⚠️ From-scratch negative result (public)
Fresh GLiNER2 heads on the raw
microsoft/deberta-v3-baseencoder (from_encoder), trained onsynthetic_sonnet5_1konly (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 counterpartwhr778/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).