checkin-quality-classifier

🌸 Bloom Check-in Quality Classifier

The Bloom Check-in Quality Classifier is a fine-tuned DistilBERT model designed to analyze daily check-ins from the Coding in Color program and classify them into one of three categories:

• Descriptive — Clear, thoughtful, and specific check-ins
• Neutral — Somewhat informative but missing depth
• Vague — Minimal or unclear updates

This model powers Bloom AI’s productivity assistant, which helps students reflect on their daily work habits and communicate effectively.

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🧠 Model Details

• Base model: distilbert-base-uncased
• Framework: 🤗 Transformers + PyTorch
• Language: English
• Task: Text Classification
• Labels: ["vague", "neutral", "descriptive"]

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📊 Training Information

• Dataset: 1,200+ anonymized check-ins from the Coding in Color program
• Split: 80% train / 10% validation / 10% test
• Epochs: 3
• Batch size: 16
• Optimizer: AdamW
• Learning rate: 5e-5

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⚙️ Inference Example

from transformers import pipeline

classifier = pipeline("text-classification", model="user6295018/checkin-quality-classifier")

classifier("Had a really productive day working on my API and debugging the UI.")
# [{'label': 'descriptive', 'score': 0.94}]