Token Classification
GLiNER2
Safetensors
GLiNER
English
extractor
named-entity-recognition
ner
pii
anonymisation
privacy
Eval Results (legacy)
Instructions to use OvermindLab/nerpa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use OvermindLab/nerpa with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("OvermindLab/nerpa") # Extract entities text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday." result = extractor.extract_entities(text, ["company", "person", "product", "location"]) print(result) - GLiNER
How to use OvermindLab/nerpa with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OvermindLab/nerpa") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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@@ -70,12 +70,6 @@ entities = detect_entities(model, text, entities={
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"LAB_VALUE": "Laboratory test result",
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})
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# Or even abstract analytical entities
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entities = detect_entities(model, text, entities={
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"COMMITMENT": "A promise or obligation",
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"ASSUMPTION": "An unstated premise or belief",
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"RISK_FACTOR": "A potential source of risk or uncertainty",
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})
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```
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This isn't prompt engineering or few-shot learning. The model's bi-encoder architecture natively supports arbitrary entity schemas. Fine-tuning on PII improves precision on those specific types without degrading the zero-shot capability.
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"LAB_VALUE": "Laboratory test result",
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})
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```
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This isn't prompt engineering or few-shot learning. The model's bi-encoder architecture natively supports arbitrary entity schemas. Fine-tuning on PII improves precision on those specific types without degrading the zero-shot capability.
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