gliner2-large-v1-rams

whr778/gliner2-large-v1-rams

gliner2-large-v1-rams is a token classification model from whr778. Use it when you need labels on individual words, such as names. It is set up for gliner2. The card lists the license as other.

A GLiNER2 multi-task information-extraction model (entities, relations, events, and classification) fine-tuned from fastino/gliner2-large-v1.

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At a glance

Task: Token Classification
Library: gliner2
License: other
Model type: extractor
Access: Public
Created: Jul 20, 2026
Updated: Aug 6, 2026
SHA: 35a351d5

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Base models

fastino/gliner2-large-v1

Model card

From the Hugging Face model README

gliner2-large-v1_rams

A GLiNER2 multi-task information-extraction model (entities, relations, events, and classification) fine-tuned from fastino/gliner2-large-v1.

⚠️ 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: fastino/gliner2-large-v1
Library: gliner2
Tasks: entity, relation, event, and classification extraction
Experiment: gliner2-large-v1_rams

Training data

1 dataset used for this run. 7,329 training records (val: 924, test: 871).

Dataset	Task(s)	Train	Val	Test	Language	License	Source
RAMS	Event extraction (trigger + args)	7,329	924	871	en	see source	link

Dataset notes

RAMS — Multi-sentence event extraction with triggers and typed arguments; 139 event types, 65 argument roles.

Training procedure

Setting	Value
Trained on	2026-07-20
Duration	32m 54s
Throughput	26.0 samples/s
Epochs	15
Batch size	4 (× 8 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_strict_micro_f1
Seed	42

Evaluation

Decision threshold: 0.5 (config default).

Blind test (held-out test splits)

Micro precision / recall / F1, strict → relaxed.

Category	Precision	Recall	F1	Support
event_type	1.000 → 1.000	1.000 → 1.000	1.000 → 1.000	848
event_trigger	0.856 → 0.860	0.955 → 0.960	0.903 → 0.907	848
event_argument	0.465 → 0.732	0.424 → 0.666	0.444 → 0.697	2016
event	0.692 → 0.828	0.677 → 0.810	0.684 → 0.819	3712

Best checkpoint (validation)

Micro precision / recall / F1, strict → relaxed.

Category	Precision	Recall	F1	Support
event_type	1.000 → 1.000	0.997 → 0.997	0.998 → 0.998	896
event_trigger	0.853 → 0.861	0.927 → 0.936	0.889 → 0.897	896
event_argument	0.445 → 0.711	0.398 → 0.636	0.420 → 0.671	2182
event	0.679 → 0.817	0.652 → 0.785	0.665 → 0.801	3974

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: gliner2-large-v1 — see model card

Unverified — verify the upstream terms before redistribution

RAMS (see source)
gliner2-large-v1 (see model card)

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 (2026-07-20).

Task: Token Classification
Library: gliner2
Type: extractor
License: other
Languages: en
Created: Jul 20, 2026
Updated: Aug 6, 2026