Token Classification
Transformers
Safetensors
PyTorch
Portuguese
qwen3
ner
pii
pii-detection
de-identification
privacy
healthcare
medical
clinical
phi
portuguese
openmed
Eval Results (legacy)
text-generation-inference
Instructions to use OpenMed/OpenMed-PII-Portuguese-QwenMed-XLarge-600M-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-PII-Portuguese-QwenMed-XLarge-600M-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-PII-Portuguese-QwenMed-XLarge-600M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-Portuguese-QwenMed-XLarge-600M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-Portuguese-QwenMed-XLarge-600M-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload Portuguese PII detection model OpenMed-PII-Portuguese-QwenMed-XLarge-600M-v1
63f47e8 verified | Classification Report for Portuguese PII Detection | |
| Model: Qwen/Qwen3-Embedding-0.6B | |
| ============================================================ | |
| precision recall f1-score support | |
| ACCOUNTNAME 0.58 0.57 0.57 83 | |
| AGE 0.91 0.59 0.71 151 | |
| AMOUNT 0.58 0.38 0.46 584 | |
| BANKACCOUNT 0.66 0.77 0.71 53 | |
| BIC 1.00 0.17 0.29 6 | |
| BUILDINGNUMBER 0.69 0.83 0.75 197 | |
| CITY 0.88 0.88 0.88 215 | |
| COUNTY 0.00 0.00 0.00 7 | |
| CREDITCARD 0.94 0.93 0.94 187 | |
| CREDITCARDISSUER 0.54 0.50 0.52 14 | |
| CURRENCY 0.45 0.81 0.58 349 | |
| CURRENCYCODE 0.35 0.53 0.42 15 | |
| CURRENCYNAME 0.00 0.00 0.00 2 | |
| CURRENCYSYMBOL 0.31 0.09 0.14 163 | |
| CVV 0.86 0.86 0.86 21 | |
| DATE 0.94 0.94 0.94 1039 | |
| DATEOFBIRTH 0.92 0.87 0.90 110 | |
| EMAIL 0.96 0.88 0.91 200 | |
| ETHEREUMADDRESS 0.00 0.00 0.00 1 | |
| FIRSTNAME 0.97 0.97 0.97 2411 | |
| GENDER 0.00 0.00 0.00 2 | |
| IBAN 0.97 0.99 0.98 666 | |
| IMEI 0.00 0.00 0.00 2 | |
| IPADDRESS 0.98 0.87 0.92 53 | |
| JOBDEPARTMENT 0.62 0.49 0.55 43 | |
| JOBTITLE 0.63 0.55 0.59 53 | |
| LASTNAME 0.96 0.98 0.97 2420 | |
| MACADDRESS 1.00 0.50 0.67 4 | |
| MASKEDNUMBER 0.50 0.33 0.40 33 | |
| MIDDLENAME 0.00 0.00 0.00 3 | |
| OCCUPATION 0.00 0.00 0.00 28 | |
| ORDINALDIRECTION 0.00 0.00 0.00 21 | |
| ORGANIZATION 0.77 0.71 0.74 502 | |
| PASSWORD 0.70 0.61 0.65 23 | |
| PHONE 0.95 0.94 0.95 363 | |
| PIN 0.80 0.67 0.73 12 | |
| PREFIX 0.94 0.96 0.95 1367 | |
| SECONDARYADDRESS 0.50 0.54 0.52 13 | |
| SSN 0.90 0.94 0.92 671 | |
| STATE 0.91 0.89 0.90 57 | |
| STREET 0.71 0.70 0.70 274 | |
| TIME 0.64 0.73 0.68 362 | |
| URL 0.88 0.85 0.87 34 | |
| USERNAME 0.44 0.19 0.27 36 | |
| VIN 0.00 0.00 0.00 1 | |
| VRM 1.00 0.50 0.67 2 | |
| ZIPCODE 0.97 0.91 0.94 80 | |
| micro avg 0.88 0.87 0.87 12933 | |
| macro avg 0.62 0.56 0.58 12933 | |
| weighted avg 0.87 0.87 0.87 12933 | |