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README.md

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+---
+license: agpl-3.0
+language:
+- en
+tags:
+- text-classification
+- bert
+- healthcare
+- risk-assessment
+- questionnaire-analysis
+pipeline_tag: text-classification
+---
+
+# BERT Classification Models for Healthcare Risk Assessment
+
+This repository contains fine-tuned BERT models for classifying healthcare questionnaire responses into risk categories.
+
+## Model Description
+
+Two BERT-base-uncased models have been fine-tuned for healthcare risk assessment:
+
+1. **Fatigue Model**: Classifies fatigue-related responses
+2. **Mental Health Model**: Classifies mental health-related responses
+
+Both 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
+
+```python
+from transformers import BertTokenizer, BertForSequenceClassification
+import torch
+
+# Load tokenizer
+tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+
+# Load fatigue model
+fatigue_model = BertForSequenceClassification.from_pretrained('keanteng/bert-classification-wqd7005', subfolder='fatigue_model')
+
+# Load mental health model
+mental_health_model = BertForSequenceClassification.from_pretrained('keanteng/bert-classification-wqd7005', subfolder='mental_health_model')
+```
+
+### Making Predictions
+
+```python
+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
+fatigue_text = "I feel extremely tired all the time and can't complete daily tasks"
+risk_category, confidence_scores = predict_risk(fatigue_text, fatigue_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:
+
+**Fatigue Model:**
+- Fatigue levels 1-2 → Low Risk
+- Fatigue level 3 → Moderate Risk  
+- Fatigue levels 4-5 → High Risk
+
+**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 fatigue levels
+- 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