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 โ€” a proper name, title, or acronym of a specific data source
  • DESCRIPTIVE_DATA โ€” a source described in words but not named
  • VAGUE_DATA โ€” 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)

Downloads last month
-
Safetensors
Model size
0.5B params
Tensor type
F32
ยท
Inference Providers NEW
This model isn't deployed by any Inference Provider. ๐Ÿ™‹ Ask for provider support