Token Classification
Transformers
Safetensors
PyTorch
Spanish
roberta
ner
pii
pii-detection
de-identification
privacy
healthcare
medical
clinical
phi
spanish
openmed
Eval Results (legacy)
Instructions to use OpenMed/OpenMed-PII-Spanish-FastClinical-Small-82M-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-PII-Spanish-FastClinical-Small-82M-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-PII-Spanish-FastClinical-Small-82M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-Spanish-FastClinical-Small-82M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-Spanish-FastClinical-Small-82M-v1") - Notebooks
- Google Colab
- Kaggle
File size: 1,213 Bytes
fdb7567 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | Classification Report for Spanish PII Detection
Model: distilbert/distilroberta-base
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precision recall f1-score support
BANKACCOUNT 0.88 0.85 0.87 178
BUILDINGNUMBER 0.85 0.89 0.87 145
CITY 0.84 0.91 0.87 350
CREDITCARD 0.69 0.96 0.81 106
DATEOFBIRTH 0.74 0.78 0.76 178
EMAIL 0.99 1.00 0.99 338
FIRSTNAME 0.79 0.85 0.82 503
LASTNAME 0.77 0.69 0.73 354
MASKEDNUMBER 1.00 0.99 1.00 121
PASSWORD 0.99 0.96 0.98 109
PHONE 0.99 0.99 0.99 229
SSN 0.95 0.98 0.97 373
STREET 0.85 0.93 0.89 150
USERNAME 0.98 0.93 0.95 301
ZIPCODE 0.88 0.94 0.91 153
micro avg 0.88 0.90 0.89 3588
macro avg 0.88 0.91 0.89 3588
weighted avg 0.88 0.90 0.89 3588
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