user6295018/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.

🧠 Model Details

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

📊 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

⚙️ 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}]