README.md

metadata

license: mit
tags:
  - text-classification
  - nlp
  - sentiment-analysis
  - transformers
datasets:
  - custom
metrics:
  - accuracy
  - f1

Roadblock Detection Classifier (v1)

🔍 Overview

The Roadblock Classifier detects whether a check-in contains signs of a roadblock or struggle.

This helps identify when students need support or intervention.

---

🎯 Purpose

Students may express confusion, frustration, or blockers in their check-ins. This model identifies those signals automatically.

---

🧠 Model Details

• Task: Binary Text Classification
• Labels:
  • ROADBLOCK → Indicates confusion, issue, or blocker
  • NOT_ROADBLOCK → No issues present
• Architecture: Transformer-based (fine-tuned)

---

📦 Use Cases

• Early detection of struggling students
• Automated support systems
• Mentor alerts
• Educational analytics

---

🧪 Example

Input

I was struggling to understand why my API deployment kept failing

Output

{
  "label": "ROADBLOCK",
  "confidence": 0.99
}

Input

Today I completed my feature and everything is working well

Output

{
  "label": "NOT_ROADBLOCK",
  "confidence": 0.97
}

---

Pipeline Integration

This model runs after check-in validation and quality analysis:

1. Check-in or Not
2. Check-in Detail
3. Roadblock Detection (this model)
4. Intervention or generation

---

Usage (Python)

from transformers import pipeline

classifier = pipeline("text-classification", model="mjpsm/roadblock-classifier-v1")

result = classifier("I was confused about my API deployment")
print(result)

---

Performance

• High accuracy and F1 score (~0.97–0.99 observed during evaluation)

---

Future Improvements

1. Detect severity of roadblocks
2. Multi-label emotions (confusion, frustration, stuck)
3. Suggest solutions automatically

---

👤 Author

• Mazamesso Meba
• Information Systems Student | AI Developer