Instructions to use ai4data/gliner_datause with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use ai4data/gliner_datause with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("ai4data/gliner_datause") - Notebooks
- Google Colab
- Kaggle
File size: 2,130 Bytes
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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 | 12275 | 8853 | 289 | 0.5810 | 0.9770 | 0.6322 | 0.7287 |
| 0.20 | 12153 | 6445 | 411 | 0.6535 | 0.9673 | 0.6988 | 0.7800 |
| 0.30 | 12013 | 5218 | 551 | 0.6972 | 0.9561 | 0.7371 | 0.8064 |
| 0.40 | 11844 | 4143 | 720 | 0.7409 | 0.9427 | 0.7740 | 0.8297 |
| 0.50 | 11489 | 3002 | 1075 | 0.7928 | 0.9144 | 0.8145 | 0.8493 |
| 0.60 | 10542 | 1935 | 2022 | 0.8449 | 0.8391 | 0.8437 | 0.8420 |
| 0.70 | 8396 | 940 | 4168 | 0.8993 | 0.6683 | 0.8411 | 0.7668 |
**Best F0.5**: 0.8437 (thr=0.6)
**Best F1**: 0.8493 (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 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| `ai4data/gliner_datause` | gliner | 0.8437 | 0.6 | 0.8493 | 0.5 | 200.9 | 46.0 |
| `ai4data/gliner2_datause` | gliner2 | 0.8634 | 0.7 | 0.8624 | 0.6 | 181.0 | 51.1 |
F0.5 by threshold (sweet spots side-by-side):
| thr | `ai4data/gliner_datause` | `ai4data/gliner2_datause` |
| --- | --- | --- |
| 0.1 | 0.6321 | 0.7321 |
| 0.2 | 0.6987 | 0.7712 |
| 0.3 | 0.7371 | 0.7979 |
| 0.4 | 0.7740 | 0.8201 |
| 0.5 | 0.8145 | 0.8363 |
| 0.6 | 0.8437 | 0.8523 |
| 0.7 | 0.8411 | 0.8634 |
<!-- NER_COMPARISON_END -->
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