Update README.md

README.md

@@ -10,4 +10,53 @@ metrics:
 - precision
 - recall
 pipeline_tag: text-classification
----
+---
+
+
+
+# Emotion Classification with BERT + RL Fine-tuning
+
+This model combines BERT architecture with Reinforcement Learning (RL) for emotion classification. Initially fine-tuned on the `dair-ai/emotion` dataset (20k English sentences with 6 emotions), we then applied PPO reinforcement learning to optimize prediction behavior.
+
+## 🔧 Training Approach
+
+1. **Supervised Phase**:  
+   - Base BERT model fine-tuned with cross-entropy loss  
+   - Achieved strong baseline performance  
+
+2. **RL Phase**:  
+   - Implemented Actor-Critic architecture  
+   - Policy Gradient optimization with custom rewards  
+   - PPO clipping (ε=0.2) and entropy regularization  
+   - Custom reward function: `+1.0` for correct, `-0.1` for incorrect predictions
+
+## 📊 Performance Comparison
+
+| Metric     | Pre-RL  | Post-RL | Δ       |
+|------------|---------|---------|---------|
+| Accuracy   | 0.9205  | 0.931   | +1.14%  |
+| F1-Score   | 0.9227  | 0.9298  | +0.77%  |
+| Precision  | 0.9325  | 0.9305  | -0.21%  |
+| Recall     | 0.9205  | 0.931   | +1.14%  |
+
+Key observation: RL fine-tuning provided modest but consistent improvements across most metrics, particularly in recall.
+
+## 🚀 Usage
+
+```python
+from transformers import pipeline
+
+# Load from your repository
+classifier = pipeline("text-classification", 
+                     model="SimoGiuffrida/SentimentRL",
+                     tokenizer="bert-base-uncased")
+
+results = classifier("I'm thrilled about this new opportunity!")
+```
+
+## 💡 Key Features
+- Hybrid training: Supervised + Reinforcement Learning
+- Optimized for nuanced emotion detection
+- Handles class imbalance (see confusion matrix in repo)
+
+For full training details and analysis, visit the [GitHub repository](https://github.com/SimoGiuffrida/DLA2).