Token Classification
GLiNER
PyTorch
ONNX
pii-detection
ner
multilingual
romanian
cee
on-device
privacy
Eval Results (legacy)
Instructions to use flowxai/cee-pii with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use flowxai/cee-pii with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("flowxai/cee-pii") 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
Commit History
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Card: remove pre-publication notes, document the ONNX export and why no int8/fp16 variant is offered.
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Add fp32 ONNX export (no quantised variant: INT8 destroyed the model, fp16 hit an unresolved converter bug). Verified against PyTorch on 7 fixtures, 0 span mismatches, max score drift 0.00001.
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Link inference contract from card (ceepii_labels_v1)
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Add inference contract: labels_ceepii_v1.json (ceepii_labels_v1)
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Add inference contract: INFERENCE.md (ceepii_labels_v1)
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Upload README.md with huggingface_hub
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Upload README.md with huggingface_hub
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Upload folder using huggingface_hub
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initial commit
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