Instructions to use rafmacalaba/gliner_datause_extended with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use rafmacalaba/gliner_datause_extended with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("rafmacalaba/gliner_datause_extended") - 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 | 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 --> | |