⚠️ From-scratch negative result (public)

Fresh GLiNER2 heads on the raw microsoft/deberta-v3-base encoder (from_encoder), trained on synthetic_sonnet5_1k only (15 epochs). This tests whether ~1,500 synthetic records can teach the extraction tasks from scratch. They cannot — every span/relation task collapses. Blind test (synthetic held-out split), strict micro-F1, vs the warm-start counterpart whr778/gliner2-base-v1-synthetic:

Task From-encoder (this) Warm-start (base-v1)
Entity 0.141 0.904
Relation 0.000 0.657
Event type 0.998 0.956
Event trigger 0.221 0.838
Event argument 0.000 (0.168 relaxed) 0.702
Classification 0.356 0.835

Only coarse event-type (few classes) is learnable from scratch; fine-grained span extraction and relations need either a warm start or the ~10⁵–10⁶-scale IE curriculum the fastino heads saw. The synthetic corpus is good (the warm-start model works) but is an adaptation set, not a from-scratch pretraining set. Not for use; documented negative result.

deberta_base_fromenc_synthetic

A GLiNER2 multi-task information-extraction model (entities, relations, events, and classification) fine-tuned from microsoft/deberta-v3-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: microsoft/deberta-v3-base
Library: gliner2
Tasks: entity, relation, event, and classification extraction
Experiment: deberta_base_fromenc_synthetic

Training data

1 dataset used for this run. 1,497 training records (val: 191, test: 194).

Dataset	Task(s)	Train	Val	Test	Language	License	Source
⚠️ `synthetic_sonnet5_1k`	unknown	—	—	—	—	UNKNOWN — not in registry	—

Training procedure

Setting	Value
Trained on	2026-08-03
Duration	40m 38s
Throughput	9.0 samples/s
Epochs	15
Batch size	8 (× 4 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=512`, `struct_loss=bce_posweight`, `struct_pos_weight=4.0`

Evaluation

Decision threshold: 0.3 (calibrated against the validation set).

Blind test (held-out test splits)

Micro precision / recall / F1, strict → relaxed.

Category	Precision	Recall	F1	Support
entity	0.082 → 0.099	0.512 → 0.616	0.141 → 0.170	6010
relation	0.000 → 0.024	0.000 → 0.019	0.000 → 0.021	1512
classification	0.411 → 0.565	0.314 → 0.432	0.356 → 0.490	762
event_type	1.000 → 1.000	0.996 → 0.996	0.998 → 0.998	809
event_trigger	0.130 → 0.133	0.725 → 0.742	0.221 → 0.226	881
event_argument	0.000 → 0.116	0.000 → 0.303	0.000 → 0.168	2954
event	0.109 → 0.176	0.311 → 0.509	0.161 → 0.262	4644

Best checkpoint (validation)

Micro precision / recall / F1, strict → relaxed.

Category	Precision	Recall	F1	Support
entity	0.088 → 0.115	0.078 → 0.102	0.083 → 0.108	5900
relation	0.000 → 0.042	0.000 → 0.028	0.000 → 0.033	1571
classification	0.394 → 0.555	0.295 → 0.415	0.338 → 0.475	766
event_type	1.000 → 1.000	0.985 → 0.985	0.992 → 0.992	799
event_trigger	0.162 → 0.164	0.594 → 0.600	0.254 → 0.257	881
event_argument	0.002 → 0.164	0.001 → 0.096	0.001 → 0.121	3022
event	0.228 → 0.278	0.279 → 0.345	0.251 → 0.308	4702

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: DeBERTa-v3-base — mit

Unverified — verify the upstream terms before redistribution

synthetic_sonnet5_1k (unknown) (unspecified)

Permissive

DeBERTa-v3-base (mit)

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).

Task	From-encoder (this)	Warm-start (base-v1)
Entity	0.141	0.904
Relation	0.000	0.657
Event type	0.998	0.956
Event trigger	0.221	0.838
Event argument	0.000 (0.168 relaxed)	0.702
Classification	0.356	0.835

whr778/deberta-base-fromenc-synthetic

⚠️ From-scratch negative result (public)