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
| 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 --> | |