Hugging Face Overview
Model Overview
This model, kingabzpro/Llama-3.1-8B-Instruct-Mental-Health-Classification, is a specialized fine-tuned version of Meta's Llama-3.1-8B-Instruct. It has been adapted for mental health text classification using the suchintikasarkar/sentiment-analysis-for-mental-health dataset.
Key Capabilities
- Mental Health Classification: Accurately classifies text into categories: Normal, Depression, Anxiety, and Bipolar.
- High Accuracy: Achieves an overall accuracy of 91.3% on the fine-tuning dataset.
- Specific Label Performance: Demonstrates strong performance for 'Normal' (97.2% accuracy) and 'Depression' (91.3% accuracy) labels.
- Llama 3.1 Base: Benefits from the robust capabilities of the Llama 3.1-8B-Instruct architecture.
Use Cases
- Sentiment Analysis in Mental Health: Ideal for identifying potential mental health indicators from textual data.
- Research and Development: Useful for researchers and developers working on applications related to mental well-being assessment.
- Educational Tool: Can be used as a practical example for fine-tuning large language models for specific classification tasks, as detailed in the associated DataCamp tutorial.