⚠️ 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_losscheckpoint 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 | Task(s) | Train | Val | Test | Language | License | Source |
|---|---|---|---|---|---|---|---|
⚠️ synthetic_sonnet5_1k | unknown | — | — | — | — | UNKNOWN — not in registry | — |
| GLiNER multilingual synthetic | NER (multilingual) | 77,259 | 9,598 | 9,749 | en, de, fr, es, it, pl, nl, pt | see card | link |
| GLiNER multi-task synthetic | NER (multi-task) | 10,319 | 1,276 | 1,288 | en | Apache-2.0 | link |
| RAMS | Event extraction (trigger + args) | 7,329 | 924 | 871 | en | see source | link |
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
| Setting | Value |
|---|---|
| Trained on | 2026-08-03 |
| Duration | 1h 40m |
| Throughput | 31.8 samples/s |
| Epochs | 2 |
| Batch size | 4 (× 8 grad-accum) |
| Encoder LR | 2e-05 |
| Task-head LR | 0.0005 |
| Weight decay | 0.01 |
| Scheduler | cosine_restarts (warmup 0.05) |
| Precision | bf16 |
| Max grad norm | 1.0 |
| Best-checkpoint metric | eval_loss |
| Seed | 42 |
| Architecture | max_width=20, max_len=8192, 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 | 0.351 → 0.433 | 0.255 → 0.315 | 0.295 → 0.364 | 61530 |
| relation | 0.001 → 0.022 | 0.001 → 0.016 | 0.001 → 0.018 | 1512 |
| classification | 0.385 → 0.433 | 0.294 → 0.331 | 0.333 → 0.375 | 762 |
| event_type | 1.000 → 1.000 | 0.791 → 0.791 | 0.883 → 0.883 | 1657 |
| event_trigger | 0.275 → 0.280 | 0.384 → 0.391 | 0.321 → 0.326 | 1729 |
| event_argument | 0.026 → 0.214 | 0.032 → 0.269 | 0.028 → 0.238 | 4970 |
| event | 0.215 → 0.334 | 0.255 → 0.398 | 0.234 → 0.363 | 8356 |
Best checkpoint (validation)
Micro precision / recall / F1, strict → relaxed.
| Category | Precision | Recall | F1 | Support |
|---|---|---|---|---|
| entity | 0.346 → 0.423 | 0.248 → 0.303 | 0.289 → 0.353 | 62301 |
| relation | 0.001 → 0.033 | 0.001 → 0.024 | 0.001 → 0.028 | 1571 |
| classification | 0.410 → 0.450 | 0.307 → 0.337 | 0.351 → 0.385 | 766 |
| event_type | 1.000 → 1.000 | 0.778 → 0.778 | 0.875 → 0.875 | 1695 |
| event_trigger | 0.268 → 0.272 | 0.359 → 0.365 | 0.307 → 0.312 | 1777 |
| event_argument | 0.021 → 0.219 | 0.024 → 0.260 | 0.022 → 0.238 | 5204 |
| event | 0.212 → 0.337 | 0.240 → 0.384 | 0.225 → 0.359 | 8676 |
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).
Model card generated automatically at the end of training (2026-08-03).