Elite13/bert-finetuned-mental-health

BERT Fine-Tuned for Mental Health Classification

This model is a fine-tuned bert-base-uncased transformer trained to classify text inputs into seven mental health categories. It is designed to support emotional analysis in mental health-related applications by detecting signs of psychological distress in user-generated content.

Try It Out

You can interact with the model in real-time via this Streamlit-powered Hugging Face Space:
👉 Live Demo on Hugging Face Spaces

Datasets Used

1. sai1908/Mental_Health_Condition_Classification
   Reddit posts from mental health forums
   ~80,000 cleaned entries from the original 100,000

2. kamruzzaman-asif/reddit-mental-health-classification
   Additional Reddit mental health posts to improve coverage and diversity

Model Overview

• Base Model: bert-base-uncased
• Type: Multi-class text classification (7 labels)
• Framework: Hugging Face Transformers
• Training Method: Trainer API (PyTorch backend)

Target Labels

• Anxiety
• Depression
• Bipolar
• Normal
• Personality Disorder
• Stress
• Suicidal

Training Configuration

Parameter	Value
Epochs	3
Learning Rate	2e-5
Batch Size	16
Max Token Length	256
Optimizer	AdamW
Hardware	2x NVIDIA Tesla T4 GPUs
Total FLOPs	25,605,736,040,851,200

Evaluation Metrics

Metric	Value
Accuracy	0.9656
Validation Loss	0.1513
Training Loss	0.0483
Samples/sec	65.354
Training Time	~1.65 hrs

Example Inference

from transformers import pipeline

classifier = pipeline("text-classification", model="Elite13/bert-finetuned-mental-health")

text = "I'm tired of everything. Nothing makes sense anymore."
result = classifier(text)
print(result)