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
Upload README.md with huggingface_hub
Browse files
README.md
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@@ -40,3 +40,26 @@ Fine-tune of `urchade/gliner_large-v2.1` for data-use mention extraction
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**Best F0.5**: 0.8506 (thr=0.6)
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**Best F1**: 0.8549 (thr=0.5)
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**Best F0.5**: 0.8506 (thr=0.6)
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**Best F1**: 0.8549 (thr=0.5)
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<!-- NER_COMPARISON_START -->
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## NER holdout comparison
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device: NVIDIA H100 NVL
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### rafmacalaba/data-use-mentions-extended (n=9249)
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| model | backend | best F0.5 | thr | best F1 | thr | wall-clock (s) | texts/s |
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| --- | --- | --- | --- | --- | --- | --- | --- |
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| `rafmacalaba/gliner_datause_extended` | gliner | 0.8506 | 0.6 | 0.8549 | 0.5 | 202.7 | 45.6 |
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| `ai4data/gliner2_datause` | gliner2 | 0.8634 | 0.7 | 0.8624 | 0.6 | 180.8 | 51.1 |
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F0.5 by threshold (sweet spots side-by-side):
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| thr | `rafmacalaba/gliner_datause_extended` | `ai4data/gliner2_datause` |
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| --- | --- | --- |
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| 0.1 | 0.6476 | 0.7321 |
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| 0.2 | 0.7121 | 0.7712 |
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| 0.3 | 0.7505 | 0.7979 |
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| 0.4 | 0.7858 | 0.8201 |
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| 0.5 | 0.8224 | 0.8363 |
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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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