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| Metric | Score |
|---|---|
| Micro avg F1 | 0.95 |
| Macro avg F1 | 0.94 |
| Weighted avg F1 | 0.95 |
classification_report.txt.1from transformers import pipeline
2
3ner = pipeline(
4 "token-classification",
5 model="rm0013/roberta-pii-ner-en",
6 aggregation_strategy="simple"
7)
8
9result = ner("Send the invoice to john.smith@acme.com, card 4111-1111-1111-1111 CVV 123.")
10for entity in result:
11 print(f"{entity['word']:30s} → {entity['entity_group']} ({entity['score']:.2f})")PERSON_NAME EMAIL PHONE_NUMBER SSN ADDRESS SECONDARYADDRESS DATE_OF_BIRTH DATE TIME AGE GENDER USERNAME PASSWORD IP_ADDRESS URL API_KEY PASSPORT_NUMBER DRIVER_LICENSE ORGANIZATION COMPANYNAME ACCOUNTNAME JOBAREA JOBTITLE JOBTYPE HEIGHT EYECOLOR ORDINALDIRECTION GPS_COORDINATES NEARBYGPSCOORDINATE USERAGENT DEVICE_ID VEHICLE_ID VEHICLEVIN VEHICLEVRM PHONEIMEICREDIT_CARD CREDIT_CARD_CVV CREDIT_CARD_EXPIRY PIN BANK_ACCOUNT BANK_ROUTING BIC AMOUNT CURRENCY CURRENCYCODE CURRENCYNAME CURRENCYSYMBOL MASKEDNUMBER BITCOINADDRESS ETHEREUMADDRESS LITECOINADDRESS| Parameter | Value |
|---|---|
| Base model | roberta-base |
| Epochs | 10 (early stopping patience 3) |
| Batch size | 32 |
| Learning rate | 2e-5 |
| Max sequence length | 256 |
| Mixed precision | FP16 |