9152	0.8224	0.8549
0.60	10519	1798	2045	0.8540	0.8372	0.8506	0.8455
0.70	8328	892	4236	0.9033	0.6628	0.8422	0.7646

Best F0.5: 0.8506 (thr=0.6) Best F1: 0.8549 (thr=0.5)

NER holdout comparison

device: NVIDIA H100 NVL

rafmacalaba/data-use-mentions-extended (n=9249)

model	backend	best F0.5	thr	best F1	thr	wall-clock (s)	texts/s
rafmacalaba/gliner_datause_extended	gliner	0.8506	0.6	0.8549	0.5	202.7	45.6
ai4data/gliner2_datause	gliner2	0.8634	0.7	0.8624	0.6	180.8	51.1

F0.5 by threshold (sweet spots side-by-side):

thr	rafmacalaba/gliner_datause_extended	ai4data/gliner2_datause
0.1	0.6476	0.7321
0.2	0.7121	0.7712
0.3	0.7505	0.7979
0.4	0.7858	0.8201
0.5	0.8224	0.8363
0.6	0.8506	0.8523
0.7	0.8422	0.8634