mmbert-base-combined

⚠️ Experimental checkpoint — negative result (private)

Research artifact from a head-initialization A/B (GLiNER2 working paper, §10.7). This broad combined base warms the GLiNER2 heads for only 2 epochs (eval_loss checkpoint selection) on a mix of synthetic multi-task data, GLiNER multilingual/multi-task NER, and RAMS events. It exists only as the base for a downstream WikiEvents fine-tune; that fine-tune showed no reliable improvement over the RAMS-only base. Its own RAMS argument-strict F1 is weak (0.028; the RAMS-only mmBERT base is 0.050 — both cold-start). Not for production; kept for reproducibility.

mmbert_base_combined

A GLiNER2 multi-task information-extraction model (entities, relations, events, and classification) fine-tuned from jhu-clsp/mmBERT-base.

⚠️ 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: jhu-clsp/mmBERT-base
• Library: gliner2
• Tasks: entity, relation, event, and classification extraction
• Experiment: mmbert_base_combined

Training data

4 datasets used for this run. 96,404 training records (val: 11,989, test: 12,102).

Dataset notes

• GLiNER multilingual synthetic — Multilingual synthetic NER (German, French, Polish, and others) for multilingual encoder training.
• GLiNER multi-task synthetic — Dense multi-type synthetic NER (~10 types per record), open vocabulary.
• RAMS — Multi-sentence event extraction with triggers and typed arguments; 139 event types, 65 argument roles.

Training procedure

Evaluation

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

Blind test (held-out test splits)

Micro precision / recall / F1, strict → relaxed.

Best checkpoint (validation)

Micro precision / recall / F1, strict → relaxed.

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: mmBERT-base — see model card

Unverified — verify the upstream terms before redistribution

• GLiNER multilingual synthetic (see card)
• RAMS (see source)
• mmBERT-base (see model card)
• synthetic_sonnet5_1k (unknown) (unspecified)

Permissive

• GLiNER multi-task synthetic (Apache-2.0)

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).