Token Classification
Transformers
Safetensors
openai_privacy_filter
privacy
pii
ner
redaction
nemotron
openmed
openai-privacy-filter
Instructions to use OpenMed/privacy-filter-nemotron-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/privacy-filter-nemotron-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/privacy-filter-nemotron-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/privacy-filter-nemotron-v2") model = AutoModelForTokenClassification.from_pretrained("OpenMed/privacy-filter-nemotron-v2") - Notebooks
- Google Colab
- Kaggle
File size: 1,103 Bytes
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"category_version": "nemotron_fine_v1",
"span_class_names": [
"O",
"account_number",
"age",
"api_key",
"bank_routing_number",
"biometric_identifier",
"blood_type",
"certificate_license_number",
"city",
"company_name",
"coordinate",
"country",
"county",
"credit_debit_card",
"customer_id",
"cvv",
"date",
"date_of_birth",
"date_time",
"device_identifier",
"education_level",
"email",
"employee_id",
"employment_status",
"fax_number",
"first_name",
"gender",
"health_plan_beneficiary_number",
"http_cookie",
"ipv4",
"ipv6",
"language",
"last_name",
"license_plate",
"mac_address",
"medical_record_number",
"national_id",
"occupation",
"password",
"phone_number",
"pin",
"political_view",
"postcode",
"race_ethnicity",
"religious_belief",
"sexuality",
"ssn",
"state",
"street_address",
"swift_bic",
"tax_id",
"time",
"unique_id",
"url",
"user_name",
"vehicle_identifier"
]
} |