Instructions to use rafmacalaba/gliner_datause with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use rafmacalaba/gliner_datause with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("rafmacalaba/gliner_datause") - Notebooks
- Google Colab
- Kaggle
gliner_datause
Fine-tune of urchade/gliner_large-v2.1 for data-use mention extraction
(dataset / survey / census / registry mentions in economics research papers).
Labels
NAMED_DATAโ a proper name, title, or acronym of a specific data sourceDESCRIPTIVE_DATAโ a source described in words but not namedVAGUE_DATAโ generic data wording with no identifiable source
Training
- base model:
urchade/gliner_large-v2.1 - dataset:
rafmacalaba/data-use-mentions(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 | 7147 | 5212 | 199 | 0.5783 | 0.9729 | 0.6293 | 0.7254 |
| 0.20 | 7082 | 3928 | 264 | 0.6432 | 0.9641 | 0.6891 | 0.7716 |
| 0.30 | 7017 | 3234 | 329 | 0.6845 | 0.9552 | 0.7256 | 0.7975 |
| 0.40 | 6901 | 2549 | 445 | 0.7303 | 0.9394 | 0.7643 | 0.8217 |
| 0.50 | 6704 | 1874 | 642 | 0.7815 | 0.9126 | 0.8046 | 0.8420 |
| 0.60 | 6212 | 1196 | 1134 | 0.8386 | 0.8456 | 0.8400 | 0.8421 |
| 0.70 | 4923 | 645 | 2423 | 0.8842 | 0.6702 | 0.8311 | 0.7624 |
Best F0.5: 0.8400 (thr=0.6) Best F1: 0.8421 (thr=0.6)
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