---
language: en
license: apache-2.0
library_name: transformers
tags:
  - mental-health
  - text-classification
  - bert
  - nlp
  - depression
  - anxiety
  - suicidal
datasets:
  - sai1908/Mental_Health_Condition_Classification
  - kamruzzaman-asif/reddit-mental-health-classification
metrics:
  - accuracy
  - loss
model-index:
  - name: bert-finetuned-mental-health
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: sai1908/Mental_Health_Condition_Classification
          type: text
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9656
          - name: Validation Loss
            type: loss
            value: 0.1513
---

# 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**](https://huggingface.co/spaces/Elite13/mental-health)

## 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

```python
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)