✅ Sanity-check checkpoint (private)
Fine-tune of
fastino/gliner2-base-v1on our multi-task synthetic corpus (synthetic_sonnet5_1k) only, to check whether our synthetic data trains cleanly across all five GLiNER2 tasks. It does — blind test on the synthetic held-out split (in-distribution):
Task Strict F1 Relaxed F1 Support Entity 0.904 0.933 6,010 Relation 0.657 0.745 1,512 Event type 0.956 — 809 Event trigger 0.838 — 881 Event argument 0.702 0.894 2,954 Classification 0.835 0.841 762 These are on the synthetic test split (in-distribution): they show the data is coherent and learnable, not a claim about the original public benchmarks. Warm-started (from_pretrained), 10 epochs,
eval_lossselection (best = epoch 7). Experimental; kept for reproducibility.
gliner2_base_v1_synthetic
A GLiNER2 multi-task information-extraction model (entities, relations, events, and classification) fine-tuned from fastino/gliner2-base-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-base-v1 - Library:
gliner2 - Tasks: entity, relation, event, and classification extraction
- Experiment:
gliner2_base_v1_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 | 32m 55s |
| Throughput | 7.4 samples/s |
| Epochs | 10 |
| Batch size | 8 (× 4 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_loss |
| Seed | 42 |
Evaluation
Decision threshold: 0.7 (calibrated against the validation set).
Blind test (held-out test splits)
Micro precision / recall / F1, strict → relaxed.
| Category | Precision | Recall | F1 | Support |
|---|---|---|---|---|
| entity | 0.914 → 0.943 | 0.894 → 0.923 | 0.904 → 0.933 | 6010 |
| relation | 0.790 → 0.895 | 0.563 → 0.638 | 0.657 → 0.745 | 1512 |
| classification | 0.964 → 0.971 | 0.736 → 0.741 | 0.835 → 0.841 | 762 |
| event_type | 1.000 → 1.000 | 0.916 → 0.916 | 0.956 → 0.956 | 809 |
| event_trigger | 0.847 → 0.866 | 0.830 → 0.848 | 0.838 → 0.857 | 881 |
| event_argument | 0.737 → 0.940 | 0.670 → 0.851 | 0.702 → 0.893 | 2954 |
| event | 0.805 → 0.935 | 0.743 → 0.862 | 0.773 → 0.897 | 4644 |
Best checkpoint (validation)
Micro precision / recall / F1, strict → relaxed.
| Category | Precision | Recall | F1 | Support |
|---|---|---|---|---|
| entity | 0.886 → 0.917 | 0.919 → 0.952 | 0.902 → 0.934 | 5900 |
| relation | 0.716 → 0.837 | 0.604 → 0.706 | 0.655 → 0.766 | 1571 |
| classification | 0.953 → 0.960 | 0.713 → 0.718 | 0.816 → 0.822 | 766 |
| event_type | 1.000 → 1.000 | 0.962 → 0.962 | 0.981 → 0.981 | 799 |
| event_trigger | 0.796 → 0.807 | 0.875 → 0.888 | 0.834 → 0.845 | 881 |
| event_argument | 0.649 → 0.893 | 0.666 → 0.911 | 0.657 → 0.902 | 3022 |
| event | 0.734 → 0.893 | 0.755 → 0.915 | 0.745 → 0.904 | 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: gliner2-base-v1 — see model card
Unverified — verify the upstream terms before redistribution
- gliner2-base-v1 (see model card)
- synthetic_sonnet5_1k (unknown) (unspecified)
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).