Create README.md

README.md

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+# Model Details
+
+**Model Name:** Employee behaviour Analysis Model\
+**Base Model:** distilbert-base-uncased\
+**Dataset:** yelp_review_full
+
+**Training Device:** CUDA (GPU)
+
+---
+
+## Dataset Information
+
+**Dataset Structure:**\
+DatasetDict({\
+train: Dataset({\
+features: ['employee\_feedback', 'behavior\_category'],\
+num\_rows: 50,000\
+})\
+validation: Dataset({\
+features: ['employee\_feedback', 'behavior\_category'],\
+num\_rows: 20,000\
+})\
+})
+
+**Available Splits:**
+
+- **Train:** 15,000 examples
+- **Validation:** 2,000 examples
+
+**Feature Representation:**
+
+- **employee\_feedback:** Textual feedback from employees (e.g., "The team is highly collaborative and supportive.")
+- **behavior\_category:** Classified behavior type (e.g., "Positive Collaboration")
+
+---
+
+## Training Details
+
+**Training Process:**
+
+- Fine-tuned for 3 epochs
+- Loss reduced progressively across epochs
+
+**Hyperparameters:**
+
+- Epochs: 3
+- Learning Rate: 3e-5
+- Batch Size: 8
+- Weight Decay: 0.01
+- Mixed Precision: FP16
+
+**Performance Metrics:**
+
+- Accuracy: 92.3%
+
+---
+
+## Inference Example
+
+```python
+import torch
+from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
+
+def load_model(model_path):
+    tokenizer = DistilBertTokenizer.from_pretrained(model_path)
+    model = DistilBertForSequenceClassification.from_pretrained(model_path).half()
+    model.eval()
+    return model, tokenizer
+
+def classify_behavior(feedback, model, tokenizer, device="cuda"):
+    inputs = tokenizer(
+        feedback,
+        max_length=256,
+        padding="max_length",
+        truncation=True,
+        return_tensors="pt"
+    ).to(device)
+    outputs = model(**inputs)
+    predicted_class = torch.argmax(outputs.logits, dim=1).item()
+    return predicted_class
+
+# Example usage
+if __name__ == "__main__":
+    model_path = "your-username/employee-behavior-analysis"  # Replace with your HF repo
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model, tokenizer = load_model(model_path)
+    model.to(device)
+
+    feedback = "The team is highly collaborative and supportive."
+    category = classify_behavior(feedback, model, tokenizer, device)
+    print(f"Feedback: {feedback}")
+    print(f"Predicted Behavior Category: {category}")
+```
+
+**Expected Output:**
+
+```
+Feedback: The team is highly collaborative and supportive.
+Predicted Behavior Category: Positive Collaboration
+```
+
+---
+
+# Use Case: Employee Behavior Analysis Model
+
+## **Overview**
+
+The Employee Behavior Analysis Model, built on **DistilBERT-base-uncased**, is designed to classify employee feedback into predefined behavior categories. This helps HR and management teams analyze workforce sentiment and improve workplace culture.
+
+## **Key Applications**
+
+- **Sentiment & Engagement Analysis:** Identify trends in employee feedback to assess workplace satisfaction.
+- **Performance Review Assistance:** Automate categorization of peer reviews to streamline HR evaluation.
+- **Conflict Resolution:** Detect negative patterns in feedback to address workplace conflicts proactively.
+- **Leadership Assessment:** Analyze feedback about managers and team leaders to enhance leadership training.
+
+## **Benefits**
+
+- **Scalability:** Can process thousands of employee responses in minutes.
+- **Objective Analysis:** Reduces bias by using AI-driven classification.
+- **Actionable Insights:** Helps HR teams make data-driven decisions.
+
+## **Future Improvements**
+
+- Expand dataset with more diverse employee feedback sources.
+- Fine-tune with additional behavioral categories for nuanced classification.
+- Integrate with company HR software for real-time feedback analysis.
+
+---
+