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license: mit tags:

• text-classification
• nlp
• productivity
• transformers datasets:
• custom metrics:
• accuracy

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Check-in Detail Classifier

🔍 Overview

The Check-in Detail Classifier evaluates whether a check-in is detailed or not detailed.

This model ensures that student check-ins provide meaningful insight into their work rather than vague or minimal responses.

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🎯 Purpose

Not all check-ins are useful. This model distinguishes between:

• High-quality, descriptive updates
• Low-effort or vague check-ins

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🧠 Model Details

• Task: Binary Text Classification
• Labels:
  • DETAILED → Clear, informative check-in
  • NOT_DETAILED → Vague or minimal check-in
• Architecture: Transformer-based (fine-tuned)

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📦 Use Cases

• Quality control for daily check-ins
• Automated feedback systems
• Performance tracking dashboards
• Educational analytics

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🧪 Example

Input

Today I worked on debugging my FastAPI deployment and fixed a memory issue on Render

Ouput

{
  "label": "DETAILED",
  "confidence": 0.96
}

Input

I worked on my project

Output

{
  "label": "NOT_DETAILED",
  "confidence": 0.94
}

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Pipeline Integration

This model runs after check-in validation:

1. Check-in or Not
2. Check-in Detail Classifier (this model)
3. Roadblock Detection
4. Generation (if needed)

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Usage (Python)

from transformers import pipeline

classifier = pipeline("text-classification", model="mjpsm/checkin-detail-classifier")

result = classifier("Today I worked on debugging my API")
print(result)

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Future Improvements

1. Add scoring system (0–10 detail rating)
2. Provide feedback suggestions
3. Expand dataset with more nuanced examples

👤 Author

• Mazamesso Meba
• Information Systems Student | AI Developer