deberta_finetuned_pii
| Property | Value |
|---|---|
| License | MIT |
| Language | English |
| Framework | PyTorch |
| Task | Token Classification |
What is deberta_finetuned_pii?
deberta_finetuned_pii is a specialized token classification model built on the DeBERTa architecture, specifically fine-tuned for identifying Personally Identifiable Information (PII) in unstructured text. With over 41,000 downloads, this model has proven its utility in privacy-focused data processing applications.
Implementation Details
The model implements a token classification pipeline using the Transformers library, making it easily deployable for production use. It's optimized for accuracy in identifying various types of PII across multiple categories, from basic personal information to complex financial identifiers.
- Built on DeBERTa architecture for superior contextual understanding
- Implements token-level classification for precise PII identification
- Supports batch processing and inference endpoints
- Easy integration with Hugging Face's Transformers library
Core Capabilities
- Account Information Detection: Identifies account names, numbers, and transaction details
- Financial Data Recognition: Detects credit card numbers, BIC, IBAN, and cryptocurrency addresses
- Personal Information Classification: Captures names, DOB, gender, and contact details
- Digital Identity Recognition: Identifies IP addresses, MAC addresses, and user agents
- Location Information: Processes address components including street, city, state, and ZIP codes
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its comprehensive coverage of PII categories and its ability to handle complex, unstructured text while maintaining high accuracy. It's particularly valuable for compliance and data privacy applications.
Q: What are the recommended use cases?
The model is ideal for document processing, email scanning, user content moderation, compliance checking, and automated PII redaction in various industries including finance, healthcare, and customer service.
