Modelpublic

whr778/mmbert-base-combined-wikievents

sourceHugging Faceotherupdated 2mo agoView on Hugging Face

0likes22downloads

Model Card

## ⚠️ 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.

mmbertbasewikievents_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	evaleventargumentstrictmicro_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).

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