Instructions to use rafmacalaba/gliner_datause with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use rafmacalaba/gliner_datause with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("rafmacalaba/gliner_datause") 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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@@ -30,25 +30,58 @@ Fine-tune of `urchade/gliner_large-v2.1` for data-use mention extraction
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| thr | tp | fp | fn | precision | recall | f0.5 | f1 |
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**Best F0.5**: 0.
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**Best F1**: 0.
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## Evaluation breakdown (holdout)
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| group | examples | spans | thr | precision | recall | f0.5 | f1 |
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| thr | tp | fp | fn | precision | recall | f0.5 | f1 |
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| 0.10 | 21384 | 11373 | 306 | 0.6528 | 0.9859 | 0.7001 | 0.7855 |
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| 0.20 | 21251 | 8687 | 439 | 0.7098 | 0.9798 | 0.7512 | 0.8232 |
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| 0.30 | 21069 | 7139 | 621 | 0.7469 | 0.9714 | 0.7831 | 0.8445 |
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| 0.40 | 20815 | 5724 | 875 | 0.7843 | 0.9597 | 0.8141 | 0.8632 |
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| 0.50 | 20317 | 4241 | 1373 | 0.8273 | 0.9367 | 0.8471 | 0.8786 |
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| 0.60 | 19018 | 2836 | 2672 | 0.8702 | 0.8768 | 0.8715 | 0.8735 |
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| 0.70 | 15666 | 1516 | 6024 | 0.9118 | 0.7223 | 0.8663 | 0.8060 |
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**Best F0.5**: 0.8715 (thr=0.6)
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**Best F1**: 0.8786 (thr=0.5)
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## Evaluation breakdown (holdout)
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| group | examples | spans | thr | precision | recall | f0.5 | f1 |
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| overall | 18709 | 22052 | 0.60 | 0.8702 | 0.8768 | 0.8715 | 0.8735 |
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| prwp | 9188 | 12359 | 0.60 | 0.8574 | 0.8469 | 0.8553 | 0.8521 |
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| fcv | 9521 | 9693 | 0.60 | 0.8863 | 0.9159 | 0.8921 | 0.9009 |
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| general_prwp | 9188 | 12359 | 0.60 | 0.8574 | 0.8469 | 0.8553 | 0.8521 |
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| fcv_pads_east_africa | 6794 | 6784 | 0.60 | 0.8991 | 0.9156 | 0.9023 | 0.9073 |
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| jdc_operational | 158 | 204 | 0.50 | 0.9212 | 0.9541 | 0.9276 | 0.9373 |
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| refugee_pads | 687 | 743 | 0.70 | 0.9237 | 0.8036 | 0.8969 | 0.8595 |
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| reliefweb | 1882 | 1962 | 0.70 | 0.8920 | 0.7735 | 0.8655 | 0.8286 |
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### Per-label (overall)
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| label | examples | spans | thr | precision | recall | f0.5 | f1 |
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| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
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| NAMED_DATA | 18709 | 8571 | 0.70 | 0.8611 | 0.7311 | 0.8315 | 0.7908 |
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| DESCRIPTIVE_DATA | 18709 | 11348 | 0.70 | 0.8525 | 0.6765 | 0.8103 | 0.7544 |
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| VAGUE_DATA | 18709 | 2133 | 0.60 | 0.6192 | 0.5741 | 0.6096 | 0.5958 |
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<details>
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<summary>Origin breakdown (per-origin metrics)</summary>
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| origin | examples | spans | thr | precision | recall | f0.5 | f1 |
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| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
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| fcv_pads_east_africa | 6794 | 6784 | 0.60 | 0.8991 | 0.9156 | 0.9023 | 0.9073 |
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| general_prwp | 9188 | 12359 | 0.60 | 0.8574 | 0.8469 | 0.8553 | 0.8521 |
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| jdc_operational | 158 | 204 | 0.50 | 0.9212 | 0.9541 | 0.9276 | 0.9373 |
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| refugee_pads | 687 | 743 | 0.70 | 0.9237 | 0.8036 | 0.8969 | 0.8595 |
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| reliefweb | 1882 | 1962 | 0.70 | 0.8920 | 0.7735 | 0.8655 | 0.8286 |
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</details>
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<details>
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<summary>Per-label details</summary>
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| label | examples | spans | thr | precision | recall | f0.5 | f1 |
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| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
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| NAMED_DATA | 18709 | 8571 | 0.70 | 0.8611 | 0.7311 | 0.8315 | 0.7908 |
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| DESCRIPTIVE_DATA | 18709 | 11348 | 0.70 | 0.8525 | 0.6765 | 0.8103 | 0.7544 |
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| VAGUE_DATA | 18709 | 2133 | 0.60 | 0.6192 | 0.5741 | 0.6096 | 0.5958 |
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</details>
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