Instructions to use ai4data/gliner2_datause with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use ai4data/gliner2_datause with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("ai4data/gliner2_datause") # Extract entities text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday." result = extractor.extract_entities(text, ["company", "person", "product", "location"]) print(result) - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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| model | backend | best F0.5 | thr | best F1 | thr | wall-clock (s) | texts/s |
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| `ai4data/gliner2_datause` | gliner2 | 0.8634 | 0.7 | 0.8624 | 0.6 |
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F0.5 by threshold (sweet spots side-by-side):
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<!-- NER_COMPARISON_END -->
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| model | backend | best F0.5 | thr | best F1 | thr | wall-clock (s) | texts/s |
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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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| 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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