update readMe

README.md

@@ -2,7 +2,175 @@
 license: apache-2.0
 language:
 - en
-pipeline_tag: question-answering
+pipeline_tag: text-classification
 tags:
 - medical
----
+---
+
+# CBT Cognitive Distortion Classifier
+
+A fine-tuned DistilBERT model for detecting cognitive distortions in text, based on Cognitive Behavioral Therapy (CBT) principles.
+
+## Model Description
+
+This model identifies 5 common cognitive distortions in conversational text:
+
+- **Overgeneralization**: Using "always", "never", "everyone", "nobody"
+- **Catastrophizing**: Using "terrible", "awful", "worst", "disaster"
+- **Black and White Thinking**: All-or-nothing, either/or patterns
+- **Self-Blame**: "My fault", "blame myself", "guilty"
+- **Mind Reading**: "They think", "must think", "probably think"
+
+## Model Details
+
+- **Base Model**: `distilbert-base-uncased`
+- **Task**: Multi-label classification
+- **Number of Labels**: 5
+- **Training Data**: 231 samples from mental health conversational data
+- **Training Split**: 196 train / 35 test
+- **Framework**: HuggingFace Transformers
+
+## Training Performance
+
+| Epoch | Training Loss | Validation Loss |
+|-------|--------------|-----------------|
+| 1     | 0.1200       | 0.0857          |
+| 2     | 0.0322       | 0.0258          |
+| 3     | 0.0165       | 0.0129          |
+| 4     | 0.0335       | 0.0084          |
+| 5     | 0.0079       | 0.0067          |
+| 6     | 0.0066       | 0.0056          |
+| 7     | 0.0311       | 0.0048          |
+| 8     | 0.0523       | 0.0045          |
+| 9     | 0.0051       | 0.0044          |
+| 10    | 0.0278       | 0.0043          |
+
+**Final Validation Loss**: 0.0043
+
+## Training Configuration
+
+```python
+- Epochs: 10
+- Batch Size: 8
+- Evaluation Strategy: Per epoch
+- Optimizer: AdamW (default)
+- Max Sequence Length: 128
+- Device: GPU (Tesla T4)
+```
+
+## Usage
+
+### Loading the Model
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+model = AutoModelForSequenceClassification.from_pretrained("./cbt_model_final")
+tokenizer = AutoTokenizer.from_pretrained("./cbt_model_final")
+
+# Load label mappings
+import json
+with open("./cbt_model_final/label_config.json", "r") as f:
+    label_config = json.load(f)
+
+id2label = label_config["id2label"]
+```
+
+### Making Predictions
+
+```python
+def predict_distortions(text, threshold=0.5):
+    # Tokenize input
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
+    
+    # Get predictions
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probabilities = torch.sigmoid(outputs.logits).squeeze()
+    
+    # Extract distortions above threshold
+    detected = []
+    for idx, prob in enumerate(probabilities):
+        if prob > threshold:
+            label = id2label[str(idx)]
+            detected.append({
+                "distortion": label,
+                "confidence": f"{prob.item():.2%}"
+            })
+    
+    return detected
+
+# Example usage
+text = "I always mess everything up. This is a disaster!"
+distortions = predict_distortions(text)
+
+for d in distortions:
+    print(f"{d['distortion']}: {d['confidence']}")
+```
+
+### Example Output
+
+```
+Input: "I always mess everything up. This is a disaster!"
+
+Detected distortions:
+  overgeneralization: 73.45%
+  catastrophizing: 68.92%
+```
+
+## Model Limitations
+
+⚠️ **Important Considerations**:
+
+- **Small Training Dataset**: Only 231 samples - model may not generalize well to all contexts
+- **Rule-Based Labels**: Training labels were created using keyword matching, not expert annotations
+- **Prototype Quality**: This is a proof-of-concept model, not production-ready
+- **Low Confidence Scores**: Average predictions are 0.25-0.73%, indicating the model is conservative
+- **Limited Context**: Only trained on short conversational patterns
+- **No Clinical Validation**: Not validated by mental health professionals
+
+## Recommendations for Improvement
+
+1. **Expand Dataset**: Collect more diverse, expert-annotated examples
+2. **Better Labeling**: Use clinical experts to label cognitive distortions
+3. **Data Augmentation**: Generate synthetic examples for underrepresented patterns
+4. **Hyperparameter Tuning**: Experiment with learning rates, batch sizes, epochs
+5. **Evaluation Metrics**: Add precision, recall, F1-score tracking
+6. **Class Balancing**: Address imbalanced distribution of distortion types
+
+## Files Included
+
+```
+cbt_model_final/
+├── config.json              # Model configuration
+├── model.safetensors        # Model weights
+├── tokenizer_config.json    # Tokenizer configuration
+├── vocab.txt                # Vocabulary
+├── special_tokens_map.json  # Special tokens
+├── tokenizer.json           # Tokenizer data
+└── label_config.json        # Label mappings
+```
+
+## License
+
+This model is based on DistilBERT and inherits its Apache 2.0 license.
+
+## Citation
+
+```
+Base Model: DistilBERT
+Original Paper: Sanh et al. (2019) - DistilBERT, a distilled version of BERT
+Fine-tuning: Custom CBT distortion detection
+```
+
+## Disclaimer
+
+⚠️ **This model is for educational and research purposes only.** It should not be used as a substitute for professional mental health diagnosis or treatment. Always consult qualified mental health professionals for clinical applications.
+
+---
+
+**Created**: December 2024  
+**Framework**: HuggingFace Transformers  
+**Hardware**: Kaggle GPU (Tesla T4)