A GLiNER2 structure-extraction model fine-tuned from fastino/gliner2-base-v1 to pull casualty figures from disaster news text — binding each number to its role (dead / injured / missing) amid distractor numbers (dates, magnitudes, displacement counts). It is the extraction front-end for a document-stream event tracker that estimates a disaster's evolving toll over time; the task it solves is number-to-role binding in multi-fact prose, which a surface parser cannot.

Usage

python

from gliner2 import GLiNER2, Schema

ex = GLiNER2.from_pretrained("whr778/gliner2-base-v1-casualty")
schema = (Schema().structure("casualty_report")
    .field("dead", dtype="str", description="number of people killed or confirmed dead")
    .field("injured", dtype="str", description="number of people injured or hurt")
    .field("missing", dtype="str", description="number of people missing or unaccounted for"))

ex.extract(
    "Officials say at least 40 were killed and about 300 injured after the "
    "magnitude-6.8 quake; roughly 2,000 people have been displaced.",
    schema,
)
# {"casualty_report": [{"dead": "40", "injured": "300", "missing": null}]}

It binds dead=40 and injured=300 (not the magnitude 6.8 or the 2,000 displaced), and correctly returns null for the unmentioned missing role.

Held-out evaluation

On a held-out test set of synthetic disaster news (12 streams, 1,044 reports, never seen in training), vs the zero-shot base model:

metric	zero-shot base	this model
role precision	0.627	0.914
role recall	0.906	0.965
value exact (number binding)	0.991	1.000

Fed end-to-end into the downstream tracker, normalized trajectory RMSE improved from 0.291 to 0.165 (clean-observation ceiling 0.115), and the hardest role (missing) from 0.458 to 0.122.

Training data

Fine-tuned on 29,198 casualty_report structure examples (val 1,303 / test 1,038) built from synthetic disaster-news streams realized with claude-sonnet-5: each report snippet states several roles' exact figures with realistic hedging ("at least", "feared", "hundreds") plus distractor numbers (dates, magnitudes, displacement counts). Deterministic generator; no real personal data. 8 epochs, batch 16×2, fp16, ~35 min on 1× A100.

Limitations

Synthetic, same-distribution. Trained and evaluated on sonnet-5-realized text. Generalization to real disaster reporting is the intended next test and is not yet validated.
The source field is unreliable. The model favors numeric spans, so a source field is often filled with a number rather than the reporting entity — use dead/injured/ missing; treat source as best-effort.
Hedges/qualifiers follow the synthetic generator's conventions.

License

Derivative of fastino/gliner2-base-v1; training data is synthetic (generated for this project). Effective license: unverified — review the base-model terms before redistribution or commercial use.