Instructions to use rafmacalaba/gliner2-datause-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use rafmacalaba/gliner2-datause-v1 with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("rafmacalaba/gliner2-datause-v1") # 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 folder using huggingface_hub
Browse files- adapter_config.json +1 -1
- adapter_weights.safetensors +1 -1
adapter_config.json
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"encoder",
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"span_rep"
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"created_at": "2026-02-
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}
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"encoder",
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"span_rep"
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],
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"created_at": "2026-02-21T11:52:21.200597Z"
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}
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adapter_weights.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 13146960
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version https://git-lfs.github.com/spec/v1
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oid sha256:78f7e65e17b854444a5f7edc3a315fa3505acfdecced5c6596c440793f192a02
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size 13146960
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