---
license: apache-2.0
pipeline_tag: token-classification
tags:
  - ner
  - gliner
  - data-use
---

# gliner_datause_extended

Fine-tune of `urchade/gliner_large-v2.1` for data-use mention extraction
(dataset / survey / census / registry mentions in economics research papers).

## Labels
- `NAMED_DATA` — a proper name, title, or acronym of a specific data source
- `DESCRIPTIVE_DATA` — a source described in words but not named
- `VAGUE_DATA` — generic data wording with no identifiable source

## Training
- base model: `urchade/gliner_large-v2.1`
- dataset: `rafmacalaba/data-use-mentions-extended` (gliner config)
- epochs: 5
- learning rate: 5e-06
- batch size: 16
- precision: bf16

## Evaluation (holdout)

| thr | tp | fp | fn | precision | recall | f0.5 | f1 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| 0.10 | 12283 | 8280 | 281 | 0.5973 | 0.9776 | 0.6477 | 0.7416 |
| 0.20 | 12192 | 6065 | 372 | 0.6678 | 0.9704 | 0.7122 | 0.7911 |
| 0.30 | 12062 | 4883 | 502 | 0.7118 | 0.9600 | 0.7506 | 0.8175 |
| 0.40 | 11845 | 3857 | 719 | 0.7544 | 0.9428 | 0.7858 | 0.8381 |
| 0.50 | 11498 | 2837 | 1066 | 0.8021 | 0.9152 | 0.8224 | 0.8549 |
| 0.60 | 10519 | 1798 | 2045 | 0.8540 | 0.8372 | 0.8506 | 0.8455 |
| 0.70 | 8328 | 892 | 4236 | 0.9033 | 0.6628 | 0.8422 | 0.7646 |

**Best F0.5**: 0.8506 (thr=0.6)
**Best F1**: 0.8549 (thr=0.5)

<!-- NER_COMPARISON_START -->
## NER holdout comparison
device: NVIDIA H100 NVL

### rafmacalaba/data-use-mentions-extended (n=9249)

| model | backend | best F0.5 | thr | best F1 | thr | wall-clock (s) | texts/s |
| --- | --- | --- | --- | --- | --- | --- | --- |
| `rafmacalaba/gliner_datause_extended` | gliner | 0.8506 | 0.6 | 0.8549 | 0.5 | 202.7 | 45.6 |
| `ai4data/gliner2_datause` | gliner2 | 0.8634 | 0.7 | 0.8624 | 0.6 | 180.8 | 51.1 |

F0.5 by threshold (sweet spots side-by-side):

| thr | `rafmacalaba/gliner_datause_extended` | `ai4data/gliner2_datause` |
| --- | --- | --- |
| 0.1 | 0.6476 | 0.7321 |
| 0.2 | 0.7121 | 0.7712 |
| 0.3 | 0.7505 | 0.7979 |
| 0.4 | 0.7858 | 0.8201 |
| 0.5 | 0.8224 | 0.8363 |
| 0.6 | 0.8506 | 0.8523 |
| 0.7 | 0.8422 | 0.8634 |
<!-- NER_COMPARISON_END -->


## FCV cross-corpus validation (precision)

Validated on the World Bank Fragility, Conflict & Violence (FCV) document corpus with
the `gpt-5.6-luna` judge (Luna validation). Precision-only: there is no gold set of all
true mentions, so recall / F1 are not reported here.

### Precision by config × label

| config | NAMED_DATA | DESCRIPTIVE_DATA | VAGUE_DATA | overall |
| --- | --- | --- | --- | --- |
| fcv_pads_east_asia | 80.1% | 81.6% | 91.8% | 81.9% |
| jdc_operational | 86.8% | 91.3% | 91.5% | 89.1% |
| refugee_pads | 83.8% | 92.8% | 91.9% | 89.1% |
| reliefweb | 83.7% | 85.4% | 95.5% | 85.6% |

### Specificity agreement (extractor label == judge specificity)

| config | NAMED_DATA | DESCRIPTIVE_DATA | VAGUE_DATA |
| --- | --- | --- | --- |
| fcv_pads_east_asia | 82.8% | 88.3% | 82.1% |
| jdc_operational | 90.2% | 83.2% | 71.7% |
| refugee_pads | 89.4% | 86.5% | 77.5% |
| reliefweb | 92.6% | 82.5% | 73.4% |

Notes:
- `VAGUE_DATA` is the safest label (91–95% precision); `NAMED_DATA` is the weakest (80–87%).
- The extractor under-specifies: `VAGUE_DATA` is often judged `descriptive`, and
  `DESCRIPTIVE_DATA` → `named` leaks occur.
- `reliefweb` carries more bad-JSON judge errors (OCR/HTML artifacts) than the other configs.