gliner-datause-catchall-singlepass

Single-pass catch-all cascade in one bundle: the rafmacalaba/gliner-datause-mentions-catch-all encoder (frozen, byte-identical) plus an inference-native probe head (probe_head.pt, trained by outputs/gliner-datause-catchall-infer-probe with --feature-source infer). One forward per doc yields proposals (GLiNER DATA_MENTION @ 0.1) and keep/drop (probe_score) together — see training/singlepass_infer.py.

• head: input_dim 2048, hidden 256, context_radius 64
• keep knob: probe_score >= thr (global best 0.5 @ F1 0.8467)

Per-origin best-F1 thresholds

origin	thr	f1
fcv_pads_east_africa	0.5	0.8046
general_prwp	0.4	0.8796
jad_paddy_docs	0.1	0.9693
jdc_operational	0.6	0.8019
refugee_pads	0.5	0.8387
reliefweb	0.4	0.7738

Inference

from training.singlepass_infer import load_bundle, predict_keep_drop
model, head, bundle = load_bundle('rafmacalaba/gliner-datause-catchall-singlepass', 'rafmacalaba/gliner-datause-catchall-singlepass', 'cuda')
rows = predict_keep_drop(texts, model, head, bundle,
                         propose_thr=0.1, keep_thr=0.3)

Files: pytorch_model.bin + gliner_config.json (encoder), probe_head.pt + head_config.json (head), thresholds.json (operating points), holdout_metrics.json (sweep).