gliner_datause_extended-onnx

rafmacalaba/gliner_datause_extended-onnx

gliner_datause_extended-onnx is a token classification model from rafmacalaba. Use it when you need labels on individual words, such as names. The card lists the license as apache-2.0.

FP16 ONNX export of rafmacalaba/glinerdatauseextended for the WebGPU demo.

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Updated Aug 16, 2026

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894 MB

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At a glance

Task: Token Classification
License: apache-2.0
Access: Public
Created: Aug 16, 2026
Updated: Aug 16, 2026
SHA: e5c8a189

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rafmacalaba/datause-extraction-webgpu

Model card

From the Hugging Face model README

gliner_datause_extended (ONNX)

FP16 ONNX export of rafmacalaba/gliner_datause_extended for the WebGPU demo.

Files

onnx/gliner_datause_extended_fp16.onnx
onnx/gliner_datause_extended_fp16.labels.json
tokenizer.json
tokenizer_config.json

Source model card

license: apache-2.0 pipeline_tag: token-classification tags:

ner
gliner
data-use

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

Task: Token Classification
License: apache-2.0
Created: Aug 16, 2026
Updated: Aug 16, 2026