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OpenMed/OpenMed-PII-Turkish-FastClinical-Small-82M-v1
OpenMed-PII-Turkish-FastClinical-Small-82M-v1 is a token classification model from OpenMed. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as apache-2.0.
This is an OpenMed token-classification checkpoint intended for Turkish (tr) personally identifiable information (PII) and protected health information (PHI) span detection.
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From the Hugging Face model README
This is an OpenMed token-classification checkpoint intended for Turkish
(tr) personally identifiable information (PII) and protected health
information (PHI) span detection.
tr)distilbert/distilroberta-basefrom transformers import pipeline
model_id = "OpenMed/OpenMed-PII-Turkish-FastClinical-Small-82M-v1"
detector = pipeline(
"token-classification",
model=model_id,
aggregation_strategy="simple",
)
text = "Örnek hasta Ayşe Yılmaz'ın e-posta adresi [email protected] ve telefon numarası +90 555 000 00 00."
print(detector(text))
The checkpoint's configured id2label mapping is authoritative for the
available entity labels. Preserve returned character offsets when applying
redaction or replacement.
No verified Turkish evaluation artifact was available during this metadata repair, so this card intentionally reports no language-specific scores. Evaluate direct-identifier recall, false negatives, span boundaries, and domain shift on representative data before deployment.
This model can miss identifiers or over-redact clinically useful context. It is not an anonymization guarantee, a compliance determination, or a medical device. Use defense in depth and human review for high-sensitivity workflows. Do not include real patient information in public examples, logs, or issue reports.