Suicidality Detection Model
| Property | Value |
|---|---|
| Parameter Count | 109M |
| Model Type | Text Classification |
| Architecture | ELECTRA |
| License | CC0 1.0 |
| Accuracy | 93.94% |
What is suicidality?
Suicidality refers to thoughts, behaviors, or intentions related to suicide. This model represents a sophisticated approach to detecting such indicators in text, utilizing advanced natural language processing techniques to identify potentially concerning content. The model serves as a valuable tool for mental health professionals and content moderators, though it should always be used in conjunction with human judgment.
Implementation Details
Built on the ELECTRA architecture, this model employs state-of-the-art transformer technology for text classification. It processes input text and classifies it into two categories: suicidal (LABEL_1) and non-suicidal (LABEL_0) content. The model achieved impressive performance metrics, including 93.94% accuracy, 93.72% recall, and 92.82% precision.
- Fine-tuned on diverse datasets from multiple sources
- Implements binary classification system
- Utilizes PyTorch backend with F32 tensor type
- Optimized for production deployment
Core Capabilities
- Binary classification of text for suicidal content
- High-accuracy sentiment analysis
- Real-time text processing
- Integration-ready via Transformers library
- Support for batch processing
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized focus on suicidality detection, combining high accuracy with robust training on diverse datasets. Its performance metrics and ethical considerations make it particularly suitable for mental health applications.
Q: What are the recommended use cases?
The model is best suited for content moderation, mental health monitoring systems, and research applications. However, it should always be used as a supplementary tool alongside professional human judgment, particularly given the sensitive nature of suicide-related content.
