finetuned-roberta-depression
| Property | Value |
|---|---|
| License | MIT |
| Base Model | RoBERTa-base |
| Accuracy | 97.45% |
| Training Loss | 0.1385 |
What is finetuned-roberta-depression?
This model is a specialized version of RoBERTa-base, fine-tuned for detecting depression indicators in text content. It demonstrates exceptional accuracy of 97.45% in evaluation, making it particularly effective for mental health content analysis.
Implementation Details
The model utilizes the PyTorch framework and employs the Transformers library (version 4.17.0). It was trained using specific hyperparameters including a learning rate of 5e-05, batch size of 8, and Adam optimizer with betas=(0.9,0.999).
- Three-epoch training process with consistent validation loss
- Implements TensorBoard for training visualization
- Supports inference endpoints for practical deployment
- Utilizes the robust RoBERTa architecture
Core Capabilities
- Depression indicator detection in text
- Binary classification of depressive vs. non-depressive content
- Real-time text analysis through inference endpoints
- High accuracy in emotional content interpretation
Frequently Asked Questions
Q: What makes this model unique?
The model's exceptional accuracy of 97.45% and specialized focus on depression detection, combined with its foundation on the robust RoBERTa architecture, makes it particularly valuable for mental health content analysis.
Q: What are the recommended use cases?
The model is well-suited for mental health monitoring applications, content moderation systems, and research studies focusing on depression detection in textual content. It can analyze both short and long-form text inputs effectively.
