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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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