# 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.

---