keanteng/bert-mental-health-response-classification-wqd7005

license: agpl-3.0 language:

• —en tags:
• —text-classification
• —bert
• —healthcare
• —risk-assessment
• —questionnaire-analysis pipeline_tag: text-classification metrics:
• —accuracy base_model:
• —google-bert/bert-base-uncased library_name: transformers ---

BERT Classification Models for Mental Health Responses

This repository contains fine-tuned BERT models for classifying healthcare questionnaire responses into risk categories.

Model Description

BERT-base-uncased models have been fine-tuned for healthcare risk assessment:

1. 1.Mental Health Model: Classifies mental health-related responses

The models predict three risk categories:

• —Low Risk (0)
• —Moderate Risk (1)
• —High Risk (2)

Training Details

• —Base Model: bert-base-uncased
• —Training Epochs: 40
• —Batch Size: 16
• —Learning Rate: 2e-5
• —Optimizer: AdamW
• —Max Sequence Length: 128

Usage

Loading the Models

from transformers import BertTokenizer, BertForSequenceClassification
import torch

# Load tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# Load mental health model
mental_health_model = BertForSequenceClassification.from_pretrained('keanteng/bert-mental-health-response-classification-wqd7005')

Making Predictions

def predict_risk(text, model, tokenizer, max_length=128):
    # Tokenize input
    inputs = tokenizer(
        text,
        padding='max_length',
        truncation=True,
        max_length=max_length,
        return_tensors='pt'
    )
    
    # Make prediction
    model.eval()
    with torch.no_grad():
        outputs = model(**inputs)
        predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
        predicted_class = torch.argmax(predictions, dim=-1)
    
    # Map to risk categories
    risk_labels = ['Low Risk', 'Moderate Risk', 'High Risk']
    return risk_labels[predicted_class.item()], predictions[0].tolist()

# Example usage
mental_health_text = "I am feeling devastated from the lost of my love ones. My heart is really painful."
risk_category, confidence_scores = predict_risk(mental_health_text, mental_health_model, tokenizer)
print(f"Risk Category: {risk_category}")
print(f"Confidence Scores: {confidence_scores}")

Model Performance

The models were trained and evaluated on healthcare questionnaire data with the following label mapping:

Mental Health Model:

• —Mental health levels 1-2 → High Risk
• —Mental health level 3 → Moderate Risk
• —Mental health levels 4-5 → Low Risk

Training Data

The models were trained on questionnaire responses containing:

• —Text descriptions of mental health status
• —Corresponding risk labels

Data was split 80/20 for training and validation with stratified sampling.

Intended Use

These models are designed for:

• —Healthcare questionnaire analysis
• —Risk assessment screening
• —Research applications in healthcare NLP

Important: These models are for research and screening purposes only and should not replace professional medical diagnosis.

Limitations

• —Models are trained on specific questionnaire formats
• —Performance may vary on different populations or text styles
• —Should be used as a screening tool, not for final diagnosis
• —May have biases present in the training data