whr778/mmbert-base-combined-wikievents

⚠️ Experimental checkpoint — negative result (private)

Treatment arm of a head-initialization A/B (GLiNER2 working paper, §10.7): mmBERT-base fine-tuned on WikiEvents from the broad combined base whr778/mmbert-base-combined. On the WikiEvents blind test it gave no reliable lift over the RAMS-only-base control: argument-strict F1 at the floor (0.007 vs 0.005), trigger edge is precision-only within noise (0.133 vs 0.085), and event-type regressed (0.573 vs 0.944). Not for production; kept for reproducibility.

mmbert_base_wikievents_combined

A GLiNER2 multi-task information-extraction model (entities, relations, events, and classification) fine-tuned from ./out/mmbert-base-combined/best.

⚠️ License at a glance

• Effective license: Unverified — review required
• Commercial use: Unverified
• All dataset licenses verified: No

See License for the full determination and per-dataset terms.

Model details

• Base model: ./out/mmbert-base-combined/best
• Library: gliner2
• Tasks: entity, relation, event, and classification extraction
• Experiment: mmbert_base_wikievents_combined

Training data

1 dataset used for this run. 206 training records (val: 20, test: 20).

Dataset	Task(s)	Train	Val	Test	Language	License	Source
WikiEvents	NER + event extraction	206	20	20	en	see source	link

Dataset notes

• WikiEvents — KAIROS-ontology event extraction co-trained with typed entity mentions; 49 event types, 57 argument roles.

Training procedure

Setting	Value
Trained on	2026-08-03
Duration	10m 11s
Throughput	4.4 samples/s
Epochs	15
Batch size	2 (× 16 grad-accum)
Encoder LR	1e-05
Task-head LR	0.0003
Weight decay	0.01
Scheduler	cosine_restarts (warmup 0.05)
Precision	bf16
Max grad norm	1.0
Best-checkpoint metric	eval_event_argument_strict_micro_f1
Seed	42
Architecture	struct_loss=bce_posweight, struct_pos_weight=4.0

Evaluation

Decision threshold: 0.5 (calibrated against the validation set).

Blind test (held-out test splits)

Micro precision / recall / F1, strict → relaxed.

Category	Precision	Recall	F1	Support
entity	0.196 → 0.249	0.472 → 0.599	0.277 → 0.352	1602
event_type	1.000 → 1.000	0.402 → 0.402	0.573 → 0.573	122
event_trigger	0.217 → 0.217	0.096 → 0.096	0.133 → 0.133	239
event_argument	0.023 → 0.264	0.004 → 0.048	0.007 → 0.081	515
event	0.306 → 0.393	0.084 → 0.113	0.132 → 0.176	876

Best checkpoint (validation)

Micro precision / recall / F1, strict → relaxed.

Category	Precision	Recall	F1	Support
entity	0.293 → 0.391	0.209 → 0.278	0.244 → 0.325	1427
event_type	1.000 → 1.000	0.140 → 0.140	0.245 → 0.245	129
event_trigger	0.400 → 0.400	0.030 → 0.030	0.055 → 0.055	269
event_argument	0.429 → 0.714	0.007 → 0.013	0.014 → 0.026	416
event	0.644 → 0.689	0.036 → 0.040	0.068 → 0.075	814

License

Effective license: Unverified — review required. This model is a derivative of its base model and every training dataset, so the most restrictive term across all of them governs the whole model.

• Commercial use: Unverified
• Share-alike obligation: No
• All licenses verified: No
• Base model: ./out/mmbert-base-combined/best — see model card

Unverified — verify the upstream terms before redistribution

• ./out/mmbert-base-combined/best (see model card)
• WikiEvents (see source)

License strings are copied verbatim from each dataset's card/source and from tools/train/dataset_registry.yaml. "see card"/"see source"/"other" mean the upstream declares no clear license — treat as unverified. This summary is informational, not legal advice; confirm terms before redistribution or commercial use.

Citation

If you use this model, please cite GLiNER2 and the underlying datasets (linked in Training data).

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Model card generated automatically at the end of training (2026-08-03).