Create README.md

README.md

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+# Model Details
+
+**Model Name:** Work Ethic 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', 'ethic_category'],  
+num_rows: 50,000  
+})  
+validation: Dataset({  
+features: ['employee_feedback', 'ethic_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., "John consistently meets deadlines and takes initiative.")  
+- **ethic_category:** Classified work ethic type (e.g., "Strong Initiative")  
+
+---
+
+## 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_ethic(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/work-ethic-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 = "John consistently meets deadlines and takes initiative."
+    category = classify_ethic(feedback, model, tokenizer, device)
+    print(f"Feedback: {feedback}")
+    print(f"Predicted Work Ethic Category: {category}")
+```
+
+**Expected Output:**  
+```plaintext
+Feedback: John consistently meets deadlines and takes initiative.
+Predicted Work Ethic Category: Strong Initiative
+```
+
+---
+
+# Use Case: Work Ethic Analysis Model
+
+## **Overview**
+
+The **Work Ethic Analysis Model**, built on **DistilBERT-base-uncased**, is designed to classify employee feedback into predefined work ethic categories. This helps HR teams and management analyze employee dedication, responsibility, and productivity.
+
+## **Key Applications**
+
+- **Performance Assessment:** Identify patterns in employee feedback for objective performance reviews.
+- **Employee Recognition:** Highlight employees demonstrating strong work ethics for rewards and promotions.
+- **Early Warning System:** Detect negative trends in work ethic and take proactive measures.
+- **Leadership and Training Enhancement:** Use feedback analysis to improve training programs for employees and managers.
+
+## **Benefits**
+
+- **Scalability:** Can process thousands of employee feedback entries in minutes.
+- **Unbiased Evaluation:** AI-driven classification removes subjective bias from evaluations.
+- **Actionable Insights:** Helps HR teams make data-driven decisions for workforce improvement.
+
+---