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ai4data/
gliner_datause

Name: gliner_datause
Rating: 2.5 (372 reviews)

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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	12275	8853	289	0.5810	0.9770	0.6322	0.7287
0.20	12153	6445	411	0.6535	0.9673	0.6988	0.7800
0.30	12013	5218	551	0.6972	0.9561	0.7371	0.8064
0.40	11844	4143	720	0.7409	0.9427	0.7740	0.8297
0.50	11489	3002	1075	0.7928	0.9144	0.8145	0.8493
0.60	10542	1935	2022	0.8449	0.8391	0.8437	0.8420
0.70	8396	940	4168	0.8993	0.6683	0.8411	0.7668

Best F0.5: 0.8437 (thr=0.6) Best F1: 0.8493 (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
`ai4data/gliner_datause`	gliner	0.8437	0.6	0.8493	0.5	200.9	46.0
`ai4data/gliner2_datause`	gliner2	0.8634	0.7	0.8624	0.6	181.0	51.1

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

thr	`ai4data/gliner_datause`	`ai4data/gliner2_datause`
0.1	0.6321	0.7321
0.2	0.6987	0.7712
0.3	0.7371	0.7979
0.4	0.7740	0.8201
0.5	0.8145	0.8363
0.6	0.8437	0.8523
0.7	0.8411	0.8634

ai4data/gliner_datause