AventIQ-AI / bert-work-ethic-analysis

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

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:

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.