gliner2_datause
Fine-tune of fastino/gliner2-large-v1 (GLiNER2) 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 sourceDESCRIPTIVE_DATA— a source described in words but not namedVAGUE_DATA— generic data wording with no identifiable source
Training
- base model:
fastino/gliner2-large-v1 - dataset:
rafmacalaba/data-use-mentions(gliner2 config) - epochs: 5
- encoder LR: 1e-05
- task LR: 0.0005
- batch size: 8
- precision: bf16
Evaluation (holdout, label-agnostic)
| thr | tp | fp | fn | precision | recall | f0.5 | f1 |
|---|---|---|---|---|---|---|---|
| 0.10 | 7037 | 3193 | 310 | 0.6879 | 0.9578 | 0.7290 | 0.8007 |
| 0.20 | 6915 | 2509 | 432 | 0.7338 | 0.9412 | 0.7676 | 0.8246 |
| 0.30 | 6789 | 2047 | 558 | 0.7683 | 0.9241 | 0.7951 | 0.8390 |
| 0.40 | 6576 | 1700 | 771 | 0.7946 | 0.8951 | 0.8128 | 0.8418 |
| 0.50 | 6300 | 1339 | 1047 | 0.8247 | 0.8575 | 0.8311 | 0.8408 |
| 0.60 | 5855 | 1001 | 1492 | 0.8540 | 0.7969 | 0.8419 | 0.8245 |
| 0.70 | 5072 | 630 | 2275 | 0.8895 | 0.6903 | 0.8410 | 0.7774 |
Best F0.5: 0.8419 (thr=0.6) Best F1: 0.8418 (thr=0.4)
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from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("rafmacalaba/gliner2_datause") # Extract entities text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday." result = extractor.extract_entities(text, ["company", "person", "product", "location"]) print(result)