mjpsm/coca-cola-contact-classifier

Coca-Cola Contact Form Relevance Classifier

This repository contains a LoRA-fine-tuned text classification model designed to determine whether messages submitted through Coca-Cola’s contact form are business-relevant or not relevant.

The model is intended to support automated intake routing, helping prioritize legitimate sales, procurement, and partnership inquiries while filtering out consumer feedback, sponsorship requests, and unrelated messages.

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🧠 Model Overview

• Base model: DistilBERT (distilbert-base-uncased)
• Fine-tuning method: LoRA (Low-Rank Adaptation)
• Task: Binary text classification
• Labels:
  • relevant — business-related inquiries (sales, procurement, distribution, vendors)
  • not_relevant — consumer feedback, sponsorships, complaints, general messages
• Training size: 300 examples (balanced)
• Evaluation: Held-out test set (80/20 split)

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🎯 Intended Use

This model is designed for:

• Contact form triage
• Business inquiry routing
• Intake prioritization workflows
• Manual review reduction

It is not intended to:

• Replace human judgment
• Make contractual or legal decisions
• Classify sentiment or emotions

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📊 Performance Summary

Evaluated on a held-out test set:

Metric	Score
Accuracy	98.3%
Precision	96.9%
Recall	100%
F1 Score	98.4%

Confusion Matrix: [28 1] [ 0 31]]

• The model prioritizes high recall, minimizing missed business inquiries.
• Borderline cases may be flagged for manual review using confidence thresholds.

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⚠️ Important Notes on Confidence

The model intentionally outputs moderate confidence scores (typically 0.55–0.70) on ambiguous inputs.
This reflects realistic uncertainty and supports safe enterprise deployment.

Recommended usage:

• Confidence ≥ 0.70 → auto-route
• Confidence < 0.70 → manual review

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