| Property | Value |
|---|---|
| License | Other |
| Paper | Intent Classification and Slot Filling for Privacy Policies |
| Base Model | mukund/privbert |
| F1 Score | 88% |
What is privacy_intent?
privacy_intent is a specialized text classification model designed to analyze and categorize privacy policy statements. Built on the mukund/privbert architecture and fine-tuned on the PolicyIE dataset, it achieves an impressive 88% F1 score, representing a 4% improvement over the original work. The model utilizes back translation for data augmentation, which contributed to a 1% performance boost for imbalanced samples.
Implementation Details
The model implements a transformer-based architecture optimized for privacy policy analysis, capable of classifying text into five distinct privacy-related categories. It's particularly notable for its ability to process complex legal text and identify specific privacy-related intents.
- Fine-tuned on PolicyIE dataset for specialized privacy policy understanding
- Implements back translation for improved handling of imbalanced data
- Optimized for English language privacy policies
- Supports inference endpoints for practical deployment
Core Capabilities
- Data Collection/Usage Classification: Identifies how user information is collected and used
- Data Sharing/Disclosure Analysis: Detects statements about third-party information sharing
- Data Storage/Retention Recognition: Classifies information about data storage duration and location
- Data Security/Protection Identification: Recognizes protection measures for user information
- Other Privacy Practices: Captures miscellaneous privacy-related statements
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized focus on privacy policy analysis and its improved performance over previous benchmarks. The implementation of back translation for handling imbalanced data makes it particularly robust for real-world applications.
Q: What are the recommended use cases?
The model is ideal for automated privacy policy analysis, legal document processing, compliance checking, and privacy statement categorization. It's particularly useful for organizations needing to analyze large volumes of privacy-related documents or maintain compliance with privacy regulations.