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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.
---
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