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") text = "Cristiano Ronaldo dos Santos Aveiro was born on 5 February 1985 in Funchal, Madeira, Portugal." labels = ["person", "date", "location"] entities = model.predict_entities(text, labels) for entity in entities: print(entity["text"], "=>", entity["label"]) - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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<!-- NER_COMPARISON_END -->
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| 0.6 | 0.8506 | 0.8523 |
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| 0.7 | 0.8422 | 0.8634 |
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<!-- NER_COMPARISON_END -->
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## FCV cross-corpus validation (precision)
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Validated on the World Bank Fragility, Conflict & Violence (FCV) document corpus with
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the `gpt-5.6-luna` judge (Luna validation). Precision-only: there is no gold set of all
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true mentions, so recall / F1 are not reported here.
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### Precision by config × label
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| config | NAMED_DATA | DESCRIPTIVE_DATA | VAGUE_DATA | overall |
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| --- | --- | --- | --- | --- |
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| fcv_pads_east_asia | 80.1% | 81.6% | 91.8% | 81.9% |
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| jdc_operational | 86.8% | 91.3% | 91.5% | 89.1% |
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| refugee_pads | 83.8% | 92.8% | 91.9% | 89.1% |
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| reliefweb | 83.7% | 85.4% | 95.5% | 85.6% |
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### Specificity agreement (extractor label == judge specificity)
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| config | NAMED_DATA | DESCRIPTIVE_DATA | VAGUE_DATA |
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| --- | --- | --- | --- |
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| fcv_pads_east_asia | 82.8% | 88.3% | 82.1% |
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| jdc_operational | 90.2% | 83.2% | 71.7% |
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| refugee_pads | 89.4% | 86.5% | 77.5% |
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| reliefweb | 92.6% | 82.5% | 73.4% |
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Notes:
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- `VAGUE_DATA` is the safest label (91–95% precision); `NAMED_DATA` is the weakest (80–87%).
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- The extractor under-specifies: `VAGUE_DATA` is often judged `descriptive`, and
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`DESCRIPTIVE_DATA` → `named` leaks occur.
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- `reliefweb` carries more bad-JSON judge errors (OCR/HTML artifacts) than the other configs.
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