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onnx/model.onnx 599 MBmodel.safetensors 599 MBonnx/model_fp16.onnx 300 MBonnx/model_q4.onnx 225 MBonnx/model_bnb4.onnx 218 MBonnx/model_int8.onnx 151 MBonnx/model_quantized.onnx 151 MBonnx/model_uint8.onnx 151 MBonnx/model_q4f16.onnx 140 MBThis model is designed to redact and classify Personally Identifiable Information (PII) from multilingual text. It has been fine-tuned on the open-pii-masking-500k-ai4privacy dataset and supports multiple languages, including French (fr), English (en), German (de), Telugu (te), Hindi (hi), Italian (it), Spanish (es), and Dutch (nl).
The table below summarizes the detailed evaluation results per PII label. Metrics are presented as percentages rounded to two decimal places. For the "O" (Non-PII) label, precision, recall, and F1 score are not applicable (n/a) due to the absence of true positives.
| Label | TP | FP | FN | Accuracy | Precision | Recall | F1 Score |
|---|---|---|---|---|---|---|---|
| O (Non-PII) | 0 | 734 | 0 | 98.97% | n/a | n/a | n/a |
| GIVENNAME | 6623 | 661 | 352 | 86.73% | 90.93% | 94.95% | 92.90% |
| SURNAME | 2786 | 877 | 162 | 72.84% | 76.06% | 94.50% | 84.28% |
| CITY | 1763 | 216 | 225 | 79.99% | 89.09% | 88.68% | 88.88% |
| DATE | 2195 | 1 | 3 | 99.82% | 99.95% | 99.86% | 99.91% |
| AGE | 176 | 7 | 2 | 95.14% | 96.17% | 98.88% | 97.51% |
| 2981 | 0 | 0 | 100.0% | 100.0% | 100.0% | 100.0% | |
| CREDITCARDNUMBER | 601 | 57 | 35 | 86.72% | 91.34% | 94.50% | 92.89% |
| SEX | 103 | 45 | 1 | 69.13% | 69.59% | 99.04% | 81.75% |
| SOCIALNUM | 364 | 134 | 20 | 70.27% | 73.09% | 94.79% | 82.54% |
| TIME | 1631 | 1 | 3 | 99.76% | 99.94% | 99.82% | 99.88% |
| TELEPHONENUM | 3537 | 10 | 9 | 99.47% | 99.72% | 99.75% | 99.73% |
| IDCARDNUM | 1540 | 314 | 148 | 76.92% | 83.06% | 91.23% | 86.96% |
| ZIPCODE | 311 | 39 | 16 | 84.97% | 88.86% | 95.11% | 91.87% |
| DRIVERLICENSENUM | 296 | 143 | 26 | 63.66% | 67.43% | 91.93% | 77.79% |
| PASSPORTNUM | 482 | 285 | 25 | 60.86% | 62.84% | 95.07% | 75.67% |
| TITLE | 224 | 68 | 78 | 60.54% | 76.71% | 74.17% | 75.42% |
| BUILDINGNUM | 292 | 45 | 14 | 83.19% | 86.65% | 95.42% | 90.85% |
| STREET | 1272 | 155 | 67 | 85.14% | 89.14% | 94.99% | 91.97% |
| TAXNUM | 471 | 101 | 34 | 77.72% | 82.34% | 93.27% | 87.47% |
| GENDER | 123 | 35 | 9 | 73.65% | 77.85% | 93.18% | 84.83% |
Accuracy: 95.03%
Precision: 87.61%
Recall: 95.76%
F1 Score: 91.50%
Total True Positives (TP): 27,771
Total False Positives (FP): 3,928
Total False Negatives (FN): 1,229
Evaluation Focus:
The metrics above reflect performance on the test split of the open-pii-masking-500k-ai4privacy dataset. This model both redacts and classifies PII into specific categories (e.g., GIVENNAME, EMAIL). Real-world performance may vary depending on text domain and language, so additional validation is recommended. For support, contact support@ai4privacy.com.
Strengths:
Limitations:
This model card details the evaluation metrics and fine-tuning parameters for the multilingual anonymiser with PII classification capabilities. Please note:
Ai4Privacy – Committed to protecting personal data in the age of AI.